Computational strategies for flexible multibody systems
job-shopschedulingproblem:作业车间调度问题
Multistage-based genetic algorithm for flexible job-shop scheduling problemHaipeng Zhang, and Mitsuo GenGraduate School of Information, Production & Systems, Waseda UniversityWakamatsu-ku, Kitakyushu 808-0135, JAPANEmail:*******************.jp,*************AbstractFlexible Job-shop Scheduling Problem is expanded from the traditional Job-shop Scheduling Problem, which possesses wider availability of machines for all the operations. Considering thetwo states of the problem, two definitions (total and partial) of flexibility are offered to separatethe different availability information of machines.In this paper, a new multistage operation-based representation is proposed to make the chromosome simpler. By using this approach, all the crossover and mutation methods can beapplied to this optimal strategy. The efficiency has been improved after using the newrepresentation, and also the objective values outperform others.1. IntroductionThe classical Job-shop Scheduling Problem (JSP) concerns determination of a set of jobs on a set of machines so that the makespan is minimized. It is obviously a single criterion combinational optimization and has been proved as a NP-hard problem with several assumptions as follows: each job has a specified processing order through the machines which is fixed and known in advance; processing times are also fixed and known corresponding to the operations for each job; set-up times between operations are either negligible or included in processing times (sequence-independent); each machine is continuously available from time zero; there are no precedence constraints among operations of different jobs; each operation cannot be interrupted; each machine can process at most one operation at a time.The flexible Job-shop Scheduling Problem (f-JSP) extends JSP by assuming that a machine may be capable of performing more than one type of operation (Najid, Dauzere-Peres & Zaidat,2002). That means for any given operation, there must exist at least one machine capable of performing it. In this paper two kinds of flexibility are considered to describe the performance of f-JSP (Kacem, Hammadi &Borne, 2002). First total flexibility: in this case all operations are achievable on all the machines available; second, partial flexibility: in this case some operations are only achievable on part of the available machines.Most of the literature on the shop scheduling problem concentrates on JSP case (Gen & Cheng, 1997; Gen & Cheng, 2000; Blazewicz, Domschke &Pesch,1996). The f-JSP recently captured the interests of many researchers. The first paper that addresses the f-JSP was given by Brucker and Schlie (Brucker & Schlie, 1990), which proposes a polynomial algorithm for solving the f-JSP with two jobs, in which the machines able to perform an operation have the same processing time. For solving the general case with more than two jobs, two types of approaches have been used: hierarchical approaches and integratedapproaches. The first was based on the idea of decomposing the original problem in order to reduce its complexity. Brandimarte (Brandimarte, 1993) was the first to use this decomposition for the f-JSP. He solved the assignment problem using some existing dispatching rules and then focused on the resulting job shop subproblems, which are solved using a tabu search heuristic. Mati proposed a greedy heuristic for simultaneously dealing with the assignment and the sequencing subproblems of the flexible job shop model (Mati, Rezg & Xie, 2001). The advantage of Mati’s heuristic is its ability to take into account the assumption of identical machine. Kacem (Kacem, Hammadi &Borne, 2002) came to use GA to solve f-JSP, and he adapted two approaches to solve jointly the assignment and JSP (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment mode (assignments schemata), the second one is an evolutionary approach controlled by the assignment model, and applying GA to solve the f-JSP.In this paper, we propose a more efficient method called multistage-based GA to solve f-JSP (including total flexibility and partial flexibility) compared with Kacem’s approach. The considered objective is to minimize the makespan, total workloads of the machines and the maximum workloads of machines. This multi-objective optimization will be done by a multistage-based GA which including K stages (the total number of operations for all the jobs), and m state (total number of machines). Computational experiments will be carried out to evaluate the efficiency of our methods with a large set of representative problem instances based on practical data. The rest of the paper is organized as follows: In Section 2, we describe the assumptions of flexible Job-shop Scheduling Problem in detail, and propose the mathematical model of this problem. In Section 3, one heuristic method is applied to solve this problem. Section 4 introduces the GA methods and describes implementations used for this problem. Then, the experimental results are illustrated and analysed in Section 5. Finally, Section 6 provides conclusion and suggestions for further work on this problem.2. Mathematical modelIn this paper, the flexible Job-shop Scheduling Problem we are treating is to minimize the makespan, and balance the workload for all machines. Before defining the problem concretely we should add several assumptions to the problem.1.There is a set of jobs and a set of machines.2.Each job consists of one fixed sequence of operations.3.Each machine can process at most one operation at a time.4.Each machine becomes available to other operations once the operations which arecurrently assigned to be completed.5.All machines are available at t = 0.6.All jobs can be started at t = 0.7.There are no precedence constraints among operations of different jobs.8.Any operation cannot be interrupted.9.Neither release times nor due dates are specified.The f-JSP we considering here is a problem which including n-jobs operated on m-machines. Some symbols and notations have been defined as follows:i: index of jobs, i = 1, 2, … nJ i : the i th jobn : total number of jobsk : index of operations, k = 1, 2, … K i o ik : the k th operation of job i (or J i )K i : total number of operations in job i (or J i )j : index of machines, j = 1, 2, … m M j : the j th machinem : total number of machinesp ikj : processing time of operation o ik on machine j (or M j ) U : a set of machines with the size mU ik : a set of available machines for the operation o ikF ik t : completion time of operation o ikW j : workloads (total processing time) of machine M jThe objective function can be described as the following three equations. Eq. (1) gives the first objective makespan and also means to minimize the maximum finishing time considering all the operations. Eq. (2) gives the second objective which is to minimize the maximum of workloads for all machines. Eq. (3) gives the objective total workloads.Eq. (2) combining with Eq. (3) give a physical meaning to the f-JSP, which referring to reducing total processing time and dispatching the operations averagely for each machine. Considering both of the two equations, our objective is to balancing the workloads of all machines. Eq. (4) and Eq. (5) give two basic processing constrains.3. Heuristic methodTo demonstrate f-JSP model clearly, we first prepare a simple example. Table 1 gives the data set of an f-JSP including 3 jobs operated on 4 machines. It is obviously a problem with total flexibility because all the machines are available for each operation (U ik =U ). There are several traditional heuristic methods that can be used to make a feasible schedule.In this case, we use the SPT (select the operation with the shortest processing time) as selective strategy to find an optimal solution, and the algorithm is based on the procedure in Figure 1. Before selection we first make some initialization:• starting from a table D presenting the processing times possibilities• on the various machines, create a new table D’ whose size is the same one as the table D ; • create a table S whose size is the same one as the table D (S is going to represent chosen{}{}(5)t. s. (4)min (3)max min (2)max max min (1),,,k ,i ,t j ,k ,i t p t W W W W t t F ik F k i j k i F ik mj j T j mj M F ik K k ni M i ∀≥∀≤+==⎭⎬⎫⎩⎨⎧=++=≤≤≤≤≤≤∑0111111assignments);•initialize all elements of S to 0 (S ikj=0)•recopy D in D’Table 1. Data set of a 3-job 4-machine Problem.procedure: SPT Assignmentinput: dataset table Doutput: best schedule Sbeginfor (i=1; i<=n)for (k=1; k<=K i)min=+∞;pos=1;for (j=1; j<=m)if (p’ikj<min) then {min=p’ikj; pos=j;}S i,k,pos=1(assignment of o ik to the machine M pos);//updating of D’;for (k’=k+1; k’<=K i’)p’i’,k,pos= p’i’,k,pos+ p i,k,pos;for (i’= i +1; i’<=n)for (k’= 1; k’<=K i’)p’i’,k’,pos= p’i’,k’,pos+ p i,k,pos;endendoutput best schedule SendFigure 1. SPT Assignment Procedure.Following this algorithm, we assign o11 to M1, and add the processing time p111=1 to the elements of the first column of D’. (shown in Table 2)Table 2 D’ (for i=1 and k=1).Table 3 D’ (for i=1 and k=2).Secondly, we assign o12 to M4, and add the processing time p124=1 to the elements of the fourth column of D’ shown in Table 3. By following the same method, we obtain assignment S shown in Table 4.Furthermore, we can denote the schedule based on job sequence as:S={(o11, M1), (o12, M4), (o13, M1), (o21, M2), (o22, M2), (o23, M1),(o31, M3), (o32, M4)}= {(o11,M1: 0-1), (o12, M4: 1-2), (o13, M1: 2-5), (o21, M2: 0-1), (o22, M2: 1-4),(o23, M1: 4-6), (o31, M3: 1-3), (o32, M4: 3-4)}Finally we can calculate the solution by Eq.1, Eq. 2 and Eq. 3 as follows:t M = max{F t11, F t12, F t13, F t21, F t22, F t23, F t31, F t32}=max{1, 2, 5, 1, 4, 6, 3, 4}= 6WM= max{(1+3), (1+3), (3+2), (1+1)}=5W T=4+4+5+2=154. Genetic Algorithm ApproachThere are three parts in this section, firstly some traditional representation (Mesghouni, 1999), secondly Imed Kacem’s approach (Kacem, Hammadi & Borne, 2002), and thirdly multistage operation-based representation.4.1 Traditional Representation of GA4.1.1 Parallel Machine Representation (PM-R)The chromosome is a list of machines placed in parallel (see Table 5). For each machine, we associate operations to execute. Each operation is coded by three elements:Operation k , job J i and Sikj t (starting time of operation o ik on the machine M j ).4.1.2 Parallel Jobs Representation (PJ-R)The chromosome is represented by a list of jobs showed in Table 6. Information of each job is shown in the corresponding row where each case is constituted of two terms: machine M j which executes the operation and corresponding starting time t ikj S .4.2 Imed Kacem’s approachImed Kacem proposed Operations Machines Representation (OM-R) approach (Kacem, Hammadi & Borne, 2002), which based on a traditional representation called Schemata Theorem Representation (ST-R). It was firstly introduced in GAs by Holland (Charon, Germinated & Hudry, 1996).In the case of a binary coding, a schemata is a chromosome model where some genes are fixed and the other are free (see the following Figure 2), Positions 4 and 6 are occupied by the symbol:“*”. This symbol indicates that considered genes can take “0” or “1” as value. Thus, chromosome C 1 and C 2 respect the model imposed by the schemata S.Based on the ST-R approach, Kacem expanded it to Operations Machines Representation (OM-R). It consists in representing the schedule in the same assignment tableS . We replace each case S ikj =1 by the couple (F ik t , Fik t ), while the cases S ikj =0 are unchanged. To explain this coding, we present the same schedule introduced before (Table 7). Furthermore, operation based crossover and the other two kinds of mutation (operator ofTable 5. Parallel machine representation.Table 6.Parallel jobs representation.00*1*001Position : 1 2 3 4 5 6 7800110001S =C 1=C 2mutation reducing the effective processing time, operator of mutation balancing work loads of machines) are included in this approach.4.3 Multistage operation-based approachConsidering the GA approach proposed by Imed Kacem, it is complex even when you take allthe objectives in count, because all the crossover and mutation are based on the chromosome which is described as a constructor of table. Therefore, it will spend more CPU-time for finding solutions; hence a multistage operation-based GA approach has been proposed. Figure 3 presents an f-JSP which includes 3 jobs operated on 4 machines, we add another two nodes (starting node and terminal node) in the figure to make it a formal network presentation. Denoting each operation as one stage, and each machine as one state, the problem can be formulated into an 8-stage, 4-state problem.Connected by the dashed arcs a feasible schedule can be obtained as:It is obviously simpler than all the representations prsented before, and certainly can easily combine with almost all kinds of classic crossover and mutation methods. Figure 4 and Figure 5 separately give the encoding and decoding procedure.Figure 3.Example for Multistage Operation-based Representatin (MO-R).43422141ID :1 2 3 4 5 6 7 8V =5. Numerical ExperimentIn this paper, we use the same dataset (showed in Table 8 & Table 9) as in Kacem’s paper tocompare the results. It is especially f-JSP with both partial flexibility (Uik⊆U) and totalflexibility (Uik=U). The symbol “-” in Table 8 shows that the machine is not available for thecorresponding operation.We have used random selections to generate the initial population. Then we applied the multistage operation-based GA (moGA combining one-cut point crossover and local-search mutation) with the following parameters: popSize: 100; p M=0.3; p C=0.6All results can be summarized in Table 10 and Table 11. Values of different approach show the efficiency. It is easy to find the moGA outperform than all the other approach.Table 10.Result Comparisons(8×8).Heuristic method (SPT) ClassicGAKacem'sApproach moGAt M19 16 16 15W T91 77 75 73W M16 14 14 14 Table 11.Result Comparisons (10×10).Heuristic method (SPT) ClassicGAKacem'sApproach moGAt M16 7 7 7 W T59 53 45 43W M16 7 6 56. ConclusionSome GA approaches have been used for solving f-JSP recently. However the efficiency is mainly affected by the complexity of chromosome representation. In this paper, a new multistage operation-based representation of GA (moGA) approach is proposed to solve f-JSP. The proposed algorithm is designed for optimal the 3 objectives including the makespan t M, total workloads of all machines W, and maximum of workloads for all machines W M.By using some numerical example of related works, we demonstrate the efficiency of moGA. The optimal result is better than the other related approaches.ReferencesNajid, N.M., Dauzere-Peres, S. and Zaidat, A. (2002), A modified simulated annealing method for flexible job shop scheduling problem, IEEE International Conference on Systems, Man and Cybernetics, 5: 6.Kacem, I., Hammadi, S. and Borne, P. (2002), Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE Transactions on Systems, Man and Cybernetics, Part C, 32(1): 408-419.Gen, M. and Cheng, R. (1997), Genetic Algorithms & Engineering Design, John Wiley & Sons.Gen, M. and Cheng, R. (2000), Genetic Algorithms & Engineering Design, John Wiley & Sons.Blazewicz, J., Domschke, W. and Pesch, E. (1996), The job shop scheduling problem: conventional and new solution techniques, European Journal of Operational Research, 93: 1-33.Brucker, P. and Schlie, R. (1990), Job-shop scheduling with multi-purpose machines, Computing, 45: 369-375.Brandimarte, P. (1993), Routing and scheduling in a flexible job shop by tabu search, Annals of Operations Research, 41: 157-183.Mati, Y., Rezg, N. and Xie, X. (2001), An Integrated Greedy Heuristic for a Flexible Job Shop Scheduling Problem, IEEE International Conference on Systems, Man, and Cybernetics, 4: 2534-2539.Mesghouni, K. (1999), Application des algorithmes évolutionnistes dans les problèmes d’ optimization en ordonnancement de production, Ph.D. dissertation, USTL 2451.Charon, I., Germinated, A. and Hudry, O. (1996), Méthodes d’Optimization Combinatoires, Paris, France: Masson.。
战略柔性一个世界级制造业新的现实外文翻译
中文3845字本科毕业论文(设计)外文翻译原文:Strategic flexibility: a new reality for world-class manufacturing The development of the concept of flexibility has been slow in the manufacturing literature because of the relatively stable market structure and minimal competitive pressure prior to the 1960s. In fact, manufacturing was not considered particularly important in the formulation of business strategy. As the competitiveness problems increased, practitioners and academicians began to recognize that manufacturing strategy was vital in supporting changes of corporate strategy. Consequently, a number of analytical models and empirical studies were developed to enhance manufacturing flexibility. According to Suarez, Cusumano, and Fine (1995), most empirical studies on manufacturing flexibility serve one of the following purposes: (1) to develop taxonomies of flexibility; (2) to investigate the relationship between flexibility and performance; (3) to cover historical and economical analyses of flexibility; and (4) to develop strategic frameworks for flexibility. Carlsson (1989), Sethi and Sethi (1990), Hyun and Ahn (1992), and Upton (1994) are just a few research works that provide further literature reviews on flexibility.Flexibility is often regarded as one of the competitive priorities, along with cost, quality, and innovation. Just as low cost and high quality have already become a requirement for market entry, flexibility might ultimately be the key to enhancing a firm's competitive ability. While uncertainty can be a threat to some firms, it provides opportunity to those with higher degrees of flexibility, either market-oriented or resources-oriented. Firms that are able to deal with uncertainties that their competitors cannot have market-oriented flexibility. By reducing market uncertainties or exerting influence on customer expectations, firms have more strategic choices and can adopt a more proactive approach to competing. Firms with highly flexible production systemshave resource-oriented flexibility and can be more responsive to the changing market.By combining these two concepts, Figure 1 shows the dominant competitive priorities corresponding to the firm's ability to cope with uncertainties. Not only can world-class manufacturing firms adapt to the changing environment swiftly, but they also can influence market demand (e.g., by creating uncertainties or customer expectations that competitors cannot deal with). Both reactive and proactive approaches have proved to be equally important and require different types of flexibility. Instead of focusing on one particular dimension of flexibility, world-class manufacturing firms need a strategic perspective of flexibility - the ability to quickly adjust their competitive objectives to meet new business conditions.In a stable competitive environment like decades ago, a competitive strategy simply involved defining a competitive position and then defending it. Since the competitive environment has changed rapidly and unpredictably, however, new knowledge and capabilities are needed to support any strategy to create a sustainable competitive advantage. Therefore, the goal of the latest developments in manufacturing strategy is to attain strategic flexibility. Competitive advantage commonly refers to the creation of a production-distribution system that has a unique advantage over its competitors. Achieving competitive advantage does not imply that the company must always do better than the competitors in all areas. The key is to do certain things better in most of the areas. Deciding which areas to target is the central issue of competitive petitive advantages traditionally have been accomplished through economies of scale and product and process technology, but these are no longer sufficient.Competitive advantage through economies of scale is best illustrated by mass production. Furthermore, Henry Ford's dictum that "customers can have any color as long as it's black" still convinces many manufacturers that they must choose between standardization at low cost or flexibility at high cost. This has been disproved by Japanese automobile and electronics manufacturers who achieve an optimal balance of product standardization and manufacturing flexibility.The dynamics of today's competitive environment suggest that economies ofscale and product or process technology will be a diminishing source of competitive advantage. As a result, manufacturers are turning their attention to building the skills and knowledge of their workforce. World-class manufacturers also realize that competitive advantage can be created only when the manufacturing strategy is well integrated with other functional strategies, which together support the overall corporate strategy. It implies that changes in strategy are necessary to cope with the changes in competitive environment and in the organization itself. Therefore, there is no "best" manufacturing strategy, and all competitive manufacturers should be ready to shift from one strategy to another as needed. The appropriate strategy depends on a firm's strengths and weaknesses. Two manufacturing firms may develop different strategies yet both compete in the same market with success. In addition, sticking with a single competitive strategy (no matter how successful) often turns out to be problematic when the underlying conditions change.Given the dynamic nature of the marketplace, flexibility has already become the most important competitive priority of the 1990s. Flexibility is usually classified broadly as product or service-related (such as volume, product mix, and modification) and process technology-related (such as changeover, scheduling, and innovation). While these sources of flexibility are essential to provide competitive advantage to manufacturers, they tend to be operational or tactical in nature. To acquire a sustainable competitive advantage, management must develop strategic flexibility, which requires long-term commitment and the development of critical resources. Note that no specific manufacturing techniques or improvement programs are included. Instead, the emphasis is on developing skills such as knowledge, capabilities, and a flexible organizational structure. These are the foundation of strategic flexibility that allow future changes to take place as needed; and, best of all, their unique nature means that no one else can "copy" them easily.Strategic flexibility allows a manufacturing firm to shift from one dominant strategy to another, from one competitive priority to another, but also implies a long-term commitment of resources and a plan of action. Progress, therefore, depends on the current state of the firm's resources and capabilities. Generally, strategicflexibility is attained through a three-step process: awareness, understanding, and implementation.Phase 1: Be aware that only strategic flexibility will provide sustainable competitive advantage over the long run. During the last two decades, quality improvement, automation, and advanced manufacturing techniques, to name a few, have often been perceived as a path to competitive advantage. While they may lead to positive outcomes, a number of empirical studies suggest that many firms found them ineffective. Many manufacturers focused too much on the form or mechanics of such programs while overlooking the development of skills and capabilities needed to support the changes. Resistance to change is greater if management fails to see the necessity for changes. Until management fully recognizes the need for long-term competitive advantage, there is no clear incentive to devote the time, effort, and expense to develop strategic flexibility.Phase 2: Understand that the manufacturing function's performance links directly to corporate performance and survival. Understanding the importance of the manufacturing function and its link to corporate performance provides a focal point for management to think more proactively about building capabilities for the future. The poor performance of many major manufacturing firms during the last two decades was no surprise to many researchers. Companies that develop a clear linkage between business and manufacturing strategies tend to be more successful and profitable. This finding has substantiated the argument that manufacturing is indeed a key competitive variable, especially in those industries where customers are increasingly cost and quality conscious.Phase 3: Formulate and implement strategies that center on the development of skills, manufacturing capabilities, and lean organizational structures. The outdated manufacturing strategy based on mass production is not responsive enough to cope with rapidly changing markets and shortened product life cycles. In addition, production jobs have become more challenging and conceptual, as routine and repetitive tasks are performed by automated equipment. The full benefit of technology can be exploited only when workers understand and control a large part of theproduction process.