1. Introduction Computational Complexity and

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METSIM软件基础教程

METSIM软件基础教程
3. Pilot plant data evaluation. 半工业试验工厂数据评估
4. Full scale plant design calculations. 全比例工厂设计计算
5. Operating plant improvement studies. 生产运行工厂改进研究
6. Actual plant operations. 现行工厂的操作
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4. METSIM allows evaluation of operating techniques and anticipation of potential problems.

ABSTRACT Progressive Simplicial Complexes

ABSTRACT Progressive Simplicial Complexes

Progressive Simplicial Complexes Jovan Popovi´c Hugues HoppeCarnegie Mellon University Microsoft ResearchABSTRACTIn this paper,we introduce the progressive simplicial complex(PSC) representation,a new format for storing and transmitting triangu-lated geometric models.Like the earlier progressive mesh(PM) representation,it captures a given model as a coarse base model together with a sequence of refinement transformations that pro-gressively recover detail.The PSC representation makes use of a more general refinement transformation,allowing the given model to be an arbitrary triangulation(e.g.any dimension,non-orientable, non-manifold,non-regular),and the base model to always consist of a single vertex.Indeed,the sequence of refinement transforma-tions encodes both the geometry and the topology of the model in a unified multiresolution framework.The PSC representation retains the advantages of PM’s.It defines a continuous sequence of approx-imating models for runtime level-of-detail control,allows smooth transitions between any pair of models in the sequence,supports progressive transmission,and offers a space-efficient representa-tion.Moreover,by allowing changes to topology,the PSC sequence of approximations achieves betterfidelity than the corresponding PM sequence.We develop an optimization algorithm for constructing PSC representations for graphics surface models,and demonstrate the framework on models that are both geometrically and topologically complex.CR Categories:I.3.5[Computer Graphics]:Computational Geometry and Object Modeling-surfaces and object representations.Additional Keywords:model simplification,level-of-detail representa-tions,multiresolution,progressive transmission,geometry compression.1INTRODUCTIONModeling and3D scanning systems commonly give rise to triangle meshes of high complexity.Such meshes are notoriously difficult to render,store,and transmit.One approach to speed up rendering is to replace a complex mesh by a set of level-of-detail(LOD) approximations;a detailed mesh is used when the object is close to the viewer,and coarser approximations are substituted as the object recedes[6,8].These LOD approximations can be precomputed Work performed while at Microsoft Research.Email:jovan@,hhoppe@Web:/jovan/Web:/hoppe/automatically using mesh simplification methods(e.g.[2,10,14,20,21,22,24,27]).For efficient storage and transmission,meshcompression schemes[7,26]have also been developed.The recently introduced progressive mesh(PM)representa-tion[13]provides a unified solution to these problems.In PM form,an arbitrary mesh M is stored as a coarse base mesh M0together witha sequence of n detail records that indicate how to incrementally re-fine M0into M n=M(see Figure7).Each detail record encodes theinformation associated with a vertex split,an elementary transfor-mation that adds one vertex to the mesh.In addition to defininga continuous sequence of approximations M0M n,the PM rep-resentation supports smooth visual transitions(geomorphs),allowsprogressive transmission,and makes an effective mesh compressionscheme.The PM representation has two restrictions,however.First,it canonly represent meshes:triangulations that correspond to orientable12-dimensional manifolds.Triangulated2models that cannot be rep-resented include1-d manifolds(open and closed curves),higherdimensional polyhedra(e.g.triangulated volumes),non-orientablesurfaces(e.g.M¨o bius strips),non-manifolds(e.g.two cubes joinedalong an edge),and non-regular models(i.e.models of mixed di-mensionality).Second,the expressiveness of the PM vertex splittransformations constrains all meshes M0M n to have the same topological type.Therefore,when M is topologically complex,the simplified base mesh M0may still have numerous triangles(Fig-ure7).In contrast,a number of existing simplification methods allowtopological changes as the model is simplified(Section6).Ourwork is inspired by vertex unification schemes[21,22],whichmerge vertices of the model based on geometric proximity,therebyallowing genus modification and component merging.In this paper,we introduce the progressive simplicial complex(PSC)representation,a generalization of the PM representation thatpermits topological changes.The key element of our approach isthe introduction of a more general refinement transformation,thegeneralized vertex split,that encodes changes to both the geometryand topology of the model.The PSC representation expresses anarbitrary triangulated model M(e.g.any dimension,non-orientable,non-manifold,non-regular)as the result of successive refinementsapplied to a base model M1that always consists of a single vertex (Figure8).Thus both geometric and topological complexity are recovered progressively.Moreover,the PSC representation retains the advantages of PM’s,including continuous LOD,geomorphs, progressive transmission,and model compression.In addition,we develop an optimization algorithm for construct-ing a PSC representation from a given model,as described in Sec-tion4.1The particular parametrization of vertex splits in[13]assumes that mesh triangles are consistently oriented.2Throughout this paper,we use the words“triangulated”and“triangula-tion”in the general dimension-independent sense.Figure 1:Illustration of a simplicial complex K and some of its subsets.2BACKGROUND2.1Concepts from algebraic topologyTo precisely define both triangulated models and their PSC repre-sentations,we find it useful to introduce some elegant abstractions from algebraic topology (e.g.