Scattered Associations in Object-Oriented Modeling

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Collaboration

Collaboration

Collaboration"The objects within a program must collaborate; otherwise, the program wouldconsist of only one big object that does everything."-- Rebecca Wirfs-Brock, et. al.,Designing Object-Oriented Software,Prentice Hall, 1990 INTRODUCTIONCollaboration, to my mind, is not discussed enough. It is one of the essential elements of object-oriented analysis and design. As Booch says:"Equally important [as inheritance] is the invention of societies of objects that responsibly collaborate with one another. ... These societies form what Icall the mechanisms of a system, and thus represent strategic architecturaldecisions because they transcend individual classes." [The C++ Journal, Vol. 2,NO. 1 1992, "Interview with Grady Booch"]In this article we will talk about what collaboarations are and why they are so important. We will discuss how collaborations are unearthed through analysis of the problem domain, and how they are designed into the application. We will also discuss the C++ "friend" mechanism, and how it aids the design of collaborations.Some of the examples in this article use a variation of the Booch Notation for describing analysis and design decisions. Where necessary I will digress to explain the notation.WHAT IS COLLABORATION?A collaboration occurs every time two or more objects interact. A collaboration can be as simple as one object sending one message to another object. Or it can be a as complex as dozens of objects exchanging messages. In fact, an entire application is really a single gigantic collaboration involving all of the objects within it.An object-oriented application can be broken down into a set of many different behaviors. Each such behavior is implemented by a distinct collaboration between the objects of the appliation. Every collaboration, no matter how small or large, always implements a behavior of the application that contains it.Imagine an object-oriented application as a network of objects connected by relationships. Collaborations are the patterns of messages that play through that network in pursuit of a particular behavior. A collaboration can be viewed as an algorithm which spans this network, using many different objects and methods. The algorithm is distributed across the network of objects, and so does not exist in any one place.This is in distinct contrast to the behaviors of a class. All behaviors pertinent to a class are methods of that class. They exist in one place. But an object-oriented application is made up of many such classes. Its behaviors are a synthesis of the individual class behaviors. So the application's behaviors are distributed through the classes as collaborations.This identification with the behaviors of the application gives collaborations a very central role in the analysis and design of object-oriented programs. It is these behaviors, after all, that we are trying to achieve. If the collaborations which implement them are not properly designed, then the application will be inaccurate or brittle.IDENTIFYING COLLABORATIONSCollaborations are typically unearthed during the analysis of the problem domain. The first step in this process is to discover the primary classes and their relationships. These are arranged into a model of the static structure of the application. To test this structure, behavioral scenarios are examined. In each scenario we ask which objects will be present, and how they will respondto one particular event. We then attempt to figure out which messages are sent between the objects in order to handle the event. It is within these scenarios that the first hints of collaboration are to be found. For example, consider an application to automate a public library. The analysis of such an application might yeild the following static model. This model is by no means complete, itsimply shows a few of the classes in the problem domain.This diagram is called a class diagram. It is typical of those produced during object-oriented analysis. It is similar to an entity relationship diagram (ERD), except that it uses Booch symbols. It shows the classes in the model, and the static relationships between those classes. In this case we see that the Library employs some number of Librarians . It also maintains a list of all the library cards which identify the Borrower s that the Library is willing to loan books to.Lets examine the behavioral scenario related to borrowing a book from the library. A Borrower takes a book up to a Librarian and presents his or her library card with a request to check the book out. The librarian enters the book id and library card number into a terminal.This creates an event from which we can trace out the flow of messages through the system.This diagram is called an object diagram. It shows the objects that we expect to participate in the behavior, and shows the messages and data that flow between those objects. Note that each message is numbered in the sequence that it occurs.We have shown the initial event as the CheckOut message which is sent to theLibrarian object (message #1). The message includes the BookCopy , which is an objectwhich represents a particular copy of a book. The message also contains the LibraryCard ofthe Borrower. The Librarian asks the Library to look up the Borrower from theLibraryCard(#2), The Library in turn asks the LibraryCardList for the same information (#3).Once in possession of the Borrower, the Librarian checks its status (#4),to see if itis allowed to check out any books. In this example, the Borrower is allowed to check outbooks, so the Location of the book is set to the Borrower (#5), and the appropriate return date is set (#6).This behavioral scenario is a first step towards identifying the collaboration for checking a book out of the library. Its purpose, at this stage, is to prove that the static model is capable of supporting the behavior. But is also gives us a very good idea of the methods that the classes will need in order to properly collaborate.Every behavior of the application should be modeled in this way. From this work a set of behavioral scenarios is generated. Each of these is an early representation of the collaborations within the application.DESIGNING COLLABORATIONSIdentification is not enough. By analyzing the problem domain we have compiled a list of proto-collaborations. Now we need to design the detailed structure of the application so that the collaboration can be supported. This involves replacing the weak relationships in the analysis model, with strong OOD relationships such as inheritance (IsA), containment (HasA) and usage relationships. This is done by inspecting the behavioral scenario to see how the messages flow.For example, the first message in the library collaboration comes to the Librarian from the outside. This implies some kind of LibrarianTerminal object which knows about the Librarian.LibrarianTerminal contains a Librarian. This relationship means that the LibrarianTerminal has intrinsic knowledge of the Librarian. This is important if the LibrarianTerminal is to send a message to the Librarian.The second message in the collaboration is between the Librarian and the Library. Since none of the data currently flowing in the collaboration has identified a particular Library object, the Librarian must has intrinsic knowledge of the Library. Oncemodel. In the analysis model the Library employed the Librarian. However, in this design, the Librarian contains the Library. Although the analysis model makes perfect sense by itself, it does not support the needed collaboration at the detailed level. Thus, the direction of the relationship must changed to support the collaboration.Message number 3 is sent from the Library to the LibraryCardList. Again, intrinsic knowledge is needed, again implying containment. Moreover, we know from the analysis model that the LibraryCardList identifies all the Borrowers. This too implies containment.Message number 4represents the Librarian interrogating the Borrowerabout its ability to borrow books. Intrinic knowledge is not implied since the Borrower was returned to the Librarianthrough message number 2 and 3. Thus we say that the Librarian usesthe Borrower , but does not contain it. The using relationship, represented by the double line and white ball, implies that the used object is somehow made available to the user via the user's interface. By the same reasoning, messages 5 and 6 imply that the Librarian uses the classBookCopy, since it finds out about the BookCopy from the LibrarianTerminal in message #1.collaboration. Similar exercises need to occur for each of the collaborations unearthed through the analysis.Notice that the static model of the analysis was used in the creation of our collaboration, and that the collaboration was then used to refine the static model. This oscillation between the static and dynamic models is typical and essential. We only showed one small oscillation, but in a real analysis and design, the oscillations would continue many more times before the design was considered sufficiently refined. Each change to the static model sheds new light on the dynamics of the collaborations. Each refinement made to the collaborations may expose deficiencies in the static model.TYPES OF COLLABORATIONWe can classify the ways in which classes collaborate into 4 broad categories. Each of these categories has to do with the relationships between the collaborating classes. The differences between these 4 classifications has to do with the intimacy of the collaboration. Some collaborations take place strictly through their public interfaces, and are therefore not very intimate. Other collaborations require closer coupling between the participants.•Peer- to-Peer collaborations All the collaborations that we have studied so far have been of the Peer-to-Peer variety.Peer-to-Peer collaborations occur when two unrelated classes exchange messages. This is the most common form of collaboration. Typically, peer-to-peer collaborations are not intimate; i.e. the collaborators do not depend upon special knowledge of each other. In C++, they are seldom declared as friends. This is not a hard and fast rule however. Sometimes intimacy is indicated. Containers and iterators are an example of peer-to-peer collaborators which are generally intimate and require friendship.•Sibling Collaborations A Sibling collaboration occurs when two or more classes, derived from a common base,exchange messages. Often such collaborations are more intimate than the Peer-to-Peer variety,BookCursor base class is abstract, which is signified by the triangular icon. BookCursor represents the set of classes which search the library for books. The three siblings represent different scopes in which such searches can occur. You can search an entire shelf with ShelfCursor , an entire aisle with AisleCursor and the whole library withLibraryCursor . Notice that the siblings make use of each other in a directional manner. TheLibraryCursor uses the AisleCursor which in-turn uses the ShelfCursor . This makes perfect sense, since searching the library is a matter of searching all the aisles, and searching an aisle is a matter of searching all the shelves within the aisle.This kind of hierarchical relationship is typical of sibiling collaborations. Each sibling builds on the facilities of the other. However, siblings are often able to deal with peer clients as well.When dealing with peers, the relationship is usually not as intimate as when dealing with aHere we see a client sending the Search message to object (x):LibraryCursor . The name of the object is 'x', but the parenthesis indicate that the name is local to this diagram, and not known to the rest of the design. It's kind of like a local variable. Object 'x' responds by sending itself the Initialize method, which is handled by the BookCursor base class.This method clears a set of counters in the BookCursor which keep track of statistics concerning the search.Since each of the siblings must be able to deal directly with clients, they must each respond to the Search method by initializing the base class with the Initialize method. However,when we are searching the entire library, we want all the statistics gathered in the base class of the LibraryCursor object, rather than spread out through a bunch of AisleCursor and ShelfCursor objects. So the LibraryCursor object 'x' tells the AisleCursor to use the statistics counters in the base class of 'x'. Moreover, the AisleCursor passes this information along to the ShelfCursor as well. This information is passed using the PrivateSearch method, which is designed for intimate use between siblings, rather than general purpose client access.Since the classes have a method that they wish to keep private amongst themselves, they should declare the method to be restricted to private access. In order for the siblings to access the methods, they must be friends of each other. Thus we modify the class diagram to show thefriendship.•Base-Derived collaborationsWe saw a small example of a Base-Derived collaboration in the previous example. Such collaborations occur when a derived class exchanges messages with its base. Such collaborations are often very intimate; base and derived classes know a lot about each other and can take advantage of that knowledge. Such collaborations typically involve short term violations of class invariants, i.e. they temporarily leave the class in an illegal state between Here we see an elaboration of part of the previous example. The LibraryCursor object initializes itself by sending itself the Initialize message. The BookCursor base class handles this message and sends the InitializeDerived message back to the derived class (probably via virtual deployment). Thus, the base portion of the class is initialized first, and then the base class initializes the derived class. In between these two messages, the object is in an invalid state, being only partially initialized. Certainly the InitializeDerived method should be private and virtual.•Auto-Collaboration Auto-collaboration occurs when an object sends a message to itself. This is the most intimate of all collaborations, since the object is generally talking to itself. Such collaboration is typically used to encapsulate portions of the implementation. For example, task x may be a component of many of the methods of class Y. Rather than coding task x in each of these methods, it makes better sense to create a new method which performs task x. Certainly such a method should be kept private, since its function is never meant to appear in isolation from theHere we see a typical case of auto-collaboration. When a LibraryCursor object is sent the Search method, it invokes the PrivateSearch method. The data item sent along is presumably its own base class. Notice how this encapsulates the task of searching within the PrivateSearch method. No other method of this class knows the details of a search.USING FRIENDSHIP IN COLLABORATIONIn one of the examples above, we used friendship to aid the collaboration of siblings. Friendship is also sometimes used in peer-to-peer collaborations. In early versions of C++, before the protected keyword was added, friendship was also used to support base-derived collaborations. In fact, the proliferation of base classes declaring their derivatives as friends was a principle factor in the decision to add protected access to the language.Friendship allows unrelated classes to participate in intimate collaborations. This is important when several classes are working together to present a single abstraction. As a case in point, take the example of the LibraryCursor. This class collaborated with its sibling AisleCursor to present a single abstraction: that of searching the entire library for books. This collaboration required that the two classes be friends.Such multi-class abstractions are an important design technique. There are situations where it is not practical or possible to represent an abstraction as a single class. A good example of this is iterators. Container classes and their iterators represent a single abstraction. But there is simply no good way to represent this abstraction as a single class.Another role of friendship is to prevent private portions of a collaboration from leaking out into the public arena. Again, the LibraryCursor class provides us with an example. The PrivateSearch method is a dangerous method to make public. It badly violates the invariants of the BookCursor abstraction. Friendship allows these dangerous functions to remain private to the abstraction, and to be used by the friends participating in that abstraction.When many classes collaborate, the use of friendship to solve the problems of access and efficiency will result in classes that are bound tightly to each other. Sometimes they can be so tightly bound that they cannot be separated from each other.Certainly we want to avoid, at all costs, huge networks of classes which are all friends and which all take great liberties with each others internal parts. Such a perversion could not be called object-oriented. Also, we want to avoid the temptation to use friendship to join two very separate abstractions. If such abstractions need to be joined in some way, the joining should generally be accomplished through their interfaces, or through an intermediary class.However, when two ore more classes are truly part of the same abstraction, then tight binding and friendship should not be discouraged. As Rumbaugh says: "Some object-oriented authors feel that every piece of information should be attached to a single class, and they argue that associations violate encapsulation of information into classes. We do not agree with this viewpoint. Some information inherently transcends a single class, and the failure to treat associations on an equal footing with classes can lead to programs containing hidden assumptions and dependencies." [Object Oriented Modeling and Design, Rumbaugh et. al., Prentice Hall, 1991]Since friendship can only be given, and cannot be taken, the choice of who to give friendship to becomes a design decision. This means that the class is designed to collaborate with certain special friends. The collaborators become members of a team which work more closely together than normal in order to achieve a single end. Thus, encapsulation is not lost, nor even compromised. The "capsule" simply widens to enclose all the friends.SUMMARYIn this article we have examined collaboration. We have shown that all the behaviors of an application are implemented through collaborations. We have shown how collaborations are first detected in the analysis phase of a project, and how their static and dynamic elements can be expressed using the Booch notation. We have shown how the static and dynamic views can be iterated to provide successive refinement of the application's design. We have discussed the various types of collaborations, and typical situations when they may be used. Finally we have discussed the role of friendship in collaborations.Collaboration is at the heart of OOA/OOD. The proper design of an object-oriented application depends upon a thorough and detailed understanding of the collaborations which implement its behaviors.。

arcmap面试题目(3篇)

arcmap面试题目(3篇)

第1篇一、基础知识1. 什么是GIS?请简述GIS的主要功能。

解析:GIS(地理信息系统)是一种将地理空间数据与属性数据相结合,用于捕捉、存储、分析、管理和展示地理空间信息的系统。

GIS的主要功能包括数据采集、数据存储、数据处理、数据分析和数据可视化。

2. 请解释以下概念:矢量数据、栅格数据、拓扑关系。

解析:- 矢量数据:以点、线、面等几何对象表示地理空间实体,适用于表示清晰的边界和形状,如道路、河流、行政区划等。

- 栅格数据:以网格的形式表示地理空间信息,每个网格单元包含一个或多个属性值,适用于表示连续的地理现象,如遥感影像、地形高程等。

- 拓扑关系:描述地理空间实体之间的相互关系,如相邻、包含、连接等,用于提高空间数据的查询和分析效率。

3. 请简述ArcGIS软件的主要组件。

解析:ArcGIS软件主要包括以下组件:- ArcGIS Desktop:用于数据采集、编辑、分析、管理和可视化。

- ArcGIS Server:用于发布GIS服务和应用程序。

- ArcGIS Online:提供云基础上的GIS服务、应用程序和地图。

- ArcGIS API for Developers:用于开发GIS应用程序。

二、ArcGIS Desktop操作1. 如何创建一个新的地图文档?解析:在ArcGIS Desktop中,可以通过以下步骤创建一个新的地图文档:- 打开ArcGIS Desktop。