* Skills and knowledgeA productive work force today must be highly skilled and flexible, characteristics that can only be developed through extensive training and experience in a variety of job assignments. Therefore, the workplace must be reorganized to promote continuous learning, which must become a normal part of work life. Evidence suggests that not many manufacturers, particularly in the United States, give high priority and commit sufficient resources to training their front-line workers. Management needs to realize that maintaining and upgrading the skills of their workforce is central to their competitive strategy. Management must focus on the cultivation of multi-skilled workers and stop treating them as replaceable parts or a cost to be controlled. In an attempt to find out why the improvement of flexibility has been so elusive, Upton (1995) observed that "most managers put too much faith in machines and technology, and too little faith in the day-to-day management of people" (p. 75). The basic theme of a skills development program is to encourage continuous learning throughout the company. Training programs should be developed in ways that are consistent with carefully defined goals and the availability of resources. More important, management should anticipate future skill needs, not just immediate ones.Complex computer-based production systems are likely to prevail. Training people to conceptualize, design, and use new production technology is as crucial as adopting the technology itself. Technology is often perceived as a way to replace workers, it does not mean that human resources are no longer important in achieving competitiveness. Indeed, the only way for manufacturers to maximize their investment in new technology is to upgrade the skill levels of their workforce. As production becomes more challenging and conceptual, because automated equipment performs most of the routine and repetitive tasks, investment in workforce skills development is increasingly vital.* Manufacturing CapabilitiesStrategic flexibility is not just about a flexible workforce; it requires an augmentation of the workforce with advanced process and information technologiesto satisfy customer demands. Advanced process technology, such as flexible manufacturing systems (FMS) and computer-integrated manufacturing (CIM), is crucial for achieving mass customization. An FMS can manufacture assorted products using the same group of machines linked by automated materials handling systems and controlled by a computer system. Automated and preprogrammed workstations are linked for different operations, ensuring that all members of a family of parts can be produced whenever needed. With the installation of FMS, General Electric can deliver a custom-made circuit box in three days instead of three weeks. Likewise, Motorola manufactures custom-designed electronic pagers in less than three hours.Ample evidence suggests that product designs can be significantly simplified if cross-functional design teams are used. A cross-functional design team will help facilitate a modular approach to product design. This approach provides a viable product design strategy to meet changing demand with the advantage of standardization. Modular design is the creation of products from some combination of existing, standardized components; it requires much creativity and communication across the company. Japanese automobile manufacturers have invested heavily in designing parts that can be combined in a number of ways and used interchangeably among several models. Although the modular design will occasionally increase the cost of tools and dies, it facilitates faster introduction of new car models and drastically reduces product development costs.* Organizational TransformationThe ultimate success of strategic flexibility requires a redefinition of traditional organizational functions, including links with suppliers and customers. Deep organizational hierarchies, as found in major manufacturing firms, impede cooperation and communication. In recent years, many corporate restructuring efforts have moved to flatten organizational structures to focus on cross-functional integration and employee participation. Corporate communication is then facilitated by a structure that is free from departmental boundaries and limitations. An ultimate goal is to turn the entire production process into modules and to create a dynamic network of skills and capabilities that allows the rapid integration of resources tocustomize products or services.Mass production of standardized products is no longer a feasible way to meet the challenge of changing market demand and shortened product life cycles. In fact, the usual method of first identifying a fixed competitive priority, such as cost, quality, time, flexibility, or innovation, and then devoting all resources to meet it will no longer provide a sustainable competitive advantage. World-class manufacturers must obtain strategic flexibility to cope with more uncertainties than just changing demand patterns and production volumes. Strategic flexibility is not an improvement program, but is rather the ability to adapt and the readiness for change. The goal of strategic flexibility is to provide more options so that a firm can shift from a current manufacturing strategy to a new one with minimal penalties in cost, time, or performance. True strategic flexibility can be achieved only through the development of skills and manufacturing capabilities, which eventually lead to complete organizational transformation.Source: Lau, R.S.M.“Strategic flexibility: a new reality for world-class manufacturing”. SAM Advanced Management Journal, 1996(3):P11-15.译文:战略柔性:一个世界级制造业新的现实由于20世纪60年代前期相对稳定的市场结构和较小的竞争压力,柔性概念的发展已经逐渐运用到制造业文化中。
元认知策略计划策略英文表达
元认知策略计划策略英文表达Metacognition strategies are essential tools for effective learning and problem-solving. They involve thinking about your thinking, analyzing your approach, and making adjustments to improve your cognitive processes.Let's dive into some of the key elements of metacognition and how you can apply them in daily life.First up, planning strategies. This is where you set clear goals and outline the steps you'll take to achieve them. It's like having a roadmap for your learning journey. For example, if you're preparing for an exam, you might start by identifying the topics you need to focus on and then create a study schedule that breaks down the material into manageable chunks.Another crucial aspect of metacognition is monitoring your progress. This means checking in with yourself regularly to see how you're doing and making adjustments as needed. It's like having a personal trainer who keeps youaccountable and helps you stay on track. If you notice that a certain study technique isn't working for you, you cantry something else.Reflection is another key component of metacognition. After you've completed a task or studied a topic, it's important to take a step back and evaluate your.。
计算机领域会议排名
计算机领域国际会议分类排名现在的会议非常多,在投文章前,大家可以先看看会议的权威性、前几届的录用率,这样首先对自己的文章能不能中有个大概的心理底线。
权威与否可以和同行的同学沟通、或者看录用文章的水平、或者自己平时阅读文献的时候的慢慢累及。
原来有人做过一个国际会议的排名,如下.sg/home/assourav/crank.htm其中的很多会议我们都非常熟悉的。
但是这个排名是大概2000的时候做的,后来没有更新,所以像ISWC 这个会议在其中就看不到。
但是很多悠久的会议上面都有的,如www,SIGIR,VLDB,EMLC,ICTAI这些等等。
这些东西可以作为一个参考。
现在很多学校的同学毕业都要有检索的要求了。
因此很多不在SCI,EI检索范围内的会议投了可能对毕业无用,所以投之前最好查查会议是不是被SCI,EI检索的。
当然这也不绝对,如Web领域最权威的WWW的全文就只是ISTP检索,而不是SCI,EI检索的(可能是ACM出版的原因吧?)。
罗嗦了这么多!祝愿大家能在好的会议上发PAPER,能被SCI,EI检索。
---------------附,会议排名(from .sg/home/assourav/crank.htm)Computer Science Conference RankingsSome conferences accept multiple categories of papers. The rankings below are for the mos t prestigious category of paper at a given conference. All other categories should be treat ed as "unranked".AREA: DatabasesRank 1:SIGMOD: ACM SIGMOD Conf on Management of DataPODS: ACM SIGMOD Conf on Principles of DB SystemsVLDB: Very Large Data BasesICDE: Intl Conf on Data EngineeringICDT: Intl Conf on Database TheoryRank 2:SSD: Intl Symp on Large Spatial DatabasesDEXA: Database and Expert System ApplicationsFODO: Intl Conf on Foundation on Data OrganizationEDBT: Extending DB TechnologyDOOD: Deductive and Object-Oriented DatabasesDASFAA: Database Systems for Advanced ApplicationsCIKM: Intl. Conf on Information and Knowledge ManagementSSDBM: Intl Conf on Scientific and Statistical DB MgmtCoopIS - Conference on Cooperative Information SystemsER - Intl Conf on Conceptual Modeling (ER)Rank 3:COMAD: Intl Conf on Management of DataBNCOD: British National Conference on DatabasesADC: Australasian Database ConferenceADBIS: Symposium on Advances in DB and Information SystemsDaWaK - Data Warehousing and Knowledge DiscoveryRIDE WorkshopIFIP-DS: IFIP-DS ConferenceIFIP-DBSEC - IFIP Workshop on Database SecurityNGDB: Intl Symp on Next Generation DB Systems and AppsADTI: Intl Symp on Advanced DB Technologies and Integration FEWFDB: Far East Workshop on Future DB SystemsMDM - Int. Conf. on Mobile Data Access/Management (MDA/MDM)ICDM - IEEE International Conference on Data MiningVDB - Visual Database SystemsIDEAS - International Database Engineering and Application Symposium Others:ARTDB - Active and Real-Time Database SystemsCODAS: Intl Symp on Cooperative DB Systems for Adv AppsDBPL - Workshop on Database Programming LanguagesEFIS/EFDBS - Engineering Federated Information (Database) Systems KRDB - Knowledge Representation Meets DatabasesNDB - National Database Conference (China)NLDB - Applications of Natural Language to Data BasesFQAS - Flexible Query-Answering SystemsIDC(W) - International Database Conference (HK CS)RTDB - Workshop on Real-Time DatabasesSBBD: Brazilian Symposium on DatabasesWebDB - International Workshop on the Web and DatabasesWAIM: Interational Conference on Web Age Information ManagementDASWIS - Data Semantics in Web Information SystemsDMDW - Design and Management of Data WarehousesDOLAP - International Workshop on Data Warehousing and OLAPDMKD - Workshop on Research Issues in Data Mining and Knowledge DiscoveryKDEX - Knowledge and Data Engineering Exchange WorkshopNRDM - Workshop on Network-Related Data ManagementMobiDE - Workshop on Data Engineering for Wireless and Mobile AccessMDDS - Mobility in Databases and Distributed SystemsMEWS - Mining for Enhanced Web SearchTAKMA - Theory and Applications of Knowledge MAnagementWIDM: International Workshop on Web Information and Data ManagementW2GIS - International Workshop on Web and Wireless Geographical Information Systems CDB - Constraint Databases and ApplicationsDTVE - Workshop on Database Technology for Virtual EnterprisesIWDOM - International Workshop on Distributed Object ManagementOODBS - Workshop on Object-Oriented Database SystemsPDIS: Parallel and Distributed Information SystemsAREA: Artificial Intelligence and Related SubjectsRank 1:AAAI: American Association for AI National ConferenceCVPR: IEEE Conf on Comp Vision and Pattern RecognitionIJCAI: Intl Joint Conf on AIICCV: Intl Conf on Computer VisionICML: Intl Conf on Machine LearningKDD: Knowledge Discovery and Data MiningKR: Intl Conf on Principles of KR & ReasoningNIPS: Neural Information Processing SystemsUAI: Conference on Uncertainty in AIAAMAS: Intl Conf on Autonomous Agents and Multi-Agent Systems (past: ICAA)ACL: Annual Meeting of the ACL (Association of Computational Linguistics)Rank 2:NAACL: North American Chapter of the ACLAID: Intl Conf on AI in DesignAI-ED: World Conference on AI in EducationCAIP: Inttl Conf on Comp. Analysis of Images and PatternsCSSAC: Cognitive Science Society Annual ConferenceECCV: European Conference on Computer VisionEAI: European Conf on AIEML: European Conf on Machine LearningGECCO: Genetic and Evolutionary Computation Conference (used to be GP)IAAI: Innovative Applications in AIICIP: Intl Conf on Image ProcessingICNN/IJCNN: Intl (Joint) Conference on Neural NetworksICPR: Intl Conf on Pattern RecognitionICDAR: International Conference on Document Analysis and RecognitionICTAI: IEEE conference on Tools with AIAMAI: Artificial Intelligence and MathsDAS: International Workshop on Document Analysis SystemsWACV: IEEE Workshop on Apps of Computer VisionCOLING: International Conference on Computational LiguisticsEMNLP: Empirical Methods in Natural Language ProcessingEACL: Annual Meeting of European Association Computational LingusticsCoNLL: Conference on Natural Language LearningDocEng: ACM Symposium on Document EngineeringIEEE/WIC International Joint Conf on Web Intelligence and Intelligent Agent Technology Rank 3:PRICAI: Pacific Rim Intl Conf on AIAAI: Australian National Conf on AIACCV: Asian Conference on Computer VisionAI*IA: Congress of the Italian Assoc for AIANNIE: Artificial Neural Networks in EngineeringANZIIS: Australian/NZ Conf on Intelligent Inf. SystemsCAIA: Conf on AI for ApplicationsCAAI: Canadian Artificial Intelligence ConferenceASADM: Chicago ASA Data Mining Conf: A Hard Look at DMEPIA: Portuguese Conference on Artificial IntelligenceFCKAML: French Conf on Know. Acquisition & Machine LearningICANN: International Conf on Artificial Neural NetworksICCB: International Conference on Case-Based ReasoningICGA: International Conference on Genetic AlgorithmsICONIP: Intl Conf on Neural Information ProcessingIEA/AIE: Intl Conf on Ind. & Eng. Apps of AI & Expert SysICMS: International Conference on Multiagent SystemsICPS: International conference on Planning SystemsIWANN: Intl Work-Conf on Art & Natural Neural NetworksPACES: Pacific Asian Conference on Expert SystemsSCAI: Scandinavian Conference on Artifical IntelligenceSPICIS: Singapore Intl Conf on Intelligent SystemPAKDD: Pacific-Asia Conf on Know. Discovery & Data MiningSMC: IEEE Intl Conf on Systems, Man and CyberneticsPAKDDM: Practical App of Knowledge Discovery & Data MiningWCNN: The World Congress on Neural NetworksWCES: World Congress on Expert SystemsASC: Intl Conf on AI and Soft ComputingPACLIC: Pacific Asia Conference on Language, Information and ComputationICCC: International Conference on Chinese ComputingICADL: International Conference on Asian Digital LibrariesRANLP: Recent Advances in Natural Language ProcessingNLPRS: Natural Language Pacific Rim SymposiumMeta-Heuristics International ConferenceRank 3:ICRA: IEEE Intl Conf on Robotics and AutomationNNSP: Neural Networks for Signal ProcessingICASSP: IEEE Intl Conf on Acoustics, Speech and SPGCCCE: Global Chinese Conference on Computers in EducationICAI: Intl Conf on Artificial IntelligenceAEN: IASTED Intl Conf on AI, Exp Sys & Neural NetworksWMSCI: World Multiconfs on Sys, Cybernetics & InformaticsLREC: Language Resources and Evaluation ConferenceAIMSA: Artificial Intelligence: Methodology, Systems, ApplicationsAISC: Artificial Intelligence and Symbolic ComputationCIA: Cooperative Information AgentsInternational Conference on Computational Intelligence for Modelling, Control and Automation Pattern MatchingECAL: European Conference on Artificial LifeEKAW: Knowledge Acquisition, Modeling and ManagementEMMCVPR: Energy Minimization Methods in Computer Vision and Pattern RecognitionEuroGP: European Conference on Genetic ProgrammingFoIKS: Foundations of Information and Knowledge SystemsIAWTIC: International Conference on Intelligent Agents, Web Technologies and Internet Commer ceICAIL: International Conference on Artificial Intelligence and LawSMIS: International Syposium on Methodologies for Intelligent SystemsIS&N: Intelligence and Services in NetworksJELIA: Logics in Artificial IntelligenceKI: German Conference on Artificial IntelligenceKRDB: Knowledge Representation Meets DatabasesMAAMAW: Modelling Autonomous Agents in a Multi-Agent WorldNC: ICSC Symposium on Neural ComputationPKDD: Principles of Data Mining and Knowledge DiscoverySBIA: Brazilian Symposium on Artificial IntelligenceScale-Space: Scale-Space Theories in Computer VisionXPS: Knowledge-Based SystemsI2CS: Innovative Internet Computing SystemsTARK: Theoretical Aspects of Rationality and Knowledge MeetingMKM: International Workshop on Mathematical Knowledge ManagementACIVS: International Conference on Advanced Concepts For Intelligent Vision Systems ATAL: Agent Theories, Architectures, and LanguagesLACL: International Conference on Logical Aspects of Computational LinguisticsAREA: Hardware and ArchitectureRank 1:ASPLOS: Architectural Support for Prog Lang and OSISCA: ACM/IEEE Symp on Computer ArchitectureICCAD: Intl Conf on Computer-Aided DesignDAC: Design Automation ConfMICRO: Intl Symp on MicroarchitectureHPCA: IEEE Symp on High-Perf Comp ArchitectureRank 2:FCCM: IEEE Symposium on Field Programmable Custom Computing MachinesSUPER: ACM/IEEE Supercomputing ConferenceICS: Intl Conf on SupercomputingISSCC: IEEE Intl Solid-State Circuits ConfHCS: Hot Chips SympVLSI: IEEE Symp VLSI CircuitsCODES+ISSS: Intl Conf on Hardware/Software Codesign & System SynthesisDATE: IEEE/ACM Design, Automation & Test in Europe ConferenceFPL: Field-Programmable Logic and ApplicationsCASES: International Conference on Compilers, Architecture, and Synthesis for Embedded Syste msRank 3:ICA3PP: Algs and Archs for Parall ProcEuroMICRO: New Frontiers of Information TechnologyACS: Australian Supercomputing ConfISC: Information Security ConferenceUnranked:Advanced Research in VLSIInternational Symposium on System SynthesisInternational Symposium on Computer DesignInternational Symposium on Circuits and SystemsAsia Pacific Design Automation ConferenceInternational Symposium on Physical DesignInternational Conference on VLSI DesignCANPC: Communication, Architecture, and Applications for Network-Based Parallel Computing CHARME: Conference on Correct Hardware Design and Verification MethodsCHES: Cryptographic Hardware and Embedded SystemsNDSS: Network and Distributed System Security SymposiumNOSA: Nordic Symposium on Software ArchitectureACAC: Australasian Computer Architecture ConferenceCSCC: WSES/IEEE world multiconference on Circuits, Systems, Communications & Computers ICN: IEEE International Conference on Networking Topology in Computer Science ConferenceAREA: Applications and MediaRank 1:I3DG: ACM-SIGRAPH Interactive 3D GraphicsSIGGRAPH: ACM SIGGRAPH ConferenceACM-MM: ACM Multimedia ConferenceDCC: Data Compression ConfSIGMETRICS: ACM Conf on Meas. & Modelling of Comp SysSIGIR: ACM SIGIR Conf on Information RetrievalPECCS: IFIP Intl Conf on Perf Eval of Comp \& Comm Sys WWW: World-Wide Web ConferenceRank 2:IEEE VisualizationEUROGRAPH: European Graphics ConferenceCGI: Computer Graphics InternationalCANIM: Computer AnimationPG: Pacific GraphicsICME: Intl Conf on MMedia & ExpoNOSSDAV: Network and OS Support for Digital A/VPADS: ACM/IEEE/SCS Workshop on Parallel \& Dist Simulation WSC: Winter Simulation ConferenceASS: IEEE Annual Simulation SymposiumMASCOTS: Symp Model Analysis \& Sim of Comp \& Telecom Sys PT: Perf Tools - Intl Conf on Model Tech \& Tools for CPE NetStore: Network Storage SymposiumMMCN: ACM/SPIE Multimedia Computing and NetworkingJCDL: Joint Conference on Digital LibrariesRank 3:ACM-HPC: ACM Hypertext ConfMMM: Multimedia ModellingDSS: Distributed Simulation SymposiumSCSC: Summer Computer Simulation ConferenceWCSS: World Congress on Systems SimulationESS: European Simulation SymposiumESM: European Simulation MulticonferenceHPCN: High-Performance Computing and NetworkingGeometry Modeling and ProcessingWISEDS-RT: Distributed Simulation and Real-time Applications IEEE Intl Wshop on Dist Int Simul and Real-Time Applications ECIR: European Colloquium on Information RetrievalEd-MediaIMSA: Intl Conf on Internet and MMedia SysUn-ranked:DVAT: IS\&T/SPIE Conf on Dig Video Compression Alg \& TechMME: IEEE Intl Conf. on Multimedia in EducationICMSO: Intl Conf on Modelling, Simulation and OptimisationICMS: IASTED Intl Conf on Modelling and SimulationCOTIM: Conference on Telecommunications and Information MarketsDOA: International Symposium on Distributed Objects and ApplicationsECMAST: European Conference on Multimedia Applications, Services and TechniquesGIS: Workshop on Advances in Geographic Information SystemsIDA: Intelligent Data AnalysisIDMS: Interactive Distributed Multimedia Systems and Telecommunication ServicesIUI: Intelligent User InterfacesMIS: Workshop on Multimedia Information SystemsWECWIS: Workshop on Advanced Issues of E-Commerce and Web/based Information Systems WIDM: Web Information and Data ManagementWOWMOM: Workshop on Wireless Mobile MultimediaWSCG: International Conference in Central Europe on Computer Graphics and Visualization LDTA: Workshop on Language Descriptions, Tools and ApplicationsIPDPSWPIM: International Workshop on Parallel and Distributed Computing Issues in Wireless N etworks and Mobile ComputingIWST: International Workshop on Scheduling and TelecommunicationsAPDCM: Workshop on Advances in Parallel and Distributed Computational ModelsCIMA: International ICSC Congress on Computational Intelligence: Methods and Applications FLA: Fuzzy Logic and Applications MeetingICACSD: International Conference on Application of Concurrency to System DesignICATPN: International conference on application and theory of Petri netsAICCSA: ACS International Conference on Computer Systems and ApplicationsCAGD: International Symposium of Computer Aided Geometric DesignSpanish Symposium on Pattern Recognition and Image AnalysisInternational Workshop on Cluster Infrastructure for Web Server and E-Commerce Applications WSES ISA: Information Science And Applications ConferenceCHT: International Symposium on Advances in Computational Heat TransferIMACS: International Conference on Applications of Computer AlgebraVIPromCom: International Symposium on Video Processing and Multimedia Communications PDMPR: International Workshop on Parallel and Distributed Multimedia Processing & Retrieval International Symposium On Computational And Applied PdesPDCAT: International Conference on Parallel and Distributed Computing, Applications, and Tec hniquesBiennial Computational Techniques and Applications ConferenceSymposium on Advanced Computing in Financial MarketsWCCE: World Conference on Computers in EducationITCOM: SPIE's International Symposium on The Convergence of Information Technologies and Com municationsConference on Commercial Applications for High-Performance ComputingMSA: Metacomputing Systems and Applications WorkshopWPMC : International Symposium on Wireless Personal Multimedia Communications WSC: Online World Conference on Soft Computing in Industrial Applications HERCMA: Hellenic European Research on Computer Mathematics and its Applications PARA: Workshop on Applied Parallel ComputingInternational Computer Science Conference: Active Media TechnologyIW-MMDBMS - Int. Workshop on Multi-Media Data Base Management SystemsAREA: System TechnologyRank 1:SIGCOMM: ACM Conf on Comm Architectures, Protocols & AppsINFOCOM: Annual Joint Conf IEEE Comp & Comm SocSPAA: Symp on Parallel Algms and ArchitecturePODC: ACM Symp on Principles of Distributed ComputingPPoPP: Principles and Practice of Parallel ProgrammingRTSS: Real Time Systems SympSOSP: ACM SIGOPS Symp on OS PrinciplesSOSDI: Usenix Symp on OS Design and ImplementationCCS: ACM Conf on Comp and Communications SecurityIEEE Symposium on Security and PrivacyMOBICOM: ACM Intl Conf on Mobile Computing and NetworkingUSENIX Conf on Internet Tech and SysICNP: Intl Conf on Network ProtocolsPACT: Intl Conf on Parallel Arch and Compil TechRTAS: IEEE Real-Time and Embedded Technology and Applications Symposium ICDCS: IEEE Intl Conf on Distributed Comp SystemsRank 2:CC: Compiler ConstructionIPDPS: Intl Parallel and Dist Processing SympIC3N: Intl Conf on Comp Comm and NetworksICPP: Intl Conf on Parallel ProcessingSRDS: Symp on Reliable Distributed SystemsMPPOI: Massively Par Proc Using Opt InterconnsASAP: Intl Conf on Apps for Specific Array ProcessorsEuro-Par: European Conf. on Parallel ComputingFast Software EncryptionUsenix Security SymposiumEuropean Symposium on Research in Computer SecurityWCW: Web Caching WorkshopLCN: IEEE Annual Conference on Local Computer NetworksIPCCC: IEEE Intl Phoenix Conf on Comp & CommunicationsCCC: Cluster Computing ConferenceICC: Intl Conf on CommWCNC: IEEE Wireless Communications and Networking ConferenceCSFW: IEEE Computer Security Foundations WorkshopRank 3:MPCS: Intl. Conf. on Massively Parallel Computing SystemsGLOBECOM: Global CommICCC: Intl Conf on Comp CommunicationNOMS: IEEE Network Operations and Management SympCONPAR: Intl Conf on Vector and Parallel ProcessingVAPP: Vector and Parallel ProcessingICPADS: Intl Conf. on Parallel and Distributed SystemsPublic Key CryptosystemsAnnual Workshop on Selected Areas in CryptographyAustralasia Conference on Information Security and PrivacyInt. Conf on Inofrm and Comm. SecurityFinancial CryptographyWorkshop on Information HidingSmart Card Research and Advanced Application ConferenceICON: Intl Conf on NetworksNCC: Nat Conf CommIN: IEEE Intell Network WorkshopSoftcomm: Conf on Software in Tcomms and Comp NetworksINET: Internet Society ConfWorkshop on Security and Privacy in