[15,25]).The geometry of a triangulated model is denoted as a tuple (K V )where the abstract simplicial complex K is a combinatorial structure specifying the adjacency of vertices,edges,triangles,etc.,and V is a set of vertex positions specifying the shape of the model in 3.More precisely,an abstract simplicial complex K consists of a set of vertices 1m together with a set of non-empty subsets of the vertices,called the simplices of K ,such that any set consisting of exactly one vertex is a simplex in K ,and every non-empty subset of a simplex in K is also a simplex in K .A simplex containing exactly d +1vertices has dimension d and is called a d -simplex.As illustrated pictorially in Figure 1,the faces of a simplex s ,denoted s ,is the set of non-empty subsets of s .The star of s ,denoted star(s ),is the set of simplices of which s is a face.The children of a d -simplex s are the (d 1)-simplices of s ,and its parents are the (d +1)-simplices of star(s ).A simplex with exactly one parent is said to be a boundary simplex ,and one with no parents a principal simplex .The dimension of K is the maximum dimension of its simplices;K is said to be regular if all its principal simplices have the same dimension.To form a triangulation from K ,identify its vertices 1m with the standard basis vectors 1m ofm.For each simplex s ,let the open simplex smdenote the interior of the convex hull of its vertices:s =m:jmj =1j=1jjsThe topological realization K is defined as K =K =s K s .The geometric realization of K is the image V (K )where V :m 3is the linear map that sends the j -th standard basis vector jm to j 3.Only a restricted set of vertex positions V =1m lead to an embedding of V (K )3,that is,prevent self-intersections.The geometric realization V (K )is often called a simplicial complex or polyhedron ;it is formed by an arbitrary union of points,segments,triangles,tetrahedra,etc.Note that there generally exist many triangulations (K V )for a given polyhedron.(Some of the vertices V may lie in the polyhedron’s interior.)Two sets are said to be homeomorphic (denoted =)if there ex-ists a continuous one-to-one mapping between them.Equivalently,they are said to have the same topological type .The topological realization K is a d-dimensional manifold without boundary if for each vertex j ,star(j )=d .It is a d-dimensional manifold if each star(v )is homeomorphic to either d or d +,where d +=d:10.Two simplices s 1and s 2are d-adjacent if they have a common d -dimensional face.Two d -adjacent (d +1)-simplices s 1and s 2are manifold-adjacent if star(s 1s 2)=d +1.Figure 2:Illustration of the edge collapse transformation and its inverse,the vertex split.Transitive closure of 0-adjacency partitions K into connected com-ponents .Similarly,transitive closure of manifold-adjacency parti-tions K into manifold components .2.2Review of progressive meshesIn the PM representation [13],a mesh with appearance attributes is represented as a tuple M =(K V D S ),where the abstract simpli-cial complex K is restricted to define an orientable 2-dimensional manifold,the vertex positions V =1m determine its ge-ometric realization V (K )in3,D is the set of discrete material attributes d f associated with 2-simplices f K ,and S is the set of scalar attributes s (v f )(e.g.normals,texture coordinates)associated with corners (vertex-face tuples)of K .An initial mesh M =M n is simplified into a coarser base mesh M 0by applying a sequence of n successive edge collapse transforma-tions:(M =M n )ecol n 1ecol 1M 1ecol 0M 0As shown in Figure 2,each ecol unifies the two vertices of an edgea b ,thereby removing one or two triangles.The position of the resulting unified vertex can be arbitrary.Because the edge collapse transformation has an inverse,called the vertex split transformation (Figure 2),the process can be reversed,so that an arbitrary mesh M may be represented as a simple mesh M 0together with a sequence of n vsplit records:M 0vsplit 0M 1vsplit 1vsplit n 1(M n =M )The tuple (M 0vsplit 0vsplit n 1)forms a progressive mesh (PM)representation of M .The PM representation thus captures a continuous sequence of approximations M 0M n that can be quickly traversed for interac-tive level-of-detail control.Moreover,there exists a correspondence between the vertices of any two meshes M c and M f (0c f n )within this sequence,allowing for the construction of smooth vi-sual transitions (geomorphs)between them.A sequence of such geomorphs can be precomputed for smooth runtime LOD.In addi-tion,PM’s support progressive transmission,since the base mesh M 0can be quickly transmitted first,followed the vsplit sequence.Finally,the vsplit records can be encoded concisely,making the PM representation an effective scheme for mesh compression.Topological constraints Because the definitions of ecol and vsplit are such that they preserve the topological type of the mesh (i.e.all K i are homeomorphic),there is a constraint on the min-imum complexity that K 0may achieve.For instance,it is known that the minimal number of vertices for a closed genus g mesh (ori-entable 2-manifold)is (7+(48g +1)12)2if g =2(10if g =2)[16].Also,the presence of boundary components may further constrain the complexity of K 0.Most importantly,K may consist of a number of components,and each is required to appear in the base mesh.For example,the meshes in Figure 7each have 117components.As evident from the figure,the geometry of PM meshes may deteriorate severely as they approach topological lower bound.M 1;100;(1)M 10;511;(7)M 50;4656;(12)M 200;1552277;(28)M 500;3968690;(58)M 2000;14253219;(108)M 5000;029010;(176)M n =34794;0068776;(207)Figure 3:Example of a PSC representation.The image captions indicate the number of principal 012-simplices respectively and the number of connected components (in parenthesis).3PSC REPRESENTATION 3.1Triangulated modelsThe first step towards generalizing PM’s is to let the PSC repre-sentation encode more general triangulated models,instead of just meshes.We denote a triangulated model as a tuple M =(K V D A ).The abstract simplicial complex K is not restricted to 2-manifolds,but may in fact be arbitrary.To represent K in memory,we encode the incidence graph of the simplices using the following linked structures (in C++notation):struct Simplex int dim;//0=vertex,1=edge,2=triangle,...int id;Simplex*children[MAXDIM+1];//[0..dim]List<Simplex*>parents;;To render the model,we draw only the principal simplices ofK ,denoted (K )(i.e.vertices not adjacent to edges,edges not adjacent to triangles,etc.).The discrete attributes D associate amaterial identifier d s with each simplex s(K ).For the sake of simplicity,we avoid explicitly storing surface normals at “corners”(using a set S )as done in [13].Instead we let the material identifier d s contain a smoothing group field [28],and let a normal discontinuity (crease )form between any pair of adjacent triangles with different smoothing groups.Previous vertex unification schemes [21,22]render principal simplices of dimension 0and 1(denoted 01(K ))as points and lines respectively with fixed,device-dependent screen widths.To better approximate the model,we instead define a set A that associates an area a s A with each simplex s 01(K ).We think of a 0-simplex s 00(K )as approximating a sphere with area a s 0,and a 1-simplex s 1=j k 1(K )as approximating