- 选择“文件”菜单中的“新建”选项。

- 选择“地图”类型,然后点击“确定”。

- 在弹出的“新建地图”对话框中,输入地图文档的名称,选择保存位置,然后点击“保存”。

2. 如何添加图层到地图文档中?解析:在ArcGIS Desktop中,可以通过以下步骤添加图层到地图文档中:- 打开地图文档。

- 在“内容”窗口中,右键点击“图层”或“组”,选择“添加数据”。

- 在弹出的“添加数据”对话框中,选择数据源,如文件、数据库或网络,然后选择要添加的图层,点击“添加”。

AI专用词汇

AI专用词汇

AI专⽤词汇LetterAAccumulatederrorbackpropagation累积误差逆传播ActivationFunction激活函数AdaptiveResonanceTheory/ART⾃适应谐振理论Addictivemodel加性学习Adversari alNetworks对抗⽹络AffineLayer仿射层Affinitymatrix亲和矩阵Agent代理/智能体Algorithm算法Alpha-betapruningα-β剪枝Anomalydetection异常检测Approximation近似AreaUnderROCCurve/AUCRoc曲线下⾯积ArtificialGeneralIntelligence/AGI通⽤⼈⼯智能ArtificialIntelligence/AI⼈⼯智能Associationanalysis关联分析Attentionmechanism注意⼒机制Attributeconditionalindependenceassumption属性条件独⽴性假设Attributespace属性空间Attributevalue属性值Autoencoder⾃编码器Automaticspeechrecognition⾃动语⾳识别Automaticsummarization⾃动摘要Aver agegradient平均梯度Average-Pooling平均池化LetterBBackpropagationThroughTime通过时间的反向传播Backpropagation/BP反向传播Baselearner基学习器Baselearnin galgorithm基学习算法BatchNormalization/BN批量归⼀化Bayesdecisionrule贝叶斯判定准则BayesModelAveraging/BMA贝叶斯模型平均Bayesoptimalclassifier贝叶斯最优分类器Bayesiandecisiontheory贝叶斯决策论Bayesiannetwork贝叶斯⽹络Between-cla ssscattermatrix类间散度矩阵Bias偏置/偏差Bias-variancedecomposition偏差-⽅差分解Bias-VarianceDilemma偏差–⽅差困境Bi-directionalLong-ShortTermMemory/Bi-LSTM双向长短期记忆Binaryclassification⼆分类Binomialtest⼆项检验Bi-partition⼆分法Boltzmannmachine玻尔兹曼机Bootstrapsampling⾃助采样法/可重复采样/有放回采样Bootstrapping⾃助法Break-EventPoint/BEP平衡点LetterCCalibration校准Cascade-Correlation级联相关Categoricalattribute离散属性Class-conditionalprobability类条件概率Classificationandregressiontree/CART分类与回归树Classifier分类器Class-imbalance类别不平衡Closed-form闭式Cluster簇/类/集群Clusteranalysis聚类分析Clustering聚类Clusteringensemble聚类集成Co-adapting共适应Codin gmatrix编码矩阵COLT国际学习理论会议Committee-basedlearning基于委员会的学习Competiti velearning竞争型学习Componentlearner组件学习器Comprehensibility可解释性Comput ationCost计算成本ComputationalLinguistics计算语⾔学Computervision计算机视觉C onceptdrift概念漂移ConceptLearningSystem/CLS概念学习系统Conditionalentropy条件熵Conditionalmutualinformation条件互信息ConditionalProbabilityTable/CPT条件概率表Conditionalrandomfield/CRF条件随机场Conditionalrisk条件风险Confidence置信度Confusionmatrix混淆矩阵Connectionweight连接权Connectionism连结主义Consistency⼀致性/相合性Contingencytable列联表Continuousattribute连续属性Convergence收敛Conversationalagent会话智能体Convexquadraticprogramming凸⼆次规划Convexity凸性Convolutionalneuralnetwork/CNN卷积神经⽹络Co-oc currence同现Correlationcoefficient相关系数Cosinesimilarity余弦相似度Costcurve成本曲线CostFunction成本函数Costmatrix成本矩阵Cost-sensitive成本敏感Crosse ntropy交叉熵Crossvalidation交叉验证Crowdsourcing众包Curseofdimensionality维数灾难Cutpoint截断点Cuttingplanealgorithm割平⾯法LetterDDatamining数据挖掘Dataset数据集DecisionBoundary决策边界Decisionstump决策树桩Decisiontree决策树/判定树Deduction演绎DeepBeliefNetwork深度信念⽹络DeepConvolutionalGe nerativeAdversarialNetwork/DCGAN深度卷积⽣成对抗⽹络Deeplearning深度学习Deep neuralnetwork/DNN深度神经⽹络DeepQ-Learning深度Q学习DeepQ-Network深度Q⽹络Densityestimation密度估计Density-basedclustering密度聚类Differentiab leneuralcomputer可微分神经计算机Dimensionalityreductionalgorithm降维算法D irectededge有向边Disagreementmeasure不合度量Discriminativemodel判别模型Di scriminator判别器Distancemeasure距离度量Distancemetriclearning距离度量学习D istribution分布Divergence散度Diversitymeasure多样性度量/差异性度量Domainadaption领域⾃适应Downsampling下采样D-separation(Directedseparation)有向分离Dual problem对偶问题Dummynode哑结点DynamicFusion动态融合Dynamicprogramming动态规划LetterEEigenvaluedecomposition特征值分解Embedding嵌⼊Emotionalanalysis情绪分析Empiricalconditionalentropy经验条件熵Empiricalentropy经验熵Empiricalerror经验误差Empiricalrisk经验风险End-to-End端到端Energy-basedmodel基于能量的模型Ensemblelearning集成学习Ensemblepruning集成修剪ErrorCorrectingOu tputCodes/ECOC纠错输出码Errorrate错误率Error-ambiguitydecomposition误差-分歧分解Euclideandistance欧⽒距离Evolutionarycomputation演化计算Expectation-Maximization期望最⼤化Expectedloss期望损失ExplodingGradientProblem梯度爆炸问题Exponentiallossfunction指数损失函数ExtremeLearningMachine/ELM超限学习机LetterFFactorization因⼦分解Falsenegative假负类Falsepositive假正类False PositiveRate/FPR假正例率Featureengineering特征⼯程Featureselection特征选择Featurevector特征向量FeaturedLearning特征学习FeedforwardNeuralNetworks/FNN前馈神经⽹络Fine-tuning微调Flippingoutput翻转法Fluctuation震荡Forwards tagewisealgorithm前向分步算法Frequentist频率主义学派Full-rankmatrix满秩矩阵Func tionalneuron功能神经元LetterGGainratio增益率Gametheory博弈论Gaussianker nelfunction⾼斯核函数GaussianMixtureModel⾼斯混合模型GeneralProblemSolving通⽤问题求解Generalization泛化Generalizationerror泛化误差Generalizatione rrorbound泛化误差上界GeneralizedLagrangefunction⼴义拉格朗⽇函数Generalized linearmodel⼴义线性模型GeneralizedRayleighquotient⼴义瑞利商GenerativeAd versarialNetworks/GAN⽣成对抗⽹络GenerativeModel⽣成模型Generator⽣成器Genet icAlgorithm/GA遗传算法Gibbssampling吉布斯采样Giniindex基尼指数Globalminimum全局最⼩GlobalOptimization全局优化Gradientboosting梯度提升GradientDescent梯度下降Graphtheory图论Ground-truth真相/真实LetterHHardmargin硬间隔Hardvoting硬投票Harmonicmean调和平均Hessematrix海塞矩阵Hiddendynamicmodel隐动态模型H iddenlayer隐藏层HiddenMarkovModel/HMM隐马尔可夫模型Hierarchicalclustering层次聚类Hilbertspace希尔伯特空间Hingelossfunction合页损失函数Hold-out留出法Homo geneous同质Hybridcomputing混合计算Hyperparameter超参数Hypothesis假设Hypothe sistest假设验证LetterIICML国际机器学习会议Improvediterativescaling/IIS改进的迭代尺度法Incrementallearning增量学习Independentandidenticallydistributed/i.i.d.独⽴同分布IndependentComponentAnalysis/ICA独⽴成分分析Indicatorfunction指⽰函数Individuallearner个体学习器Induction归纳Inductivebias归纳偏好I nductivelearning归纳学习InductiveLogicProgramming/ILP归纳逻辑程序设计Infor mationentropy信息熵Informationgain信息增益Inputlayer输⼊层Insensitiveloss不敏感损失Inter-clustersimilarity簇间相似度InternationalConferencefor MachineLearning/ICML国际机器学习⼤会Intra-clustersimilarity簇内相似度Intrinsicvalue固有值IsometricMapping/Isomap等度量映射Isotonicregression等分回归It erativeDichotomiser迭代⼆分器LetterKKernelmethod核⽅法Kerneltrick核技巧K ernelizedLinearDiscriminantAnalysis/KLDA核线性判别分析K-foldcrossvalidationk折交叉验证/k倍交叉验证K-MeansClusteringK–均值聚类K-NearestNeighb oursAlgorithm/KNNK近邻算法Knowledgebase知识库KnowledgeRepresentation知识表征LetterLLabelspace标记空间Lagrangeduality拉格朗⽇对偶性Lagrangemultiplier拉格朗⽇乘⼦Laplacesmoothing拉普拉斯平滑Laplaciancorrection拉普拉斯修正Latent DirichletAllocation隐狄利克雷分布Latentsemanticanalysis潜在语义分析Latentvariable隐变量Lazylearning懒惰学习Learner学习器Learningbyanalogy类⽐学习Learn ingrate学习率LearningVectorQuantization/LVQ学习向量量化Leastsquaresre gressiontree最⼩⼆乘回归树Leave-One-Out/LOO留⼀法linearchainconditional randomfield线性链条件随机场LinearDiscriminantAnalysis/LDA线性判别分析Linearmodel线性模型LinearRegression线性回归Linkfunction联系函数LocalMarkovproperty局部马尔可夫性Localminimum局部最⼩Loglikelihood对数似然Logodds/logit对数⼏率Lo gisticRegressionLogistic回归Log-likelihood对数似然Log-linearregression对数线性回归Long-ShortTermMemory/LSTM长短期记忆Lossfunction损失函数LetterM Machinetranslation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majorityvoting绝对多数投票法Manifoldassumption流形假设Manifoldlearning流形学习Margintheory间隔理论Marginaldistribution边际分布Marginalindependence边际独⽴性Marginalization边际化MarkovChainMonteCarlo/MCMC马尔可夫链蒙特卡罗⽅法MarkovRandomField马尔可夫随机场Maximalclique最⼤团MaximumLikelihoodEstimation/MLE极⼤似然估计/极⼤似然法Maximummargin最⼤间隔Maximumweightedspanningtree最⼤带权⽣成树Max-P ooling最⼤池化Meansquarederror均⽅误差Meta-learner元学习器Metriclearning度量学习Micro-P微查准率Micro-R微查全率MinimalDescriptionLength/MDL最⼩描述长度Minim axgame极⼩极⼤博弈Misclassificationcost误分类成本Mixtureofexperts混合专家Momentum动量Moralgraph道德图/端正图Multi-classclassification多分类Multi-docum entsummarization多⽂档摘要Multi-layerfeedforwardneuralnetworks多层前馈神经⽹络MultilayerPerceptron/MLP多层感知器Multimodallearning多模态学习Multipl eDimensionalScaling多维缩放Multiplelinearregression多元线性回归Multi-re sponseLinearRegression/MLR多响应线性回归Mutualinformation互信息LetterN Naivebayes朴素贝叶斯NaiveBayesClassifier朴素贝叶斯分类器Namedentityrecognition命名实体识别Nashequilibrium纳什均衡Naturallanguagegeneration/NLG⾃然语⾔⽣成Naturallanguageprocessing⾃然语⾔处理Negativeclass负类Negativecorrelation负相关法NegativeLogLikelihood负对数似然NeighbourhoodComponentAnalysis/NCA近邻成分分析NeuralMachineTranslation神经机器翻译NeuralTuringMachine神经图灵机Newtonmethod⽜顿法NIPS国际神经信息处理系统会议NoFreeLunchTheorem /NFL没有免费的午餐定理Noise-contrastiveestimation噪⾳对⽐估计Nominalattribute列名属性Non-convexoptimization⾮凸优化Nonlinearmodel⾮线性模型Non-metricdistance⾮度量距离Non-negativematrixfactorization⾮负矩阵分解Non-ordinalattribute⽆序属性Non-SaturatingGame⾮饱和博弈Norm范数Normalization归⼀化Nuclearnorm核范数Numericalattribute数值属性LetterOObjectivefunction⽬标函数Obliquedecisiontree斜决策树Occam’srazor奥卡姆剃⼑Odds⼏率Off-Policy离策略Oneshotlearning⼀次性学习One-DependentEstimator/ODE独依赖估计On-Policy在策略Ordinalattribute有序属性Out-of-bagestimate包外估计Outputlayer输出层Outputsmearing输出调制法Overfitting过拟合/过配Oversampling过采样LetterPPairedt-test成对t检验Pairwise成对型PairwiseMarkovproperty成对马尔可夫性Parameter参数Parameterestimation参数估计Parametertuning调参Parsetree解析树ParticleSwarmOptimization/PSO粒⼦群优化算法Part-of-speechtagging词性标注Perceptron感知机Performanceme asure性能度量PlugandPlayGenerativeNetwork即插即⽤⽣成⽹络Pluralityvoting相对多数投票法Polaritydetection极性检测Polynomialkernelfunction多项式核函数Pooling池化Positiveclass正类Positivedefinitematrix正定矩阵Post-hoctest后续检验Post-pruning后剪枝potentialfunction势函数Precision查准率/准确率Prepruning预剪枝Principalcomponentanalysis/PCA主成分分析Principleofmultipleexplanations多释原则Prior先验ProbabilityGraphicalModel概率图模型ProximalGradientDescent/PGD近端梯度下降Pruning剪枝Pseudo-label伪标记LetterQQuantizedNeu ralNetwork量⼦化神经⽹络Quantumcomputer量⼦计算机QuantumComputing量⼦计算Quasi Newtonmethod拟⽜顿法LetterRRadialBasisFunction/RBF径向基函数RandomFo restAlgorithm随机森林算法Randomwalk随机漫步Recall查全率/召回率ReceiverOperatin gCharacteristic/ROC受试者⼯作特征RectifiedLinearUnit/ReLU线性修正单元Recurr entNeuralNetwork循环神经⽹络Recursiveneuralnetwork递归神经⽹络Referencemodel参考模型Regression回归Regularization正则化Reinforcementlearning/RL强化学习Representationlearning表征学习Representertheorem表⽰定理reproducingke rnelHilbertspace/RKHS再⽣核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residu alMapping残差映射ResidualNetwork残差⽹络RestrictedBoltzmannMachine/RBM受限玻尔兹曼机RestrictedIsometryProperty/RIP限定等距性Re-weighting重赋权法Robu stness稳健性/鲁棒性Rootnode根结点RuleEngine规则引擎Rulelearning规则学习LetterS Saddlepoint鞍点Samplespace样本空间Sampling采样Scorefunction评分函数Self-Driving⾃动驾驶Self-OrganizingMap/SOM⾃组织映射Semi-naiveBayesclassifiers半朴素贝叶斯分类器Semi-SupervisedLearning半监督学习semi-SupervisedSupportVec torMachine半监督⽀持向量机Sentimentanalysis情感分析Separatinghyperplane分离超平⾯SigmoidfunctionSigmoid函数Similaritymeasure相似度度量Simulatedannealing模拟退⽕Simultaneouslocalizationandmapping同步定位与地图构建SingularV alueDecomposition奇异值分解Slackvariables松弛变量Smoothing平滑Softmargin软间隔Softmarginmaximization软间隔最⼤化Softvoting软投票Sparserepresentation稀疏表征Sparsity稀疏性Specialization特化SpectralClustering谱聚类SpeechRecognition语⾳识别Splittingvariable切分变量Squashingfunction挤压函数Stability-plasticitydilemma可塑性-稳定性困境Statisticallearning统计学习Statusfeaturefunction状态特征函Stochasticgradientdescent随机梯度下降Stratifiedsampling分层采样Structuralrisk结构风险Structuralriskminimization/SRM结构风险最⼩化S ubspace⼦空间Supervisedlearning监督学习/有导师学习supportvectorexpansion⽀持向量展式SupportVectorMachine/SVM⽀持向量机Surrogatloss替代损失Surrogatefunction替代函数Symboliclearning符号学习Symbolism符号主义Synset同义词集LetterTT-Di stributionStochasticNeighbourEmbedding/t-SNET–分布随机近邻嵌⼊Tensor张量TensorProcessingUnits/TPU张量处理单元Theleastsquaremethod最⼩⼆乘法Th reshold阈值Thresholdlogicunit阈值逻辑单元Threshold-moving阈值移动TimeStep时间步骤Tokenization标记化Trainingerror训练误差Traininginstance训练⽰例/训练例Tran sductivelearning直推学习Transferlearning迁移学习Treebank树库Tria-by-error试错法Truenegative真负类Truepositive真正类TruePositiveRate/TPR真正例率TuringMachine图灵机Twice-learning⼆次学习LetterUUnderfitting⽋拟合/⽋配Undersampling⽋采样Understandability可理解性Unequalcost⾮均等代价Unit-stepfunction单位阶跃函数Univariatedecisiontree单变量决策树Unsupervisedlearning⽆监督学习/⽆导师学习Unsupervisedlayer-wisetraining⽆监督逐层训练Upsampling上采样LetterVVanishingGradientProblem梯度消失问题Variationalinference变分推断VCTheoryVC维理论Versionspace版本空间Viterbialgorithm维特⽐算法VonNeumannarchitecture冯·诺伊曼架构LetterWWassersteinGAN/WGANWasserstein⽣成对抗⽹络Weaklearner弱学习器Weight权重Weightsharing权共享Weightedvoting加权投票法Within-classscattermatrix类内散度矩阵Wordembedding词嵌⼊Wordsensedisambiguation词义消歧LetterZZero-datalearning零数据学习Zero-shotlearning零次学习。