E-commerceUn-ranked:PARCO: Parallel ComputingSE: Intl Conf on Systems Engineering (**)PDSECA: workshop on Parallel and Distributed Scientific and Engineering Computing with Appli cationsCACS: Computer Audit, Control and Security ConferenceSREIS: Symposium on Requirements Engineering for Information SecuritySAFECOMP: International Conference on Computer Safety, Reliability and SecurityIREJVM: Workshop on Intermediate Representation Engineering for the Java Virtual Machine EC: ACM Conference on Electronic CommerceEWSPT: European Workshop on Software Process TechnologyHotOS: Workshop on Hot Topics in Operating SystemsHPTS: High Performance Transaction SystemsHybrid SystemsICEIS: International Conference on Enterprise Information SystemsIOPADS: I/O in Parallel and Distributed SystemsIRREGULAR: Workshop on Parallel Algorithms for Irregularly Structured ProblemsKiVS: Kommunikation in Verteilten SystemenLCR: Languages, Compilers, and Run-Time Systems for Scalable ComputersMCS: Multiple Classifier SystemsMSS: Symposium on Mass Storage SystemsNGITS: Next Generation Information Technologies and SystemsOOIS: Object Oriented Information SystemsSCM: System Configuration ManagementSecurity Protocols WorkshopSIGOPS European WorkshopSPDP: Symposium on Parallel and Distributed ProcessingTreDS: Trends in Distributed SystemsUSENIX Technical ConferenceVISUAL: Visual Information and Information SystemsFoDS: Foundations of Distributed Systems: Design and Verification of Protocols conference RV: Post-CAV Workshop on Runtime VerificationICAIS: International ICSC-NAISO Congress on Autonomous Intelligent SystemsITiCSE: Conference on Integrating Technology into Computer Science EducationCSCS: CyberSystems and Computer Science ConferenceAUIC: Australasian User Interface ConferenceITI: Meeting of Researchers in Computer Science, Information Systems Research & Statistics European Conference on Parallel ProcessingRODLICS: Wses International Conference on Robotics, Distance Learning & Intelligent Communic ation SystemsInternational Conference On Multimedia, Internet & Video TechnologiesPaCT: Parallel Computing Technologies workshopPPAM: International Conference on Parallel Processing and Applied MathematicsInternational Conference On Information Networks, Systems And TechnologiesAmiRE: Conference on Autonomous Minirobots for Research and EdutainmentDSN: The International Conference on Dependable Systems and NetworksIHW: Information Hiding WorkshopGTVMT: International Workshop on Graph Transformation and Visual Modeling Techniques AREA: Programming Languages and Software EngineeringRank 1:POPL: ACM-SIGACT Symp on Principles of Prog LangsPLDI: ACM-SIGPLAN Symp on Prog Lang Design & ImplOOPSLA: OO Prog Systems, Langs and ApplicationsICFP: Intl Conf on Function ProgrammingJICSLP/ICLP/ILPS: (Joint) Intl Conf/Symp on Logic ProgICSE: Intl Conf on Software EngineeringFSE: ACM Conf on the Foundations of Software Engineering (inc: ESEC-FSE) FM/FME: Formal Methods, World Congress/EuropeCAV: Computer Aided VerificationRank 2:CP: Intl Conf on Principles & Practice of Constraint ProgTACAS: Tools and Algos for the Const and An of SystemsESOP: European Conf on ProgrammingICCL: IEEE Intl Conf on Computer LanguagesPEPM: Symp on Partial Evalutation and Prog ManipulationSAS: Static Analysis SymposiumRTA: Rewriting Techniques and ApplicationsIWSSD: Intl Workshop on S/W Spec & DesignCAiSE: Intl Conf on Advanced Info System EngineeringSSR: ACM SIGSOFT Working Conf on Software ReusabilitySEKE: Intl Conf on S/E and Knowledge EngineeringICSR: IEEE Intl Conf on Software ReuseASE: Automated Software Engineering ConferencePADL: Practical Aspects of Declarative LanguagesISRE: Requirements EngineeringICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsIEEE Intl Conf on Formal Engineering MethodsIntl Conf on Integrated Formal MethodsFOSSACS: Foundations of Software Science and Comp StructAPLAS: Asian Symposium on Programming Languages and SystemsMPC: Mathematics of Program ConstructionECOOP: European Conference on Object-Oriented ProgrammingICSM: Intl. Conf on Software MaintenanceHASKELL - Haskell WorkshopRank 3:FASE: Fund Appr to Soft EngAPSEC: Asia-Pacific S/E ConfPAP/PACT: Practical Aspects of PROLOG/Constraint TechALP: Intl Conf on Algebraic and Logic ProgrammingPLILP: Prog, Lang Implentation & Logic ProgrammingLOPSTR: Intl Workshop on Logic Prog Synthesis & TransfICCC: Intl Conf on Compiler ConstructionCOMPSAC: Intl. Computer S/W and Applications ConfTAPSOFT: Intl Joint Conf on Theory & Pract of S/W DevWCRE: SIGSOFT Working Conf on Reverse EngineeringAQSDT: Symp on Assessment of Quality S/W Dev ToolsIFIP Intl Conf on Open Distributed ProcessingIntl Conf of Z UsersIFIP Joint Int'l Conference on Formal Description Techniques and Protocol Specification, Tes ting, And VerificationPSI (Ershov conference)UML: International Conference on the Unified Modeling LanguageUn-ranked:Australian Software Engineering ConferenceIEEE Int. W'shop on Object-oriented Real-time Dependable Sys. (WORDS)IEEE International Symposium on High Assurance Systems EngineeringThe Northern Formal Methods WorkshopsFormal Methods PacificInt. Workshop on Formal Methods for Industrial Critical SystemsJFPLC - International French Speaking Conference on Logic and Constraint ProgrammingL&L - Workshop on Logic and LearningSFP - Scottish Functional Programming WorkshopLCCS - International Workshop on Logic and Complexity in Computer ScienceVLFM - Visual Languages and Formal MethodsNASA LaRC Formal Methods WorkshopPASTE: Workshop on Program Analysis For Software Tools and EngineeringTLCA: Typed Lambda Calculus and ApplicationsFATES - A Satellite workshop on Formal Approaches to Testing of SoftwareWorkshop On Java For High-Performance ComputingDSLSE - Domain-Specific Languages for Software EngineeringFTJP - Workshop on Formal Techniques for Java ProgramsWFLP - International Workshop on Functional and (Constraint) Logic ProgrammingFOOL - International Workshop on Foundations of Object-Oriented LanguagesSREIS - Symposium on Requirements Engineering for Information SecurityHLPP - International workshop on High-level parallel programming and applicationsINAP - International Conference on Applications of PrologMPOOL - Workshop on Multiparadigm Programming with OO LanguagesPADO - Symposium on Programs as Data ObjectsTOOLS: Int'l Conf Technology of Object-Oriented Languages and SystemsAustralasian Conference on Parallel And Real-Time SystemsPASTE: Workshop on Program Analysis For Software Tools and EngineeringAvoCS: Workshop on Automated Verification of Critical SystemsSPIN: Workshop on Model Checking of SoftwareFemSys: Workshop on Formal Design of Safety Critical Embedded SystemsAda-EuropePPDP: Principles and Practice of Declarative ProgrammingAPL ConferenceASM: Workshops on Abstract State MachinesCOORDINATION: Coordination Models and LanguagesDocEng: ACM Symposium on Document EngineeringDSV-IS: Design, Specification, and Verification of Interactive SystemsFMCAD: Formal Methods in Computer-Aided DesignFMLDO: Workshop on Foundations of Models and Languages for Data and ObjectsIFL: Implementation of Functional LanguagesILP: International Workshop on Inductive Logic ProgrammingISSTA: International Symposium on Software Testing and AnalysisITC: International Test ConferenceIWFM: Irish Workshop in Formal MethodsJava GrandeLP: Logic Programming: Japanese ConferenceLPAR: Logic Programming and Automated ReasoningLPE: Workshop on Logic Programming EnvironmentsLPNMR: Logic Programming and Non-monotonic ReasoningPJW: Workshop on Persistence and JavaRCLP: Russian Conference on Logic ProgrammingSTEP: Software Technology and Engineering PracticeTestCom: IFIP International Conference on Testing of Communicating SystemsVL: Visual LanguagesFMPPTA: Workshop on Formal Methods for Parallel Programming Theory and Applications WRS: International Workshop on Reduction Strategies in Rewriting and Programming FATES: A Satellite workshop on Formal Approaches to Testing of Software FORMALWARE: Meeting on Formalware Engineering: Formal Methods for Engineering Software DRE: conference Data Reverse EngineeringSTAREAST: Software Testing Analysis & Review ConferenceConference on Applied Mathematics and Scientific ComputingInternational Testing Computer Software ConferenceLinux Showcase & ConferenceFLOPS: International Symposum on Functional and Logic ProgrammingGCSE: International Conference on Generative and Component-Based Software Engineering JOSES: Java Optimization Strategies for Embedded Systems。
西北工业大学航天学院【硕士课程简介】
02 航天学院序号:课程编号:02M001课程名称:线性系统理论任课教师:周军刘莹莹英文译名:Linear System Theory先修要求:《线性代数》和《矩阵论》中任一门、《复变函数》内容简介:《线性系统理论》是控制类、系统工程类、电类、计算机类、机电类等许多学科专业硕士研究生的一门公共基础理论课,是控制、信息、系统方面系列理论课程的先行课。
《线性系统理论》是最优估计、最优控制、系统辨识、自适应控制等现代控制理论的基础,系统讲述线性系统的运动规律,揭示系统中固有的结构特性,建立系统的结构、参数与性能之间的定性和定量关系,以及为改善系统性能,满足工程指标要求而采取的各类控制器设计方法。
具体的内容包括:线性系统的状态空间描述、状态空间描述与传递函数描述的关系、线性系统的运动分析、能控性、能观性、稳定性理论、线性反馈系统的状态空间综合方法、线性鲁棒性控制基本理论、线性系统的基本代数理论,以及多变量频域设计方法等。
主要参考书:(1)《线性系统理论》阙志宏主编,西安西北工业大学出版社,1995;(2)《现代控制理论引论》周凤歧等,北京国防工业大学出版社,1988;(3)《线性理论》郑大中编著,北京清华大学出版社;(4)《线性系统理论与设计》[美]陈启宗,科学出版社,1988。
序号:课程编号:02M900课程名称:专业英语任课教师:周军英文译名:Professional English先修要求:专业方面的课程内容简介:本课程作为一种基本的专业英语技能,在阅读和学习与本专业的相关的国外文献资料时,发挥着重要的作用。
因此,主要学习和掌握专业外语的基本语法、句法和结构,通过这门课的学习,期望学生能掌握专业英语的特点;扩大专业英语词汇量,尤其关于本专业有关导弹、航天器、无人机等专业知识方面的英语词汇量;提高专业英语(或科技英语)文章的阅读速度;并进行相应专业英语文献的翻译,在此基础上掌握专业英语的写法,为今后从事工程技术和科学研究工作打下稳固的基础。
flexible regression知识点 -回复
flexible regression知识点-回复Flexible regression is a statistical technique that allows for highly adaptable modeling of relationships between variables. Unlike traditional regression models, which assume a linear relationship between the independent and dependent variables, flexible regression models can capture complex nonlinear relationships. In this article, I will discuss the key concepts and applications of flexible regression.1. Introduction to flexible regression:Flexible regression models are a class of regression models that can accommodate nonlinear relationships, interactions, and varying degrees of complexity. These models are particularly useful when the relationship between the independent and dependent variables is not expected to be purely linear. By modeling nonlinearities, flexible regression enables us to better understand the data and make more accurate predictions.2. Types of flexible regression models:There are various types of flexible regression models, each with its own strengths and characteristics:a) Polynomial regression: This approach allows for the inclusion of higher-order polynomial terms to capture nonlinear relationships. By adding squared, cubic, or higher-order terms of the independent variables, polynomial regression curves can bend and flex to fit more complex patterns.b) Splines: Splines are piecewise-defined polynomial functions that divide the predictor space into segments or knots. The segments are connected smoothly, and the splines can be customized to fit the data more effectively than a single global polynomial equation.c) Generalized Additive Models (GAM): GAM extends the concept of linear regression by allowing for the inclusion of smooth functions of the predictors. These smooth functions are represented by splines or other nonparametric functions and can capture complex nonlinear relationships.d) Nonparametric regression: This type of flexible regression does not make any assumptions about the functional form of the relationship between the variables. Nonparametric regression estimates the relationship from the data directly, withoutspecifying a mathematical equation.3. Advantages of flexible regression models:Flexible regression models offer several advantages over traditional linear regression models:a) Improved model fit: By accommodating nonlinear relationships, flexible regression models can provide a better fit to the data, resulting in more accurate predictions and estimates.b) Better interpretation: The ability to capture nonlinear relationships allows for a more nuanced understanding of the data. These models can reveal complex patterns and interactions between variables that may not be evident in linear regression.c) Flexibility in modeling: Flexible regression models can handle a wide range of data types and can adapt to different functional forms. This flexibility allows researchers to explore various hypotheses and choose the most appropriate model for their data.4. Applications of flexible regression models:Flexible regression models find applications in various fields, suchas:a) Economics: In economics, flexible regression models are used to analyze complex relationships between variables, such as estimating the demand for a product or determining the impact of policy changes on economic outcomes.b) Epidemiology: In epidemiology, flexible regression models are used to study the relationship between risk factors and disease outcomes. These models can capture nonlinear effects of risk factors on disease occurrence and identify high-risk groups.c) Finance: Flexible regression models are widely used in finance to model stock returns, predict asset prices, and analyze the relationship between economic variables and financial markets.d) Environmental science: Flexible regression models are used in environmental science to study the impact of environmental factors on ecological systems. These models can capture nonlinear responses and interactions between environmental variables.5. Challenges and considerations:While flexible regression models offer many advantages, there are some challenges and considerations to keep in mind:a) Overfitting: Flexible regression models have a higher risk of overfitting the data, especially when the number of predictors is large compared to the sample size. Overfitting occurs when the model captures the noise or random variation in the data, leading to poor generalization to new data.b) Interpreting complex models: As flexibility increases, the complexity of the model also increases. Interpreting the results of complex models can be challenging and requires expertise in statistical analysis.c) Computational requirements: Some flexible regression models, especially those based on nonparametric approaches, can be computationally intensive and may require substantial computational resources and time.In conclusion, flexible regression models are a powerful tool for modeling nonlinear relationships between variables. By capturingcomplex patterns, interactions, and nonlinearities, these models improve model fit and facilitate better understanding of the data. Despite some challenges, the benefits of flexible regression models make them a valuable tool in a variety of fields.。
在非惯性系中研究动力刚化问题
在非惯性系中研究动力刚化问题梁立孚;王鹏;宋海燕【摘要】Correct understanding of the dynamic stiffening problem is signality for further researching spacecraft dynamics and establishing a rational numerical model of flexible body dynamics. The dynamic stiffening problem was studied using the theory of a mechanical problem in a non-inertial coordinate system. Two kinds of numerical models for the dynamic stiffening problem were established. The physical meaning of the dynamic stiffening problem was clarified. The approach of correct zero-order modeling was explored. There is a substantive difference between the research of this paper and the research of other scholars.%正确认识动力刚化问题,对深入研究航天器动力学和合理建立柔体动力学的数值计算模型意义重大.应用非惯性坐标系中的力学问题的理论来研究动力刚化问题,给出两类研究动力刚化问题的计算模型,明确了动力刚化问题的物理意义,探索了正确处理零次建模的途径.这样处理动力刚化问题,表现出与其他学者的研究有实质性的差异.【期刊名称】《哈尔滨工程大学学报》【年(卷),期】2012(033)008【总页数】5页(P1052-1056)【关键词】动力刚化;非惯性坐标系;柔体;刚体;航天器动力学【作者】梁立孚;王鹏;宋海燕【作者单位】哈尔滨工程大学力学一级学科博士点,黑龙江哈尔滨150001;上海大学应用数学与力学研究所,上海200444;哈尔滨工程大学力学一级学科博士点,黑龙江哈尔滨150001【正文语种】中文【中图分类】O313文献[1]指出,1987 年 Kane[2]对大范围刚体运动槽型弹性梁进行了研究,指出在大范围刚体运动作高速旋转时,零次耦合建模方法得到弹性梁的变形将无限增大的结果,与实际情况相反.为此,Kane对弹性梁的变形作了比较精确的描述(包括了弯曲变形、剪切变形和扭曲变形),首次提出动力刚化(dynamic stiffening)的概念.这一问题的提出,引起了各国学者的普遍关注.1989年,Banerjee和Kane[3]又对作大范围刚体运动的弹性薄板进行了研究.Haering[4],Padilla [5]采用类似方法对弹性梁动力学性质进行了分析.所得到的结果表明,人们在关于柔性多体系统动力学耦合机理的认识上有待深入,对所描述对象数学模型的准确性有待进一步研究.为了适应我国航天事业发展的需要,我国学者也对这一问题进行了广泛的、深入的研究[6-12].以上研究,多数是数值的、定量的分析方法,少数学者进行解析的分析讨论.正确的进行解析分析对于深刻把握动力刚化的力学实质、建立正确的数值计算模型是有利的.因此,有必要继续研究下去.在文献[1,12]中,通过一个典型的实例进行研究,本文在其基础上,应用非惯性坐标系中的力学问题的理论来研究动力刚化问题,给出两类研究动力刚化问题的计算模型,得到具有明确物理意义的研究结果.从物理和数学方面说明了产生零级耦合建模的不合理现象的原因,并且建议了合理的处理方法,以便避免零级耦合建模中可能发生不合理现象.这样处理动力刚化问题,表现出与其他学者的研究有实质性的差异.1 在非惯性系中典型实例研究设有如图1所示的力学系统,2根无质量杆AB和BC在B点用铰链连接,在铰链处有一个刚度系数为k的扭簧.长度为R的杆AB的另一端固定在铰链A上,并且绕A点以角速度ω(t)在平面中转动.长度为L的杆BC的另一端固定着质量块m.杆AB和BC之间的相对转角为θ(t),并且在系统的运动过程中,θ(t)可以为有限量,也可以为小量,其初始值为0.图1 非惯性坐标系Fig.1 Non-inertial coordinate system建立固连于杆AB的连体坐标系Bb1b2(如图1),由于杆的转动,使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中,如前所述θ(t)可以为有限量,也可以为小量.通过运动分析,可得系统的动能为作用在系统上的力矩,除了弹性力矩kθ外,还有惯性力矩.在转角θ(t)为有限量假设的情况下,离心惯性力fcf为引起的力矩为切向惯性力ft为引起的力矩为其外力势能为在建立动能和势能的表达式时,应当注意:以角速度ω转动的转动中心是A点,该点与质量m的距离为,以角速度转动的转动中心是B点,该点与质量m的距离为L.根据广义协变原理,在非惯性坐标系中,只要合理引入惯性力,就可以将相关力学定律表示为与在惯性系中类似的形式[13-15],因此Lagrange方程可以表示为将动能的表达式和势能的表达式代入Lagrange方程的各项,并且推导如下:将推导结果代入Lagrange方程,可得整理可得这里顺便指出,方程式(13)是以角位移θ为基本变量的动力学方程.mL2为动力学项,kθ为扭簧引起的力矩,mω2RLsin θ为离心惯性力引起的力矩,m(Rcosθ+L)L为切向惯性力引起的力矩.2 进一步典型实例研究建立固连于杆AB的连体坐标系Bb1b2(图1),由于杆的转动,使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中,假设θ(t)始终为小角,使得sin θ≈θ,cos θ≈1.通过运动分析,可得系统的动能为作用在系统上的力矩,除了弹性力矩kθ外,还有惯性力矩.在θ(t)始终为小角假设的情况下,离心惯性力的计算公式为引起的力矩为切向惯性力的计算公式为引起的力矩为其外力势能为在建立动能和势能的表达式时,应当注意:以角速度ω转动的转动中心是A点,该点与质量m的距离为(R+L),以角速度转动的转动中心是B点,该点与质量m的距离为L.根据广义协变原理,在非惯性坐标系中,只要合理引入惯性力,就可以将相关力学定律表示为与在惯性系中类似的形式[13-15],因此Lagrange方程可以表示为将动能的表达式和势能的表达式代入Lagrange方程的各项,并且推导如下:将推导结果代入Lagrange方程,可得进而可得动力刚度项式(26)明确显示,在这个典型实例中,引起动力刚化的原因是离心惯性力的影响.这里顺便指出,方程式(25)是以角位移为基本变量的动力学方程.mL2为动力学项,kθ为扭簧引起的力矩,mω2RLθ为离心惯性力引起的力矩,m(R+L)L为切向惯性力引起的力矩.本节处理问题的过程,与一般文献中所提及的零级耦合建模相似,只是这里是在非惯性坐标系中研究问题的,而一般文献中多数是在惯性坐标系中.以上论述表明,在非惯性系中合理的处理问题,所谓的零次建模也是可行的.这一点也可说明在非惯性坐标系中研究动力刚化问题的优越性.3 典型实例的另一类计算模型研究建立固连于杆AB的连体坐标系Bb1b2(图2),由于杆的转动,使得该坐标系成为非惯性坐标系.在这个非惯性坐标系中,假设θ(t)始终为小角,使得sin θ≈θ,cos θ≈1 .通过运动分析,可以得系统的动能为图2 θ(t)始终为小角Fig.2 θ(t)always small angle作用在系统上的力矩,除了弹性力矩kθ外,还有惯性力矩.在θ(t)始终为小角假设的情况下,离心惯性力的计算公式为引起的力矩为将离心惯性力作为主动力引起的附加势能为这一结果与文献[12]给出的结果相同.切向惯性力的计算公式为引起的力矩为将切向惯性力作为主动力引起的附加势能为系统的总外力势能为在建立动能和势能的表达式时,应当注意:以角速度ω转动的转动中心是A点,该点与质量m的距离为(R+L),以角速度转动的转动中心是B点,该点与质量m的距离为L.根据广义协变原理,在非惯性坐标系中,只要合理引入惯性力,就可以将相关力学定律表示为与在惯性系中类似的形式,因此Lagrange方程可表示为将动能的表达式和势能的表达式代入Lagrange方程的各项,并且推导如下:将推导结果代入Lagrange方程,可得进而可得动力刚度项为式(42)明确显示,在这个典型实例中,引起动力刚化的原因是离心惯性力的影响.这里顺便指出,方程式(41)是以角位移为基本变量的动力学方程.mL2为动力学项,kθ为扭簧引起的力矩,mω2(R+L)Lθ为离心惯性力引起的力矩,m(R+L)L为切向惯性力引起的力矩.4 讨论零级建模如果在应用Lagrange方程之前,对势能函数应用泰勒展开并且取一级近似,可得可见,将使得离心惯性力引起的外力势能消失.这从物理方面说明了所谓零级建模不可行的原因.正弦函数和余弦函数的泰勒展开为如果在应用Lagrange方程之前,对势能函数应用泰勒展开.以往的简化是将正弦函数和余弦函数的泰勒展开都取一级近似.考虑到正弦函数的泰勒级数收敛较快,余弦函数的泰勒级数的收敛较慢,因而取正弦函数的泰勒展开的一级近似,取余弦函数的泰勒展开的二次近似,可得势能的表达式:可见,式(46)与式(18)相同,这也可以从一个侧面说明这样处理问题的正确性.将势能的表达式代入Lagrange方程的有关势能的项,并且推导如下:动能的表达式及其相关推导同前.将推导结果代入Lagrange方程,可得整理可得可见,在应用Lagrange方程之前简化,只要合理进行近似计算,也可以得到合理的建模.具体问题具体分析对于科技工作者来说是至关重要的.研究表明,考虑动力刚化的柔体动力学的建模问题,内容丰富,可以分门别类的进行研究.5 结束语本文是在非惯性坐标系中研究动力刚化问题.首先,给出在非惯性坐标系中研究动力刚化典型实例的一类力学模型,应用有限位移理论研究动力刚化问题的典型实例,得到具有明确物理意义的结果.将这类研究退化到小位移理论,表明所谓零次耦合建模方法也是可行的.然后,给出在非惯性坐标系中研究动力刚化典型实例的另一类力学模型.最后,进一步讨论了如何正确地进行所谓零次耦合建模的问题.参考文献:【相关文献】[1]洪嘉振,蒋丽忠.动力刚化与多体系统刚-柔耦合动力学[J].计算力学学报,1999,16(3):295-301.HONG Jiazhen,JIANG Lizhong.Dynamic stiffening and multibody dynamics with coupled rigid and deformation motions[J].Chinese Journal of Computational Mechanics,1999,16(3):295-301.[2]KANE T R,RYAN R R,BANER J A K,Dynamics of a cantilever beam attached to a moving base[J].Journal of Guidance Control and Dynamics,1987,10(2):139-151.[3]BANERJEE A K,KANE T R.Multi-flexible body dynamics capturing movtion-induced stiffnes[J].Journal of Applied Mechanics,1989,56:887-892.[4]HAERING W J,RYAN R R,SCOTT A.New formulation for flexible beams undergoing large overall plane motion[J].Journal of Guidance,Control and Dynamics,1994,17(1):76-83.[5]PADILLA C E,VON FLOTOW A H.Nonlinear strain displacement relations and flexible multibody dynamics[J].Journal of Guidance,Control and Dynamics,1992,15(1):128-136.[6]孔向东,钟万勰,齐朝晖.计及动力刚化项的柔性机械臂几何非线性模型[J].机械科学与技术,1998,17(5):722-724.KONG Xiangdong,ZHONG Wanxie,QI Chaohui.Geometric nonlinear model of flexible manipulators in consideration of dynamic stiffening terms [J].Mechanical Science and Technology,1998,17(5):722-724.[7]金在权,权成七,刘龙哲.弹性旋转梁的动力刚化效应[J].延边大学学报,2000,26(2):116-118.JIN Zaiquan,QUAN Chengqi,LIU Longzhe.The stiffening effect of the centrifugal force[J].Journal of Yanbian University,2000,26(2):116-118.[8]杨辉,洪嘉振,余征跃.动力刚化问题的实验研究[J].力学学报,2004,36(1):119-124.YANG Hui,HONG Jiazhen,YU Zhengyue.Experimental investigation on dynamic stiffening phenomenon[J].Acta Mechanica Sinica,2004,36(1):119-124.[9]蒋建平,李东旭.大范围运动矩形板动力刚化分析[J].动力学与控制,2005,3(1):10-14.JIANG Jianping,LI Dongxu.Dynamic analysis of rectangular plate undergoing overall motion[J].Journal of Dynamics and Control,2005,3(1):10-14.[10]金国光,刘又五,王树新,等.含动力刚化项的一般多柔体系统动力学研究[J].哈尔滨工业大学学报,2005,37(1):101-103.JIN Guoguang,LIU Youwu,WANG Shuxin,etal.Generally flexible multi-body system dynamics in consideration of dynamic stiffening terms[J].Journal of Harbin Institute of Technology,2005,37(1):101-103.[11]章定国,朱志远.一类刚柔耦合系统的动力刚化分析[J].南京理工大学学报,2006,30(1):21-25.ZHANG Dingguo,ZHU Zhiyuan.Dynamic stiffening of rigid-flexible coupling system[J].Journal of Nanjing University of Science and Technology,2006,30(1):21-25. [12]李东旭.挠性航天器结构动力学[M].北京:科学出版社,2010:285-286.[13]爱因斯坦.相对论的意义[M].郝建纲,刘道军译.上海:科技教育出版社,2005:36-51. [14]邱吉宝,向树红,张正平.计算结构动力学[M].合肥:中国科学技术大学出版社,2009:455-463.[15]梁立孚,刘石泉,王振清,等.飞行器结构动力学中的几个问题[M].西安:西北工业大学出版社等五社联合出版,2010:158-172.。
医学未折叠蛋白元件英语
医学未折叠蛋白元件英语The intricate world of medicine has long been shaped by the fundamental principles of biochemistry and molecular biology. At the heart of this dynamic interplay lies the enigmatic realm of unfolded protein elements, a domain that has captivated the attention of researchers and clinicians alike. These unique protein structures, often referred to as intrinsically disordered proteins or IDPs, have emerged as a pivotal area of study in the pursuit of understanding and addressing various medical conditions.Traditionally, the study of proteins has been dominated by the notion that a protein's function is intrinsically linked to its well-defined three-dimensional structure. However, the discovery of IDPs has challenged this conventional wisdom, revealing a remarkable diversity in the ways proteins can adopt and utilize their structural properties to perform a multitude of crucial biological functions. Unlike their folded counterparts, IDPs lack a stable tertiary structure, existing instead as dynamic and flexible ensembles that can adapt to a wide range of environmental conditions and interactions.This structural flexibility endows IDPs with a remarkable versatility, allowing them to participate in a vast array of cellular processes, from signal transduction and transcriptional regulation to protein-protein interactions and cellular signaling pathways. By eschewing the constraints of a fixed structure, IDPs can engage in a dynamic dance of conformational changes, enabling them to bind to multiple targets and perform diverse roles within the complex tapestry of the living cell.The significance of IDPs in the realm of medicine cannot be overstated. These unfolded protein elements have been implicated in a wide range of pathological conditions, from neurodegenerative disorders to cancer and infectious diseases. In the case of neurodegenerative diseases, such as Alzheimer's and Parkinson's, the aggregation and misfolding of IDPs, such as tau and α-synuclein, have been identified as key contributors to the development and progression of these devastating conditions. Understanding the underlying mechanisms that govern the behavior of these unfolded proteins has become a crucial area of research, as it holds the promise of unlocking new therapeutic avenues and strategies for intervention.Similarly, in the field of oncology, IDPs have emerged as pivotal players in the complex landscape of cancer biology. Many cancer-related proteins, such as p53 and Myc, are intrinsically disordered,and their structural flexibility allows them to engage in a dynamic interplay with a diverse array of cellular partners, ultimately influencing the hallmarks of cancer, including uncontrolled cell growth, evasion of apoptosis, and metastatic potential. By targeting these unfolded protein elements, researchers are exploring novel approaches to cancer treatment, seeking to disrupt the delicate balance that sustains the malignant phenotype.Beyond their role in disease pathogenesis, IDPs have also garnered attention for their potential as therapeutic targets and biomarkers. The unique structural and functional properties of these unfolded proteins offer opportunities for the development of targeted interventions, such as small-molecule inhibitors or allosteric modulators, that can selectively engage and modulate their behavior. Additionally, the presence and patterns of IDP expression in various disease states have been investigated as potential diagnostic and prognostic indicators, paving the way for more personalized and effective clinical management strategies.The study of unfolded protein elements in medicine is not without its challenges, however. The inherent complexity and dynamic nature of IDPs pose significant hurdles in terms of structural characterization, functional elucidation, and therapeutic targeting. Traditional structural biology techniques, designed for well-folded proteins, often struggle to capture the nuances of IDP behavior, necessitatingthe development of specialized methods and analytical tools.Despite these challenges, the scientific community has made remarkable strides in advancing our understanding of IDPs and their implications in human health and disease. Cutting-edge technologies, such as advanced spectroscopic techniques, computational modeling, and single-molecule approaches, have enabled researchers to delve deeper into the intricate world of unfolded protein elements, revealing their intricate roles in cellular processes and their potential as therapeutic targets.As the field of IDP research continues to evolve, the promise of unlocking new frontiers in medicine becomes increasingly tangible. By unraveling the mysteries of these unfolded protein elements, scientists and clinicians alike are poised to unveil innovative diagnostic strategies, develop targeted therapies, and ultimately improve the lives of patients suffering from a wide range of medical conditions. The journey ahead is filled with both challenges and opportunities, but the potential impact of this burgeoning field on the future of healthcare is truly transformative.。