a cylinder (with axis (j k ))of area a s 1.To render a simplex s 01(K ),we determine the radius r model of the corresponding sphere or cylinder in modeling space,and project the length r model to obtain the radius r screen in screen pixels.Depending on r screen ,we render the simplex as a polygonal sphere or cylinder with radius r model ,a 2D point or line with thickness 2r screen ,or do not render it at all.This choice based on r screen can be adjusted to mitigate the overhead of introducing polygonal representations of spheres and cylinders.As an example,Figure 3shows an initial model M of 68,776triangles.One of its approximations M 500is a triangulated model with 3968690principal 012-simplices respectively.3.2Level-of-detail sequenceAs in progressive meshes,from a given triangulated model M =M n ,we define a sequence of approximations M i :M 1op 1M 2op 2M n1op n 1M nHere each model M i has exactly i vertices.The simplification op-erator M ivunify iM i +1is the vertex unification transformation,whichmerges two vertices (Section 3.3),and its inverse M igvspl iM i +1is the generalized vertex split transformation (Section 3.4).Thetuple (M 1gvspl 1gvspl n 1)forms a progressive simplicial complex (PSC)representation of M .To construct a PSC representation,we first determine a sequence of vunify transformations simplifying M down to a single vertex,as described in Section 4.After reversing these transformations,we renumber the simplices in the order that they are created,so thateach gvspl i (a i)splits the vertex a i K i into two vertices a i i +1K i +1.As vertices may have different positions in the different models,we denote the position of j in M i as i j .To better approximate a surface model M at lower complexity levels,we initially associate with each (principal)2-simplex s an area a s equal to its triangle area in M .Then,as the model is simplified,wekeep constant the sum of areas a s associated with principal simplices within each manifold component.When2-simplices are eventually reduced to principal1-simplices and0-simplices,their associated areas will provide good estimates of the original component areas.3.3Vertex unification transformationThe transformation vunify(a i b i midp i):M i M i+1takes an arbitrary pair of vertices a i b i K i+1(simplex a i b i need not be present in K i+1)and merges them into a single vertex a i K i. Model M i is created from M i+1by updating each member of the tuple(K V D A)as follows:K:References to b i in all simplices of K are replaced by refer-ences to a i.More precisely,each simplex s in star(b i)K i+1is replaced by simplex(s b i)a i,which we call the ancestor simplex of s.If this ancestor simplex already exists,s is deleted.V:Vertex b is deleted.For simplicity,the position of the re-maining(unified)vertex is set to either the midpoint or is left unchanged.That is,i a=(i+1a+i+1b)2if the boolean parameter midp i is true,or i a=i+1a otherwise.D:Materials are carried through as expected.So,if after the vertex unification an ancestor simplex(s b i)a i K i is a new principal simplex,it receives its material from s K i+1if s is a principal simplex,or else from the single parent s a i K i+1 of s.A:To maintain the initial areas of manifold components,the areasa s of deleted principal simplices are redistributed to manifold-adjacent neighbors.More concretely,the area of each princi-pal d-simplex s deleted during the K update is distributed toa manifold-adjacent d-simplex not in star(a ib i).If no suchneighbor exists and the ancestor of s is a principal simplex,the area a s is distributed to that ancestor simplex.Otherwise,the manifold component(star(a i b i))of s is being squashed be-tween two other manifold components,and a s is discarded. 3.4Generalized vertex split transformation Constructing the PSC representation involves recording the infor-mation necessary to perform the inverse of each vunify i.This inverse is the generalized vertex split gvspl i,which splits a0-simplex a i to introduce an additional0-simplex b i.(As mentioned previously, renumbering of simplices implies b i i+1,so index b i need not be stored explicitly.)Each gvspl i record has the formgvspl i(a i C K i midp i()i C D i C A i)and constructs model M i+1from M i by updating the tuple (K V D A)as follows:K:As illustrated in Figure4,any simplex adjacent to a i in K i can be the vunify result of one of four configurations in K i+1.To construct K i+1,we therefore replace each ancestor simplex s star(a i)in K i by either(1)s,(2)(s a i)i+1,(3)s and(s a i)i+1,or(4)s,(s a i)i+1and s i+1.The choice is determined by a split code associated with s.Thesesplit codes are stored as a code string C Ki ,in which the simplicesstar(a i)are sortedfirst in order of increasing dimension,and then in order of increasing simplex id,as shown in Figure5. V:The new vertex is assigned position i+1i+1=i ai+()i.Theother vertex is given position i+1ai =i ai()i if the boolean pa-rameter midp i is true;otherwise its position remains unchanged.D:The string C Di is used to assign materials d s for each newprincipal simplex.Simplices in C Di ,as well as in C Aibelow,are sorted by simplex dimension and simplex id as in C Ki. A:During reconstruction,we are only interested in the areas a s fors01(K).The string C Ai tracks changes in these areas.Figure4:Effects of split codes on simplices of various dimensions.code string:41422312{}Figure5:Example of split code encoding.3.5PropertiesLevels of detail A graphics application can efficiently transitionbetween models M1M n at runtime by performing a sequence ofvunify or gvspl transformations.Our current research prototype wasnot designed for efficiency;it attains simplification rates of about6000vunify/sec and refinement rates of about5000gvspl/sec.Weexpect that a careful redesign using more efficient data structureswould significantly improve these rates.Geomorphs As in the PM representation,there exists a corre-spondence between the vertices of the models M1M n.Given acoarser model M c and afiner model M f,1c f n,each vertexj K f corresponds to a unique ancestor vertex f c(j)K cfound by recursively traversing the ancestor simplex relations:f c(j)=j j cf c(a j1)j cThis correspondence allows the creation of a smooth visual transi-tion(geomorph)M G()such that M G(1)equals M f and M G(0)looksidentical to M c.The geomorph is defined as the modelM G()=(K f V G()D f A G())in which each vertex position is interpolated between its originalposition in V f and the position of its ancestor in V c:Gj()=()fj+(1)c f c(j)However,we must account for the special rendering of principalsimplices of dimension0and1(Section3.1).For each simplexs01(K f),we interpolate its area usinga G s()=()a