埃农映射的不动点

埃农映射的不动点

埃农映射的不动点埃农映射(Arnold's cat map)是一种具有周期性的非线性映射,最早由Vladimir I. Arnold在1968年引入,用来说明质点在二维平面上的运动轨迹。

这个映射的非线性特点使得它具有许多有趣的数学性质,其中之一是存在不动点。

埃农映射可以形象地理解为将一个正方形的平面划分为一个个小的正方形,并按照一定的规则对小正方形进行旋转和重新排列,从而得到新的平面。

这个过程可以表示为一个二维的矩阵乘法。

具体而言,给定一个矩阵```A = [[2, 1],[1, 1]]```和一个二维向量```X = [x, y]```则埃农映射可以表示为```A * X (mod 1)```其中mod 1表示取余数。

根据埃农映射的定义,我们可以通过反复将一个点进行映射来观察它在平面上的运动轨迹。

令一个初始点为(X0, Y0),经过n次映射后得到(Xn, Yn)。

当(Xn, Yn) = (X0, Y0)时,我们称它为一个周期为n的不动点,表示在n次映射后,点回到了初始的位置。

埃农映射具有非常有趣的周期性特征。

事实上,对于一个正方形的平面而言,埃农映射的周期为n的不动点共有n个。

这意味着在平面上存在很多很多的周期性轨迹,这些轨迹之间又有着复杂的交织关系。

这种周期性特征是埃农映射非线性性质的直接体现,也是它被广泛研究和应用的原因之一。

埃农映射及其不动点的研究在许多领域中都有重要的应用。

在动力系统中,埃农映射是研究混沌现象的一个重要模型。

混沌现象是指非线性动力系统中出现的无规律、无周期的运动。

埃农映射的周期特性使得它成为理解混沌现象的基础模型之一。

在密码学中,埃农映射的周期性特征被应用于设计密码算法。

利用埃农映射的特性,可以生成一系列随机数,并通过一定的操作将这些随机数转化为密码所需的扰动序列。

这种基于埃农映射的密码算法被广泛应用于信息安全领域。

此外,在图像处理和数据压缩领域,埃农映射也有一定的应用。

稀疏表示目标关联

稀疏表示目标关联

稀疏表示目标关联Company Document number:WUUT-WUUY-WBBGB-BWYTT-1982GT无重叠视域多摄像头目标关联综述摘要:随着视频监控技术的快速发展,多监控摄像机信息融合的研究逐渐被重视起来,作为核心技术之一的无重叠视域多目标关联也成为了研究的焦点。

这个研究焦点是计算机视觉研究领域的一个新兴的、多学科交叉的研究方向。

文章对国际上关于此方向从开始到现在的重要研究成果,做出了比较详细的论述,把对该问题的研究归纳为三个主要的组成部分,并依次介绍了这三部分的研究进展,最后简要分析了这个方面研究的难点和未来的发展趋势。

关键字:视频监控、无重叠视域多摄像头、目标关联Abstract: With the rapid development of visual surveillance technology, the research about multi-camera information fusion is gradually paid more attention to. Thus, as one of the core techniques, objects tracking across non-overlapping multi-camera become the focus. This focus is a rising direction of computer vision, which crosses several subjects. This paper discusses the important achievements of this topic all over the world from beginning to present in detail. All of them are clustered into three parts and introduced orderly. Finally, some difficulties and future directions are analyzed concisely.Keywords: visual surveillance, non-overlapping multi-camera, objects matching1引言在单摄像机智能监控算法逐渐成熟的同时,近年来,多摄像机之间的信息关联和信息融合的研究逐渐被重视。

自组织映射(SOM)R包说明书

自组织映射(SOM)R包说明书

Package‘som’October14,2022Version0.3-5.1Date2010-04-08Title Self-Organizing MapAuthor Jun Yan<***************.edu>Maintainer Jun Yan<***************.edu>Depends R(>=2.10)Description Self-Organizing Map(with application in gene clustering).License GPL(>=3)Repository CRANDate/Publication2016-07-0610:26:15NeedsCompilation yesR topics documented:filtering (1)normalize (2)plot.som (3)qerror (4)som (4)summary.som (7)yeast (7)Index9 filtering Filter data before feeding som algorithm for gene expression dataDescriptionFiltering data by certainfloor,ceiling,max/min ratio,and max-min difference.12normalizeUsagefiltering(x,lt=20,ut=16000,mmr=3,mmd=200)Argumentsx a data frame or matrix of input data.ltfloor value replaces those less than it with the valueut ceiling value replaced those greater than it with the valuemmr the max/min ratio,rows with max/min<mmr will be removedmmd the max-min difference,rows with(max-min)<mmd will be removed ValueAn dataframe or matrix after thefilteringAuthor(s)Jun Yan<***************.edu>See Alsonormalize.normalize normalize data before feeding som algorithmDescriptionNormalize the data so that each row has mean0and variance1.Usagenormalize(x,byrow=TRUE)Argumentsx a data frame or matrix of input data.byrow whether normalizing by row or by column,default is byrow.ValueAn dataframe or matrix after the normalizing.Author(s)Jun Yan<***************.edu>plot.som3See Alsofiltering.plot.som Visualizing a SOMDescriptionPlot the SOM in a2-dim map with means and sd bars.Usage##S3method for class somplot(x,sdbar=1,ylim=c(-3,3),color=TRUE,ntik=3,yadj=0.1,xlab="",ylab="",...)Argumentsx a som objectsdbar the length of sdbar in sd,no sdbar if sdbar=0ylim the range of y axies in each cell of the mapcolor whether or not use color plottingntik the number of tiks of the vertical axisyadj the proportion used to put the number of obsxlab x labelylab y label...other options to plotNoteThis function is not cleanly written.The original purpose was to mimic what GENECLUSTER does.The ylim is hardcoded so that only standardized data could be properly plotted.There are visualization methods like umat and sammon in SOM\_PAK3.1,but not implemented here.Author(s)Jun Yan<***************.edu>Examplesfoo<-som(matrix(rnorm(1000),250),3,5)plot(foo,ylim=c(-1,1))4som qerror quantization accuracyDescriptionget the average distortion measureUsageqerror(obj,err.radius=1)Argumentsobj a‘som’objecterr.radius radius used calculating qerrorValueAn average of the following quantity(weighted distance measure)over all x in the sample,||x−m i||h ciwhere h ci is the neighbourhood kernel for the ith code.Author(s)Jun Yan<***************.edu>Examplesfoo<-som(matrix(rnorm(1000),100),2,4)qerror(foo,3)som Function to train a Self-Organizing MapDescriptionProduces an object of class"som"which is a Self-Organizing Mapfit of the data.som5 Usagesom.init(data,xdim,ydim,init="linear")som(data,xdim,ydim,init="linear",alpha=NULL,alphaType="inverse",neigh="gaussian",topol="rect",radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL)som.train(data,code,xdim,ydim,alpha=NULL,alphaType="inverse",neigh="gaussian",topol="rect",radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL) som.update(obj,alpha=NULL,radius=NULL,rlen=NULL,err.radius=1,inv.alp.c=NULL)som.project(obj,newdat)Argumentsobj a‘som’object.newdat a new dataset needs to be projected onto the map.code a matrix of initial code vector in the map.data a data frame or matrix of input data.xdim an integer specifying the x-dimension of the map.ydim an integer specifying the y-dimension of the map.init a character string specifying the initializing method.The following are per-mitted:"sample"uses a radom sample from the data;"random"uses randomdraws from N(0,1);"linear"uses the linear grids upon thefirst two principlecomponents directin.alpha a vector of initial learning rate parameter for the two training phases.Decreaseslinearly to zero during training.alphaType a character string specifying learning rate funciton type.Possible choices arelinear function("linear")and inverse-time type function("inverse").neigh a character string specifying the neighborhood function type.The following arepermitted:"bubble""gaussian"topol a character string specifying the topology type when measuring distance in themap.The following are permitted:"hexa""rect"radius a vector of initial radius of the training area in som-algorithm for the two trainingphases.Decreases linearly to one during training.rlen a vector of running length(number of steps)in the two training phases.err.radius a numeric value specifying the radius when calculating average distortion mea-sure.inv.alp.c the constant C in the inverse learning rate function:alpha0*C/(C+t);6somValue‘som.init’initializes a map and returns the code matrix.‘som’does the two-step som training ina batch fashion and return a‘som’object.‘som.train’takes data,code,and traing parameters andperform the requested som training.‘som.update’takes a‘som’object and further train it with updated paramters.‘som.project’projects new data onto the map.An object of class"som"representing thefit,which is a list containing the following components: data the dataset on which som was applied.init a character string indicating the initializing method.xdim an integer specifying the x-dimension of the map.ydim an integer specifying the y-dimension of the map.code a metrix with nrow=xdim*ydim,each row corresponding to a code vector of a cell in the map.The mapping from cell coordinate(x,y)to the row index in thecode matrix is:rownumber=x+y*xdimvisual a data frame of three columns,with the same number of rows as in data:x and y are the coordinate of the corresponding observation in the map,and qerror is thequantization error computed as the squared distance(depends topol)betweenthe observation vector and its coding vector.alpha0a vector of initial learning rate parameter for the two training phases.alpha a character string specifying learning rate funciton type.neigh a character string specifying the neighborhood function type.topol a character string specifying the topology type when measuring distance in the map.radius0a vector of initial radius of the training area in som-algorithm for the two training phases.rlen a vector of running length in the two training phases.qerror a numeric value of average distortion measure.code.sum a dataframe summaries the number of observations in each map cell.Author(s)Jun Yan<***************.edu>ReferencesKohonen,Hynninen,Kangas,and Laaksonen(1995),SOM-PAK,the Self-Organizing Map Pro-gram Package(version3.1).http://www.cis.hut.fi/research/papers/som\_tr96.ps.ZExamplesdata(yeast)yeast<-yeast[,-c(1,11)]yeast.f<-filtering(yeast)yeast.f.n<-normalize(yeast.f)foo<-som(yeast.f.n,xdim=5,ydim=6)foo<-som(yeast.f.n,xdim=5,ydim=6,topol="hexa",neigh="gaussian")plot(foo)summary.som7 summary.som summarize a som objectDescriptionprint out the configuration parameters of a som objectUsage##S3method for class somsummary(object,...)##S3method for class somprint(x,...)Argumentsobject,x a‘som’object...nothing yetAuthor(s)Jun Yan<***************.edu>yeast yeast cell cycleDescriptionThe yeast data frame has6601rows and18columns,i.e.,6601genes,measured at18time points. Usagedata(yeast)FormatThis data frame contains the following columns:Gene a character vector of gene nameszero a numeric vectorten a numeric vectortwenty a numeric vectorthirty a numeric vectorfourty a numeric vector8yeastfifty a numeric vectorsixty a numeric vectorseventy a numeric vectoreighty a numeric vectorninety a numeric vectorhundred a numeric vectorone.ten a numeric vectorone.twenty a numeric vectorone.thirty a numeric vectorone.fourty a numeric vectorone.fifty a numeric vectorone.sixty a numeric vectorSourceReferencesTamayo et.al.(1999),Interpreting patterns of gene expression with self-organizing maps:Methods and application to hematopoietic differentiation,PNAS V96,pp2907-2912,March1999.Index∗arithqerror,4∗clustersom,4∗datasetsyeast,7∗hplotplot.som,3∗manipfiltering,1normalize,2∗printsummary.som,7filtering,1,3normalize,2,2plot.som,3print.som(summary.som),7qerror,4som,4summary.som,7yeast,79。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘 要
在竞争激烈的工业自动化生产过程中,机器视觉对产品质量的把关起着举足 轻重的作用,机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比,自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在,像用于半导体装配和封装底板和发光二极管,现代 化电子 系统中的印制电路板,以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究,为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征,纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差,对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义,如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时,在参考图像可行的情况下,本 算法可用于同质纹理物体和非同质纹理物体的检测,对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中,我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同,我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法,来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段,我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测;纹理;物体变形;可调控金字塔;重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