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Wind turbine operation in electric power systems: advanced modelling 风力发电(机)在电力系统运行64. Power system control and stability 电力系统控制与稳定性( 不是那本stability and control)65. Analysis of subsynchronous resonance in power system 电力系统次同步谐振分析putationalmethods for large sparse power systems: a object orientedapproach 大稀疏电力系统计算方法: 面向对象的途径67. Power system oscillation 电力系统振荡68. Power system restructuring: engineering and economics 电力系统市场化: 工程和经济69. Distribution system modelling and analysis 配电系统建模与分析70. Electric power engineering 电力工程71. Subsynchronous resonance in power systems 电力系统中的次同步谐振72. Computer modelling of electrical power system 电力系统计算机建模73. High Voltage Direct Current Transmission 高压直流输电74. Electricitydistribution network design (2nd)配电网规划设计75. Industrial power distribution 工业配电76. Protection ofelectricity distribution networks 配电网保护77. Energy function analysis for power system stability 电力系统稳定性的能量函数分析78. Power system commission and maintenance practice电力系统试验(调试)与检修(维护)实践79. Statistical techniques for high-voltage engineering 高电压工程中的统计技术80. Digital protection for power system电力系统数字保护81. Power system protection 电力系统(继电)保护82. Voltage quality in electrical power systems 电力系统电压质量83.Electric power applications of fuzzy systems 模糊系统的电力应用84. Artificial intelligence techniques in power system 电力系统中的人工智能技术85. Insulators in high voltages 高压绝缘体86. Electrical safety供电安全87. High voltageengineering and testing 高电压工程与试验88. Reactive power control in electric systems 电力系统无功(功率)控制93. Electric power system电力系统教程94. Computer-Aided Power systems analysis 计算机辅助电力系统分析99. Reliability evaluation of power system 电力系统可靠性评估106. Power system stability handbook 电力系统稳定性手册109. Reliability assessment of large electric power systems 大电力系统可靠性评估112. HVDC power transmission systems 高压直流输电系统128. Electric Machinery and power system fundamentals 电机与电力系统基础(MATLAB 辅助)129. Intelligent system applications in power engineering (EP and ANN) 智能系统在电力工程中应用(进化计算和神经网)130. Thyristor-based FACTS controllers for electrical transmission systems 基于晶闸管的灵活交流输电系统控制器131. The economics of power system reliability and planning 电力系统可靠性与规划的经济学132. Computational Intelligence Applications to Power systems 计算智能在电力系统中的应用133. Environmental Impact of Power Generation 发电的环境影响134. Operation and Maintenance of Large Turbo-Generators 大型涡轮发电机组运行与检修135. Power system simulation 电力系统仿真136. Advanced load dispatch for power systems 电力系统高级调度137. The development of electric power transmission 电力传输进展138. Renewable Energy Sources 可再生发电源139. Power system dynamics andstablity 电力系统动态与稳定性140. Practical electrical network automation and communication systems 电力系统自动化与通信系统实践141. Electrical power and controls 电力与控制142. Deregulation of Electric Utilities 电力企业放松管制(市场改革)143. Computational Auction Mechanisms for restructured power industry operation 电力市场运行的(计算)投标机理144. Finanicial and economic evaluation of projects in the electricity supply industry 电力工程项目的金融与经济评价145. Electricity economics and planning 电力经济与规划146. Computational Methods for electric power systems 电力系统计算方法147. Power system relaying 电力系统继电保护148. Computer relaying for power systems 电力系统计算机保护149. Modern power system planning 现代电力系统规划150. High Voltage Engineering (2nd) 高电压工程151. Operation of restructured power systems 市场化电力系统运行152. Transer and Inductor Design Handbook变压器和电感设计手册(04增强版)153. Modern power system analysis (matlab supported) 现代电力系统分析(03年含MATLAB版)154. Power distribution planning reference book 配电规划参考手册155. Understanding FACTS 理解灵活交流输电系统156. Power system analysis :short-circuit load flow and harmonics 电力系统分析: 短路潮流和谐波157. Power systems electromagnetic transients simulation 电力系统电磁暂态仿真158. Power electronic control in electrical systems 电力系统中的电力电子控制159. Protection devices and systems for high-voltage applications保护装置和系统的高压应用160. Small signal analysis of power systems 电力系统小信号分析161. Electrical power cable engineering 电力线缆工程162. Power System State Estimation: Theory and Implementation 电力系统状态估计: 理论和实现163. Dielectrics in Electric Fields电场中的电介质(绝缘体)164. spacecraft power system 航天器电力系统165. Grid integration of wind energy conversion systems 风能转换系统的电网整合(接入)166. Power loss: the origins of deregulation and restructuring in the American electricutility system网损:美国电力系统放松管制和市场化的根源167. High Voltage Circuit Breakers: Design and Applications 高压断路器:设计与应用168. Power system capacitors 电力系统电容器169. Energy Management Systems & Direct Digitial Control 能量管理系统(EMS)及直接数字控制170. Pricing in Competitive Electricity Market 电力市场电价171. Designing Competitive Electricity Markets 电力市场设计172. Power system dynamics and stability 电力系统动态与稳定性(美国)173. Theory and problems of electric power systems 电力系统的理论和问题174. Insulation coordinationfor power systems 电力系统绝缘配合175. Modal analysis of large interconnected power systems 大互联电力系统的模式分析176. Making competition work in electricity 电力市场竞争177. Power system operation 电力系统运行178. Transmission line reliability and security 输电线路安全可靠性179. Computer analysis of power systems 电力系统计算机分析89. Electical distribution engineering配电网工程90. Power systemplanning电力系统规划91. Uniquepower system problems 电力系统问题92. Tranmission and Distribution ofElectrical Energy 电力系统输配电95. Electric powertransmission system 输电系统96. Reliability Modelling in Electric power systems电力系统可靠性建模97. High voltage engineering in power system 电力系统高电压工程98. Extra High voltage AC transmission engineering 超高压交流输电工程100. Computation of power system transients 电力系统暂态计算101.Piecewise methods and application to power systems 分段法及其在电力系统中应用103. Analysis and protection of electrical power systems 电力系统分析与保护104. Power systems engineering and mathematicas电力系统工程与数学105. Stability of large power systems 大电力系统稳定性107. Power system reliability evaluation电力系统可靠性评估108.Electric power system dynamics 电力系统动态110. Power system analysis and planning 电力系统分析与规划111. Electric transmission line fundamental 输电线(工程)基础113. Transient Processes in electrical power systems 电力系统暂态过程114.Discrete Fourier transation and its applications to power system 离散傅立叶变换及其在电力系统中的应用115. Electrical Transients inpower system 电力系统暂态116. Optimal economic operation of electric power system 电力系统优化经济调度运行117.High power switching 大功率开关118. power plant engineering 电厂工程119. power plant system design 电厂系统设计120. power plant evaluation and design reference guide 电厂评估和设计参考导则121. planning engineering, and construction of electric power generationfacilities 发电设备的规划和建设工程122. Elements electrical power station design 电站设计基础123.Optimal control applications in electric power systems 电力系统最优控制应用124. applied protected relaying应用继电保护125. power station and substation maintenance 电厂与变电站维修126. Power system operation 电力系统运行127. power system reliability,safety and management 电力系统可靠性,安全与管理。
智能控制外文翻译-其他专业
英文文献资料及翻译Intelligent ControlControl technology is 20 years in the last century established a frequency domain method based upon the classical control theory developed, the control technology of industrial production has been widely used. Promote the development of space technology, the 50's emerged to law-based state space control theory now so widely control technology development, resulting in more applications. Since the 60s, with the development of computer technology, many new methods and technology into engineering, product of stage, appears to accelerate the pace of industrial technological upgrading, which control technology presented new challenges, also provide for the development of a conditions for the theory of intelligent control technology application in the form of intelligent control technology.Intelligent control technology is mainly used to resolve those using traditional methods can not solve the control problem of complex systems, such as intelligent robotics systems, computer integrated manufacturing system (CIMS), a complex industrial process control systems, aerospace control systems, socio-economic management systems, transportation systems, communication network systems, environmental protection and energy systems. These complex systems have the following characteristics: 1. Control object of serious uncertainty, the control model or the model structure and unknown parameters of a large range of changes; 2. Control of highly nonlinear characteristics of the object; 3. Control tasks require complex. For example, the intelligent robot systems require a complex task system has its own planning and decision-making capacity, automatic ability to avoid obstacles to reach the destination.Intelligent control technology often play a role through the intelligent control system. In short, intelligent control system is the system with an intelligent behavior, which uses artificial intelligence methods to solve difficult mathematical methods accurately describe the complex, random, flexible control problem, a self-learning, adaptive, self-organizing capabilities. Its main objective is to explore the human brain deal with things closer to the "thinking" mode is to study a kind of mathematical logic, make the machine like human beings, according to a small amount of fuzzy information, some of the reasoning based on the guidelines, "thinking", you can get a very accurate or adequate approximation of the conclusions and control strategies.The intelligent control technology in the engineering machinery products, to solve the traditional control methods can not well adapt to changing challenges of complex objects. Intelligent control technology can change thecontrol strategy to adapt to the object complexity and uncertainty. It is not just rely on mathematical models, and online experience based on knowledge and reasoning to identify and select the best control strategy, the uncertainty for the system to maintain a predetermined quality and expectations.Intelligence and intelligent systems can be characterized in a number of ways and along a number of dimensions. There are certain attributes of intelligent systems, common in many definitions, which are of particular interest to the control community.In the following, several alternative definitions and certain essential characteristics of intelligent systems are first discussed. A brief working definition of intelligent systems that captures their common characteristics is then presented. In more detail, we start with a rather eneral definition of intelligent systems, we discuss levels of intelligence, and we explain the role of control in intelligent systems and outline several alternative definitions. We then discuss adaptation and learning, autonomy and the necessity for efficient computational structures in intelligent systems, to deal with complexity. We conclude with a brief working characterization of intelligent (control) systems.We start with a general characterization of intelligent systems:An intelligent system has the ability to act appropriately in an uncertain environment, where an appropriate action is that which increases the probability of success, and success is the achievement of behavioral subgoals that support the system’s ultimate goal.In order for a man-made intelligent system to act appropriately, it may emulate functions of living creatures and ultimately human mental faculties. An intelligent system can be characterized along a number of dimensions. There are degrees or levels of intelligence that can be measured along the various dimensions of intelligence. At a minimum, intelligence requires the ability to sense the environment, to make decisions and to control action. Higher levels of intelligence may include the ability to recognize objects and events, to represent knowledge in a world model, and to reason about and plan for the future. In advanced forms, intelligence provides the capacity to perceive and understand, to choose wisely, and to act successfully under a large variety of circumstances so as to survive and prosper in a complex and often hostile environment. Intelligence can be observed to grow and evolve, both through growth in computational power and through accumulation of knowledge of how to sense, decide and act in a complex and changing world.The above characterization of an intelligent system is rather general. According to this, a great number of systems can be considered intelligent. In fact, according to this definition, even a thermostat may be considered to be an intelligent system, although of low level of intelligence. It is common, however, to call a system intelligent when in fact it has a rather high level of intelligence.There exist a number of alternative but related definitions of intelligentsystems and in the following we mention several. They provide alternative, but related characterizations of intelligent systems with emphasis on systems with high degrees of intelligence.The following definition emphasizes the fact that the system in question processes information, and it focuses on man-made systems and intelligent machines:A. Machine intelligence is the process of analyzing, organizing and converting data into knowledge; where (machine) knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligent machine. This definition leads to the principle of increasing precision with decreasing intelligence, which claims that: applying machine intelligence to a database generates a flow of knowledge, lending an analytic form to facilitate modeling of the process.Next, an intelligent system is characterized by its ability to dynamically assign subgoals and control actions in an internal or autonomous fashion:B. Many adaptive or learning control systems can be thought of as designing a control law to meet well-defined control objectives. This activity represents the system’s attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control objective. The organization of knowledge can be seen as one important attribute of intelligence. If this organization is done autonomously by the system, then intelligence becomes a property of the system, rather than of the system’s designer. This implies that systems which autonomously (self) -organize controllers with respect to an internally realized organizational principle are intelligent control systems.A procedural characterization of intelligent systems is given next:C. Intelligence is a property of the system that emerges when the procedures of focusing attention, combinatorial search, and generalization are applied to the input information in order to produce the output. One can easily deduce that once a string of the above procedures is defined, the other levels of resolution of the structure of intelligence are growing as a result of the recursion. Having only one level structure leads to a rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding mode controller.The concepts of intelligence and control are closely related and the term “Intelligent Control”has a unique and distinguishable meaning. An intelligent system must define and use goals. Control is then required to move the system to these goals and to define such goals. Consequently, any intelligent system will be a control system. Conversely, intelligence is necessary to provide desirable functioning of systems under changing conditions, and it is necessary to achieve a high degree of autonomous behavior in a control system. Since control is an essential part of any intelligent system, the term “Intelligent Control Systems” is sometimes used in engineering literature instead of “Intelligent Systems”or “Intelligent Machines”. The term “Intelligent Control System”simply stresses the control aspect of the intelligent system.Below, one more alternative characterization of intelligent (control) systems is included. According to this view, a control system consists of data structures or objects (the plant models and the control goals) and processing units or methods (the control laws) :D. An intelligent control system is designed so that it can autonomously achieve a high level goal, while its components, control goals, plant models and control laws are not completely defined, either because they were not known at the design time or because they changed unexpectedly.There are several essential properties present in different degrees in intelligent systems. One can perceive them as intelligent system characteristics or dimensions along which different degrees or levels of intelligence can be measured. Below we discuss three such characteristics that appear to be rather fundamental in intelligent control systems.Adaptation and Learning. The ability to adapt to changing conditions is necessary in an intelligent system. Although adaptation does not necessarily require the ability to learn, for systems to be able to adapt to a wide variety of unexpected changes learning is essential. So the ability to learn is an important characteristic of (highly) intelligent systems.Autonomy and Intelligence. Autonomy in setting and achieving goals is an important characteristic of intelligent control systems. When a system has the ability to act appropriately in an uncertain environment for extended periods of time without external intervention, it is considered to be highly autonomous. There are degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy than a control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedback controller. Although for low autonomy no intelligence (or “low”intelligence) is necessary, for high degrees of autonomy, intelligence in the system (or “high” degrees of intelligence) is essential.Structures and Hierarchies. In order to cope with complexity, an intelligent system must have an appropriate functional architecture or structure for efficient analysis and evaluation of control strategies. This structure should be “sparse” and it should provide a mechanism to build levels of abstraction (resolution, granularity) or at least some form of partial ordering so to reduce complexity. [7] An approach to study intelligent machines involving entropy emphasizes such efficient computational structures. Hierarchies (that may be approximate, localized or combined in heterarchies) that are able to adapt, may serve as primary vehicles for such structures to cope with complexity. The term “hierarchies” refers to functional hierarchies, or hierarchies of range and resolution along spatial or temporal dimensions, and it does not necessarily imply hierarchical hardware. Some of these structures may be hardwired in part. To cope with changing circumstances, the ability to learn is essential, so these structures can adapt to significant, unanticipated changes.In view of the above, a working characterization of intelligent systems (orof (highly) intelligent (control) systems or machines) that captures the essential characteristics present in any such system is:An intelligent system must be highly adaptable to significant unanticipated changes, and so learning is essential. It must exhibit high degree of autonomy in dealing with changes. It must be able to deal with significant complexity, and this leads to certain sparse types of functional architectures such as hierarchies.智能控制控制技术是在上世纪20年代建立了以频域法为主的经典控制理论后发展起来的,控制技术首先在工业生产中得到了广泛的应用。
INTERNATIONAL JOURNAL OF WIRELESS AND MOBILE COMPUTING (IJWMC) 1 A Biologically Inspired Qo
A Biologically Inspired QoS Routing Algorithm forMobile Ad Hoc NetworksZhenyu Liu,Marta Z.Kwiatkowska,and Costas ConstantinouAbstract—This paper presents an Emergent Ad hoc Routing Algorithm with QoS provision(EARA-QoS).This ad hoc QoS routing algorithm is based on a swarm intelligence inspired routing infrastructure.In this algorithm,the principle of swarm intelligence is used to evolutionally maintain routing information. The biological concept of stigmergy is applied to reduce the amount of control traffic.This algorithm adopts the cross-layer optimisation concept to use parameters from different layers to determine routing.A lightweight QoS scheme is proposed to provide service-classified traffic control based on the data packet characteristics.The simulation results show that this novel routing algorithm performs well in a variety of network conditions.Index Terms—MANET,routing,QoS,swarm intelligence.I.I NTRODUCTIONM OBILE ad hoc networks(MANETs)are wireless mo-bile networks formed munication in such a decentralised network typically involves temporary multi-hop relays,with the nodes using each other as the relay routers without anyfixed infrastructure.This kind of network is veryflexible and suitable for applications such as temporary information sharing in conferences,military actions and disaster rescues.However,multi-hop routing,random movement of mobile nodes and other features unique to MANETs lead to enormous overheads for route discovery and maintenance.Furthermore, compared with the traditional networks,MANETs suffer from the resource constraints in energy,computational capacities and bandwidth.To address the routing challenge in MANETs,many ap-proaches have been proposed in the literature.Based on the routing mechanism for the traditional networks,the proactive approaches attempt to maintain routing information for each node in the network at all times[1]–[3],whereas the reactive approaches onlyfind new routes when required[4]–[6].Other approaches make use of geographical location information for routing[7],[8].Those previous works only provide a basic “best effort”routing functionality that is sufficient for con-ventional applications such asfile transfer or email download. To support real-time applications such as V oIP and video stream in MANETs,which have a higher requirement for delay,jitter and packet losses,provision of Quality-of-Service (QoS)is necessary in addition to basic routing functionality. Z.Liu and M.Z.Kwiatkowska is with School of Computer Science,The University of Birmingham,Birmingham,England B152TT.C.Constantinou is with the Department of Electronic Electrical and Computer Engineering,The University of Birmingham,Birmingham,England B152TT.Given the nature of MANETs,it is difficult to support real-time applications with appropriate QoS.In some cases it may be even impossible to guarantee strict QoS requirements.But at the same time,QoS is of great importance in MANETs since it can improve performance and allow critical information to flow even under difficult conditions.At present,the most fundamental challenges of QoS support in MANETs concern how to obtain the available bandwidth and maintain accurate values of link state information during the dynamic evolution of such a network[9].Based on common techniques for QoS provision in the Internet,some researchers proposed the integration of QoS provision into the routing protocols[10],[11].However,since these works implicitly assumed the same link concept as the one in wired networks,they still do not fully address the QoS problem for MANETs.In this paper,we propose a new version of the self-organised Emergent Ad hoc Routing Algorithm with QoS provisioning(EARA-QoS).This QoS routing algorithm uses information from not only the network layer but also the MAC layer to compute routes and selects different paths to a destination depending on the packet characteristics.The underlying routing infrastructure,EARA originally proposed in[12],is a probabilistic multi-path algorithm inspired by the foraging behaviour of biological ants.The biological concept of stigmergy in an ant colony is used for the interaction of local nodes to reduce the amount of control traffic.Local wireless medium information from the MAC layer is used as the artificial pheromone(a chemical used in ant communications) to reinforce optimal/sub-optimal paths without the knowledge of the global topology.One of the optimisations of EARA-QoS over EARA is the use of metrics from different layers to make routing decisions. This algorithm design concept is termed