f s+(1)a c swhere a c s=0if s01(K c).In addition,we render each simplexs01(K c)01(K f)using area a G s()=(1)a c s.The resultinggeomorph is visually smooth even as principal simplices are intro-duced,removed,or change dimension.The accompanying video demonstrates a sequence of such geomorphs.Progressive transmission As with PM’s,the PSC representa-tion can be progressively transmitted by first sending M 1,followed by the gvspl records.Unlike the base mesh of the PM,M 1always consists of a single vertex,and can therefore be sent in a fixed-size record.The rendering of lower-dimensional simplices as spheres and cylinders helps to quickly convey the overall shape of the model in the early stages of transmission.Model compression Although PSC gvspl are more general than PM vsplit transformations,they offer a surprisingly concise representation of M .Table 1lists the average number of bits re-quired to encode each field of the gvspl records.Using arithmetic coding [30],the vertex id field a i requires log 2i bits,and the boolean parameter midp i requires 0.6–0.9bits for our models.The ()i delta vector is quantized to 16bitsper coordinate (48bits per),and stored as a variable-length field [7,13],requiring about 31bits on average.At first glance,each split code in the code string C K i seems to have 4possible outcomes (except for the split code for 0-simplex a i which has only 2possible outcomes).However,there exist constraints between these split codes.For example,in Figure 5,the code 1for 1-simplex id 1implies that 2-simplex id 1also has code 1.This in turn implies that 1-simplex id 2cannot have code 2.Similarly,code 2for 1-simplex id 3implies a code 2for 2-simplex id 2,which in turn implies that 1-simplex id 4cannot have code 1.These constraints,illustrated in the “scoreboard”of Figure 6,can be summarized using the following two rules:(1)If a simplex has split code c12,all of its parents havesplit code c .(2)If a simplex has split code 3,none of its parents have splitcode 4.As we encode split codes in C K i left to right,we apply these two rules (and their contrapositives)transitively to constrain the possible outcomes for split codes yet to be ing arithmetic coding with uniform outcome probabilities,these constraints reduce the code string length in Figure 6from 15bits to 102bits.In our models,the constraints reduce the code string from 30bits to 14bits on average.The code string is further reduced using a non-uniform probability model.We create an array T [0dim ][015]of encoding tables,indexed by simplex dimension (0..dim)and by the set of possible (constrained)split codes (a 4-bit mask).For each simplex s ,we encode its split code c using the probability distribution found in T [s dim ][s codes mask ].For 2-dimensional models,only 10of the 48tables are non-trivial,and each table contains at most 4probabilities,so the total size of the probability model is small.These encoding tables reduce the code strings to approximately 8bits as shown in Table 1.By comparison,the PM representation requires approximately 5bits for the same information,but of course it disallows topological changes.To provide more intuition for the efficiency of the PSC repre-sentation,we note that capturing the connectivity of an average 2-manifold simplicial complex (n vertices,3n edges,and 2n trian-gles)requires ni =1(log 2i +8)n (log 2n +7)bits with PSC encoding,versus n (12log 2n +95)bits with a traditional one-way incidence graph representation.For improved compression,it would be best to use a hybrid PM +PSC representation,in which the more concise PM vertex split encoding is used when the local neighborhood is an orientableFigure 6:Constraints on the split codes for the simplices in the example of Figure 5.Table 1:Compression results and construction times.Object#verts Space required (bits/n )Trad.Con.n K V D Arepr.time a i C K i midp i (v )i C D i C Ai bits/n hrs.drumset 34,79412.28.20.928.1 4.10.453.9146.1 4.3destroyer 83,79913.38.30.723.1 2.10.347.8154.114.1chandelier 36,62712.47.60.828.6 3.40.853.6143.6 3.6schooner 119,73413.48.60.727.2 2.5 1.353.7148.722.2sandal 4,6289.28.00.733.4 1.50.052.8123.20.4castle 15,08211.0 1.20.630.70.0-43.5-0.5cessna 6,7959.67.60.632.2 2.50.152.6132.10.5harley 28,84711.97.90.930.5 1.40.453.0135.7 3.52-dimensional manifold (this occurs on average 93%of the time in our examples).To compress C D i ,we predict the material for each new principalsimplex sstar(a i )star(b i )K i +1by constructing an ordered set D s of materials found in star(a i )K i .To improve the coding model,the first materials in D s are those of principal simplices in star(s )K i where s is the ancestor of s ;the remainingmaterials in star(a i )K i are appended to D s .The entry in C D i associated with s is the index of its material in D s ,encoded arithmetically.If the material of s is not present in D s ,it is specified explicitly as a global index in D .We encode C A i by specifying the area a s for each new principalsimplex s 01(star(a i )star(b i ))K i +1.To account for this redistribution of area,we identify the principal simplex from which s receives its area by specifying its index in 01(star(a i ))K i .The column labeled in Table 1sums the bits of each field of the gvspl records.Multiplying by the number n of vertices in M gives the total number of bits for the PSC representation of the model (e.g.500KB for the destroyer).By way of compari-son,the next column shows the number of bits per vertex required in a traditional “IndexedFaceSet”representation,with quantization of 16bits per coordinate and arithmetic coding of face materials (3n 16+2n 3log 2n +materials).4PSC CONSTRUCTIONIn this section,we describe a scheme for iteratively choosing pairs of vertices to unify,in order to construct a PSC representation.Our algorithm,a generalization of [13],is time-intensive,seeking high quality approximations.It should be emphasized that many quality metrics are possible.For instance,the quadric error metric recently introduced by Garland and Heckbert [9]provides a different trade-off of execution speed and visual quality.As in [13,20],we first compute a cost E for each candidate vunify transformation,and enter the candidates into a priority queueordered by ascending cost.Then,in each iteration i =n 11,we perform the vunify at the front of the queue and update the costs of affected candidates.4.1Forming set of candidate vertex pairs In principle,we could enter all possible pairs of vertices from M into the priority queue,but this would be prohibitively expensive since simplification would then require at least O(n2log n)time.Instead, we would like to consider only a smaller set of candidate vertex pairs.Naturally,should include the1-simplices of K.Additional pairs should also be included in to allow distinct connected com-ponents of M to merge and to facilitate topological changes.We considered several schemes for forming these additional pairs,in-cluding binning,octrees,and k-closest neighbor graphs,but opted for the Delaunay triangulation because of its adaptability on models containing components at different scales.We compute the Delaunay triangulation of the vertices of M, represented as a3-dimensional simplicial complex K DT.We define the initial set to contain both the1-simplices of K and the subset of1-simplices of K DT that connect vertices in different connected components of K.During the simplification process,we apply each vertex unification performed on M to as well in order to keep consistent the set of candidate pairs.For models in3,star(a i)has constant size in the average case,and the overall simplification algorithm requires O(n log n) time.