物体级slam的四种表示

物体级slam的四种表示

物体级slam的四种表示随着机器人视觉技术的不断发展,场景重建(Scene Reconstruction)技术也在迅速发展。

物体级SLAM(Simultaneous Localization and Mapping)也逐渐成为越来越多研究者关注的热点话题。

物体级SLAM用于重建欧氏空间中许多小物体,它可以大大提高三维构建的准确性,并可用于更多的应用场景。

因此,在物体级SLAM中,物体的表示方式至关重要。

本文尝试从三个方面(特征以及特征的姿态和位置)介绍物体级SLAM中的四种物体表示方式。

首先,物体表示的基本要素是特征,即描述任何物体的独特属性。

特征建模通常使用点云,使用特征识别算法确定物体表面的关键点。

为了精确定位物体,这些特征点必须有足够的泛化能力。

目前,使用特征进行建模的方法有很多,比如FPFH(Fast Point Feature Histogram),SHOT(Shape-Hession Transform)等等,它们都有不同的精度和效率要求。

特征-姿态表示是在特征表示的基础上建立起来的,它将特征点进行聚类,以确定物体的几何结构。

其中最常见的是ICP(Iterative Closest Point)算法,它可以使用最短的迭代次数求出物体的最优姿态,从而加快特征识别的效率。

此外,还有许多特征量化方法,可以将物体特征映射到离散空间,以获得更高精度的物体检测效果。

特征-位置表示则是将特征表示和姿态表示拼接到一起,以确定物体的位置和尺度。

主要的方法有四种:1)视觉里程计,它使用视觉信息来计算相对运动;(2)特征检测,它用特征点的匹配来估计位置;(3)特征跟踪,它从一帧到另一帧的图像中跟踪特征点,从而确定位置;(4)特征匹配,它使用预定义的3D模型,来估计物体的位置。

最后,我们介绍最典型的物体表示方法完整姿态表示(Full Pose Representation),它是物体级SLAM最重要的形式之一,它能够实现准确的物体三维重建。

ISO_10289_盐雾判定标准

ISO_10289_盐雾判定标准
Méthodes d'essai de corrosion des revêtements métalliques et inorganiques sur substrats métalliques — Cotation des éprouvettes et des articles manufacturés soumis aux essais de corrosion
Although these functions overlaparately in terms of: a protection rating (Rp) relating to the corrosion of the base metal; an appearance rating (RA) relating to the deterioration of the coating.
iii
SIS Multi User Licence: ABB TECHNOLOGY LTD.. 12/16/2009
ISO 10289:1999(E)
©
ISO
Introduction
The rating method described in this International Standard recognizes that decorative and protective metallic and inorganic coatings on metallic substrates can be either anodic or cathodic to the substrate. In rating these coatings for the effects of corrosion, two evaluations shall be made: the ability of the coating to protect the substrate from corrosion and thus prevent degradation of the base metal; the ability of the coating to retain its integrity and thus maintain a satisfactory appearance.

西门子Minicap FTC260、FTC262电容点级传感器说明书

西门子Minicap FTC260、FTC262电容点级传感器说明书

Products Solutions Services TI00287F/00/EN/14.1471247138Technical InformationMinicapFTC260, FTC262CapacitivePoint level switch with buildup compensationNo calibration necessaryApplicationThe Minicap is designed for point level detection in light bulk solids with a grain sizeup to max. 30mm (1.18 in) and a dielectric constant εr≥1.6e.g. grain products, flour, milk powder, animal feed, cement, chalk or gypsum.Versions:•Minicap FTC260: with rod probe for bulk solids and liquids•Minicap FTC262: with rope probe up to 6 m (20 ft); for bulk solids•Relay output (potential-free change-over contact / SPDT) with AC or DC power•PNP output with three-wire DC powerYour Benefits•Complete unit consisting of the probe and electronic insert:– simple mounting– no calibration on start-up•Active build-up compensation– accurate switch point– high operational safety•Mechanically rugged– no wearing parts– long operating life– no maintenance•The rope probe of the Minicap FTC262 can be shortened– optimum matching to the measuring point– less stocks requiredMinicap FTC260, FTC2622Endress+HauserTable of contentsFunction and system design . . . . . . . . . . . . . . . . . . . . . .3Measuring principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Measuring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Function Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Setting the Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Fail-safe mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5Measured variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Measuring range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6Output signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Signal on alarm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Switching delay when free or covered . . . . . . . . . . . . . . . . . . . . 6Overvoltage category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Protection class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Power supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6Electrical connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Supply voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Terminal compartment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Performance characteristics . . . . . . . . . . . . . . . . . . . . . .8Reference operating conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 8Hysteresis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Switch point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Power up response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Long-term drift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Influence of medium temperature . . . . . . . . . . . . . . . . . . . . . . . . 8Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Installation instructions FTC260 . . . . . . . . . . . . . . . . . . . . . . . . . 9Installation instructions FTC262 . . . . . . . . . . . . . . . . . . . . . . . . 10Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Ambient temperature Ta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Storage temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Climate class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Degree of protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Impact resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Vibrational resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Electromagnetic compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . 11Operating height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Process temperature Tp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Process pressure range pp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Temperature diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Mechanical construction . . . . . . . . . . . . . . . . . . . . . . . 12Design and dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Material for wetted parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Process connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Housing, cable entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Tensile strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Operability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Display elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Operating elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Certificates and approvals. . . . . . . . . . . . . . . . . . . . . . 14CE mark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Ex approval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Ordering information. . . . . . . . . . . . . . . . . . . . . . . . . . 14Accessories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Adapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Rope shortening set for FTC262 . . . . . . . . . . . . . . . . . . . . . . . . 14Supplementary documentation. . . . . . . . . . . . . . . . . . 14Operating manual (BA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Safety instructions (XA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Minicap FTC260, FTC262Endress+Hauser 3Function and system designMeasuring principlePoint Level DetectionA metal plate at the end of the probe, within the insulation, the integrated counter-electrode and the surroundings combine to form the two electrodes of a capacitor.If the probe is covered or free of process medium, the capacitance changes and the Minicap switches.Active Build-up CompensationThe Minicap detects build-up on the probe and compensates for its effects so that the switch point is always observed. The effects of build-up compensation depend on:•the thickness of the buildup on the probe,•the conductivity of the buildup,•the sensitivity setting on the electronic insert.Measuring SystemMinicap is an electronic switch. The complete measuring system consists of:•the Minicap FTC260 or FTC262•a power supply and•controllers, switching devices, signal transmitters (e.g. lamps, horns, PCS, PLC, etc.)Point level detection in silos containing solidsMinicap FTC260, FTC2624Endress+HauserFunction RangeThere is a loose relationship between the dielectric constant εr and the density ρ of the material.The table on the left indicates whether the Minicap can be used or if application limits are exceeded.In general:If the dielectric constant of the process medium is not known, then the bulk density can be a deciding factor.Experience shows that the Minicap functions in foodstuffs with a density of 250 g/l and above or in plastic or mineral materials with a density of 600 g/l and above.Setting the SensitivityThe Minicap is so calibrated at the factory that it correctly switches in most materials.Greater sensitivity can be set using a switch on the electronic insert. This is necessary if there is very strong build-up on the probe, or if the dielectric constant εr of the material is very small.The switch positions show the factory settings: 1LEDs 2 Switch for setting sensitivity 3Switch for selecting safety positionGrain, seed, legumes and their products Minerals, inorganic materialsPlasticsExamples ρ in g/l (approx.) εr(approx.)Examples ρ in g/l (approx.) εr(approx.)Examples ρ in g/l (approx.) εr(approx.)Rice770 3.0Cement 1050 2.2ABS granulate 630 1.7Cornstarch (packed)680 2.6Plaster 730 1.8PA granulate 620 1.7Flour (wheat)580 2.4Chalk (packed)*540 1.6PE granulate*560 1.5Corn grist 500 2.1Chalk (loose)*3601.4PVC powder*550 1.4Sunflower seeds 380 1.9PU dust*801.1Noodles 370 1.9Bran (wheat)250 1.7Popcorn*301.1* Gray background: Application limits not reached => Use Soliphant FTM as point level switch.Minicap FTC260, FTC262Endress+Hauser5Fail-safe mode MIN-/MAX detection on the electronic insert, switchable.MINThe output switches if the probe is uncovered or if the supply voltage is disconnected in a safety-oriented manner (signal on alarm). Used for dry-running protection and pump protection, for example.MAXThe output switches if the probe is covered or if the supply voltage is disconnected in a safety-oriented manner (signal on alarm). Used for overfill protection, for example.Function and selection of fail-safe modeInputMeasured variable Point level Measuring range•FTC260: εr ≥ 1.6•FTC262: εr ≥ 1.5Minicap FTC260, FTC2626Endress+HauserOutputOutput signal•DC, PNP transistor output:Switching: PNP I max 200 mA–overload and short circuit protection –residual voltage at transistor at I max < 2.9 V •AC / DC, Relay output:Contact: change-over,potential-freeU~max 253 V, I~max 4 A (AC)P~max 1000 VA, cos ϕ = 1P~max 500 VA, cos ϕ > 0.7I %max4 A for U %30 V I %max 0.2 A for U % 253 VSignal on alarm•DC, PNP transistor output: <100 μA•AC / DC, Relay output: relay de-energized Switching delay when free or covered•FTC260: 0.5 s •FTC262: 0.8 sOvervoltage category Category II (as per EN 61010-1)Protection classClass I (as per EN 61010-1)Power supplyElectrical connectionTo ensure that the Minicap operates safely and without electrical interference, it must be connected to an earthed silo with metal or reinforced concrete walls.For silos made of non-conductive materials, the external earth wire of the Minicap must be connected to a conductive and earthed component which is earthed near to the silo. The protective earth can be connected to the internal earth terminal of the Minicap.Connections can be made with standard instrument cabling.See TI00241F/00/EN for information on EMC (testing procedures, installation).Connect the potential matching lead (PAL) when using in dust explosion hazardous areas. Note national regulations!Minicap FTC260, FTC262Endress+Hauser 7Minicap AC or DC connection and relay output Minicap with three-cable DC connection; Transistor output PNP Supply voltage•DC, PNP transistor output:U 10.8 to 45 V DC–transient pulses to 55 V–current consumption max. 30 mA –reverse polarity protected•AC / DC, Relay output:U 20 to 253 V AC or U 20 to 55 V DC–current consumption max. 130 mATerminal compartment•Stranded wires max. 1.5 mm 2 (16 AWG) in end sleeves •Electric wire max. 2.5 mm 2 (14 AWG)F1:F2:M:E:Fine-wire fuse to protect the relay contact,dependent on the connected load Fine-wire fuse, 500 mA Earth connection to silo or metal components on silo EarthMinicap FTC260 with F14 housing: no ground lines (PE) or potential matching lines (PAL) are required.F:R:M:E:Fine-wire fuse, 500 mAConnected load, e.g. PLC, PCS, relay Earth connection to silo or metal components on silo EarthMinicap is protected against reverse polarity. The green LED for standby goes out if the connections are reversed.Minicap FTC260 with F14 housing: no ground lines (PE) or potential matching lines (PAL) are required.Minicap FTC260, FTC2628Endress+HauserPerformance characteristicsReference operating conditionsIn plastic container:•Ambient temperature: 23 °C (73 °F)•Medium temperature: 23 °C (73 °F)•Medium pressure p e : 0 bar (0 psi)•Medium: dielectric constant r = 2.6 •Conductivity: < 1 μS •Sensitivity setting: CHysteresis•FTC260: 4 mm (0.16 in) horizontal, 7 mm (0.28 in) vertical •FTC262: 5 mm (0.2 in) verticalSwitch point•FTC260: Probe center –5 mm (-0.2 in) horizontal, above probe tip 40 mm (1.57 in) vertical •FTC262: Above probe tip: 35 mm (1.38 in) vertical Probe length tolerances; mm (in):–Probe length L Tolerances–up to 1000 (39.4)+0/–10 (+0/–0.39)–up to 3000 (118) +0/–20 (+0/–0.79)–up to 6000 (236) +0/–30 (+0/–1.18)Power up response•FTC260: Correct switching after max. 1.5 s •FTC262: Correct switching after max. 2 sLong-term drift•FTC260: 3 mm (0.12 in) horizontal, 6 mm (0.24 in) vertical •FTC262: Vertical 6 mm (0.24 in)Influence of medium temperatureDepending on material to be measuredInstallationInstallationSilo MaterialThe Minicap can be used in a range of silos made of different materials. Mounting PointNote the angle of the material mounds and the outlet funnel when determining the mounting point or probe length of the FTC262.The material flow must not be directed at the probe!Minicap FTC260, FTC262Endress+Hauser 9Installation instructions FTC260Correct Installation a.Minimum distances:To prevent mutual interference with the FTC260, there must be a minimum distance of 200 mm (7.87 in) between two probe tips.b.Mounting point:Tip of probe points slightly downwards so that process medium can slide off more easily.The protective cover protects the probe rod from collapsing mounds or mechanical strain at the outflow when the Minicap FTC260 is set to minimum detection.c.Mechanical load:The maximum lateral load on the probe rode must be taken into account when used for minimum detection. It should therefore only be used for minimum detection with loose materials that have good flow characteristics.Incorrect Installation d.The probe may be damaged by inflowing material and cause faulty switching. Cable gland pointed upwards can allow moisture to enter.e.Threaded socket too long with material build-up on the silo wall. (Minimum mounting depth 100 mm (3.94 in) not reached).f.Mounted near build-up in the silo.The probe tip is too near to a silo wall (less than a minimum distance of 200 mm (7.87 in) ).Correct Installation Incorrect InstallationGeneral information and recommendations for installing a Minicap FTC260 point level switchMinicap FTC260, FTC26210Endress+HauserInstallation instructions FTC262Correct Installation a.Minimum distances:Sufficient distance from the material filling curtain and the other probe.b.Mounting point:Do not install in the center of the outlet cone. Ensure there is sufficient distance from the silo wall and from material build-up on the wall.c.Mechanical load:Note the tensile strain on the probe rope and the strength of the silo roof when used for minimum detection.Very high tensile forces may occur at the material outlet especially with heavy, powderybulk materials which tend to form build-up. These forces are significantly greater over the outlet than at the silo wall.For minimum detection Minicap FTC262 should only be used for light, easily flowing solids, and that do not tend to form build-up.Incorrect Installation d.In the center of the material outflow; the high tensile forces at this point may tear off the probe or damage the silo roof.e.The probe may be damaged by inflowing materialf.Mounted laterallyg.Too near silo wall; when swinging gently the probe can hit the wall or touch any build-up which may have formed. This can result in error switchingCorrect InstallationIncorrect InstallationGeneral notes and recommendations for installing a Minicap FTC262 point level switchMinicap FTC260, FTC262Endress+Hauser 11EnvironmentAmbient temperature T a–40 to +80 °C (–40 to +176 °F)For Dust-Ex version: –40 to +60 °C (–40 to +140 °F)Storage temperature –40 to +80 °C (–40 to +176 °F)Climate class As per EN 60068 part 2-38 (Z/AD), (IEC 68-2-38)Degree of protectionIP66; Type 4 encl. (with F14 housing)IP66; Type 4x encl. (with F34 housing)Impact resistance Probe with F34 housing: 7 JVibrational resistanceEN 60068-2-64 (IEC 68-2-64),a(RMS) = 50 m/s 2; ASD = 1.25 (m/s 2)2/Hz; f = 5 to 2000 Hz, t = 3x2 hElectromagnetic compatibilityInterference Emission to EN 61326, Electrical Equipment Class AInterference Immunity to EN 61326, Annex A (Industrial) and NAMUR Recommendation NE 21 (EMC)See TI00241F for general instructions regarding the EMC test conditions for E+H instruments.Operating heightUp to 2000 m (6600 ft) above mean sea level.ProcessProcess temperature T p•FTC260: –40 to +130 °C (–40 to +266 °F)For Dust-Ex version: –40 to +80 °C (–40 to +176 °F)•FTC262: –40 to +80 °C (–40 to +176 °F)Process pressure range p p•FTC260: –1 to +25 bar (-14.5 to +362 psi)•FTC262: –1 to +6 bar (-14.5 to +87 psi)Minicap FTC260, FTC26212Endress+HauserMechanical constructionDesign and dimensionsAll dimensions in mm (in)!F14 = Polyester PBT-FR housing, IP66F34 = Aluminum housing, IP66* Cover with sight glass for F34 housing, cover with sight glass for F14 housingMinicap FTC260, FTC262Endress+Hauser 13Material for wetted parts•Probe FTC260/FTC262: PPS GF40FDA: FCN No. 40, 21 CFR 177.1520; Regulation (EC) No. 1935/2004 and No. 10/2011•Probe rope FTC262: PE-HD •Probe rope seal FTC262: VMQ FDA: 21 CFR 177.2600Process connectionsThreaded boss:•FTC260– R 1, ISO 7/1 (DIN 2999), BSPT; adapter for R 1½ and G 1½, →ä14 "Accessories"– 1 NPT, ANSI B 1.20.1; adapter for 1¼ NPT, →ä14 "Accessories"•FTC262– R 1½, ISO 7/1 (DIN2999), BSPT – 1½ NPT, ANSI B 1.20.1Housing, cable entry•Housing F14: polyester PBT-FR, IP66–Coupling M20 –Thread NPT 1/2–Thread G 1/2•Housing F34: aluminum, IP66–Coupling M20 –Thread NPT 1/2–Thread G 1/2Tensile strengthFTC262max. 3000 N up to 40 °C (104 °F)max. 2800 N at 80 °C (176 °F)OperabilityDisplay elements•Green LED: stand-by •Red LED: switch status Operating elementsSwitch on electronic insert•switching between minimum and maximum fail-safe mode•Sensitivity setting (depending on dielectric constant εr and buildup).It is usually not necessary to adjust the sensitivity (see "Measuring principle" section →ä3)Minicap FTC260, FTC26214Endress+HauserCertificates and approvalsCE markThe device complies with the legal requirements of the EU directives.In attaching the CE Mark, Endress+Hauser confirms that the device conforms to all relevant EU directives.Ex approvalATEX (in conjunction with F34 aluminum housing) FM and CSA (in preparation)Ordering informationDetailed ordering information is available from the following sources:•In the Product Configurator on the Endress+Hauser website: --> Select country --> Instruments --> Select instrument --> Product page function: Configure this product •From your Endress+Hauser Sales Center: /worldwide Product Configurator - the tool for individual product configuration•Up-to-the-minute configuration data•Depending on the instrument: Direct input of measuring point-specific information such as measuring range or operating language •Automatic verification of exclusion criteria•Automatic creation of the order code and its breakdown in PDF or Excel output format •Direct ability to order in the Endress+Hauser Online ShopAccessoriesAdapter•FTC260, 1 BSPT female (Rc1), ISO 7/1 (see dimensional sketch):Part No.: 943215-1001,for 1½ BSPT (R 1½ ISO 7/1), PPS Part No.: 943215-1021,for 1½ BSP (G 1½ DIN ISO228), PPS •FTC260, 1 NPT female:Part No.: 943215-0042,for 1¼ NPT, steelPart No.: 943215-0043,for 1¼ NPT, AISI 316Ti (1.4571)Rope shortening set for FTC262Part No.: 52005918Supplementary documentationOperating manual (BA)•Minicap FTC260KA00093F/00/A6•Minicap FTC262KA00155F/00/A6•Rope shortening set for FTC262KA00157F/00/A6Safety instructions (XA)•Safety instructions (ATEX) for FTC260XA00011F/00/a3ATEX II 1/3D•Safety instructions (ATEX) for FTC262XA00092F/00/A3ATEX II 1/3DMinicap FTC260, FTC262Endress+Hauser1571247138。