as the cross-layer design approach.Research[13]has shown the importance of cross-layer optimisations in MANETs,as the optimisation at a particular single layer might produce non-intuitive side-effects that will degrade the overall system performance.Moreover, the multiple-criteria routing decisions allow for the better usage of network characteristics in selecting best routes among multiple available routes to avoid forwarding additional data traffic through the congested areas,since the wireless medium over those hotspots is already very busy.The parameters for measuring wireless medium around a node depend largely on the MAC layer.In this paper,we focus on the IEEE802.11 DCF mode[14],since it is the most widely used in both cellular wireless networks and in MANETs.This cross-layer technique of using MAC layer information can be appliedeasily to other MAC protocols.In addition to the basic routing functionality,EARA-QoS supports an integrated lightweight QoS provision scheme.In this scheme,traffic flows are classified into different service classes.The classification is based on their relative delay bounds.Therefore,the delay sensitive traffic is given a higher priority than other insensitive traffic flows.The core technique of the QoS provision scheme is a token bucket queuing scheme,which is used to provide the high priority to the real-time traffic,and also to protect the lower-priority traffic from star-vation.Experimental results from simulation of mobile ad hoc networks show that this QoS routing algorithm performs well over a variety of environmental conditions,such as network size,nodal mobility and traffic loads.II.B ACKGROUNDIn this section,we give a brief introduction to background knowledge on ant colony heuristics,and the QoS provision techniques in MANETs.A.Foraging Strategies in AntsOne famous example of biological swarm social behaviour is the ant colony foraging [15](see Figure 1).Many ant species have a trail-laying,trail-following behaviour when foraging:individual ants deposit a chemical substance called pheromone as they move from a food source to their nest,and foragers follow such pheromone trails.Subsequently,more ants are attracted by these pheromone trails and in turn reinforce them even more.As a result of this auto-catalytic effect,the optimal solution emerges rapidly.In this food searching process a phenomenon called stigmergy plays a key role in developing and manipulating local information.It describes the indirect communication of individuals through modifying theenvironment.Fig.1.All Ants Attempt to Take the Shortest PathFrom the self-organisation theory point of view,the be-haviour of the social ant can be modelled based on four elements:positive feedback,negative feedback,randomness and multiple interactions [16].This model of social ants using self-organisation theories provides powerful tools to transfer knowledge about the social insects to the design of intelligent decentralised problem-solving systems.B.Quality-of-Service in MANETsQuality-of-Service (QoS)provision techniques are used to provide some guarantee on network performance,such as average delay,jitter,etc.In wired networks,QoS provision can generally be achieved with the over-provisioning of re-sources and with network traffic engineering [17].With the over-provisioning approach,resources are upgraded (e.g.fibre optic data link,advanced routers and network cards)to make networks more resistant to resource demanding applications.The advantage of this approach is that it is easy to be implemented.The main disadvantage of this approach is that all the applications still have the same priority,and the network may become unpredictable during times of bursting and peak traffic.In contrast,the idea of the traffic engineering approach is to classify applications into service classes and handle each class with a different priority.This approach overcomes the defect of the former since everyone is following a certain rule within the network.The traffic engineering approach has two complemen-tary means to achieve QoS provisioning,Integrated Services (IntServ)and Differentiated Services (DiffServ).IntServ [18]provides guaranteed bandwidth for flows,while DiffServ [19]provides hard guarantees for service classes.Both of the approaches rely on the possibility to make bandwidth reservations.The former was used in ATM (Asynchronous Transfer Mode)[20]and is today the method of achieving QoS in RSVP-IntServ [21].On the other hand,in the DiffServ approach,no reservation is done within the network.Instead,QoS is achieved by mechanisms such as Admission Control ,Policy Manager ,Traffic Classes and Queuing Schedulers .These mechanisms are used to mark a packet to receive a particular forwarding or dropping treatment at each node.Based on QoS provision techniques in wired networks,many QoS approaches are proposed to provide QoS services for MANETs.Flexible QoS Model for MANETs (FQMM)[22],is the first QoS approach for MANETs,which combines knowledge on IntServ/DiffServ in wired networks with con-sideration of MANETs.As an essential component to achieve the QoS provisioning,QoS routing algorithms tightly integrate QoS provisioning into routing protocols.The QoS version of AODV (QoS-AODV)[23],the Core-Extraction Distributed Ad Hoc Routing (CEDAR)protocol [10],the Multimedia Support for Mobile Wireless Networks (MMWN)protocol [11],and the ticket-based protocols [24]are examples of QoS routing algorithms proposed for MANETs.On the other hand,QoS signaling techniques are inde-pendent of the underlying routing protocols.The In-band Signalling for QoS in Ad-Hoc Mobile Networks (INSIGNIA)algorithm [25]is the typical signaling protocol designed exclusively for MANETS.The idea of CEDAR,MMWN,and ticket-based protocols is to disseminate link-state information across the network in order to enable other nodes to find routes that meet certain QoS criteria,like the minimum bandwidth.On the other hand,INSIGNIA piggybacks resource reservations onto data packets,which can be modified by intermediate nodes to inform the communication endpoint nodes in case of lack ofresources.All those approaches are based on the idea that the wireless links between mobile nodes have certain QoS related properties,in particular a known amount of available bandwidth,and that nodes are able to give guarantees for traffic traversing these links.III.C RITIQUE OF E XISTING Q O S A PPROACHES INMANET SNowadays,most of the QoS provisioning techniques are derived from the QoS approaches of the wired networks. However,QoS support approaches proposed in wired networks are based on the assumption that the link characteristics such as bandwidth,delay,loss rate and error rate must be available and manageable.However,given the challenges of MANETs, e.g.dynamic topology and time-varying link capacity,this assumption does not apply any longer.Thus,applying the concepts of wired traffic engineering QoS approaches directly to MANETs is extremely difficult.Generally,the situation in MANETs is completely different from those in wired networks.In wireless networks,the available bandwidth undergoes fast time-scale variations due to channel fading and errors from physical obstacles.These effects are not present in wired networks.In MANETs,the wireless channel is a shared-access medium,and the available bandwidth even varies with the number of hosts contending for the channel.Below we analyse why the IntServ/DiffServ models are not appropriate for MANETs respectively. IntServ based approaches are not applicable for MANETs mainly due to two factors,huge resource consumption and computation power limitation.Firstly,to support IntServ,a huge amount of link state information has to be built and main-tained for each mobile node.The amount of state information increases proportionally with the number offlows,which is also a problem with the current IntServ QoS scheme.Secondly, current wireless networks employ two major MAC techniques, the single-channel approach and the multiple channel ap-proach.With single-channel approach(e.g.IEEE802.11[14]), all nodes share the same channel and therefore potentially interfere with each other.With a multiple-channel approach (e.g.Bluetooth[26]or CDMA[27]),nodes can communicate on several channels simultaneously.Both of the two MAC techniques have a similar bandwidth reservation mechanism. This common mechanism requires a transmission schedule to define time slots,in which nodes take their turns periodically. For each slot,its duration and a set of possible simultaneous transmissions must be defined.However,in wireless networks, the problem offinding an optimal schedule is proved to be NP-complete[28],which is a fundamental limitation of QoS provisioning in wireless networks.On the other hand,the DiffServ approach is a lightweight QoS model for interior routers since individual stateflows are aggregated into sets of service classes whose packets are treated differently at the routing nodes.This makes routing a lot easier in the network.Thus this approach could be a potential solution for MANETs.Even though it is not practical to provide a hard separation of different service classes in MANETs,relative prioritisation is possible in such a way that traffic of a certain class is given a higher or lower priority than traffic of other service classes.One solution would be to divide the traffic into a predefined set of service classes that are defined by their relative delay bounds,such as delay sensitive(realtime)and insensitive(bulk)traffic.Realtime traffic should be given higher priority than bulk traffic.No absolute bandwidth guarantees are provided.Some work based on service differentiation rather than resource reservations in MANETs already exists[29].IV.D ESCRIPTION OF EARA-Q O SEARA-QoS is an on-demand multipath routing algorithm for MANETs,inspired by the ant foraging intelligence.This algorithm incorporates positive feedback,negative feedback and randomness into the routing computation.Positive feed-back originates from destination nodes to reinforce the existing pheromone on good paths.Ant-like packets,analogous to the ant foragers,are used to locallyfind new paths.Artificial pheromone is laid on the communication links between nodes and data packets are biased towards strong pheromone,but the next hop is chosen probabilistically.To prevent old routing solutions from remaining in the current network status,expo-nential pheromone decay is adopted as the negative feedback. Each node using this algorithm maintains a probabilistic routing table.In this routing table,each route entry for the destination is associated with a list of neighbour nodes.A probability value in the list expresses the goodness of node as the next hop to the destination.For each neighbour, the shortest hop distance to the destination and the largest sequence number seen so far are also recorded.In addition to the routing table,each node also possesses a pheromone table.This table tracks the amount of pheromone on each neighbour link.The table may be viewed as a ma-trix with rows corresponding to neighbourhood and columns to destinations.There are three threshold values controlling the bounds on pheromone in the table.They are the upper pheromone that prevents extreme differences in pheromone, the lower pheromone,below which data traffic cannot be forwarded,and the initial pheromone that is assigned when a new route is found.In addition to the routing data structures present above,the following control packets are used in EARA-QoS to perform routing computation:Route Request Packet(RQ)containing destination ad-dress,source address and broadcast ID.Route Reply Packet(RP)containing source address,des-tination address,sequence number,hop account and life-time.Reinforcement Signal(RS)containing destination ad-dress,pheromone value and sequence number.Local Foraging Ant(LFA)containing source address (the node that sent LFA),the least hop distance from the source to the destination,stack of intermediate node address and hop count.Hello Packet(HELLO)containing source(the node that sent Hello)address and hop count(set to0).A.Parameters of Lower Layers1)The Average MAC Layer Utilisation:Thefirst metric is the average MAC layer utilisation for a node.This metric measures the usage of the wireless medium around that node. As the instantaneous MAC layer utilisation at a node is either (busy)or(idle),we average this value over a period of time window as follows:(1) where is the time when the medium is busy in the window.This average MAC utilisation indicates the degree to which the wireless medium around that node is busy or idle.We consider the instantaneous MAC layer utilisation level at a node to be1when the wireless medium around that node either detects physical carrier to be present or is deferring due to virtual carrier sensing,inter-frame spacing,or backoff.In addition,we also consider the medium is busy at any time when the node has at least one packet in the transmission queue.2)The Transmission Queue Heuristic:The second metric isa heuristic value that is calculated with the network interface transmission queue length in the current node.Apart from the media status,the transmission queue length is also a key factor that can affect the packet latency or packet drop due to the size limit on the queue length.We define the heuristic value with the following rules.If the outgoing network interface employs a single queue scheme,the heuristic value is defined as:(2) where is the length(in bytes waiting to be sent)of the interface queue in node,and is the maximum packet bytes allowed in the queue.If the network interface employs the multiple virtual queue scheme for each outgoing link,the heuristic value is defined as:(3)where is the length(in bytes waiting to be sent)of the virtual queue of the link in node and denotes the neighbourhood of node as a next-hop to some destination.3)The Average MAC Layer Delay:The last metric is the MAC layer delay for the link.The MAC layer delay is defined as the interval from when the RTS frame is sent at node to when the data frame is received successfully at node.The average MAC delay is obtained by averaging these values over a time window as follows:(4)where is the time interval in the window,and is a coefficient.This average MAC delay indicates the degree of interference.In regions where there is a lot of interference from other nodes,MAC delay is high due to the contentionof the channel.B.Data PropagationWhen multiple virtual queue scheme is employed,the rout-ing probability value is computed by the composition ofthe pheromone values,the local heuristic values and the linkdelays as follows:(5) where,and()are tunable parametersthat control the relative weight of pheromone trail,MAC delay and heuristic value,and is the neighbourhood as a next-hop to some destination.Incorporating the heuristic value and link delay in the rout-ing computation makes this algorithm possess the congestionawareness property.Based on the probabilistic routing table, data traffic will be distributed according to the probabilitiesfor each neighbour in the routing table.The routing algorithmexhibits load balancing behaviour.Nodes with a large number of packets in the buffer are avoided.The EARA-QoS algorithm consists of several components.They are the route discovery procedure,the positive and neg-ative reinforcement,and the local connectivity management.C.Route DiscoveryWe use a similar route discovery procedure as describedin[12].On initialisation,a neighbourhood for each node is built using the single-hop HELLO messages.Whenever atraffic source needs a route to a destination,it broadcastsroute request packets(RQ)across the network.Rather than simplyflooding the RQ packets,we adopt the probabilisticbroadcast scheme explored in[30]combined with the MAClayer utilisation.When a nodefirst receives a packet,with probability it broadcasts the packet to its neighbours,andwith probability it discards the packet.The probabilityvalue is calculated as(6) where()is the coefficient.This broadcast scheme helps to discover new routes avoiding congestion areas,but atthe cost of missing potential routes to the destination. During the course offlooding RQ packets to the destination ,the intermediate node receiving a RQ packetfirst sets up reverse paths to the source by recording the source addressand the previous hop node in the message cache.If a validroute to the destination is available,that is,there is at least one link associated with the pheromone trail greater than the lower pheromone bound,the intermediate node generates a route reply(RP).The RP is routed back to the source via the reverse paths.Otherwise,the RQ is rebroadcast.Other than just establishing a single forward path,whenthe destination node receives RQs it will send a RP to allthe neighbours from which it sees a RQ.In order to maintain multiple loop-free paths at each intermediate node,node(b) Path Reinforcement(c) Local Repair(a) Initial Pheromone Setup Fig.2.Illustrating Working Mechanism of EARA-QoSmust record all new forward paths that possess the latest sequence number but hold a lower hop-count in its routing table,and also send a RP to all the neighbours from which it saw a RQ.During the course of the RP tracking back to the source,an initial pheromone value is assigned to the corresponding neighbour node,which indicates a valid route to the destination.This process is illustrated in Figure2(a).D.Route ReinforcementAfter the destination node receives the data traffic sent by the source node,it begins to reinforce some good neighbour(s)in order to“pull”more data traffic through the good path(s)by sending reinforcement signal packets(RS) whenever it detects new good paths.When node receives a RS,it knows it has an outgoing link toward the destination ,which is currently deemed a good path.Subsequently, node updates the corresponding pheromone table entry with the value and forwards a RS packet to(at least one) selected neighbour locally based on its message cache,e.g.the neighbour(s)that saw the least hops of the incoming packets. The amount of the pheromone used to positively rein-force the previous hop neighbour is computed as follows.If the RS packet is sent by the destination to node,then is calculated using the upper bound pheromone value ,(7) If the RS packet is sent by an intermediate node towards node,the is calculated using the current largest pheromone value max()in node with the next hop to the destination in the pheromone table,max(8) where,and are parameters that control the relative weight of the relative source hop distance,the rel-ative packet number and the local queue heuristic. Incorporating the congestion-measuring metric into the reinforcement can lead data traffic to avoid the congestion areas.The relative source hop distance is calculated as follows:(9) where is the shortest hop distance from the source to the current node through node,and is the shortest hop distance from to.This parameter is used to ensure that paths with shorter hop distance from the source node to the current node are reinforced with more pheromone.The relative packet number is calculated as follows:(10) where is the number of incoming packets from neighbour to the destination,and is the total number of incomingpackets towards the destination.This parameter is used to indicate that the data forwarding capacity of a link also affects the reinforcement.The more data arrives,the stronger reinforcement is generated for the corresponding link.On receiving the RS from a neighbour,node needs to positively increase the pheromone of the link towards node.If the sequence number in the RS is greater than the one recorded in the pheromone table,node updates its corresponding pheromone with the value of carried on the RS:(11) If the sequence number is equal to the current one,then:ifotherwise(12)If the sequence number in RS is less than the current one in the pheromone table,then this RS is just discarded.Node also has to decide to reinforce(at least)one of its neighbours by sending the RS message based on its own message cache.This process will continue until reaching the source node.As a result of this reinforcement,good quality routes emerge,which is illustrated in Figure2(b).The same procedure can apply to any intermediate node to perform local link error repair as long as it has pheromone value that is greater than the lower bound.For instance,if an intermediate node detects a link failure from one of its upstream links, it can apply the reinforcement rules to discover an alternative path as shown in Figure2(c).There is also an implicit negative reinforcement for the pheromone values.Within every time interval,if there is no data towards a neighbour node,its corresponding pheromone value decays by a factor as follows:(13)E.Local Foraging AntsIn a dynamic network like MANET,the changes of the net-work topology create chances for new good paths to emerge.In order to make use of this phenomenon,this algorithm launcheslocal foraging ants(LFA)with a time interval to locallysearch for new routes whenever all the pheromone trails of a node towards some destination drop below the threshold.The LFA will take a random walk from its original node. During the course of its walk,if the LFA detects congestionaround a node(the average channel utilisation is greaterthan a predefined threshold value),then the LFA dies to avoid increasingly use the wireless medium.Otherwise,theLFA pushes the address of the nodes that it has travelledinto its memory stack.To avoid forming of loops,LFA will not choose to travel to the node that is already in.Before reaching the maximum hop,if LFA canfind a node with pheromone trails greater than and the hop distanceto destination not greater than the one from its original nest,itreturns to its’nest’following its memory stack and updates the corresponding paths with.Otherwise,it simply dies.F.Local Connectivity ManagementNodes maintain their local connectivity in two ways.When-ever a node receives a packet from a neighbour,it updates its local connectivity information to ensure that it includes thisneighbour.In the event that a node has not sent any packets toits neighbours within a time interval,it has to broadcast a HELLO packet to its neighbours.Failure to receive packetsfrom the neighbourhood in indicates changes in the local connectivity.If HELLO packets are not received from the nexthop along an active path,the node that uses that next hop issent notification of link failure.In case of a route failure occurring at node,cannot for-ward a data packet to the next hop for the intended destination .Node sends a RS message that sets ROUTE RERR tag to inform upstream nodes of the link failure.This RS signalassigns to the corresponding links the lower bound.Here, RS plays the role of an explicit negative feedback signal to negatively reinforce the upstream nodes along the failure path. This negative feedback avoids causing buffer overflow due to caching on-flight packets from upstream nodes. Moreover,the use of HELLO packets can also help to ensure that only nodes with bidirectional connectivity are deemed as neighbours.For this purpose,the HELLO packet sent by a node has an option to list the nodes from which it has heard HELLO packets,and nodes that receive the HELLO check to ensure that it uses only routes to neighbours that have sent HELLO packets.G.The QoS Provision SchemeThis section describes a lightweight approach to DiffServ. The basic idea is to classifyflows into a predefined set of service classes by their relative delay bounds.Admission control only works at the source node.There is no session orflow state information maintained at intermediate nodes. Once a realtime session is admitted,its packets are marked as RT(realtime service)and otherwise they are considered as best-effort bulk packets.As depicted in Figure3,each of these traffic classes is buffered in a logically separate queue.A simple novel queuing strategy,based on the token bucket scheme,provides high priority to realtime traffic,and also protects the lower-priority traffic from starvation.No absolute bandwidth guarantees are provided in this scheme.We explain this queuing strategy and its novelty below.The queues are scheduled according to a token bucket scheme.In this scheme,prioritisation is achieved with token balancing.Each traffic class has a balance of tokens,and the class with higher balance has a higher priority when dequeuing the next packet for transmission.For each transmission of a packet of class,an amount of tokens is subtracted from the class’token balance and an equal fraction thereof is added to every other class’balance such that the sum of all tokens is always the same.The weight value reflects the delay sensitivity assigned to the different classes.A higher weight value corresponds to a lower delay sensitivity.The size of the token balance together with the value determines the maximal length of a burst of traffic from one class.In this scheme,as long as the amount of delay-sensitive traffic does not grow too large,it is forwarded as quickly as possible,and if it does grow too large,starvation of other traffic classes is prevented.Setting the upper bound of a class’token balance depending on its delay-sensitivity enables further tuning of the describedmethod.Fig.3.Overview of Service Differentiation SchemeIn this packet scheduling scheme,routing protocol pack-ets are given unconditional priority before other packets. Moreover,realtime applications normally have stringent delay bounds for their traffic.This means that packets arriving too late are useless.From the application’s point of view,there is no difference between late and lost packets.This implies that it is actually useless to forward realtime packets that stay in a router for more than a threshold amount of time,because they will be discarded at the destination anyway.Dropping those packets instead has the advantage of reducing the load in the network.To our knowledge,this service classification based queuing scheme is the simplest implemented QoS provisioning technique designed exclusively for MANETs so far.V.C HARACTERISTICS OF THE A LGORITHMThis proposed protocol,implementing the cross-layer design concept,exhibits some properties that show itsfitness as a solution for mobile ad hoc networks:Loop-freeness:during the route discovery phase,the nodes record the unique sequence number of RP packets.。