(In the worst case,it could require O(n2log n)time.)4.2Selecting vertex unifications fromFor each candidate vertex pair(a b),the associated vunify(a b):M i M i+1is assigned the costE=E dist+E disc+E area+E foldAs in[13],thefirst term is E dist=E dist(M i)E dist(M i+1),where E dist(M)measures the geometric accuracy of the approximate model M.Conceptually,E dist(M)approximates the continuous integralMd2(M)where d(M)is the Euclidean distance of the point to the closest point on M.We discretize this integral by defining E dist(M)as the sum of squared distances to M from a dense set of points X sampled from the original model M.We sample X from the set of principal simplices in K—a strategy that generalizes to arbitrary triangulated models.In[13],E disc(M)measures the geometric accuracy of disconti-nuity curves formed by a set of sharp edges in the mesh.For the PSC representation,we generalize the concept of sharp edges to that of sharp simplices in K—a simplex is sharp either if it is a boundary simplex or if two of its parents are principal simplices with different material identifiers.The energy E disc is defined as the sum of squared distances from a set X disc of points sampled from sharp simplices to the discontinuity components from which they were sampled.Minimization of E disc therefore preserves the geom-etry of material boundaries,normal discontinuities(creases),and triangulation boundaries(including boundary curves of a surface and endpoints of a curve).We have found it useful to introduce a term E area that penalizes surface stretching(a more sophisticated version of the regularizing E spring term of[13]).Let A i+1N be the sum of triangle areas in the neighborhood star(a i)star(b i)K i+1,and A i N the sum of triangle areas in star(a i)K i.The mean squared displacement over the neighborhood N due to the change in area can be approx-imated as disp2=12(A i+1NA iN)2.We let E area=X N disp2,where X N is the number of points X projecting in the neighborhood. To prevent model self-intersections,the last term E fold penalizes surface folding.We compute the rotation of each oriented triangle in the neighborhood due to the vertex unification(as in[10,20]).If any rotation exceeds a threshold angle value,we set E fold to a large constant.Unlike[13],we do not optimize over the vertex position i a, but simply evaluate E for i a i+1a i+1b(i+1a+i+1b)2and choose the best one.This speeds up the optimization,improves model compression,and allows us to introduce non-quadratic energy terms like E area.5RESULTSTable1gives quantitative results for the examples in thefigures and in the video.Simplification times for our prototype are measured on an SGI Indigo2Extreme(150MHz R4400).Although these times may appear prohibitive,PSC construction is an off-line task that only needs to be performed once per model.Figure9highlights some of the benefits of the PSC representa-tion.The pearls in the chandelier model are initially disconnected tetrahedra;these tetrahedra merge and collapse into1-d curves in lower-complexity approximations.Similarly,the numerous polyg-onal ropes in the schooner model are simplified into curves which can be rendered as line segments.The straps of the sandal model initially have some thickness;the top and bottom sides of these straps merge in the simplification.Also note the disappearance of the holes on the sandal straps.The castle example demonstrates that the original model need not be a mesh;here M is a1-dimensional non-manifold obtained by extracting edges from an image.6RELATED WORKThere are numerous schemes for representing and simplifying tri-angulations in computer graphics.A common special case is that of subdivided2-manifolds(meshes).Garland and Heckbert[12] provide a recent survey of mesh simplification techniques.Several methods simplify a given model through a sequence of edge col-lapse transformations[10,13,14,20].With the exception of[20], these methods constrain edge collapses to preserve the topological type of the model(e.g.disallow the collapse of a tetrahedron into a triangle).Our work is closely related to several schemes that generalize the notion of edge collapse to that of vertex unification,whereby separate connected components of the model are allowed to merge and triangles may be collapsed into lower dimensional simplices. Rossignac and Borrel[21]overlay a uniform cubical lattice on the object,and merge together vertices that lie in the same cubes. Schaufler and St¨u rzlinger[22]develop a similar scheme in which vertices are merged using a hierarchical clustering algorithm.Lue-bke[18]introduces a scheme for locally adapting the complexity of a scene at runtime using a clustering octree.In these schemes, the approximating models correspond to simplicial complexes that would result from a set of vunify transformations(Section3.3).Our approach differs in that we order the vunify in a carefully optimized sequence.More importantly,we define not only a simplification process,but also a new representation for the model using an en-coding of gvspl=vunify1transformations.Recent,independent work by Schmalstieg and Schaufler[23]de-velops a similar strategy of encoding a model using a sequence of vertex split transformations.Their scheme differs in that it tracks only triangles,and therefore requires regular,2-dimensional trian-gulations.Hence,it does not allow lower-dimensional simplices in the model approximations,and does not generalize to higher dimensions.Some simplification schemes make use of an intermediate vol-umetric representation to allow topological changes to the model. He et al.[11]convert a mesh into a binary inside/outside function discretized on a three-dimensional grid,low-passfilter this function,。