Scattering

Scattering

Shading and Lighting• Shade objects so their images appear three-dimensional • Light-material interactions • Phong modelWhy we need shading• Suppose we build a model of a sphere using many polygons and color it with glColor. We get something like• But we wantShading• Why does the image of a real sphere look likeScattering• Light strikes A– Some scattered – Some absorbed• Some of scattered light strikes B• Light-material interactions cause each point to have a different color or shade • Need to consider– – – – Light sources Material properties Location of viewer Surface orientation– Some scattered – Some absorbed• Some of this scattered light strikes A and so on1Rendering Equation• The infinite scattering and absorption of light can be described by the rendering equation– Cannot be solved in general – Ray tracing is a special case for perfectly reflecting surfacesGlobal Effectsshadow• Rendering equation is global and includes– Shadows – Multiple scattering from object to objectmultiple reflection translucent surfaceRay Tracing SceneLocal vs Global Rendering• Correct shading requires a global calculation involving all objects and light sources– Incompatible with pipeline model which shades each polygon independently (local rendering)• However, in computer graphics, especially real time graphics, we are happy if things “look right”– Exist many techniques for approximating global effects2Light-Material Interaction• Light that strikes an object is partially absorbed and partially scattered (reflected) or refracted • The amount reflected determines the color and brightness of the object– A surface appears red under white light because the red component of the light is reflected and the rest is absorbedLight SourcesGeneral light sources are difficult to work with because we must integrate light coming from all points on the source• The reflected light is scattered in a manner that depends on the smoothness and orientation of the surfaceSimple Light Sources• Point source– Model with position and color – Distant source = infinite distance away (parallel)Surface Types• The smoother a surface, the more reflected light is concentrated in the direction a perfect mirror would reflected the light • A very rough surface scatters light in all directions• Spotlight– Restrict light from ideal point source• Ambient light– Same amount of light everywhere in scene – Can model contribution of many sources and reflecting surfacessmooth surfacerough surface3Phong Model• A simple model that can be computed rapidly • Has three components – Diffuse – Specular – Ambient • Uses four vectors – To source – To viewer – Normal – Perfect reflectorIdeal Reflector• Normal is determined by local orientation • Angle of incidence = angle of reflection • The three vectors must be coplanarr = 2 (l · n ) n - lLambertian Surface• Perfectly diffuse reflector • Light scattered equally in all directions • Amount of light reflected is proportional to the vertical component of incoming light– reflected light ~cos θi – cos θi = l · n if vectors normalized – There are also three coefficients, kr, kb, kg that show how much of each color component is reflectedSpecular Surfaces• Most surfaces are neither ideal diffusers nor perfectly specular (ideal reflectors) • Smooth surfaces show specular highlights due to incoming light being reflected in directions concentrated close to the direction of a perfect reflectionspecular highlight4Modeling Specular Reflections• Phong proposed using a term that dropped off as the angle between the viewer and the ideal reflection increasedIr ~ ks I cosαφ φ shininess coef reflected incoming intensity intensity absorption coefThe Shininess Coefficient• Values of α between 100 and 200 correspond to metals • Values between 5 and 10 give surface that look like plasticcosα φ-90φ90• Ambient light is the result of multiple interactions between (large) light sources and the objects in the environment • Amount and color depend on both the color of the light(s) and the material properties of the object • Add ka Ia to diffuse and specular termsreflection coef intensity of ambient lightAmbient LightLight Sources• In the Phong Model, we add the results from each light source • Each light source has separate diffuse, specular, and ambient terms to allow for maximum flexibility even though this form does not have a physical justification • Separate red, green and blue components5Material Properties• Hence, 9 coefficients for each point source– Idr, Idg, Idb, Isr, Isg, Isb, Iar, Iag, IabAdding up the ComponentsFor each light source and each color component, the Phong model can be written as I =kd Id l · n + ks Is (v · r )α + ka Ia For each color component we add contributions from all sources• Material properties match light source properties– Nine absorbtion coefficients • kdr, kdg, kdb, ksr, ksg, ksb, kar, kag, kab – Shininess coefficient αModified Phong Model• The specular term in the Phong model is problematic because it requires the calculation of a new reflection vector and view vector for each vertex • Blinn suggested an approximation using the halfway vector that is more efficientThe Halfway Vector• h is normalized vector halfway between l and vh = ( l + v )/ | l + v |6Using the halfway angle• Replace (v · r )α by (n · h )βExample• β is chosen to match shineness• Note that halfway angle is half of angle between r and v if vectors are coplanar • Resulting model is known as the modified Phong or Blinn lighting model– Specified in OpenGL standardOnly differences in these teapots are the parameters in the modified Phong modelComputation of Vectors• • • • l and v are specified by the application Can compute r from l and n Problem is determining n For simple surfaces n is can be determined but how we determine n differs depending on underlying representation of surface • OpenGL leaves determination of normal to application– Exception for GLU quadrics and Bezier surfacesRay Tracing Algorithm• References– Glassner, Andrew (Ed.) (1989). An Introduction to Ray Tracing. Academic Press – Shirley, Peter and Morley Keith, R. (2001) Realistic Ray Tracing,2nd edition. A.K. Peters – Free ray tracing program POV-Ray at – Siggraph 2005 course notes • Introduction to Real-Time Ray Tracing– Many graphics books…7。