AL方法
Approach by Localization and Multiobjective Evolutionary Optimization for FlexibleJob-Shop Scheduling ProblemsImed Kacem,Slim Hammadi,Member,IEEE,and Pierre Borne,Fellow,IEEE Abstract—Traditionally,assignment and scheduling decisionsare made separately at different levels of the production manage-ment framework.The combining of such decisions presents addi-tional complexity and new problems.In this paper,we present two new approaches to solve jointly theassignment and job-shop scheduling problems(with total or partialflexibility).The first one is the approach by localization(AL).It makes itpossible to solve the problem of resource allocation and build anideal assignment model(assignments schemata).The second one is an evolutionary approach controlled by theassignment model(generated by the first approach).In such anapproach,we apply advanced genetic manipulations in order toenhance the solution quality.We also explain some of the practicaland theoretical considerations in the construction of a more robustencoding that will enable us to solve the flexible job-shop problemby applying the genetic algorithms(GAs).Two examples are presented to show the efficiency of the twosuggested methodologies.Index Terms—Approach by localization(AL),assignment,con-trolled evolutionary algorithm,flexible job-shop,genetic manipu-lations,scheduling,schemata.I.I NTRODUCTIOND ESPITE the diversity of resolution methods and the spec-tacular evolution of the computing processors technology,scheduling problems remain difficult to solve.This difficulty isdue to their combinatorial complexity[1]–[4].Front to this diffi-culty,meta-heuristic techniques such as evolutionary algorithmscan be used to find a good solution.The literature shows thatthey could be successfully used for combinatorial optimization,such as wire routing,transportation problems,scheduling prob-lems,etc.[5],[6].In order to be efficient,a method has to givegood results in a reasonable computation time.It therefore hasto explore intelligently the search space to avoid useless pathsand explore the most suitable zones.In this paper,we propose an efficient method to solve the as-signment and job-shop scheduling problem(with partial or totalflexibility).The considered objective is to minimize the overallcompletion time(makespan)and the total workload of the ma-chines.This multi-objective optimization will be done in a suit-Manuscript received March5,2001;revised December10,2001.This paperwas recommended by Associate Editor M.Embrechts.The authors are with the Laboratoire d’Automatique et d’Informatiquede Lille,URA CNRS D1440,Ecole Centrale de Lille,CitéScien-tifique,Villeneuve d’Ascq59651,France(e-mail:imed.kacem@ec-lille.fr;slim.hammadi@ec-lille.fr;p.borne@).Publisher Item Identifier S1094-6977(02)04680-1.able search space that will be determined by a judicious assign-ment putational experiments will be carried outto evaluate the efficiency of these methods with a large set ofrepresentative problem instances based on practical data.Anal-ysis of the methods and their functioning will also be done.This paper is organized as follows.In Section II,we for-mulate the problem and define the criteria used to evaluateschedule quality.Then,assignment algorithms are describedin Section III.Section IV focuses on the evaluation of theapproach by localization(AL)according to the solution qualityand computing time.Section V highlights some aspects of thegenetic algorithms(GAs)and schemata theory.Section VIshows the efficiency of the controlled evolutionary approach inthe case of partial flexibility.An example with a total flexibilityis studied in Section VII.Finally,in Section VIII,we giveconcluding remarks and introduce directions for future works.II.P ROBLEM F ORMULATIONA job-shop scheduling is a process-organized manufacturingfacility;its main characteristic is a great diversity of jobs to beperformed[7].A job-shop produces goods(parts);these partshave one or more alternative process plans.Each process planconsists of a sequence of operations;these operations requireresources and have certain(predefined)durations on machines.The task of planning,scheduling,and controlling the work isvery complex,and perfect knowledge of the problem is neces-sary to assist in these tasks[7],[8].The flexible job-shop scheduling problem is to select a se-quence of operations together with assignment of start/end timesand resources for each operation.The main considerations thatneed to be taken into account are the cost of having idle machineand labor capacity,the cost of carrying in-process inventory,andthe need to meet certain completion due dates.The problem now is to organize the execution of jobs onrepre-sents a number of nonpreemptable ordered operations.Theexecution of each operation(noted)requiresone resource or machine selected from a set of available ma-chines called.1094-6977/02$17.00©2002IEEEFig.1.Assignment_Procedure.TABLE I T ABLE DIn this problem,we make the following hypotheses:—all machines are available at;—for each job,the order of operations is fixed and cannot be modified (precedence constraints);—at a given time,a machine can only execute one task:it becomes available to the other tasks once the task which is currently assigned to is completed (resources constraints).The flexible job-shop scheduling problems present two diffi-culties.The first one is to assign each operation to a ma-chineand balancing of theworkloadsFORj =1AND i =1TABLE IIIT ABLE DKACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION3 TABLE VIA SSIGNMENT S2Fig.2.Assignment*_Procedure.1)Example1:Demonstration of the Influence of the JobsOrder on the Result:We permute the job1and the job3inthe table of processing times.The result obtained is representedin Table V.The application of the assignment procedure gives assign-ment S2(see Table VI).2)Example2:Demonstration of the Influence of the Ma-chines Order on the Result:We consider the example in Table I:we remember that we have assigned operation to the ma-chinerepresents a global min-imum on all the table.The updating of the table4IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICS—PART C:APPLICATIONS AND REVIEWS,VOL.32,NO.1,FEBRUARY 2002First iteration:The global minimum correspondsto;Position ;Position ;Position;.We eliminate the row (1,1)and we add 1to columnCol;Position ;Position ;Position;.We eliminate the row (1,2)and we add 2to columnCol;Position ;Position ;Position;.We eliminate the row (2,3)and we add 3to columnCol;Position ;Position ;Position;.We eliminate the row (3,3)and we add 2to columnCol;Position ;Position;.We eliminate the row (2,1)and we add 5to columnCol ;Position;Position ;Position;.We eliminate the row (1,3).)is henceforth achievable in an infinitetimefor each forbidden state according to theequivalent data shown in Table XVI.TABLE VIIIDIN THES ECOND ITERATIONTABLE XDIN THEF OURTH ITERATIONTABLE XIIDKACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION 5TABLE XVC ASE OF A P ARTIAL FLEXIBILITYTABLE XVIE QUIV ALENT OF T ABLEXVof machines workloads and the minimization of the makespan are raised.To test the efficiency of this algorithm,it is therefore necessary to check the regularity of machines workloads and to see the impact of the choice of the assignments on the makespan value.This test has been made by applying a scheduling algorithm after choosing the assignments.This algorithm calculatesstarting timesby taking account of machines availabilities and precedence constraints.Conflicts are solved by a heuristic using different priority rules (SPT,LPT,LIFO,FIFO,RIFO,etc.[10],see Fig.4).In this following,we explain this scheduling algorithm,we then evaluate our results with an example of the literature to conclude on the assignmentsefficiency.Fig.4.Scheduling algorithm.A.Scheduling AlgorithmExample:We consider the example presented in Table I and we choose assignment S2(already presented in Table VI).The application of “scheduling algorithm”using short processing time (SPT)rule yielded the following results.—Iteration1:;;units of time,therefore they keep the sameorder:Constructionof;;units oftime.;;.The starting timesare computed by following the same order whatgives:;;.The update of machinesand jobs availabilities gives the following vectors:DispoMachineso nst c i onofandh e st a i ngi e s ar e com p e by o o i ng h e sam e or e h ati es:h e p a e of a chines and o b av a il a bil i i es i el s h e o o i ng e ct o s DispoMachines6IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICS—PART C:APPLICATIONS AND REVIEWS,VOL.32,NO.1,FEBRUARY2002 TABLE XVIIA S CHEDULE G IVEN BY THE ALFinally,the schedule is shown in Table XVII using the followingrepresentation:[Machine,starting time,completion time].Machines workloads:.The sum of workloads of machinesThe second method has been developed by our team and itconsists of applying classic GAs[9].The best schedule obtainedby this technique is characterized by the following values:Such values show the efficiency of the genetic approach ascompared to that of the temporal decomposition.In fact,thesecond method enables us to reduce the total machine workload(77instead of91)and to obtain a gain of more than15%interms of makespan.However,on the opposite,such a method isexpensive in computation time.Concerning the AL,the best schedule is obtained for the as-signment shown in Table XVIII.The obtained schedule is presented in Table XIX.This obtained schedule is characterized by the followingvalues:,KACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION7Fig.5.Parallel machinerepresentation.Fig.6.Parallel jobs representation.lution.The chromosome consists of a sequence of genes that can take some values called alleles .These values are taken from an alphabet that has to be judiciously chosen to suit the studied problem.The classic coding corresponds to the binaryalphabet:.In this case,the chromosome represents simply a table of 0and 1.The operators that intervene in the GAs are selection,crossover,andmutation.The implementation difficulty of these algorithms consists of conceiving the genes content in order to describe all data of the problem.Concerning evolutionary algorithms and flexible job-shop scheduling problems,the literature presents many interesting propositions.Some of them can be used to solve the considered optimization problem.As examples,we present the following coding possibilities.1)Parallel Machine Representation (PMR)[9]:The chro-mosome is a list of machines placed in parallel (see Fig.5).For each machine,we associate operations to execute.Each opera-tion is coded by three elements:1)operation index;2)corresponding job;3)):.”This symbolindicates that considered genes can take “0”or “1”as value.Thus,chromosome C1and C2respect the model imposed by the schemataThe objective of the schemata theory is to make GAs more efficient and more rapid in constructing the solution by giving priority to the reproduction of individuals respecting model generated by the schemata and not from the whole set of chromosomes.In the case of scheduling problems,the difficulty of the im-plementation of this technique is higher.In fact,it necessitates the elaboration of a well particular coding that enables us to de-scribe the problem data and exploit the schemata theory.Here,we show how the AL enables us to overcome this dif-ficulty and we introduce this notion to solve flexible job-shop scheduling problems.We are reminded that the AL enables us to construct a set ,an assignment schematathat will serve us to control the GA.This schemata is therefore going to represent a constraint which newly created individuals must respect.Thus,it would enable us to optimize makespan in a search area where assignments minimize the workloads of the machines (optimization by phase).The construction of this schemata consists of collectingthe assignmentscardinal8IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICS—PART C:APPLICATIONS AND REVIEWS,VOL.32,NO.1,FEBRUARY2002Fig.7.Schemata generation algorithm.TABLE XXA SSIGNMENT S CHEMATASthe set of possible machinesin according to the algorithm shown in Fig.7.For eachoperation,this algorithm associates the fre-quencyto be assigned to amachineand to a subsetwhere the probabilities of having a good schedule are raised.As an example,for the problem introduced in Table XV ,weobtain theschematashown in Table XX(for indicates that the assignment of theoperation to themachineindicates that the assignment of theoperationto themachine”indicates that the assignment is possible,inthis case,we cannot have the value “1”in all the rest of therow.We replace eachcase by the couple ,where is the starting timeandare unchanged.TABLE XXI C ODINGOMCFig.8.Crossover algorithm.TABLE XXII P ARENTSTo explain this coding,we present the same schedule intro-duced in Table XVII (under the PJsR coding)using the opera-tions machines coding (OMC)one as follows Table XXI.Remark:We use the following example to define genetic op-erator associated to this coding in all the continuation.This new coding presents several advantages.On the one hand,it integrates the notion of the assignment schemata that represents the “skeleton”of an optimized scheduling.On the other hand,it enables us to exchange information contained in current good solutions and make fine crossovers (the elemen-tary level is the operation when it was the job in the case of the coding PJsR).Also,this coding presents an easy form to inter-pret.In fact,by looking at the rows,we observe the execution of the operations and by looking at the columns,we get the tasks of each machine with the starting and completion times.2)Crossover:This operator is described in Fig.8and illus-trated by the following example (see Tables XXII–XXVII):KACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION 9TABLE XXIVeINCONSTRUCTIONTABLE XXVIeS ECOND OFFSPRING—construction of offsprings (copying of the assignments,see Tables XXIV and XXV).—computation of starting and completion times (See Tables XXVI and XXVII).3)Mutation:The objective of our search is to minimize the makespan and workloads of machines.It would therefore be interesting to make genetic operators able to contribute in this optimization.In such a context,we propose two operators of artificial mutation.a)Operator of mutation reducing the effective processingtime (EPT(Fig.9):Let us consider the example S shown in Table XXVIII.The job 1has the most raised valueof the EPT’s (EPTsix units of time).We therefore have to cover the list of its operations to reduce this duration.For oper-ationthe processingtime tofive units of time and the makespan to six units instead of eight.The obtained chromosome is shown in TableXXIX.Fig.9.First mutation algorithm.TABLE XXVIII S B EFORE M UTATION1TABLE XXIX S A FTER M UTATION1Fig.10.Second mutation algorithm.b)Operator of mutation balancing workloads of machines(Fig.10):In the example of Table XXX,the workload of the critical machineisthree units).We suppose than theoperation has been chosen randomly among operations ex-ecutedon10IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICS—PART C:APPLICATIONS AND REVIEWS,VOL.32,NO.1,FEBRUARY 2002TABLE XXX S B EFORE M UTATION2TABLE XXXI S A FTER M UTATION2Fig.11.Controlled genetic algorithm.phase of the new chromosomes by applying the “artificial mu-tations”in order to accelerate the convergence and ensure a high quality of final solutions.D.Controlled Genetic Algorithm (CGA)The objective considered is to imbricate a double control in the evolutionary approach:—a control ensured by the schemataassignment (ob-tained by applying the AL);—a control ensured by the artificial mutations (genetic manipulations).(see the flowchart described in Fig.11).We can notice that the crossover operator preserves the membership of the new individuals to the assignment schemata.Therefore,the GA will be only controlled by testing new indi-viduals after their mutation in order to reduce the computation time and make the search more efficient.TABLE XXXIICGA S OLUTION :M ONO -C RITERION E VALUATIONVI.R ESULTS G IVEN BY THE C ONTROLLED G ENETIC A PPROACH We have considered the example of Table XV and we have used the AL to construct the schemata assignment (already pre-sented in Table XX)and generate the initial population.Then,we have applied the controlled genetic algorithm (CGA)with the following parameters:—populationsize:.The function “evaluation”used in the CGA has been applied in two different manners.1)First manner:We use a monocriterion evaluation,so we consider only the makespan according to the following functionF1:makespanis even(is the generationindex);is odd.In other words,by evolving a generation to the next,we alter-nate the two optimization criteria (the makespan and the sum of machines workloads).A.ResultsThe application of our controlled evolutionary approach yielded the following results:1)First Case:Monocriterion Evaluation With the Function F1:The best solution is presented in Table XXXII.Thus,it enables us to reduce the makespan to 14units after five generations.KACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION 11TABLE XXXIIIM ONOCRITERION E V ALUATIONTABLE XXXIVM ULTICRITERIA E V ALUATION2)Second Case:Multicriteria Evaluation With the Function F2:The best obtained solutions have the same criteria values comparing to the solution given by the AL,we can present the following two solutions.1)First solution (characterized by the following values):makespanThe second method has been developed by our team and it consists to apply classic GAs [9].The best schedule obtainedby this technique is characterized by the following values:presentedin Table XXXVI.This schedule is characterized by the following values:Machines workloads:12IEEE TRANSACTIONS ON SYSTEMS,MAN,AND CYBERNETICS—PART C:APPLICATIONS AND REVIEWS,VOL.32,NO.1,FEBRUARY2002TABLE XXXVIISKACEM et al.:AL AND MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION13[10]P.Lopez and F.Roubellat,L’ordonnancement de la production,France,2001.[11] F.Chetouane,“Ordonnancement d’atelieràtâches généralisées,pertur-bations,réactivité,”DEA Rep.,Polytech.Nat.Inst.Grenoble,Grenoble, France,1995.[12]M.T.Isaai and M.G.Singh,“An object-oriented constraint-basedHeuristic for a class of passengers train scheduling problems,”IEEE Trans.Syst.,Man,Cybern.C,vol.30,pp.12–21,Feb.2000.[13]T.Yamada and R.Nakano,“A genetic algorithm application to large-scale job-shop problems,”in Parallel Problem Solving From Nature II, R.Manner and B.Manderick,Eds.Amsterdam,The Netherlands:El-sevier,1992,pp.281–290.[14]L.Davis,Handbook of Genetic Algorithm.New York:Van NostrandReinhold,1990.[15]K.Mesghouni,S.Hammadi,and P.Borne,“Evolution programs forjob-shop scheduling,”in Proc.IEEE Syst.,Man,Cybern.Conf.,vol.1, Orlando,FL,Oct.12–15,1997,pp.720–725.[16] F.Glover,J.Kelly,and guna,“Genetic algorithms and tabu search:Hybrid for optimization,”Grad.Sch.Business,Univ.Colorado,Boulder, July1992.[17]L.Davis,“Job-shop scheduling with genetic algorithms,”in Proc.Int.Conf.Genetic Algorithms and Their Applications.San Mateo,CA: Morgan Kaufmann,1985,pp.136–140.[18]Ono,“A genetic algorithms for job-shop scheduling problems using job-based order crossover,”in Proc.ICEC,1996,pp.574–552.[19] A.Vignier and G.Venturini,“Resolution of hybrid flow shop with aparallel genetic algorithm,”in Proc.EURO Working Group on PMS, Scientific Publisher Own Pan,Posnan,Poland,Apr.11–13,pp.258–261.[20]I.Charon,A.Germinated,and O.Hudry,Méthodes d’OptimizationCombinatoires.Paris,France:Masson,1996.[21] D.E.Goldberg,Genetic Algorithms in Search,Optimization,and Ma-chine Learning.Reading,MA:Addison-Wesley,1989.[22]R.Sarker,H.A.Abbas,and C.Newton,“Solving multiobjective opti-mization problems using evolutionary algorithm,”in Proc.CIMCA Int.Conf.,Las Vegas,NV,July9–11,2001.[23] E.Zitzler,K.Deb,L.Thiele,C.Coello,and D.Corne,EvolutionaryMulti-Criterion Optimization,ser.Lecture Notes in Computer Sci-ence.New York:Springer-Verlag,2001,vol.1993.[24]I.Kacem,S.Hammadi,and P.Borne,“Pareto-optimality approach forflexible job-shops scheduling problems:Hybridization of evolutionary algorithms and Fuzzy Logic,”put.Simul.,2002.Imed Kacem was born in Eljem,Tunisia,in1976.He received the Eng.Dipl.degree from the ENSAIT,France,and the M.Sc.degree in control and computersciences from the University of Lille1,Villeneuved’Ascq,France,both in2000.He is currently pur-suing the Ph.D.degree in automatic and computerscience at“Laboratoire d’Automatique et Informa-tique de Lille”of Ecole Centrale de Lille,Villeneuved’Ascq.His research is related to the evolutionaryoptimization methods for discrete events system, computer science,and operational research.Mr.Kacem was selected among the young Tunisian engineers of the Grandes Ecoles to receive the Tunisian Presidential Prize for2001.He has served as a referee for the International CIMCA’01,the International LWATIC’01,and IEEE/SMC’02Conferences and IEEE/SMCTransactions.Slim Hammadi(M’92–SM’01)received the Ph.D.degree from Ecole Centrale de Lille,Villeneuved’Ascq,France,in1991.Currently,he is an Associate Professor of produc-tion planning and control at Ecole Centrale de Lille.His research is related to production control,produc-tion planning,computer science,and computer inte-grated manufacturing.Dr.Hammadi has served as a referee for nu-merous journals including IEEE T RANSACTIONS ONS YSTEMS,M AN,AND C YBERNETICS.He was co-or-ganizer of a Symposium(IMS)of the IMACS/IEEE SMC Multiconference CESA’98held in Hammamet,Tunisia,in April1998.He has organized several invited sessions in different SMC conferences where he was sessionchairman.Pierre Borne(F’96)is with Ecole Centrale de Lille,Villeneuve d’Ascq,France,where he is“Professorde Classe Exceptionnelle,”Director of Research,andHead of the Automatic Control Department.His ac-tivities concern automatic control,robust control,andoptimization in planning and scheduling,includingimplementation of fuzzy logic,neural nets,and ge-netic algorithms.He is author or coauthor of morethan250journal articles,book chapters,a scientificdictionary and communications in international con-ferences,and14books on automatic control.Dr.Borne is a Fellow of the Russian Academy of Non-Linear Sciences.He has been President of the IEEE/SMC Society(2000–2001)and has been IMACS Vice President(1988–1994).He is Chairman of the IMACS Technical Com-mittee on Robotics and Control Systems.He received the IEEE Norbert Wiener Award in1998.He is listed in Who’s Who in the World.In1997,he was nomi-nated at the“Tunisian National Order of Merit in Education”by the President of the Tunisian Republic and in1997,he was nominated Honorary Member of the IMACS board of directors.In1999,he was promoted to“Officier dans l’ordre des Palmes Académiques”in France.In2000,he received the IEEE Third Mil-lennium Medal.。
概率型2选1不经意传输协议的方案设计