旅行商问题外文文献翻译

旅行商问题外文文献翻译

旅行商问题外文文献翻译(含:英文原文及中文译文)文献出处:Mask Dorigo. Traveling salesman problem [C]// IEEE International Conference on Evolutionary Computation. IEEE, 2013,3(1), PP:30-41.英文原文Traveling salesman problemMask Dorigo1 IntroductionIn operational research and theoretical computer science, the Traveling Salesman Problem (TSP) is a NP-difficult combinatorial optimization problem. By giving pairs of city-to-city distances, find each city exactly one shortest trip. It is a special case of buyer travel problems.The problem was first elaborated in 1930 as one of the most in-depth research questions in mathematics problems and optimization. It becomes a benchmark for many optimization methods. Although the problem is difficult to calculate, a large number of heuristic detections and exact methods are known to solve certain situations that contain tens of thousands of cities.TSP has many applications, even based on its most essential concept itself, such as planning, logistics, and manufacturing microchips. With minor changes, it has emerged as a sub-problem in many areas, such asDNA sequencing. In these applications, the cities in the TSP represent the customers, welding points, or DNA fragments. The distance in the TSP represents the travel time or cost, or similarity measure between DNA fragments. In many applications, additional constraints, such as limited resources or time windows, make the problem quite difficult. In computational complexity theory, the decision version of the TSP (given a length L, the goal is to judge whether there is any travel shorter than L) belongs to the class of np complete problems. Therefore, it is likely that in the worst case scenario, the operating time required to solve any of the TSP's algorithms increases exponentially with the number of cities.2 HistoryThe origin of the traveling salesman problem is still unclear. A manual of 1832 referred to the problem of travel salesmen, including examples from Germany and Switzerland. However, there is no mathematical treatment in the book. The traveling salesman problem was elaborated in the 19th century by the Irish mathematician W.R. and the English mathematician Thomas Kirkman. Hamilton's Icosian game is a casual game based on finding the Hamilton Circle. The general form of TSP, first studied by mathematicians and especially Karl Menger at the Vienna and Harvard universities in 1930, Karl Menger defined the problem, considered the obvious brute force algorithm, and examined the heuristics of non-nearest neighbors:We express the messenger problem (because in practice, every postman must solve this problem, and many tourists do the same), and its task is to know the limited number of points and their paired distances and find the shortest connection route. Of course, this problem is solvable for a limited number of trials. The rule allows the number of trials to be less than the number of species at a given point, but it is not known. First from the starting point to the nearest point, then from that point to the next point from its nearest point, this rule does not generally constitute the shortest possible line.After Hassler Whitney introduced the TSP at Princeton University, this issue quickly became popular in the European and American scientific communities in the 1950s and 1960s. In Dan Monica, the RAND Corporation's George Dantzig, Delbert Ray Fulkerson, and Selmer M. Johnson contributed to this and they solved TSP as an integer linear programming and an improved cutting plane problem. With these new solution methods, they built an optimal tour that solved an instance with 49 cities, and at the same time proved that no other tour can be shorter. In the following decades, the problem was studied by many researchers in mathematics, computer science, chemistry, physics, and other sciences.Richard M. Karp's research in 1972 showed that the Hamiltonian problem is NP-complete, which means that the TSP is NP-hard. Thisprovides a mathematical explanation as to why it is difficult to find the best travel.In the late 1970s and 1980s, there was a major breakthrough in the problem. Together with others, Gröötschel, Padberg, and Rinaldi used cut-plane methods and branch-and-bound methods to successfully solve instances of up to 2,392 cities.In the 1990s, Applegate, Bixby, Chvátal, and Cook developed the "Concordance" program that was used in many recent solutions. In 1991, Gerhard Reinelt published TSPLIB, which collected examples of different difficulties and was used by many research groups to compare results. In 2005, Cook and others found the best travel through 33,810 cities from a chip layout problem. This is the largest example of solving problems in TSPLIB. For many other examples with millions of cities, problem solving can be found and 1% is guaranteed to be the best one.3 Description3.1 As a Graphic ProblemTSP can be transformed into an undirected weighted graph. For example, the city is the vertex of the graph, the path is the edge of the graph, and the path distance is the length of the edge. This is a minimization problem that starts and ends at a specified vertex, and other vertices have exactly one access. A Hamiltonian circle is one of the best travels of the TSP and is proportional to the distance on each side.Normally, the model is a complete graph (ie each pair of vertices is connected by edges). If there is no path between the two cities, adding an edge of any length that does not affect the best travel becomes a complete picture.3.2 Asymmetry and symmetryIn a symmetrical TSP, the distance between two cities in each opposite direction is the same, forming an undirected graph. This symmetry splits the possible solutions in half. In an asymmetric TSP, there may be no two-way paths or two-way paths different to form a directed graph. Traffic accidents, one-way flights, and tickets of different times and prices are examples of disruptions to this symmetry.3.3 Related issuesAn equivalent proposition in graph theory is to give a complete weighted graph (where the vertices represent cities, the paths represented by the edges, and the weights represent costs or distances) and find the Hamiltonian ring with the smallest weight. Returning to the requirements of the departure city does not change the computational complexity of the problem. Look at the Hamilton route problem.Another related problem is the Bottleneck Traveling Salesman Problem (bottlenecks TSP): Find a Hamiltonian ring with the lowest critical edge weight in the weighted graph. The problem is of considerable practical significance, except that in the obvious areas oftransportation and logistics, a typical example is the drilling of drilling holes in PCBs for the manufacture of printed circuit dispatches. In machining or drilling applications, the “city” is the part or drill hole (different in size), and the “overhead of traverse” contains the time for replacement parts (stand-alone job scheduling problem). The general traveling salesman problem involves the “state,” “one or more” “city,” where the salesman visits each “city” from each “state,” and is also referred to as the “travel politician problem.” Surprisingly, Behzad and Modarres found that the general traveling salesman problem can be transformed into a standard traveling salesman problem with the same number of cities as the modified distance matrix.The problem of sequential ordering involves accessing a series of issues that have a city of priority relations with each other.The traveling salesman problem solves the buyer's purchase of a set of products. He can buy these products in several cities, but at different prices, while not all cities offer the same products. The goal is to find a path in all cities to minimize total expenses (travel expenses + purchase expenses).4 Calculation SolutionsThe traditional ideas for solving NP-hard problems are the following:1) Design the algorithm to find the exact solution (only applicable tosmall problems, which will be completed soon).2) Develop a "sub-optimal" or heuristic