Handbook_of_X-Ray_Spectrometry_CH01

Handbook_of_X-Ray_Spectrometry_CH01

1X-ray PhysicsAndrzej A.MarkowiczVienna,AustriaI.INTRODUCTIONIn this introductory chapter,the basic concepts and processes of x-ray physics that relate to x-ray spectrometry are presented.Special emphasis is on the emission of the continuum and characteristic x-rays as well as on the interactions of photons with matter.In the latter,only major processes of the interactions are covered in detail,and the cross sections for different types of interactions and the fundamental parameters for other processes involved in the emission of the characteristic x-rays are given by the analytical expressions and=or in a tabulated form.Basic equations for the intensity of the characteristic x-rays for the different modes of x-ray spectrometry are also presented(without derivation). Detailed expressions relating the emitted intensity of the characteristic x-rays to the concentration of the element in the specimen are discussed in the subsequent chapters of this handbook dedicated to specific modes of x-ray spectrometry.II.HISTORYX-rays were discovered in1895by Wilhelm Conrad Ro ntgen at the University of Wu rzburg,Bavaria.He noticed that some crystals of barium platinocyanide,near a dis-charge tube completely enclosed in black paper,became luminescent when the discharge occurred.By examining the shadows cast by the rays.Ro ntgen traced the origin of the rays to the walls of the discharge tube.In1896,Campbell-Swinton introduced a definite target (platinum)for the cathode rays to hit;this target was called the anticathode.For his work x-rays,Ro ntgen received thefirst Nobel Prize in physics,in1901.It was thefirst of six to be awarded in thefield of x-rays by1927.The obvious similarities with light led to the crucial tests of established wave optics: polarization,diffraction,reflection,and refraction.With limited experimental facilities, Ro ntgen and his contemporaries couldfind no evidence of any of these;hence,the des-ignation‘‘x’’(unknown)of the rays,generated by the stoppage at anode targets of the cathode rays,identified by Thomson in1897as electrons.The nature of x-rays was the subject of much controversy.In1906,Barkla found evidence in scattering experiments that x-rays could be polarized and must therefore by waves,but W.H.Bragg’s studies of the produced ionization indicated that they werecorpuscular.The essential wave nature of x-rays was established in1912by Laue, Friedrich,and Knipping,who showed that x-rays could be diffracted by a crystal(copper sulfate pentahydrate)that acted as a three-dimensional diffraction grating.W.H.Bragg and W.L.Bragg(father and son)found the law for the selective reflection of x-rays.In 1908,Barkla and Sadler deduced,by scattering experiments,that x-rays contained com-ponents characteristic of the material of the target;they called these components K and L radiations.That these radiations had sharply defined wavelengths was shown by the diffraction experiments of W.H.Bragg in1913.These experiments demonstrated clearly the existence of a line spectrum superimposed upon a continuous(‘‘White’’)spectrum.In 1913,Moseley showed that the wavelengths of the lines were characteristic of the element of the which the target was made and,further,showed that they had the same sequence as the atomic numbers,thus enabling atomic numbers to be determined unambiguously for thefirst time.The characteristic K absorption wasfirst observed by de Broglie and in-terpreted by W.L.Bragg and Siegbahn.The effect on x-ray absorption spectra of the chemical state of the absorber was observed by Bergengren in1920.The influence of the chemical state of the emitter on x-ray emission spectra was observed by Lindh and Lundquist in1924.The theory of x-ray spectra was worked out by Sommerfeld and others.In1919,Stenstro m found the deviations from Bragg’s law and interpreted them as the effect of refraction.The anomalous dispersion of x-ray was discovered by Larsson in 1929,and the extendedfine structure of x-ray absorption spectra was qualitatively in-terpreted by Kronig in1932.Soon after thefirst primary spectra excited by electron beams in an x-ray tube were observed,it was found that secondaryfluorescent x-rays were excited in any material ir-radiated with beams of primary x-rays and that the spectra of thesefluorescent x-rays were identical in wavelengths and relative intensities with those excited when the specimen was bombarded with electrons.Beginning in1932,Hevesy,Coster,and others investigated in detail the possibilities offluorescent x-ray spectroscopy as a means of qualitative and quantitative elemental analysis.III.GENERAL FEATURESX-rays,or Ro ntgen rays,are electromagnetic radiations having wavelengths roughly within the range from0.005to10nm.At the short-wavelength end,they overlap with g-rays,and at the long-wavelength end,they approach ultraviolet radiation.The properties of x-rays,some of which are discussed in detail in this chapter,are summarized as follows:InvisiblePropagated in straight lines with a velocity of36108m=s,as is lightUnaffected by electrical and magneticfieldsDifferentially absorbed while passing through matter of varying composition, density,or thicknessReflected,diffracted,refracted,and polarizedCapable of ionizing gasesCapable of affecting electrical properties of liquids and solidsCapable of blackening a photographic plateAble to liberate photoelectrons and recoil electronsCapable of producing biological reactions(e.g.,to damage or kill living cells and to produce genetic mutations)Emitted in a continuous spectrum whose short-wavelength limit is determined onlyby the voltage on the tubeEmitted also with a line spectrum characteristic of the chemical elementsFound to have absorption spectra characteristic of the chemical elementsIV.EMISSION OF CONTINUOUS RADIA TIONContinuous x-rays are produced when electrons,or other high-energy charged particles,such as protons or a -particles,lose energy in passing through the Coulomb field of a nucleus.In this interaction,the radiant energy (photons)lost by the electron is called bremsstrahlung (from the German bremsen ,to brake,and Strahlung ,radiation;this term sometimes designates the interaction itself).The emission of continuous x-rays finds a simple explanation in terms of classic electromagnetic theory,because,according to this theory,the acceleration of charged particles should be accompanied by the emission of radiation.In the case of high-energy electrons striking a target,they must be rapidly decelerated as they penetrate the material of the target,and such a high negative accel-eration should produce a pulse of radiation.The continuous x-ray spectrum generated by electrons in an x-ray tube is char-acterized by a short-wavelength limit l min ,corresponding to the maximum energy of the exciting electrons:l min ¼hceV 0ð1Þwhere h is Planck’s constant,c is the velocity of light,e is the electron charge,and V 0is the potential difference applied to the tube.This relation of the short-wavelength limit to the applied potential is called the Duane–Hunt law.The probability of radiative energy loss (bremsstrahlung)is roughly proportional to q 2Z 2T =M 20,where q is the particle charge in units of the electron charge e ,Z is the atomic number of the target material,T is the particle kinetic energy,and M 0is the rest mass of the particle.Because protons and heavier particles have large masses compared to the electron mass,they radiate relatively little;for example,the intensity of continuous x-rays generated by protons is about four orders of magnitude lower than that generated by electrons.The ratio of energy lost by bremsstrahlung to that lost by ionization can be approximated bym 0M 0 2ZT 1600m 0c ð2Þwhere m 0the rest mass of the electron.A.Spectral DistributionThe continuous x-ray spectrum generated by electrons in an x-ray tube (thick target)is characterized by the following features:1.Short-wavelength limit,l min [Eq.(1)];below this wavelength,no radiation isobserved.2.Wavelength of maximum intensity l max ,approximately 1.5times l min ;however,the relationship between l max and l min depends to some extent on voltage,voltage waveform,and atomic number.3.Total intensity nearly proportional to the square of the voltage and the firstpower of the atomic number of the target material.The most complete empirical work on the overall shape of the energy distribution curve for a thick target has been of Kulenkampff(1922,1933),who found the following formula for the energy distribution;I ðv Þdv ¼i aZ v 0Àv ðÞþbZ 2ÂÃdvð3Þwhere I ðn Þd n is the intensity of the continuous x-rays within a frequency range ðn ;n þdv Þ;i is the electron current striking the target,Z is the atomic number of the target material,n 0is the cutofffrequency ð¼c =l min Þabove which the intensity is zero,and a and b are constants independent of atomic number,voltage,and cutoffwavelength.The second term in Eq.(3)is usually small compared to the first and is often neglected.The total integrated intensity at all frequencies isI ¼i ða 0ZV 20þb 0Z 2V 0Þð4Þin which a 0¼a ðe 2=h 2Þ=2and b 0¼b ðe =h Þ.An approximate value for b 0=a 0is 16.3V;thus,I ¼a 0iZV 0ðV 0þ16:3Z Þð5ÞThe efficiency Effof conversion of electric power input to x-rays of all frequencies is given byEff ¼I V 0i ¼a 0Z ðV 0þ16:3Z Þð6Þwhere V 0is in volts.Experiments give a 0¼ð1:2Æ0:1ÞÂ10À9(Condon,1958).The most complete and successful efforts to apply quantum theory to explain all features of the continuous x-ray spectrum are those of Kramers (1923)and Wentzel (1924).By using the correspondence principle,Kramers found the following formulas for the energy distribution of the continuous x-rays generated in a thin target:I ðv Þdv ¼16p 2AZ 2e53ffiffi3p m 0V 0c 3dv ;v <v 0I ðv Þdv ¼0;v >v 0ð7Þwhere A is the atomic mass of the target material.When the decrease in velocity of the electrons in a thick target was taken into account by applying the Thomson–Whiddington law (Dyson,1973),Kramers found,for a thick target,I ðv Þdv ¼8p e 2h 3ffiffiffi3p lm 0c 3Z ðv 0Àv Þdv ð8Þwhere l is approximately 6.The efficiency of production of the x-rays calculated via Kramers’law is given byEff ¼9:2Â10À10ZV 0ð9Þwhich is in qualitative agreement with the experiments of Kulenkampff(Stephenson,1957);for example,Eff ¼15Â10À10ZV 0ð10ÞIt is worth mentioning that the real continuous x-ray distribution is described only ap-proximately by Kramers’equation.This is related,inter alia ,to the fact that the derivation ignores the self-absorption of x-rays and electron backscattering effects.Wentzel (1924)used a different type of correspondence principle than Kramers,and he explained the spatial distribution asymmetry of the continuous x-rays from thin targets.An accurate description of continuous x-rays is crucial in all x-ray spectrometry (XRS).The spectral intensity distributions from x-ray tubes are of great importance for applying fundamental mathematical matrix correction procedures in quantitative x-ray fluorescence (XRF)analysis.A simple equation for the accurate description of the actual continuum distributions from x-ray tubes was proposed by Tertian and Broll (1984).It is based on a modified Kramers’law and a refined x-ray absorption correction.Also,a strong need to model the spectral Bremsstrahlung background exists in electron-probe x-ray microanalysis (EPXMA).First,fitting a function through the background portion,on which the characteristic x-rays are superimposed in an EPXMA spectrum,is not easy;several experimental fitting routines and mathematical approaches,such as the Simplex method,have been proposed in this context.Second,for bulk multielement specimens,the theoretical prediction of the continuum Bremsstrahlung is not trivial;indeed,it has been known for several years that the commonly used Kramers’formula with Z directly sub-stituted by the average "Z ¼P i W i Z i (W i and Z i are the weight fraction and atomic number of the i th element,respectively)can lead to significant errors.In this context,some im-provements are offered by several modified versions of Kramers’formula developed for a multielement bulk specimen (Statham,1976;Lifshin,1976;Sherry and Vander Sande,1977;Smith and Reed,1981).Also,a new expression for the continuous x-rays emitted by thick composite specimens was proposed (Markowicz and Van Grieken,1984;Markowicz et al.,1986);it was derived by introducing the compositional dependence of the continuum x-rays already in the elementary equations.The new expression has been combined with known equations for the self-absorption of x-rays (Ware and Reed,1973)and electron back-scattering (Statham,1979)to obtain an accurate description of the detected continuum radiation.A third problem is connected with the description of the x-ray continuum gen-erated by electrons in specimens of thickness smaller than the continuum x-ray generation range.This problem arises in the analysis of both thin films and particles by EPXMA.A theoretical model for the shape of the continuous x-rays generated in multielement specimens of finite thickness was developed (Markowicz et al.,1985);both composition and thickness dependence have been considered.Further refinements of the theoretical approach are hampered by the lack of knowledge concerning the shape of the electron interaction volume,the distribution of the electron within the interaction volume,and the anisotropy of continuous radiation for different x-ray energies and for different film thickness.B.Spatial Distribution and PolarizationThe spatial distribution of the continuous x-rays emitted by thin targets has been in-vestigated by Kulenkampff(1928).The author made an extensive survey of the intensity at angles between 22 and 150 to the electron beam in terms of dependence on wavelength and voltage.The target was a 0.6-m m-thick Al foil.Figure 1shows the continuous x-ray intensity observed at different angles for voltages of 37.8,31.0,24.0,and 16.4kV filtered by 10,8,4,and 1.33mm of Al,respectively (Stephenson,1957).Curve (a)is repeated as a dotted line near each of the other curves.The angle of the maximum intensity varied from50 for 37.8kV to 65 for 16.4kV.Figure 2illustrates the intensity of the continuous x-rays observed in the Al foil for different thicknesses as a function of the angle for a voltage of 30kV (Stephenson,1957).The theoretical curve is from the theory of Scherzer (1932).The continuous x-ray intensity drops to zero at 180 ,and although it is not zero at 0 as the theory of Scherzer predicts,it can be seen from Figure 2that for a thinner foil,a lower intensity at 0 is obtained.Summarizing,it appears that the intensity of the con-tinuous x-rays emitted by thin foils has a maximum at about 55 relative to the incident electron beam and becomes zero at 180 .The continuous radiation from thick targets is characterized by a much smaller anisotropy than that from thin targets.This is because in thick targets the electrons are rarely stopped in one collision and usually their directions have considerable variation.The use of electromagnetic theory predicts a maximum energy at right angles to the in-cident electron beam at low voltages,with the maximum moving slightly away from perpendicularity toward the direction of the elctron beam as the voltage is increased.In general,an increase in the anisotropy of the continuous x-rays from thick targets is ob-served at the short-wavelength limit and for low-Z targets (Dyson,1973).Figure1Intensity of continuous x-rays as a function of direction for different voltages.(Curve (a)is repeated as dotted line.)(From Stephenson,1957.)Continuous x-ray beams are partially polarized only from extremely thin targets;the angular region of polarization is sharply peaked about the photon emission angle y ¼m 0c 2=E 0,where E 0is the energy of the primary electron beam.Electron scattering in the target broadens the peak and shifts the maximum to larger angles.Polarization is defined by (Kenney,1966)P ðy ;E 0;E n Þ¼d s ?ðy ;E 0;E n ÞÀd s kðy ;E 0;E n Þd s ?ðy ;E 0;E n Þþd s kðy ;E 0;E n Þð11Þwhere an electron of energy E 0radiates a photon of energy E n at angle y ;d s ?ðy ;E 0;E n Þand d s kðy ;E 0;E n Þare the cross sections for generation of the continuous radiation with the electric vector perpendicular (?)and parallel (k )to the plane defined by the incident electron and the radiated photon,respectively.Polarization is difficult to observe,and only thin,low-yield radiators give evidence for this effect.When the electron is relativistic before and after the radiation,the electrical vector is most probably in the ?direction.Practical thick-target Bremsstrahlung shows no polarization effects whatever (Dyson,1973;Stephenson,1957;Kenney,1966).V.EMISSION OF CHARACTERISTIC X-RA YSThe production of characteristic x-rays involves transitions of the orbital electrons of atoms in the target material between allowed orbits,or energy states,associated with ionization of the inner atomic shells.When an electron is ejected from the K shell by electron bombardment or by the absorption of a photon,the atom becomes ionized and the ion is left in a high-energy state.The excess energy the ion has over the normal state of the atom is equal to the energy (the binding energy)required to remove the K electron to a state of rest outside the atom.If this electron vacancy is filled by an electron coming from an L level,the transition is accompanied by the emission of an x-ray line known as the K a line.This process leaves a vacancy in the L shell.On the other hand,if the atom contains sufficient electrons,the K shell vacancy might be filled by an electron coming from an M level that is accompanied by the emission of the K b line.The L or M state ions that remain may also give rise to emission if the electron vacancies are filled by electrons falling from furtherorbits.Figure 2Intensity of continuous x-rays as a function of direction for different thicknesses of the A1target together with theoretical prediction.(From Stephenson,1957.)A.Inner Atomic Shell IonizationAs already mentioned,the emission of characteristic x-ray is preceded by ionization of inner atomic shells,which can be accomplished either by charged particles(e.g.,electrons, protons,and a-particles)or by photons of sufficient energy.The cross section for ion-ization of an inner atomic shell of element i by electrons is given by(Bethe,1930;Green and Cosslett,1961;Wernisch,1985)Q i¼p e4n s b s ln UUEc;ið12Þwhere U¼E=E c;i is the overvoltage,defined as the ratio of the instantaneous energy of the electron at each point of the trajectory to that required to ionize an atom of element i,E c;i is the critical excitation energy,and n s and b s are constants for a particular shell: s¼K:n s¼2;b s¼0:35s¼L:n s¼8;b s¼0:25The cross section for ionization Q i is a strong function of the overvoltage,which shows a maximum at Uffi3–4(Heinrich,1981;Goldstein et al.,1981).The probability(or cross section)of ionization of an inner atomic shell by a charged particle is given by(Merzbacher and Lewis,1958)s s¼8p r2q2f sZ Z sð13Þwhere r0is the classic radius of the electron equal to2.818610À15m,q is the particle charge,Z is the atomic number of the target material,f s is a factor depending on the wave functions of the electrons for a particular shell,and Z s is a function of the energy of the incident particles.In the case of electromagnetic radiation(x or g),the ionization of an inner atomic shell is a result of the photoelectric effect.This effect involves the disappearance of a ra-diation photon and the photoelectric ejection of one electron from the absorbing atom, leaving the atom in an excited level.The kinetic energy of the ejected photoelectron is given by the difference between the photon energy h n and the atomic binding energy of the electron E c(critical excitation energy).Critical absorption wavelengths(Clark,1963)re-lated to the critical absorption energies(Burr,1974)via the equation l(nm)¼1.24=E(ke V) are presented in Appendix I.The wavelenghts of K,L,M,and N absorption edges can also be calculated by using simple empirical equations(Norrish and Tao,1993).For energies far from the absorption edge and in the nonrelativistic range,the cross section t K for the ejection of an electron from the K shell is given by(Heitler,1954)t K¼32ffiffiffi2p3p r20Z5ð137Þm0c2hv7=2ð14ÞEquation(14)is not fully adequate in the neighborhood of an absorption edge;in this case,Eq.(14)should be multiplied by a correction factor f(X)(Stobbe,1930):fðXÞ¼2p D hv1=2eÀ4X arccot X1ÀeÀ2p Xð15ÞwhereX¼DhvÀD1=2ð15aÞwithDffi12ðZÀ0:3Þ2m0c2ð137Þ2ð15bÞWhen the energy of the incident photon is of the order m0c2or greater,relativistic cross sections for the photoelectric effect must be used(Sauter,1931).B.Spectral Series in X-raysThe energy of an emission line can be calculated as the difference between two terms,each term corresponding to a definite state of the atom.If E1and E2are the term values re-presenting the energies of the corresponding levels,the frequency of an x-ray line is given by the relationv¼E1ÀE2hð16ÞUsing the common notations,one can represent the energies of the levels E by means of the atomic number and the quantum numbers n,l,s,and j(Sandstro m,1957):E Rh ¼ðZÀS n;lÞ2n2þa2ðZÀd n;l;jÞ2n31lþ12À34n!Àa2ðZÀd n;l;jÞ4n3jðjþ1ÞÀlðlþ1ÞÀsðsþ1Þ2lðlþ12Þðlþ1Þð17Þwhere S n;l and d n;l;j are screening constants that must be introduced to correct for the effect of the electrons on thefield in the atom,R is the universal Rydberg constant valid for all elements with Z>5or throughout nearly the whole x-ray region,and a is thefine-structure constant given bya¼2p e2hcð17aÞThe theory of x-ray spectra reveals the existence of a limited number of allowed transitions;the rest are‘‘forbidden.’’The most intense lines create the electric dipole ra-diation.The transitions are governed by the selection rules for the change of quantum numbers:D l¼Æ1;D j¼0orÆ1ð18ÞThe j transition0?0is forbidden.According to Dirac’s theory of radiation(Dirac,1947),transitions that are forbidden as dipole radiation can appear as multipole radiation(e.g.,as electric quadrupole and magnetic dipole transitions).The selection rules for the former areD l¼0orÆ2;D j¼0;Æ1;orÆ2ð19ÞThe j transitions0?0,12?12,and0$1are forbidden.The selection rules for magnetic dipole transitions areD l¼0;D j¼0orÆ1ð20ÞThe j transition0?0is forbidden.The commonly used terminology of energy levels and x-ray lines is shown in Figure3.A general expression relating the wavelength of an x-ray characteristic line with the atomic number of the corresponding element is given by Moseley’s law(Moseley,1914): 1¼kðZÀsÞ2ð21Þlwhere k is a constant for a particular spectral series and s is a screening constant for the repulsion correction due to other electrons in the atom.Moseley’s law plays an important role in the systematizing of x-ray spectra.Appendix II tabulates the energies and wave-lengths of the principal x-ray emission lines for the K,L,and M series with their ap-proximate relative intensities,which can be defined either by means of spectral line peak intensities or by area below their intensity distribution curve.In practice,the relativeFigure3Commonly used terminology of energy levels and x-ray lines.(From Sandstro m,1957.)intensities of spectral lines are not constant because they depend not only on the electron transition probability but also on the specimen composition.Considering the K series,the K a fraction of the total K spectrum is defined by the transition probability p K a,which is given by(Schreiber and Wims,1982)p K a¼IðK a1þK a2ÞIðK a1þK a2ÞþIðK b1þK b2Þð22ÞWernisch(1985)proposed a useful expression for the calculation of the transition prob-ability p K a for different elements:p K a;i¼1:052À4:39Â10À4Z2i;11Z i190:896À6:575Â10À4Z i;20Z i291:0366À6:82Â10À3Z iþ4:815Â10À5Z2i;30Z2i608<:ð23ÞFor the L level,split into three subshells,several electron transitions exist.The transition probability p L a,defined as the fraction of the transitions resulting in L a1and L a2radiation from the total of possible transitions into the L3subshell,can be calculated by the ex-pression(Wernisch,1985)p L a;i¼0:944;39Z i44À4:461Â10À1þ5:493Â10À2Z iÀ7:717Â10À4Z2iþ3:525Â10À6Z3i;45Z i828<:ð24ÞRadiative transition probabilities for various K and L x-ray lines(West,1982–83)are presented in detail in Appendix III.The experimental results,together with the estimated 95%confidence limits,for the relative intensity ratios for K and L x-ray lines for selected elements with atomic number from Z¼14to92have been reported by Stoev and Dlouhy (1994).The values are in a good agreement with other published experimental data.Because the electron vacancy created in an atom by charged particles or electro-magnetic radiation has a certain lifetime,the atomic levels E1and E2[Eq.(16)]and the characteristic x-ray lines are characterized by the so-called natural widths(Krause and Oliver,1979).The natural x-ray linewidths are the sums of the widths of the atomic levels involved in the transitions.Semiempirical values of the natural widths of K,L1,L2and L3 levels,K a1and K a2x-ray lines for the elements10Z110are presented in Appendix IV.Uncertainties in the level width values are from3%to10%for the K shell and from 8%to30%for the L subshell.Uncertainties in the natural x-ray linewidth values are from 3%to10%for K a1;2.In both cases,the largest uncertainties are for low-Z elements (Krause and Oliver,1979).C.X-ray SatellitesA large number of x-ray lines have been reported that do notfit into the normal energy-level diagram(Clark,1955;Kawai and Gohshi,1986).Most of the x-ray lines,called satellites or nondiagram lines,are very weak and are of rather little consequence in ana-lytical x-ray spectrometry.By analogy to the satellites in optical spectra,it was supposed that the origin of the nondiagram x-ray lines is double or manyfold ionization of an atom through electron impact.Following the ionization,a multiple electron transition results in emission of a single photon of energy higher than that of the characteristic x-rays.The majority of nondiagram lines originate from the dipole-allowed deexcitation of multiply。