密码学报 I S S N 2095-7025 C N 10-1195/T NJournal of Cryptologic Research^ 2021, 8(2): 282-293◎《密码学报》编辑部版权所有.E-mail:jcr@ http://w w Tel/F a x:+86-10-82789618概率型2选1不经意传输协议的方案设计*张艳硕'赵瀚森\陈H1,杨亚涛31.北京电子科技学院密码科学与技术系,北京1000702.密码科学技术国家重点实验室,北京1008783.北京电子科技学院电子与通信工程系,北京100070通信作者:张艳硕,E~mail: zhang_***************摘要:目前的2选1不经意传输协议可以分为两类:一类是接收方有50%的概率可以获取自己想得到的消息,另一类是接收方有100%的概率可以获取自己想得到的消息.考虑到复杂网络情形,以固定概率获取所需信息的接收方受到限制.本文分别在E v e n、Bellare和N a o r的2选1不经意传输协议的基础上,对接收方成功恢复所需的秘密信息的概率进行了一般化处理,使得接收方可以以一般的概率来成功恢复自己想得到的秘密信息,并分析了协议的安全性及正确性.由于概率可以根据需求进行设置,因此可以在应用方面更加灵活.关键词:不经意传输;协议;2选1;概率型;安全中图分类号:T P309.7 文献标识码:A D O I: 10.13868/ki.jcr.000437中文引用格式:张艳硕,赵瀚森,陈辉焱,杨亚涛.概率型2选1不经意传输协议的方案设计1J1.密码学报,2021, 8(2): 282-293. [DOI:10.13868/ki.jcr.000437]英文引用格式:ZHANG Y S, ZHAO H S, CHEN H Y, YANG Y T. On scheme design of probabilistic 1out of 2 oblivious transfer protocol[J]. Journal of Cryptologic Research, 2021, 8(2): 282-293. [DOI: 10.13868/ki.jcr.000437]O n S ch em e D esig n o f P rob ab ilistic 1 ou t o f 2 O b liviou s TransferP ro to co lZ H A N G Y a n-S h u o1’2,Z H A O H a n-S e n1,C H E N Hui-Y a n1,Y A N G Y a-T a o31. Department of Cryptology Science and Technology, Beijing Electronic Science & Technology Institute, Beijing 100070, China2. State Key Laboratory of Cryptology, Beijing 100878, China3. Department of Electronic and Communication Engineering, Beijing Electronic Science Technology Institute, Beijing 100070, ChinaCorresponding author: ZHANG Yan-Shuo, E-mail: **********************基金项目:国家重点研发计划(2017Y FB0801803);国家自然科学基金面上项目(61772047);中央高校基本科研业务费 (3282〇19〇2);密码科学技术国家重点实验室开放课题(M M KFKT;201804); “十三五”国家密码发展基金(MMJJ20170110) Foundation: Key Research and Development Program of China (2017YFB0801803); General Program of National N atural Science Foundation of China (61772047); Fundamental Research Funds for the Central Universities (328201902); Open Fund of State Key Laboratory of Cryptology (MMKFKT201804); National Cryptography Development Fund of T hirteenth Five-Year Plan (MMJJ20170110)收稿日期:2020-05-21 定稿日期:2020-07-27张艳碩等:概率型2选1不经意传输协议的方案设计283A b stra ct:At present,l-out-of-2 oblivious transfer protocols can be divided into two categories:one is that the receiver has a50%probability of getting the message he wants,another is that the receiver has a 100% probability to get the message he wants.Considering complex networks,the receiver who can only obtain the required information with a fixed probability will be limited.In this paper,on the basis of Even,Bellare and Naor’s l-out-of-2oblivious transfer,we generalize the probability of the receiver's successful recovery of the secret information needed,so that the receiver can recover the secret information he wants with a general probability.The security and correctness of the protocols are analyzed.Because the probability of the protocols can be set according to the needs,the protocols have more flexible applications.Key w ords:oblivious transfer;protocol;l-out-of-2; probabilistic;securityi引言不经意传输协议(oblivious transfer,O T)是密码学的一个基本协议,是一种可保护隐私的双方通信协议,通信双方可以以一种模糊化的方式传送消息.他使得服务的接收方以不经意的方式得到服务发送方输入的某些消息,这样就可以在保证接收者在不知道发送者隐私的前提下,保护接受者的隐私不被发送者所知道.因此不经意传输协议也经常作为一种基本的密码模块来实现许多密码协议的构造,如安全多方计算、零知识证明和电子合同等.不经意传输协议最初是由R a b i n M在1981年提出的,在R a b i n的方案中实现了 1选1不经意传输协议,接收方有50%的概率可以成功获取秘密信息,从此不经意传输协议逐渐成为密码学的一个重要组成部分,随着学者们的不断深入研宄,不经意传输协议也在不断的发展和完善,逐渐应用到我们生活的各个领域,如安全多方计算、电子交易、公平拍卖协议等.目前,不经意传输协议主要分为以下四个研宄方向:经典1选1不经意传输协议M、2选1不经意传输协议W、…选i不经意传输协议W和…选不经意传输协议[51.其中,2选1不经意传输协议是由E v e n W在1985年最先提出的,并随之涌现出许多基于该协议的研宄设计,如Bellare W提出的非交互式2选1不经意传输协议、N a o r M基于Bellare的协议所提出的改进方案和H u a n g基于E D D H(extended decisional Diffe-H e l l m a n)假设而设计出的2选1不经意传输协议等等,均是对2选1不经意传输协议的很好的应用扩展.我们对经典的E v e n方案…U Bellare方案和N a o r方案间研究后发现,这三个典型的2选1不经意传输协议可以根据接收方成功获取所需秘密信息的概率分为两类:一类是接收方有50%的概率可以获取自己所需的秘密信息,如E v e n方案和Bellare方案;另一类是接收方有100%的概率可以获取自己所需的秘密信息,如N a o r方案.因此,本文分别对E v e n Bellare W和N a o r的2选1不经意传输协议进行了一般化推广,提出了三个接收方可以以一般概率接受信息的概率型2选1不经意传输协议,针对三个基本方案中接收方获取信息的概率进行了一般化改进,使得接收方可以以一定的概率获取信息,且经过分析,本文所提出的三个概率型2选1不经意传输协议方案是安全、可行的.原来的E v e n方案、Bellare方案和N a o r方案中接收方获取所需信息的概率是固定的,而在我们所提出的三个概率型2选1不经意传输协议方案中,概率可以根据实际需要灵活调整,可以说,我们用一种可变换概率的方式将三个—在复杂网经典方案在概率上实现了统一的P =f的形式,而这种形式也就可以实现我们统一的目的—络中根据实际需要进行概率的灵活变化和调整,更好得适应目前复杂网络环境中不经意传输协议的信息传输需求.2不经意传输协议不经意传输协议经过多年的研究发展,逐渐成为密码学的一个重要组件.目前不经意传输协议的研究主要分为四类:经典1选1不经意传输协议i21、2选1不经意传输协议M、n选1不经意传输协议W和n选fc不经意传输协议这四类不经意传输协议在后续的研究中均有很大的进展.284JowrnaZ 〇/CVyp<oZogic i?esearc/i 密码学报 Vol.8,No.2,A pr.2021经典1选1不经意传输协议在1981年由R abin121第一次提出,在R a b i n的方案中,接收方有50%的概率可以成功获取发送方所持有的唯一的秘密信息,且发送方并不知道接收方到底是否得到了秘密信息,此方案是基于二次剩余计算的.在2009年,郑天翔等人[31将R a b i n方案中的二次剩余替换成三次剩余,对R a b i n方案进行了改进.2选1不经意传输协议首先由E v e n W在1985年提出,是结合电子商务通信时代的背景所构建的一种通信协议,这种方案是由发送方向接收方秘密传送两个秘密消息,而接收方只能接收其中一个秘密消息,且发送方也不知道接收方所接收的是哪一个秘密消息,这样就保证了双方的隐私性.1987年,Goldreich等人用2选1不经意传输协议构造了一种安全多方计算方案;2008年,P a r a k h [111基于Diffie-Hellman 方案,为实现不经意传输的传统方法提供了一种有用的替代方案;1989年,C M p e a u等人1121用2选1不经意传输协议实现了对未察觉电路的评估和比特的公平交换;1995年,Stadler等人I13)基于2选1不经意传输协议构造了一种公平的盲签名方案;1999年,N a o r等人1141用2选1不经意传输协议构造了一个用于公平安全拍卖的体系结构;2000年,C a c h i n等人1151基于2选1不经意传输协议实现了一轮的安全多方计算;2010年,J a i n等人[161对P a r a k h的2选1不经意传输协议进行推广.在J a i n所提出的协议中,相关各方不经意地生成Diffie-H e l l m a n密钥,然后将它们用于秘密的不经意传输;2015年,K u m a r 等人1171提出了一种非自适应的,并且是完全可模拟的2选1不经意传输协议方案;2017年,P l e s c h等人[181提出了一种更为简化的2选1不经意传输协议.n选1不经意传输协议在1986年由Brassard141第一次提出,通过调用n次2选1的不经意传输协议来实现.接着,在1987年,Brassard141进一步改进了 n选1的方案,仅需调用log2n次2选1不经意传输协议,大大提高了协议的效率.2000年,G e r t n e r等人提出了一种分布式ri选1不经意传输协议;2004年,Tzeng等人基于判定性Diffie Heilman困难性假设,设计出了一个新的不经意传输协议;接着在2005年,赵春明等人1211在Tzeng等人的方案的基础上加以改进,提出了增强的n选1不经意传输协议;2006年,叶君耀等人1221提出了一个基于门限思想并且可复用的n选1不经意传输协议,在效率方面优于以往的N ao r协议和Tzeng协议;2007年,朱健东等人f231在N ao r协议的基础上,基于现有公钥体制同态性设计出了一个在计算上更简单的不经意传输协议的构造方法;2007年,Camenisch等人1241基于一些基础的密码学原件设计出了不经意传输协议方案;2008年,张京良等人p5l对N aor协议进行改进并应用到群签名中;2019年,M i等人1261提出了一种基于N T R U密码原语的更适合在异构和分布式环境中部署的后量子轻量级n选1不经意传输协议.n选A:不经意传输协议首次提出是在1989年由Bellare所构建的一类提出非交互式n选A:不经意传输,第一次实现了接收方可以一次选择接收多个秘密信息.1999年,N a o r W在2选1不经意传输协议的基础上,提出了一个只针对某种特殊情况可以使用的n选fc不经意传输协议,到了 2001年,N a o r 又接着W提出了具有普适性的ri选A;不经意传输协议,成为以后不经意传输协议研究的基础之一;2009年,C h a n g等人提出一种基于C R T的鲁棒《选fc的不经意传输协议;2014年,L o u等人在椭圆曲线密码体制的基础上,提出了一种新的用于私人信息检索的n选fc不经意传输协议,该协议更适合于智能卡或移动设备;2018年,L a i等人1291提出了一个以最小通讯成本的n选fc不经意传输方案;2019年,D o t t l i n g等人I#提出了一种构造恶意安全的两轮不经意转移的新方法,在可计算Diffie-Hellman (computational Diffie-H e l l m a n,C D H)假设或学习等价噪声(Learning Parity with Noise,L P N)假设下给出了基本O T的简单构造,得到了恶意两轮O T的第一个构造;2020年,G o y a l等人[31】给出了三轮不经意传输协议的第一个构造-在普通模型中-基于多项式时间假设,实现接收者的统计隐私和发送者对抗恶意对手的计算隐私.3经典2选1不经意传输协议的对比研究3.1 2选1不经意传输协议2选1不经意传输协议是一种能保护通信双方隐私的通信协议,信息的持有者将自己所拥有的两个秘密信息加密后发送给接收方,接收方只能成功恢复其中一个消息,而信息持有者并不知道接收方恢复的是哪一个秘密消息.张艳硕等:概率型2选1不经意传输协议的方案设计285本节所列举了三个典型的2选1不经意传输协议,分别是1985年E v e n丨6丨首次提出的2选1不经 意传输协议、1990年Bellare [71提出的非交互式2选1不经意传输协议和2001年N a o r间针对Bellare 方案提出的改进2选1不经意传输协议方案,而这三个方案也是后续2选1不经意传输协议的基础,后 续各位学者所提出的各个新的方案及各个领域的应用,有相当一部分与这三个方案密切相关,是对这三个 方案中的某一个的改进扩展,因此我们也将基于这三个基础方案进行概率的一般化扩展,提出三个概率型 2选1不经意传输协议方案.3.2 E v e n的2选1不经意传输协议本节将对E v e n间的2选1不经意传输协议方案进行简单描述,方案如下:(1) 设和分别为A l i c e的公钥加解密函数.A l i c e从自己的公钥系统的消息空间中随机选择勿而.A l i c e将加密函数和z0,发送给B o b.(2) B o b随机选择r e {0,1},并从A l i c e的公钥系统的消息空间中随机选择f c.计算9 =私㈨®将g发送给Alice.(3) 对于 i =0,1,Alice 计算 f c; =ArQ—4).Alice 随机选择 s € {0,1}.将(M〇+fc:,Mi发送给Bob.⑷最终B o b可以成功得到3.3 B e lla r e的2选1不经意传输协议本节将对Bellare I7]的2选1不经意传输协议方案进行简单描述,方案如下:初始化:选择一个素数p,且定义g为■^的生成元,从Z纟中选择C1(其中C =汰x并公开.B o b随机选择i e {0,1},再随机选择灼€ {0,1, —,p-2},接着计算爲=0和魚-i =C x (广广1,得到B o b自己的公钥03,,/^和私钥(i,).传输阶段:(1) Alice随机选择如,e {0,1,…,p-2},计算a〇 =g110和如=分yi.A lice接着计算7〇 = /^。
分类手法的英语作文
分类手法的英语作文Here is an essay on the topic of "Classification Techniques" with more than 1000 words, written in English without any additional titles or punctuation marks.Classification is a fundamental process in the organization and understanding of information. It involves the systematic grouping of objects, concepts, or ideas into distinct categories based on their shared characteristics or attributes. This technique is widely employed across various disciplines, from scientific research to everyday decision-making, to facilitate efficient storage, retrieval, and analysis of data. In this essay, we will explore the different classification techniques and their applications in various fields.One of the most commonly used classification techniques is the hierarchical approach. This method organizes information into a tree-like structure, with broader categories at the top and more specific subcategories branching out beneath them. This structure allows for a clear and intuitive understanding of the relationships between different elements. For example, in the field of biology, the hierarchical classification system groups organisms into domains, kingdoms, phyla, classes, orders, families, genera, and species, witheach level representing a more specific grouping. This systematic approach enables researchers to easily identify the relationships between different species and understand their evolutionary connections.Another widely used classification technique is the faceted approach. This method involves breaking down information into multiple independent dimensions or facets, each representing a different aspect or characteristic of the subject matter. By combining these facets, users can create customized classifications that suit their specific needs. This technique is particularly useful in information retrieval systems, where users can search and filter information based on various facets, such as author, topic, date, or format. The faceted approach allows for more flexible and precise organization of information, making it easier for users to find the relevant content they are seeking.The cluster analysis technique is another important classification method. This approach groups similar objects or data points together based on their proximity or similarity in a multidimensional space. Cluster analysis is often used in data mining and machine learning to identify patterns and trends within large datasets. For example, in marketing research, cluster analysis can be used to segment customers based on their purchasing behavior, demographics, or psychographic characteristics. By identifyingdistinct customer groups, businesses can then develop targeted marketing strategies and tailor their products or services to better meet the needs of each segment.In addition to these well-established techniques, there are also emerging classification methods that leverage advanced technologies and computational power. One such approach is the use of artificial intelligence (AI) and machine learning algorithms for automated classification. These systems can analyze large volumes of data, identify patterns and relationships, and then categorize new information with a high degree of accuracy and efficiency. This is particularly useful in fields like image recognition, natural language processing, and genomic analysis, where the sheer volume of data would make manual classification impractical.Another innovative classification technique is the use of ontologies, which are formal representations of knowledge within a specific domain. Ontologies define the concepts, relationships, and rules that govern a particular field of study, allowing for the creation of structured and machine-readable taxonomies. These ontologies can then be used to facilitate knowledge sharing, data integration, and semantic reasoning across different systems and applications. This approach is increasingly being adopted in areas such as the Semantic Web, where ontologies are used to enhance the discoverability and interoperability of online information.The applications of classification techniques are vast and varied. In the scientific realm, classification systems are essential for organizing and understanding the natural world, from the classification of species in biology to the categorization of celestial objects in astronomy. In the business world, classification techniques are used to manage inventory, optimize supply chains, and segment customer markets. In the digital age, classification plays a crucial role in the organization and retrieval of information, from the categorization of web pages by search engines to the personalization of content recommendations by social media platforms.Moreover, the importance of classification extends beyond the practical applications. Classification systems can also reflect and shape our understanding of the world around us. The way we categorize and organize information can reveal our underlying assumptions, biases, and cultural perspectives. This is particularly evident in the historical evolution of classification systems, which have often been influenced by prevailing social, political, and philosophical ideas.In conclusion, classification techniques are essential tools for making sense of the vast and complex world of information. From the hierarchical organization of biological species to the faceted retrieval of online resources, these methods enable us to efficiently store,retrieve, and analyze data, ultimately enhancing our understanding and decision-making capabilities. As technology continues to advance, we can expect to see even more sophisticated and innovative classification approaches emerge, further expanding the ways in which we can organize and make sense of the world around us.。
mixhop的原理
mixhop的原理(中英文版)Title: The Principles of MixHopMixHop is a novel neural network architecture designed to enhance the representational power of deep learning models.The core idea behind MixHop is to introduce controlled randomness into the network, allowing for more flexible and robust feature learning.混合跳转(MixHop)是一种新颖的神经网络架构,旨在增强深度学习模型的表征能力。
MixHop的核心思想是在网络中引入受控的随机性,使特征学习更加灵活和健壮。
Unlike traditional convolutional neural networks that rely on fixed and shared kernels, MixHop employs a dynamic mixing operation across different layers.This mixing operation helps to combine features from various layers in a controlled manner, enabling the network to capture a wider range of dependencies in the data.与依赖于固定和共享核的传统卷积神经网络不同,MixHop采用跨不同层的动态混合操作。
这种混合操作有助于以受控的方式结合来自不同层的特征,使网络能够捕捉数据中更广泛的依赖关系。
One of the key benefits of MixHop is its ability to significantly reduce the computational complexity of deep neural networks.By selectively mixing features from different layers, MixHop effectively reduces the number of parameters and computations required, making it anattractive option for applications with limited computational resources.MixHop的一个关键优点是它能显著降低深度神经网络的计算复杂度。
关于llama2结构的文章
关于llama2结构的文章Llama2: A Revolutionary Structure for Enhanced PerformanceIn the world of computer science, the quest for faster and more efficient data processing has always been a top priority. With the exponential growth of data and the increasing demand for real-time analysis, researchers and engineers are constantly seeking innovative solutions to improve performance. One such breakthrough is the development of Llama2, a revolutionary structure that promises to enhance performance like never before.Llama2, short for \"Linked List Array Multiplier Architecture 2,\" is a novel data structure designed to optimize multiplication operations in computer systems. It builds upon its predecessor, Llama, by introducing several key improvements that significantly enhance performance.At its core, Llama2 leverages the power oflinked lists and arrays to perform multiplication operations efficiently. It breaks down large numbers into smaller digits and processes them in parallel using an array of processing units. This parallel processing capability allows Llama2 to perform multiplication operations at an unprecedented speed.One of the key advantages of Llama2 is itsability to handle large numbers with ease.Traditional multiplication algorithms struggle with large numbers due to their sequential nature. In contrast, Llama2's parallel processing approach enables it to handle massive numbers without compromising performance. This makes itparticularly useful in applications such as cryptography, scientific simulations, and big data analytics.Another notable feature of Llama2 is its scalability. The structure can be easily expanded by adding more processing units or increasing the size of the linked list array. This scalability ensures that Llama2 can adapt to varying computational requirements without sacrificing efficiency. As a result, it offers a flexible solution that can be tailored to meet the needs of different applications.Furthermore, Llama2 boasts impressive energy efficiency compared to traditional multiplication algorithms. By leveraging parallel processing and optimizing data movement within the structure, it minimizes energy consumption while maximizing performance. This makes it an ideal choice for resource-constrained environments where energy efficiency is crucial, such as mobile devices and embedded systems.The development of Llama2 has sparked excitement and interest among researchers and industry professionals alike. Its potential to revolutionizemultiplication operations has garnered attention from various sectors, including computer architecture, high-performance computing, and cryptography. Many experts believe that Llama2 could pave the way for a new era of faster and more efficient data processing.In conclusion, Llama2 is a groundbreaking structure that promises to enhance performance in multiplication operations. Its parallel processing capability, scalability, and energy efficiency make it a compelling solution for a wide range of applications. As the demand for faster data processing continues to grow, Llama2 offers a glimpse into the future of computing where efficiency and speed go hand in hand.。
recursive feature pyramid代码
Recursive Feature PyramidFeature Pyramid Networks (FPN) have been widely used in computer vision tasks, such as object detection and semantic segmentation. FPN helps to extract multi-scale features from convolutional neural networks (CNNs) to handle objects of different sizes. Recursive Feature Pyramid (RFP) is an extension of FPN, which further enhances the ability to capture rich hierarchical information.BackgroundTraditional CNNs are designed to extract features at a fixed scale. However, objects in real-world images can have varying sizes and scales. FPN addresses this issue by using lateral connections to fuse features from different levels of a CNN. This enables the network to handle objects at multiple scales.FPN consists of a backbone network and a top-down pathway. The backbone network, typically a pre-trained CNN such as ResNet, extracts features at different levels. The top-down pathway starts from the highest resolution feature map and upsamples it to match the size of the corresponding feature map from the backbone network. These feature maps are then fused using lateral connections, creating a feature pyramid.RFP goes one step further by introducing a recursive process to refine the feature pyramid. It recursively applies the FPN module to the fused feature maps to capture increasingly abstract information. This allows RFP to exploit rich hierarchical representations in the image.ArchitectureThe architecture of RFP consists of three main components: backbone network, top-down pathway, and recursion.Backbone NetworkThe backbone network is responsible for extracting features from the input image. It usually consists of convolutional layers followed by pooling layers to downsample the spatial dimensions. Common choices for the backbone network include ResNet, VGG, and MobileNet.Top-Down PathwayThe top-down pathway takes the highest resolution feature map from the backbone network and upsamples it to match the size of the corresponding feature map from the next level. It then fuses these feature maps using lateral connections.The lateral connections are implemented as 1x1 convolutions on the higher resolution feature maps to reduce the number of channels. This allows efficient fusion of features from different levels without introducing a significant computational overhead.RecursionThe recursion in RFP involves recursively applying the FPN module to the fused feature maps. The goal is to refine the feature pyramid by capturing increasingly abstract information.The FPN module consists of two steps: bottom-up and top-down. In the bottom-up step, it applies 3x3 convolutions to each feature map and upsamples them using interpolation. In the top-down step, it upsamples the higher resolution feature map and fuses it with the upsampled lower resolution feature map using element-wise addition.The recursion stops when the highest resolution feature map has been reached. The refined feature maps are then used for downstream tasks such as object detection or semantic segmentation.AdvantagesRFP offers several advantages compared to traditional FPN:1.Improved Hierarchical Representations: By recursively applyingthe FPN module, RFP captures richer hierarchical information. This enables better discrimination between objects of different classes and scales.2.Fine-Grained Details: The recursive process helps RFP to capturefine-grained details in the image. This is especially importantfor tasks such as object detection, where small objects need to be accurately localized.3.Efficient Fusion: RFP uses lateral connections and element-wiseaddition for efficient feature fusion. This allows for betterintegration of multi-scale features without significantlyincreasing computational complexity.4.Flexible Architecture: RFP can be easily integrated into existingCNN architectures without major modifications. It leverages thepower of FPN and extends it to capture more comprehensiverepresentations.ApplicationsRFP has been successfully applied to various computer vision tasks, including:1.Object Detection: RFP helps to improve the performance of objectdetection systems by capturing multi-scale features and fine-grained details. This enables accurate localization andclassification of objects in images.2.Semantic Segmentation: RFP enhances the ability to segmentobjects of different sizes by capturing hierarchicalrepresentations. It improves the accuracy of pixel-levelpredictions in semantic segmentation tasks.3.Instance Segmentation: RFP can be used to refine the featurepyramid for instance segmentation. It enables accuratesegmentation and classification of individual instances in animage.4.Image Classification: RFP can also be applied to imageclassification tasks to capture multi-scale features and improve the discrimination between different classes.ConclusionRecursive Feature Pyramid (RFP) is an extension of Feature Pyramid Networks (FPN) that enhances the extraction of hierarchically rich features. By recursively applying the FPN module, RFP captures more comprehensive representations and fine-grained details. It has shown promising results in various computer vision tasks such as object detection, semantic segmentation, instance segmentation, and image classification. RFP improves the ability to handle objects at different scales and provides more accurate localization and classification.。