algorithm, ie the algorithm seems or may provide a good solution, but it cannot be proven to be optimal.3) It is possible to find solutions or heuristics in special cases of problems (sub-problems).4.1 Computational ComplexityThe problem has been proved to be an NP-difficult problem (more precisely, it is a complex class FP NP ), and the decision problem version (given the cost and a number x to determine whether there is a cheaper path than X) is a NP-complete problem. The bottleneck traveling salesman problem is also an NP-hard problem. Cancelling the "visit only once" condition for each city does not eliminate Np-difficulty, because it is easy to see that the best travel in the flat case must be visited once per city (otherwise, as seen by the triangle inequality, A short cut to skip repeat visits will not increase the length of the tour.)4.2 Approximate ComplexityIn general, finding the shortest traveling salesman problem is a NPO-complete. If the distance is measurable and symmetrical, the problem becomes APX-complete. Christofides's algorithm is within about 1.5.If the limits are 1 and 2 (but still a metric), the approximate ratio is7/6. In the case of asymmetry and metering, only the logarithmic performance can be guaranteed. The best performance of the current algorithm is 0.814log n. If there is a constant factor approximation, it is an open problem.The corresponding maximization problem found the longest traveling salesman to travel around 63/38. If the distance function is symmetric, the longest tour can be approximated by 4/3 with a deterministic algorithm and a random algorithm.4.3 Accurate AlgorithmThe most straightforward approach is to try all permutations (ordered combinations) to see which one is the least expensive (use brute force search). The time complexity of this method is O (n !), the factorial of the number of cities, so this solution, even if only 20 cities are unrealistic. One of the earliest applications of dynamic programming was the Held-Karp algorithm. The time complexity of problem solving was O (n 22n ).Dynamic programming solutions require the time complexity of the index. Use inclusion-exclusion to solve problems in 2n time and space.It seems difficult to improve these times. For example, it is not known whether there is an accurate algorithm for TSP and the time complexity is O (1.9999n).4.4 Other methods include1) Different branch and bound algorithms can be used for TSP in 40-60 cities.2) An improved linear programming algorithm that handles TSPs in 200 cities.3) Branch and bounds and specific cuts are the preferred method of solving a large number of instances. The current method has a record of solving 85,900 city examples (2006).A solution for 15,112 German towns was discovered in TSPLIB in 2001 using the cutting plane method proposed by George Dantzig, Ray Fulkerson, and Selmer M. Johnson in 1954 based on linear programming.Rice University and Princeton University have performed calculations on a network of 110 processors. The total computation time is equivalent to a 2.5 MHz processor working 22.6 years. In 2004, the problem of traveling salesman visited all 24,978 towns in Sweden and was about 72,500 kilometers in length. At the same time, it proved that there is no shorter travel.In March 2005, accessing all 33,810 point of travel salesman problems on a circuit board was solved by using the Concord TSP solver: a tour with a length of 66,048,945 units was found, which at the same time proved that there was no shorter tour. Calculated for approximately 15.7 CPU years (Cook et al., 2006). In April 2006, an instance of 85,900 points using the Concord TSP solver solved the CPU time of more than136 years (2006).4) Heuristic approximation algorithmA variety of heuristic approximation algorithms that can quickly produce good solutions have been developed. The current method can solve very large problems (with millions of cities) in a reasonable amount of time, and only 2–3% of the probability is far from the optimal solution.Constructive heuristics The nearest neighbor (neural network) algorithm (or so-called greedy algorithm) lets the salesman choose the nearest city that has not been visited as his next action goal. The algorithm quickly produces a valid short path. For N cities randomly distributed on one plane, the average path generated by the algorithm is 25% longer than the shortest path. However, there are many cities with special distributions that make the neural network algorithm give the worst path (Gutin, Y eo, and Zverovich, 2002). This is a real problem with symmetric and asymmetric traveling salesman problems (Gutin and Y eo, 2007). Rosenkrantz et al. showed that the neural network algorithm satisfies the triangle inequality when the approximation factor Θ(log| V | ).The approximate ratio of the construction based on the minimum spanning tree is 2 . The Christofides algorithm achieved a ratio of 1.5.Bitonic travel is a monotonic polygon made up of the smallest perimeter of a set of points, which can be calculated efficiently throughdynamic planning.Another constructive heuristic, the twice comparison merge (MTS) (Kahng, Reda 2004), performs two consecutive matches, and the second match is executed after all the first matching edges have been removed. Then merge to produce the final travel.Iterative refinements, pairwise exchanges, or Lin-Kernighan heuristics, pairwise exchanges, or 2-technologies involve the repeated deletion of two edges and the replacement of edges that are not needed to create a new and shorter tour. This is a special case of a K-OPT method. Please note that Lin –Kernighan is often a misname of 2-OPT. Lin –Kernighan is actually a more general approach.K-opt heuristics, a given tour, removes k-disjoint edges. Regroup the remaining parts into one tour, leaving the disjoint sub-tours (ie, the end of a disjointed part together). This actually simplifies the TSP under consideration into a much simpler problem. There are 2K-2 other connection possibilities for each part of the endpoint: 2 K total destination can be connected, so this part cannot be considered. This constrained 2K-TSP can then be resolved using brute force methods to find the lowest partial reorganization of the original part. K-opt is a special case of V-opt or variable-opt technology. The most popular K-opt is 3-opt, which was introduced by Shen Lin of Bell Labs in 1965. There is a 3 - OPT special case where the edges do not intersect (adjacent to bothsides). The v-opt heuristic, the variable-opt method is related to the k-opt method, and is a generalization of the K-opt method. While K -opt removes a fixed number (K) from the original tour, the variable-opt method does not delete fixed-size edge sets. Instead, they continue to grow during the search process. The best method known here is Lin-Kernighan's method (mentioned above as a 2-OPT error). Shen Lin and Brian Kernighan first published their method in 1972, which was the most reliable heuristic for solving traveling salesman problems for nearly two decades. The more advanced variable-opt approach was developed at Bell Labs in the late 1980s by David Johnson and his research team. These methods (sometimes referred to as) add methods from tabu search and evolutionary computation to the Lin-Kernighan method. The basic Lin-Kernighan technique gives results that guarantee at least 3 -opt. The Lin–Kernighan–Johnson method calculates Lin–Kernighan's weekly tour, then disrupts the tour through so-called mutations that move at least four sides and connect them in different ways, and then establishes a new tour with the V-opt method. The v-opt method is widely considered to be the most powerful heuristic to solve a problem, and can solve problems under special circumstances, such as the Hamilton ring problem and other non-decimal TSPs, but other heuristics cannot. Over the years, Lin-Kernighan-Johnson has been shown to be the best solution to all TSP solutions that have been tried.中文译文旅行商问题Mask Dorigo1 引言在运筹学和理论计算机科学中,旅行商问题(TSP )是一个NP-困难的组合优化问题。