In this part

In this part
2
In the single antenna Additive White Gaussian Noise (AWGN) channel, 1 bit per second per hertz can be
achieved with every 3dB increase at high SNR. 3 By realistic, we mean models representing our state of knowledge of reality which might be different from reality.
and related documents can be downloaded at http://www.fl
January 14, 2004 SUBMITTED VERSION
2
I. Introduction The problem of modelling channels is crucial for the efficient design of wireless systems [4]. The wireless channel suffers from constructive/destructive interference signaling [5] and yields a randomized channel with certain statistics to be discovered. Recently ([6], [7]), the need to increase spectral efficiency has motivated the use of multiple antennas at both the transmitter and the receiver side. Hence, in the case of i.i.d Gaussian entries of the MIMO link and perfect channel knowledge at the receiver, it has been proved [8] that the ergodic capacity increase is min(nr ,nt ) bits per second per hertz for every 3dB increase (nr is the number of receiving antennas and nt is the number of transmitting antennas) at high Signal to Noise Ratio (SNR)2 . However, for realistic3 channel models, results are still unknown and may seriously put into doubt the MIMO hype. As a matter of fact, the actual design of efficient codes is tributary of the channel model available: the transmitter has to know in what environment the transmission occurs in order to provide the codes with the adequate properties: as a typical example, in Rayleigh fading channels, when coding is performed, the hamming distance (also known as the number of distinct components of the multi-dimensional constellation) plays a central role whereas maximizing the Euclidean distance is the commonly approved design criteria for Gaussian channels (see Giraud and Belfiore [9] or Boutros and Viterbo [10]). As a consequence, channel modelling is the key in better understanding the limits of transmissions in wireless and noisy environments. In particular, questions of the form: ”what is the highest transmission rate on a propagation environment where we only know the mean of each path, the variance of each path and the directions of arrival?” are crucially important. It will justify the use (or not) of MIMO technologies for a given state of knowledge. Let us first introduce the modelling constraints. We assume that the transmission takes place between a mobile transmitter and receiver. The transmitter has nt antennas and the receiver has nr antennas. Moreover, we assume that the input transmitted signal goes through a time invariant linear filter channel. Finally, we assume that the interfering noise

细化用于将二维图像应用于三维模型的局部参数化[发明专利]

细化用于将二维图像应用于三维模型的局部参数化[发明专利]

专利名称:细化用于将二维图像应用于三维模型的局部参数化专利类型:发明专利
发明人:E·盖姆巴雷特,V·金,周擎楠,M·E·于梅
申请号:CN201811198558.X
申请日:20181015
公开号:CN110176072A
公开日:
20190827
专利内容由知识产权出版社提供
摘要:某些实施例涉及细化将二维(“2D”)图像应用于三维(“3D”)模型的局部参数化。

例如,基于目标网格区域的一个或多个特征来选择特定参数化初始化过程。

从该参数化初始化过程生成2D图像的初始局部参数化。

计算初始局部参数化的质量度量,并且修改局部参数化以改进质量度量。

通过根据经修改的局部参数化将来自2D图像的图像点应用于目标网格区域来修改3D模型。

申请人:奥多比公司
地址:美国加利福尼亚州
国籍:US
代理机构:北京市金杜律师事务所
更多信息请下载全文后查看。

图着色下的树顶点邻集的行为

图着色下的树顶点邻集的行为
注意 到 , . 厂是 , 3的一个 O 全 等着 色 .图 lb T ()中 的树 承认 一 个优 化全 着 色 g 使 得 , c () () [ 6. 1c = = 1 ] 图 ()中树 日 的着色 则包 含 了更多 的信 息.首先 ,树 日 承 认 一个 ,
(;,) 112 饱和全着色 h 5= 。) 2v =[6. , ] ( ≠c () 1 ]接下来, h满足 () () , ≠ 对不
摘要:图着色下 的树顶点邻集的行为展示 了有趣的问题 . 该文证 明了树顶点邻集的行为对一些 图全着色有着非常强的性质,或多 或少地 揭示了树顶点邻集 的行为与着色 猜想的一些关系. 作 者期望应用这种邻集的行 为去深刻地研 究图着色 问题 .
关键词:图着色 ;可区分着 色;树.
M R(0 0 2 0 1主题分类: 5 1 中图分类号: 1. 文献标识码: 0C 5 021 4 A
文章编号:10—982 1) —6 0 0339 ( 10 5  ̄1 0 2
1 引言和术语
确定 图的着色 数是 NP困难 问题 [1】在 图的全 着色 中, 够看到 一个现象 : , 61 ,. 能 设 是图 G 的一个正常全着色, 对每一个顶点 ∈ ( )总有 l ( ) U G, { u :V∈E G ) { ()l a u+1 f v ( )u ,札)=d () , 其 中 d () 示顶 点 t的度数 .因而 ,我 们称 ,为 图 G 的一个度 饱和 全着 色 .那 么,对 于 au 表 t 其他的图着色我们能够发现相似于上述现象的性质,且它们与着色之间存在何种关系 ? 换 句话说, 我们期望认识那些 由许多个体 ( 顶点) 组成的庞大而复杂的系统 ( . 图)这种思路早 已 被应用于图论的各种着色研究和 网络结构分析 [ 1112 . 9 0 380 - ,,,] 本文论及的图均为简单、连通、有限图,且采用标准的图论术语和记号 [ 3 除特别声 2] -. 明外 ,文 中出现 的集合 均为 非负整 数集合 .为简便 起见 ,记 号 [t ] ?, 表示整 数 集合 { m + Tn m, 1… ,}其 中 礼>m 0 集合 s的基 数为 = 后 我 们也称 为 k集 .集 合 s 的最 大和 , n, . , 最 小 者分别 记 为 ma( ) mi() 个具 有 P个 顶 点和 q条边 的 图 G 叫做 (,)图,记 xS 和 nS .一 Pq P=『 a l P l1在一个图 G 中,我们把一度顶点叫做叶子,所有度数为 d的顶点集 v( )或 = G . 合 记为 ( . 而 ,图 G 中度 数为 d的顶 点 的个 数 为 n ( = fdG) G)从 dG) v( 1 . 定义 1 图 G的一个正常 k 着色 .是将集合 s v a u a 划分为 k 厂 ( ) ) E( 个相互不交的 非 空子集 s ,z… , , 1s , 使得 每一 个子 集 中的任何 两个 元 素在 图 G 中既不相 邻 ,也 不关 联 .对 ∈ , 称 着 有色 i记 作 fx = i 使 得 图 G 承 认正 常 k着 色 的最小 的 叫做 G , () . 的e 着色数,记为 )( ) 简记 fS ={ () ∈s , =m xfS)或 =ma() (G . () , : ) a(( ) , xf.

两类图的连续边着色的开题报告

两类图的连续边着色的开题报告

两类图的连续边着色的开题报告引言:图着色是图论中经典的问题之一,其中边着色问题是指给定一个无向图,对图中所有的边进行涂色,使得相邻的边颜色不同,并使用最少的颜色。

在图的连续边着色问题中,相邻的边按照一定顺序排列,需要求出至少需要涂多少种颜色才能满足相邻边颜色不同的条件。

本篇开题报告主要探讨两类图的连续边着色问题:树和圆方图。

首先介绍背景和意义,然后阐述问题描述和已有的相关研究,最后提出研究的主要目标和实现步骤。

背景:图着色问题是图论中的经典问题,是许多实际问题的抽象及其应用的基础,如地图着色、课表安排、调度问题等等。

边着色问题是应用领域中的经典问题之一,具有实际意义。

圆方图是一类特殊的图,在各种应用场景中都有着重要地位,如电路设计、图形表示、生物学等等。

研究意义:在实际应用中,能够有效地解决连续边着色问题可以提高工作效率和解决实际问题,如保证电路的正确设计和分析。

因此,研究连续边着色问题具有重要的理论和应用意义。

问题描述:树的连续边着色问题:给定一棵树,求其连续边着色数。

圆方图的连续边着色问题:给定一个圆方图,求其连续边着色数。

已有的相关研究:对于树的连续边着色问题,Gallian给出了一种线性时间求解方法,即一个树可由其一棵DFS树唯一确定,所以只需要对其DFS树进行边着色即可。

而对于圆方图的连续边着色问题,目前尚无明确的解决方法。

主要研究目标和实现步骤:树的连续边着色问题已有较为严谨的研究成果,因此本文的主要研究目标是解决圆方图连续边着色问题,具体的实现步骤如下:1. 对圆方图建模:选择合适的模型表示圆方图,便于对其进行求解。

2. 研究圆方图的特性:分析圆方图的特殊性质,探究其连续边着色问题的解决方法。

3. 设计算法并实现:在已有的研究基础上,设计一种有效的算法并进行实现,运用数学计算和计算机编程处理问题。

4. 对算法进行评价和应用:在实验数据下评价算法的效果,并将算法应用于实际问题,验证算法的实用性和有效性。

史上最全的语义SLAM论文笔记整理

史上最全的语义SLAM论文笔记整理

史上最全的语义SLAM论文笔记整理语义信息的挖掘和应用,是目前SLAM问题中的研究热点,本文从特征选择、动态SLAM、单目SLAM的尺度恢复、long-term的定位、提高定位精度等方面介绍了当前最新的一些论文中的研究工作。