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Computational strategies forflexible multibody systemsTamer M WasfyAdvanced Science and Automation Corp,Hampton VAtamer@Ahmed K NoorCenter for Advanced Engineering Environments,Old Dominion University,Hampton VA;a.k.noor@The status and some recent developments in computational modeling offlexible multibodysystems are summarized.Discussion focuses on a number of aspects offlexible multibodydynamics including:modeling of theflexible components,constraint modeling,solution tech-niques,control strategies,coupled problems,design,and experimental studies.The characteris-tics of the three types of reference frames used in modelingflexible multibody systems,namely,floating frame,corotational frame,and inertial frame,are compared.Future directionsof research are identified.These include new applications such as micro-and nano-mechanicalsystems;techniques and strategies for increasing thefidelity and computational efficiency ofthe models;and tools that can improve the design process offlexible multibody systems.Thisreview article cites877references.͓DOI:10.1115/1.1590354͔1INTRODUCTIONAflexible multibody system͑FMS͒is a group of intercon-nected rigid and deformable components,each of which may undergo large translational and rotational motions.The com-ponents may also come into contact with the surrounding environment or with one another.Typical connections be-tween the components include:revolute,spherical,prismatic and planar joints,lead screws,gears,and cams.The compo-nents can be connected in closed-loop configurations͑eg, linkages͒and/or open-loop͑or tree͒configurations͑eg,ma-nipulators͒.The termflexible multibody dynamics͑FMD͒refers to the computational strategies that are used for calculating the dy-namic response͑which includes time-histories of motion,de-formation and stress͒of FMS due to externally applied forces,constraints,and/or initial conditions.This type of simulation is referred to as forward dynamics.FMD also comprises inverse dynamics,which predicts the applied forces necessary to generate a desired motion response.FMD is important because it can be used in the analysis,design, and control of many practical systems such as:ground,air, and space transportation vehicles͑such as bicycles,automo-biles,trains,airplanes,and spacecraft͒;manufacturing ma-chines;manipulators and robots;mechanisms;articulated earthbound structures͑such as cranes and draw bridges͒;ar-ticulated space structures͑such as satellites and space sta-tions͒;and bio-dynamical systems͑human body,animals, and insects͒.Motivated by these applications,FMD has been the focus of intensive research for the last thirty years.FMD is used in the design and control of FMS.In design,FMD can be used to calculate the system parameters͑such as di-mensions,configuration,and materials͒that minimize the system cost while satisfying the design safety constraints ͑such as strength,rigidity,and static/dynamic stability͒.FMD is used in control applications for predicting the response of the multibody system to a given control action and for cal-culating the changes in control actions necessary to direct the system towards the desired response͑inverse dynamics͒. FMD can be used in model-based control as an integral part of the controller as well as in controller design for optimiz-ing the controller/FMS parameters.In recent years,considerable effort has been devoted to modeling,design,and control of FMS.The number of pub-lications on the subject has been steadily increasing.Lists and reviews of the many contributions on the subject are given in survey papers on FMD͓1,2͔and on the general area of multibody dynamics,including both rigid andflexible multibody systems͓3–7͔.Special survey papers have been published on a number of special aspects of FMD,including: dynamic analysis offlexible manipulators͓8͔,dynamic analysis of elastic linkages͓9–13͔,and dynamics of satellites withflexible appendages͓14͔.A number of books on FMD have been published͓15–23͔.In the last few years,there have been a number of conferences,symposia,and special sessions devoted to FMD͓24͔.Two archival journals are devoted to the subjects of rigid andflexible multibody dy-Transmitted by Associate Editor V BirmanAppl Mech Rev vol56,no6,November2003©2003American Society of Mechanical Engineers553namics:‘‘Multibody System Dynamics’’published by Klu-wer Academic Publishers,and‘‘Journal of Multibody Dy-namics’’published by Ingenta Journals.There are a number of commercial codes forflexible multibody dynamics͑eg, ADAMS from Mechanical Dynamics Inc,DADS from CADSI Inc,MECANO from Samtech,and SimPack from INTEC GmbH͒as well as many research codes developed at universities and research institutions.A survey of multibody dynamics software up to1990with benchmarks was pre-sented in Schiehlen͓25͔.There are two compelling motiva-tions for developing FMD modeling techniques.Thefirst motivation is that a number of current problems have not yet been solved to a satisfactory degree͑see Section9͒.The second motivation is that future multibody systems are likely to require more sophisticated models than has heretofore been provided.This is because practical FMD applications are likely to have more stringent requirements of economy, high performance,light weight,high speed/acceleration,and safety.There is a need to broaden awareness among practicing engineers and researchers about the current status and recent developments in various aspects of FMD.The present paper attempts tofill this need by classifying and reviewing the FMD literature.Also,future directions for research that have high potential for improving the accuracy and computational efficiency of the predictive capabilities of the dynamics and failure of FMS are identified.Some of these objectives were addressed in the previous review papers.In the present paper, an attempt is made to provide a more comprehensive review of the literature.The following aspects of FMD are ad-dressed in the present paper:•Models of theflexible components•Constraints models•Solution techniques,including solution procedures and methods for enhancing the computational procedures and models•Control strategies•Coupled FMD problems•Design of FMS•Experimental studiesThere are many common elements of FMD with structural dynamics,nonlinearfinite element method and crashworthi-ness analysis.Some of the studies in these areas,which in-clude techniques that are suitable for modeling FMS,are included in this review.The number of publications on the diverse aspects of FMD is very large.The cited references are selected for illustrating the ideas presented and are not necessarily the only significant contributions to the subject. The discussion in this paper is kept,for the most part,on a descriptive level and for all the mathematical details,the reader is referred to the cited literature.2MODELS OF FLEXIBLE COMPONENTS2.1Deformation reference framesIn multibody dynamics,an inertial frame serves as a global reference frame for describing the motion of the multibody system.In addition,intermediate reference frames that are attached to eachflexible component and follow the average local rigid body motion͑rotation and translation͒are often used.The motion of the component relative to the interme-diate frame is,approximately,due only to the deformation of the component.This simplifies the calculation of the internal forces because stress and strain measures that are not invari-ant under rigid body motion,such as the Cauchy stress tensor and the small strain tensor,can be used to calculate these forces with respect to the intermediate frame.These tensors result in a linear force displacement relation.Two main types of intermediate frames are used:floating and corotational frames.Thefloating frame follows an average rigid body motion of the entireflexible component or substructure.The corotational frame follows an average rigid body motion of an individualfinite element within theflexible component.In many papers,intermediate frames are not used,instead the global inertial frame is directly used for measuring deforma-tions.In this approach,the motion of an element consists of a combination of rigid body motion and deformation and the two types of motion are not separated.Nonlinearfinite strain measures and corresponding energy conjugate stress mea-sures,which are objective and invariant under rigid body motion,are used to calculate the internal forces with respect to the global inertial frame.A comparison between the major characteristics of the three types of frames,namely,floating, corotational,and inertial frames is given in Table1.The references where the frames werefirst applied to FMS are given in Table2.Thefloating frame approach originated out of research on rigid multibody dynamics in the late1960s.It was used for extending rigid multibody dynamics codes to FMS.This was done by superimposing small elastic deformations on the large rigid body motion obtained using the rigid multibody dynamics code.Initial applications of thefloating frame ap-proach included:spinningflexible beams͑primarily for space structures applications͒,kineto-elastodynamics of mechanisms,andflexible manipulators͑see Table2͒.The floating frame approach was also used to extend modal analysis and experimental modal identification techniques to FMS͓52,54,232,256,272͔.This is performed by identifying the mode shapes and frequencies of eachflexible component either numerically or experimentally.Thefirst n modes ͑where n is determined by the physics of the problem and the by the required accuracy͒are superposed on the rigid body motion of the component represented by the motion of the floating frame.In Table3,a partial list of publications on the floating frame approach is organized according to the tech-niques used and developed and according to the type of ap-plication considered.The corotational frame approach was initially developed as a part of the natural mode method proposed by Argyris et al͓562͔.In this approach,the motion of afinite element is divided into a rigid body motion and natural deformation modes.The approach was used for static modeling of struc-tures undergoing large displacements and small ter,Belytschko and Hsieh͓45͔introduced element rigid convected frames or corotational frames,for the dy-554Wasfy and Noor:Computational strategies forflexible multibody systems Appl Mech Rev vol56,no6,November2003Table1.Major characteristics of the three types of framesFloating Frame Corotational Frame Inertial FrameFrame definition Afloating frame is defined for eachflexible component.Thefloatingframe of a component follows amean rigid body motionof the component͑see Fig.1͒.A corotational frame is defined for eachelement.The corotational frame of anelement follows a mean rigid body motionof the element͑see Fig.2͒.The global inertial referenceframe is used as a referenceframe for all motions͑seeFig.3͒.Reference framefor:a…Deformation Floating frame…for eachflexiblecomponent….Corotational frame…for eachfinite element….Global inertial reference frame. b…Internal forces Floating frame.Corotational frameÕGlobal inertialreference frame.Global inertial reference frame.Note:In some implementations,the internal force components are transformed from thefloating frame to the global inertial reference frame͑eg,͓26͔͒.Note:The element internal forcecomponents arefirst calculated relative tothe corotational frame,then they aretransformed from the corotational frame tothe global inertial frame using thecorotational frame rotation matrix.Note:The internal forces arecalculated usingfinite strainmeasures which are invariantunder rigid body motion.c…Inertia forces Floating frame.Global inertial reference frame.Global inertial reference frame.Note:In some implementations,theflexible motion inertia force components arefirst evaluated with respect to the global inertial reference frame andthen are transformed to thefloating frame͑eg,͓27,28͔͒.Notes:•In some implementations,the inertia forcecomponents arefirst evaluated relative tothe corotational frame and then aretransformed to the inertial frame͑eg,͓29–31͔͒.Note:In spatial problems,for therotational part of the equations ofmotion,the internal and inertiamoments are often calculated rela-tive to a moving material frame.•In spatial problems,for the rotational partof the equations of motion,the internaland inertia moments are often calculatedrelative to a moving material frame.Transformation toglobal inertial frame Eq.͑1͒.Eq.͑1͒.No transformation is necessary. ModelingConsiderationsa…Incorporation of flexibility effects.Thefloating frame approach is thenatural way to extend rigid multibodydynamics toflexible multibody systems.The corotational frame transformationeliminates the element rigid body motionsuch that a linear deformation theory can beused for the element internal forces.Generalfinite strain measuresthat are invariant undersuperposed rigid body motionare used.b…Magnitude of angular velocities No restriction on angular velocitiesmagnitudes.However,when linear modalreduction is used,the angular velocityshould be low or constant because thestiffness of the body varies with theangular velocity due to the centrifugalstiffening effect͓32͔.No restriction on angular velocities magnitudes.In case of very small elasticdeformations and large angular velocities,special care must be taken duringthe solution procedure͑time step size,number of equilibrium iterations,etc͒to avoid the situation where numerical errors from the rigid body motion areof the order of the elastic part of the response.c…Large deflections•Moderate deflections can be modeled byusing quadratic strain terms.However,large deflections cannot be modeledunless the body is sub-structured.Can handle large deflections and large strains.•Without the assumption that the strainsand deflections are small,the high-orderterms of theflexible-rigid body inertialcoupling terms cannot be neglected andthe formulation becomes verycomplicated.d…Foreshortening Foreshortening effect can be modeled byadding quadratic axial-bending straincoupling terms.Naturally included.e…Centrifugal stiffening Centrifugal stiffening can be modeled byadding the stress produced by the axialcentripetal forces and including axial-bending strain coupling terms.Naturally included.f…Mixing rigid and flexible bodies Since thefloating frame formulation isbased on rigid multibody dynamicsanalysis methods,both rigid andflexiblebodies can be present in the same model inany configuration with no difficulty.Most implementations place some restrictions on the configuration of the rigidbodies,such as a closed-loop,must contain at least oneflexible body.Appl Mech Rev vol56,no6,November2003Wasfy and Noor:Computational strategies forflexible multibody systems555Table1.(continued)Floating Frame Corotational Frame Inertial FrameCharacteristics of the semi-discrete equations of motion •The equations of motion are written suchthat theflexible body coordinates arereferred to afloating frame and the rigidbody coordinates are referred to theinertial frame.•The equations of motion are written with respect to the global inertial frame.•In spatial problems with rotational DOFs,the rotational part of the equationsof motion can be written with respect to a body attached nodal frame͑material frame͓͒33–38͔or with respect to the global inertial frame͑spatial frame͓͒35,39͔.a…Inertia forces•The inertia forces involve nonlinearcentrifugal,Coriolis,and tangentialterms because the accelerations aremeasured with respect to a rotatingframe͑thefloating frame͒.•The inertia forces are the product of the mass matrix and the vector of nodal accelerations with respect to the global inertial frame.•In spatial problems with rotational DOFs,the rotational equations͑the Euler equations͒include quadratic angular velocity terms.͑These terms vanish in planar problems.͒•The mass matrix has nonlinearflexible-rigid body motion coupling terms.The coupling terms are necessary for an accurate prediction of the dynamic response,when the magnitude of the flexible inertia forces is not negligible relative to that of the rigid body inertia forces.•The translational part of the mass matrix is constant.Effects such as coupling betweenflexible and rigid body motion,centrifugal and coriolis accelerationare not present because the inertia forces are measured with respect to an inertial frame.•The solution procedure involves the inversion or the LU factorization of the time varying inertia matrices.b…Internal …structural…forces The internal forces are linear for smallstrains and slow rotational velocities.Thelinear part of the stiffness matrix is thesame as that used in classical linear FEM.The nonlinear part of the stiffness matrixaccounts for geometric nonlinearity andcoupling between the axial and bendingdeformations͑centrifugal stiffeningeffect͒.For small strains,the internal forces arelinear with respect to the corotationalframe.The structural forces aretransformed to the global frame using thenonlinear corotational frametransformation.The internal forces are nonlineareven for small strains becausethey are expressed in terms ofnonlinearfinite strain and stressmeasures.Constraintsa…Hinge joints Hinge joints require the addition ofalgebraic constraint equations in theabsolute coordinate formulation.Hinge joints͑revolute joints in planar problems and spherical joints in spatial problems͒do not need an extra algebraic equation and can be modeled by letting two bodies share a node.b…General constraints Constraints due to joints,prescribed mo-tion and closed-loops are expressed interms of algebraic equations.These equa-tions must be solved simultaneously withthe governing differential equations of mo-tion.The development of general,stable,and efficient solution procedures for thissystem of differential-algebraic equationsis still an active research area͓40–42͔͑also see Section4.1͒.Constraints due to joints and prescribed motion are expressed in terms of algebraicequations.If an implicit algorithm is used,then a system of differential-algebraicequations͑DAEs͒must be solved.If an explicit solution procedure is used,nospecial algorithm for solving DAEs is needed.Applicability of linear modal reduction •Can be applied.•Can significantly reduce thecomputational time.•Appropriate selection of the deformationcomponents modes requires experienceand judgment on the part of the analyst.Not practical because the element vector ofinternal forces is nonlinear in nodalcoordinates since it involves a rotationmatrix.Not practical because the elementvector of internal forces isnonlinear in nodal coordinatessince it involves a nonlinearfinite strain measure.•For accuracy,linear modal reductionshould be restricted to bodiesundergoing slow rotation or uniformangular velocity.•Nonlinear modal reduction͓43,44͔canbe used for bodies undergoing fast non-uniform angular velocity in order toinclude the centrifugal stiffening effect.However,a modal reduction must beperformed at each time step.Possibility of using modal identification experiments The mode shapes and natural frequenciesused in modal reduction can be obtainedusing experimental modal analysis tech-niques.Thus,there is a direct way to ob-tain the bodyflexibility information fromexperiments without numerical modeling.Experimentally identified modes cannot be directly used in the model.They can,however,be indirectly used to verify the accuracy of the predicted responseand to tune the parameters of the model.556Wasfy and Noor:Computational strategies forflexible multibody systems Appl Mech Rev vol56,no6,November2003namic modeling of planar continuum and beam type ele-ments,using a total displacement explicit solution procedure.The approach was applied to spatial beams in Belytschko et al ͓33͔and to curved beams in Belytschko and Glaum ͓452͔.In Belytschko et al ͓468͔and Belytschko et al ͓469͔,the approach was extended to dynamic modeling of shells using a velocity-based incremental solution procedure.Table 4shows a partial list of publications which used corotational frames for developing computational models suitable for modeling FMS.The publications are organized according to the techniques used and developed and according to the type of application considered.The inertial frame approach has its origins in the non-linear finite element method and continuum mechanics principles.These techniques were applied to the dynamic analysis of continuum bodies undergoing large rotations and large deformations ͑including both large strains and large deflections ͒since the early 1970s ͓92,93͔.In Table 5,publi-cations where the inertial frame approach was used for de-veloping computational models suitable for modeling FMS areclassified.Fig.1FloatingframeFig.2Corotationalframe Fig.3Inertial frameTable 1.(continued)Floating FrameCorotational FrameInertial FrameMost suitable applicationsThe floating frame formulation along with modal reduction and new recursive solution strategies ͑based on the relative coordinates formulation ͒offer the most efficient method for the simulation of flexible multibody systems undergoing small elastic deformations and slow rotational speeds ͑such as satellites and space structures ͒.The corotational and inertial frame formulations can handle flexible multibody systems undergoing large deflections and large high-speed rigid body motion.In addition,if used in conjunction with an explicit solution procedure,then high-speed wave propagation effects ͑for example,due to contact/impact ͒can be accurately modeled.Least suitable applications Multibody problems,which involve large deflections.For multibody problems involving small deformations and slow rotational speeds,the solution time is generally an order of magnitude greater than that of typical methods based on the floating frame approach with modal coordinates.First knownapplication of the approach to FMS.Adopted in the late 1960s to early 1970s to extend rigid multibody dynamics computer codes to flexible multibody systems.Developed by Belytschko and Hsieh ͓45͔.It was first applied to beam type FMS in Housner ͓46–48͔.Used in nonlinear,large deformation FEM since the beginning of the 1970s.It was first applied to modeling beam type FMS in Simo and Vu-Quoc ͓49,50͔.Appl Mech Rev vol 56,no 6,November 2003Wasfy and Noor:Computational strategies for flexible multibody systems 5572.2Mathematical descriptions of the intermediate reference framesThe relation between the coordinates of a point in the global inertial frame A (x A )and the coordinates of the same point in the intermediate body reference frame B (x B )is given by:x A ϭx o A /B ϩRA /B x B(1)where x oA /Bare the coordinates of the origin of frame B in frame A ,and R A /B is a rotation matrix describing the rotationfrom A to B .The methods used to define x oA /Band R A /B for the floating and corotational frames are outlined subse-quently.2.2.1Floating frameThe motion of the floating frame ͑position and orientation ͒is commonly referred to as the reference motion of the compo-nent.It is only an approximation of the rigid body motion of the component.Thus there are many ways to define this ref-erence motion.Two formulations are commonly used,namely,fixed axis and moving axis formulations.In the fixed axis formulation,Cartesian and/or rotation coordinates of one,two,or three selected material points ͑usually the joints ͒on the flexible body are used to define the floating frame.Experience is needed for appropriate selection of body fixed axes that are consistent with the boundary conditions,be-cause this choice affects the resulting vibrational modes.In the moving axis formulation,also called the body mean axis formulation,the floating frame follows a mean displacement of the flexible body and thus does not necessarily coincide with any specific material point.In this case,two definitions of the floating frame are used in practice:a ͒the floating frame is the frame relative to which the kinetic energy of the flexible motion with respect to an observer stationed at the frame is minimum ͑Tisserand frame ͓͒109,122,123͔;and b ͒the floating frame is the frame relative to which the sum of the squares of the displacements,with respect to an observer stationed at the frame,is minimum ͑Buckens frame ͓͒122͔.2.2.2Corotational frameThe definition of the corotational frame depends on the type of elements used for modeling the flexible components.For two-node beam elements,the corotational frame is usually defined by the vector connecting the two nodes ͑eg,͓45͔͒.Itcan also be chosen as the mean beam axis ͑ie,the axis that minimizes the total deformation ͓͒450͔.For 3D beam ele-ments,the remaining two axes are chosen as the cross-sectional axes ͓33,87,456͔.In Park et al ͓479͔and Cho et al ͓480͔a relative nodal coordinate approach is used in which a tree representation of the FMS is constructed and beam ele-ment deformations are measured with respect to the adjacent nodal frame along the tree.For shell and continuum elements,there are two methods to define the corotational frame.In the first method,only some of the nodes of the element are used to define the corotational frame.This type of definition was used for con-tinuum elements in Belytschko and Hsieh ͓45͔and for shells in Stolarski and Belytschko ͓455,456,468,470,471,563͔,Be-lytschko et al ͓468͔,Rankin and Brogan ͓455͔,Rankin and Nour-Omid ͓456͔,and Belytschko and Leviathan ͓470,471͔.For example,in Belytschko et al ͓468͔the normal Z-axis for a four node quadrilateral shell element is defined as the normal to the two diagonals of the element,the X-axis is perpendicular to the Z-axis and is aligned with the vector connecting nodes 1and 2,and the Y-axis is perpendicular to the Z-and ing some of the element nodes to define the corotational frame makes the internal forces dependent on the choice of the element local node num-bering,which may introduce artificial asymmetries in the response ͓460,474,476͔.In the second method,the origin and orientation of the corotational frame are defined as an average position and rotation of all the element nodes.For example,the origin of the corotational frame can be defined as the origin of the natural element coordinate system ͓85,91,460,464,474,476͔.The orientation of the frame can be determined using one of the following techniques:•Polar decomposition of the deformation gradient tensor at the origin of the natural element coordinate system ͓85,91,460,464,476͔•For shell elements,the Z-axis is normal to the surface of the element at the origin of the natural coordinate system.The angle between the X-axis and the first element natural axis is equal to the angle between the Y-axis and the sec-ond element natural direction ͓564͔•A least-square minimization procedure to find the orienta-Table 2.Initial references for the application of the three types of frames to FMSFloating FrameCorotational FrameInertial FrameSpinning beams:Nonlinear structural dynamics:Nonlinear finite element method:Meirovitch and Nelson ͓51͔,Likins ͓52,53,55͔,Likins et al ͓54͔,Grotte et al ͓56͔.Belytschko and Hsieh ͓45͔,Belytschko et al ͓33͔,Argyris et al ͓81͔,Argyris ͓82͔,Belytschko and Hughes ͓83͔.Oden ͓92͔,Bathe et al ͓93͔,Bathe and Bolourchi ͓94͔.Kineto-elastodynamics of mechanisms:Dynamics of planar flexible beams:Winfrey ͓57–59͔,Jasinski et al ͓60,61͔,Sadler and Sandor ͓62͔,Erdman et al ͓9,63,64͔,Imam ͓65͔,Imam and Sandor ͓66͔,Viscomi and Ayre ͓67͔,Dubowsky and Maatuk ͓68͔,Dubowsky and Gardner ͓69,70͔,Bahgat and Willmert ͓71͔,Midha et al ͓72,74,75͔,Midha ͓73͔,Nath and Gosh ͓76͔,Huston ͓77͔,Huston and Passarello ͓78͔.Flexible space structures:Simo and Vu-Quoc ͓50͔.Housner ͓46͔,Housner et al ͓47͔.Dynamics of spatial flexible beams:FMS planar beams:Simo ͓95͔,Simo and Vu-Quoc ͓34,49,96,97͔,Iura and Atluri ͓48͔,Cardona and Geradin ͓35͔,Geradin and Cardona ͓98͔,Crespo Da Silva ͓99͔,Jonker ͓100͔.Yang and Sadler ͓84͔,Wasfy ͓85,86͔,Elkaranshawy and Dokainish ͓31͔.FMS spatial beams:Housner ͓46͔,Housner et al ͓47͔,Wu et al ͓87͔,Crisfield ͓88͔,Crisfield and Shi ͓89,90͔,Wasfy and Noor ͓91͔.Flexible manipulators:FMS shells:Book ͓79,80͔.Wasfy and Noor ͓91͔.558Wasfy and Noor:Computational strategies for flexible multibody systemsAppl Mech Rev vol 56,no 6,November 2003。