一:语义信息于特征选择二:语义信息用于动态slam三:语义信息用于单目SLAM的尺度恢复四:语义信息用于long-term定位五:语义信息用于提高定位精度一语义信息用于特征选择1.1 选择感兴趣区域的点SalientDSO:Bringing Attention to Direct Sparse Odometry (直接法)本文是在DSO的基础上,改变了跟踪点的选取策略。

DSO是在图像上均匀选取点,如图a所示,本文是在图像上的感兴趣区域上选取点,下面具体介绍:a. 显著性图获取分为两步:首先使用SalGAN网络提取图像中的显著性图,如图e 所示,显著性定义为人类对图像中每个像素的关注量,颜色越接近红色表示显著性越高。

接着使用PSPNet进行获取语义分割的结果,如图 f 所示。

利用语义分割的结果对显著性图进行filter,即重新调整每个像素点的显著性得分,目的是降低无信息区域的显著性得分,例如墙,天花板和地板等。

论文中为每种语义类别设置一个经验权重 Wc,根据得到的语义分割图 Cj,对显著性图 Sj 的每个像素点乘以该像素点对应类别的经验权重,得到 Sj [weighted] :这还不是最终的显著性图,论文中提到为了平滑和为每种类别维持一个一致的显著性图,每个像素的显著性得分被图中所有该类像素的显著性得分的中值代替,得到最终的显著性图Sj [final] :b. 点的选择策略首先,将图像分割为K×K的patch, 对于每个patch,计算显著性中值,显著性中值作为该patch的被选择的权重,如果该patch的显著性高,则被选择的概率越大,然后在选择的patch内根据梯度值进一步选择点,可参考DSO的策略。

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Scattered Associations in Object-Oriented ModelingKasper ØsterbyeNorwegian Computing Center, Postboks 114 Blindern, 0314 OSLO, Norway.Kasper.Osterbye@nr.noJohnny OlssonWM-data, Hermodsvej 22, 8230 Åbyhøj, Århus, Denmark,jooln@Abstract. The popularity of UML has bought a new abstraction mechanism, as-sociations, into the realm of object-oriented programming. Associations areused in the analysis and design phases of software development. We have usedUML in a paper and pencil redesign of a large existing system where the goalwas to move from a relational model to an object-oriented model. Our experi-ence has shown us that the concept of associations in UML is very useful, butalso that there are several shortcomings regarding their expressive power. Scat-tered associations are global association structures whose definition is scatteredthroughout a UML diagram. We have identified two kinds of scattered associa-tions. A structural association is a scattered association that defines the struc-ture of a set of objects, and a co-variant association is a scattered associationthat describes pair-wise parallel inheritance hierarchies.1IntroductionThe graphical modeling language UML [8] has brought associations into object-oriented modeling, and seem to complement the existing modeling capabilities well. A hallmark of object-oriented modeling has been its closeness to human conceptualiza-tion, with objects representing nouns, and methods/functions/procedures representing verbs. Associations can be said to model the word class, transitive verbs, with one object being the subject and the other the object, e.g. WM-data employs Johnny. When we talk about the association in general, we will often use a substantiated verb, e.g. employment.In a model of a large system concerning administration of students, student pro-grams, teaching, exam planning, and public scholarships, we have encountered the situation that the same association ties together elements of different types. We call the situation “scattered associations”, as the typical characteristic is that the same as-sociation type is used many places in the same class diagram.The concrete modeling case from which the experiences are drawn will be pre-sented first, followed by a discussion of the notion of scattered associations. Two dif-ferent types of scattered associations are identified, structural and co-variant scatter-ing. The general principles behind these two are presented, and several possible solutions are discussed.1.1BackgroundIntroducing associations into object-oriented modeling was first done in [5]. The un-derlying programming language DSM [7], which supports relations1been used very 1 In this paper we use the term association as a synonym for relation.successfully for several cases. Since then, associations have come into object-oriented analysis and design notations. However, there seem to have been little research into the deeper semantic implications of doing this, and the list of open issues is fairly long:•Computed associations. Associations are a useful abstraction mechanism. How-ever, sometimes it is too costly to maintain explicit data structure for a given asso-ciation. The associated object must then be found through computation. It is an open question how to specify computed associations. Can a declarative model be used, or must computed associations be hand-coded?•Association objects or extended tuples. Should an association be considered an object2 with unique id, or a tuple that derives its identity from the objects it is as-sociated with. The practical implication is whether two objects can be associated more than once through the same association (using different association objects).Both models seem to have their merits.•Associations as first class objects. If we introduce association objects as above, it would seem a natural extension to associate state and behavior to association ob-jects. In [2] this idea is pursued in great length.•Inheritance. Can associations be specialized? There are several open issues in giving this a useful interpretation, both at the practical, conceptual, and at the theo-retical level.•Constraints. In many situations associations are not independent of each other, e.g.if X is associated to Y, and Y to Z, then X should be associated to Z.•Property propagation. This issue is discussed in [6]and deals with the practical observation that properties of one association in some situation propagate across the association, and become a property of the associated property as well.Our experiences using UML in a real case, made the list grow rather than shrink. A secondary purpose of this paper is to bring this long research agenda to the attention of a Scandinavian with a long tradition in modeling.2The STADS rule checkerThe case deals with one aspect of a large generic administration system called STADS3. The selected part deals with describing educations and the courses and ex-ams that constitute these educations. It deals with describing the concrete career of in-dividual students, and, it deals with describing the rules that make up the passing crite-ria and structural constraints of the educations themselves. The analysis and design of the STADS system is further described in [4].To provide a framework for describing educations, a number of concepts have been defined, ranging from government regulations and degrees, to courses and indi-vidual exams. Each of these concepts can be described using rules. For an individual exam, the rule might say something about the passing grade, for a course it might say2 An association is declared between classes, but associate objects. Sometimes, like here, we need to refer to the substance that binds two concrete objects together. We will call this the association object.3 STADS is a short for ’STudieADminstrationsSystem’ (Education Administration System in Danish). The system concerns administration of students, student programs, teaching, exam planning, and public scholar-ships. STADS has been developed since 1993. The first universities started to use the system in 1996. As the system gets more developed, more universities are taking in into use. The system is implemented on a Oracle 7 platform and works for UNIX as well as VMSsomething about the minimum grade average needed for its constituent grades. The rules associated with a degree might state that a certain number of courses must be passed, or that in general all exams must be passed with a certain minimum grade. The career of a specific student contains the courses actually followed by the student. Most educations are structured to have required as well as voluntary courses, and the career of each student therefore varies in structure as well as in grade results, even if theyThey have been used to describe the association between the different descriptive elements (degrees etc.), the association between the corresponding career elements, and to associate career elements with their descriptions. Associations are also used to associate descriptive elements to their rules, and to associate individual dispensations from the rules with career elements for the student who has the dispensation. The above diagram gives a fuller picture of the model.In the diagram, there is a single association between Career element and Descrip-tion element. This association should be interpreted as a pair-wise association between specializations within the two hierarchies. That is, it is interpreted as associating Ca-reer programs (the program followed by a specific student) to programs as offered by the educational institution. The association is repeated between the Career program element and the program element as an illustration.It is worth to notice that the career elements are not instances of the descriptive elements. The reason is that the descriptive elements are tangible objects in the do-main of the system. The users of the system are used to having such things as course descriptions in their problem domain. This is a good example of the object-oriented analysis pattern “item-description” described in [1].2.1ExperiencesA practical matter arose though, which was one of naming. Each association on the UML diagram must be given a name to be implemented. To come up with meaningful names for each association was very hard, and in the end we just settled for a simple systematic naming involving the first part of each participating class. We believe, however, that the problem has a more profound explanation. Throughout the work with the STADS system, we were lacking a proper object that captured the structureof a program and a career. Many of the associations in both career and description are structural associations, and a compelling idea is to interpret these unnamable associa-tions as making up a single association (one for career and one for description), which exactly defines the structure.A program element can be part of both a program and a group (which is a model-ing concept used to provide some reuse of rules and descriptions). This leaves us with a typing problem because we cannot tell the type of the object when we traverse the association.An implementation of associations should allow us to move from one object to the other by the role names specified as part of the association definition. The association binding career elements to program elements raised the problem that the return type of the roles is different for each matching pair of specialized element type.3Scattered associationsAs mentioned in the previous section, our overall experience of using UML for mod-eling was a mixed blessing. There are two points where we have encountered prob-lems. In both the description part and the career part of the model, the different ele-ments make up a hierarchical structure. We need a way to view this structure as a single property of the model, but as it is represented in the UML diagram, the struc-ture declaration is scattered all over the diagram.Another problem is the association that binds career elements to their associated description elements. This association needs to be refined (not done in the diagram) to specify that career programs are associated to programs, that career groups are associ-ated to groups, that is, the two hierarchies are parallel. Two problems arise from this. First it would be incredible tiresome to create all the actual associations in the dia-gram. Second, and more serious, the semantics of this is not quite simple to work out. Again, we believe that this pattern of associations across parallel inheritance hierar-chies is a reoccurring pattern, and it is addressed in section 3.2.3.1Structural associationsThe career elements are organized into a career structure, as are the description ele-ments. In both cases this structure is made up from a number of individual associa-tions. However, it was our experience that we needed to be able to talk about the structure as such, as it in many situations provide the right level of abstraction. At an intuitive level, we feel that the structural association can be decomposed into the indi-vidual associations. Or rather that one is able to talk about the same association at dif-ferent levels.We feel that the structure is in nature a scattered association. Our solution is to as-sume that an association can be declared more than once in a class diagram. This mean that the structure relation maps exams to programs, programs to educations etc. This works well in most cases, but a problem arise when the same object can partici-pate two different places in the structure, e.g. an exam can be part of a program or a group.To solve the problem, we extend the simple Relation definition to allow several clauses as in:Relation EducationSctructure is(element: Education ↔ regulation:Regulation OR super_education: Education) AND (program: Program ↔ group: Education 0:M) AND(element: ProgElement ↔ program: Program OR group: Group 0:M)This states that an education is associated to either a regulation, or to another educa-tion (itself being a sub-education). In the actual STADS system the OR’s are actually not exclusive, as the education structure is hierarchical, though not tree structured (it is a directed acyclic graph). The above seem like a solution at the moment, but the in-troduction of boolean connectives indicates to us that the final solution has not yet been reached.3.2Covariant associationsThe exact semantics of the association between career elements and descriptive ele-ments is so that it does not associate arbitrary career elements to arbitrary description elements. This association is indeed the association that establishes that the two hier-archies are parallel. The problems relate to uncertain semantics of associations in con-nection with specialization. If we assume that association A associate class C to class X. Then one will expect that A also associate a subclass D of C to the X class. If Y and Z are specializations of X, then A might associate a D object to both an X, Y, and Z object. This seems a reasonable way to interpret the interplay between an associa-tion and class inheritance.But in the case of parallel hierarchies, the situation is such that A should be con-strained in some way so that the general element to element association will not en-able all possible associations, but can be restricted in the proper way.As before, the fundamental question is if this the restricted association is a new as-sociation, or if it is a restriction of the existing one. However, it seems that in defining a new association for each parallel pair the general picture is easily lost, e.g. the new relation need to specify that it is a elaboration of the general one. Another problem is that the general one still exists but is useless, as all concrete associations will be done using new associations. We therefore believe that it is most promising to find a way to restrict the existing association. That way we can maintain the view that there is really only one association, which “does the right thing”.The title of the section indicates a hybrid solution, which has its origin in virtual classes as known from BETA [3]. If the general relation is described as associating a virtual career element to a virtual description element, then new concrete associations can bind these appropriately. In a concrete syntax, this might be:Relation Description(career: virtual CareerElement ; description: virtual DescriptionElement). Without full understanding of what specialization of associations is, Description could then be specialized as:Relation ExamDescription is-a Description(carrer: bindTo CareerExam; description: bindTo Exam)The term covariant in the title refers to the idea that both association ends are special-ized simultaneously.In [7] access methods are automatically compiled into the classes that are associ-ated. This means that a method for accessing the description will be compiled into the CareerElement class. If association ends are virtual, this should imply that the returntype of the description access method is virtual, and that the ExamDescription asso-ciation overides this vitual to return an Exam element rather than a DescriptionEle-ment. The notion of virtual classes seem to provide an elegant solution for covariant associations. However, no appropriate solution has been found for notations that does not support virtual classes.4ConclusionDuring our work in modeling the STADS rule checker using the UML diagram nota-tion, we found associations to be quite natural. However, we came across two situa-tions in which the notation seemed in adequate in its support for associations. The common theme in these two situations was that a number of associations seemed really to be only constituents of a larger association, which could not be expressed in UML. The two situations were named structural associations and covariant associa-tion, and their general term is scattered associations. For both kinds of associations linguistic solutions were proposed.In the case of structural associations, we propose to expand the notion of a binary association, so that the same association can be used to associate different kinds of objects. The concrete proposal introduced boolean connectives. While this seem to solve the problem, it is not quite satisfactory, as we believe that a linguistic solution which captures the solution in a combination of aggregation and inheritance will ulti-mately prove more elegant.Such a solution present itself for covariant association. Here the problem was how to relate parallel hierarchies. Here a proper linguistic solution did arise. By declaring the endpoints of an association virtual, these endpoints can then be further bound in specializations of a general association.However, it is important to point out that specialization of associations has no known well-defined semantics. We believe that work like this may aid in laying down the experience needed to define such semantics.Acknowledgements. This work has been supported by the Danish Center for Infor-mation Technology in the subproject “COT project 74.1”.5References[1] P. Coad, Object-Oriented Patterns, 1992, Communications of the ACM, September 1992, pp. 152-159.[2] B. B. Kristensen. ‘‘Complex Associations: Abstractions in Object-Oriented Modeling’’. Proceedings ofOOPSLA’94.[3] O. L. Madsen, B. Møller-Pedersen and K. Nygaard. Object-Oriented Programming in the BETA Pro-gramming language. Addison-Wesley, 1993.[4] J. Olsson, K. H. Nielsen, K. Østerbye, A. R. Lassen. Objektorienteret Analyse og Design af udvalgtedele af STADS. Technical report from Center for Object Technology, Dansk Teknologisk Institut, Teknologiparken, DK-8000 Aarhus C, DENMARK, COT/4-03. (In Danish)[5] J. Rumbaugh. Relations as Semantic Constructs in an Object Oriented Language, Proceedings ofOOPSLA’87. Pages 466-481.[6] J. Rumbaugh. Controlling Propagation of Operations using Attributes on Relations, Proceedings ofOOPSLA’88. Pages 285-296.[7] A. V. Shan, J. Rumbaugh, J. H. Hamel, and R. A. Borsari. DSM: An Object-Relationship ModelingLanguage. Proceedings of OOPSLA’89. Pages 191-202.[8] UML resource center, /uml/.。

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