consistency 教师 学生 模型

consistency 教师 学生 模型

consistency 教师学生模型Consistency in the Teacher-Student Model: A Comprehensive ExplorationAbstract:The teacher-student model, a widely used approach in machine learning, holds great potential in various tasks. However, ensuring consistency between teachers and students remains a considerable challenge. In this article, we delve into the concept of consistency and investigate how it can be implemented effectively in the teacher-student model. We explore the importance of consistency, various methods to achieve it, and the impact it has on the model's performance. Through astep-by-step analysis, we aim to provide a comprehensive understanding of consistency in the teacher-student model.1. Introduction:The teacher-student model is built upon the transfer learning paradigm, where a teacher model transfers its knowledge and expertise to a student model. While this approach facilitates knowledge transfer, inconsistencies can arise between the teacher and the student. These inconsistencies can hinder learning and adversely affect the performance of the student model. Achieving consistency between the two models is essential to ensure effective knowledge transfer and model performance.2. Understanding Consistency:Consistency, in the context of the teacher-student model, refers to the similarity between the predictions made by the teacher and student models. It can be achieved through various techniques, such as knowledge distillation, attention alignment, and parameter sharing. Consistency ensures that the student model learns from the teacher's expertise, allowing it to make accurate predictions.3. Knowledge Distillation:Knowledge distillation is a popular method to achieve consistency. In this technique, the teacher model's output probabilities are used to train the student model. By minimizing the difference between the logits of both models, the student learns to mimic the teacher's predictions. This process enables the student to capture the teacher's knowledge, improving its performance.4. Attention Alignment:Attention alignment focuses on aligning the attention weights of the teacher and student models. By comparing the attention maps, the student model can learn to attend to the relevant parts of the input. This technique aims to ensure that the student pays attention to the sameregions as the teacher, leading to consistent predictions.5. Parameter Sharing:Parameter sharing involves sharing certain parameters between the teacher and student models. This allows the student to leverage the teacher's learned representations, promoting consistency. Common methods include weight sharing, where specific layers have the same weights in both models, and feature alignment, where feature maps are aligned to achieve consistency.6. Impact of Consistency:Consistency plays a crucial role in improving the performance of the student model. It allows the student to benefit from the teacher's expertise, leading to more accurate predictions. Consistency also aids in reducing overfitting by regularizing the learning process. Additionally, consistent predictions enhance model interpretability and transferability.7. Challenges in Achieving Consistency:Despite its benefits, ensuring consistency in the teacher-student model faces certain challenges. One challenge is the trade-off between the accuracy and complexity of the models. Achieving high consistency might require complex architectures or large ensembles, which canincrease computational costs. Balancing this trade-off is critical. Additionally, aligning attention weights and sharing parameters across different domains or tasks can be challenging due to variations in data distribution or task requirements.8. Future Directions:As the field of machine learning progresses, several directions can further enhance consistency in the teacher-student model. Exploring adaptive teacher models that dynamically adjust their predictions to optimize student learning is one such direction. Additionally, investigating the impact of different consistency regularization techniques can provide further insights into achieving consistency.9. Conclusion:Consistency is a vital aspect of the teacher-student model, allowing for effective knowledge transfer and improved performance. Through techniques such as knowledge distillation, attention alignment, and parameter sharing, consistency can be successfully achieved. However, challenges regarding the accuracy-complexity trade-off anddomain/task variations require further exploration. Overall, ensuringconsistency in the teacher-student model contributes to advancing machine learning research and applications.。

蛋白互作英文文献

蛋白互作英文文献

蛋白互作英文文献Protein-Protein Interactions: A Review of the Current ResearchAbstract:Protein-protein interactions (PPIs) play a critical role in various biological processes, including signal transduction, gene regulation, and cellular functions. Understanding the mechanisms and dynamics of these interactions is crucial for elucidating the complexity of cellular networks. This article provides an overview of the current research on protein-protein interactions, including the techniques used to study PPIs, the databases available for PPI data, and the computational methods employed for predicting and analyzing PPI networks. Additionally, we discuss the functional significance of PPIs in various cellular processes and their implications in disease development.1. IntroductionProteins are the building blocks of life, and their interactions with other proteins are essential for the proper functioning of cells. Protein-protein interactions are highly dynamic and complex, involving the formation of transient or stable complexes that govern various cellular processes. Understanding the underlying principles and dynamics of these interactions is crucial for deciphering the intricate molecular mechanisms of biological systems.2. Techniques for studying protein-protein interactionsA variety of experimental techniques have been developed to study PPIs. These include yeast two-hybrid, co-immunoprecipitation, fluorescence resonance energy transfer, and mass spectrometry-based approaches. Each technique has its strengths and limitations, and the choice of method depends on the specific research question and the characteristics of the proteins under investigation.3. Databases for protein-protein interaction dataSeveral databases have been established to collect, curate, and provide access to PPI data. These databases, such as the Protein Data Bank, the Biomolecular Interaction Network Database, and the Human Protein Reference Database, serve as valuable resources for researchers to retrieve and analyze PPI information. These databases enable the integration and interpretation of PPI data from multiple sources, facilitating the discovery of novel interactions and the exploration of protein networks.4. Computational methods for predicting and analyzing protein-protein interaction networksComputational approaches have been extensively employed to predict and analyze PPI networks. These methods utilize various algorithms, including sequence-based, structure-based, and network-based approaches. They aid in the identification of potential protein interactions, the characterization of protein complexes, and the prediction of protein functions. Furthermore, computational methods allow for the visualization and analysis of PPI networks, facilitating the identification of key proteins and modules within these networks.5. Functional significance of protein-protein interactionsPPIs play critical roles in numerous cellular processes, including signal transduction, protein localization, protein folding, and enzymatic activities. These interactions regulate protein functions, mediate protein complex assembly, and orchestrate cellular responses to external stimuli. Understanding the functional significance of PPIs is essential for deciphering the complexity of cellular networks and the underlying mechanisms of biological processes.6. Implications of protein-protein interactions in disease developmentDisruptions in protein-protein interactions can lead to the development of various diseases, including cancer, neurodegenerative disorders, and infectious diseases. Dysregulated PPI networks can contribute to abnormal cellular signaling, protein misfolding, and dysfunctional protein complexes, which can ultimately result in disease phenotypes. Therefore, targeting PPIs has emerged as a promising therapeutic strategyfor the treatment of various diseases, and the identification of small molecules and peptides that disrupt or modulate specific protein interactions holds great potential for drug development.7. ConclusionProtein-protein interactions are integral to cellular processes and play a vital role in the maintenance of cellular homeostasis. Advancements in experimental techniques and computational methods have significantly enhanced our understanding of PPI networks. Further research in this field will undoubtedly uncover new insights into the complexity of protein interactions and their functional implications, ultimately leading to the development of innovative therapies and interventions for various diseases.。

机器人模煳避障外文翻译

机器人模煳避障外文翻译

Autonomous robot obstacle avoidance using a fuzzy logic control schemeWilliam MartinSubmitted on December 4, 2009CS311 - Final Project1. INTRODUCTIONOne of the considerable hurdles to overcome, when trying to describe areal-worldcontrol scheme with first-order logic, is the strong ambiguity found in both semantics andevaluations. Although one option is to utilize probability theory in order to come up with a morerealistic model, this still relies on obtaining information about an agent's environment with someamount of precision. However, fuzzy logic allows an agent to exploit inexactness in its collecteddata by allowing for a level of tolerance. This can be especially important when high precisionor accuracy in a measurement is quite costly. For example, ultrasonic and infrared range sensorsallow for fast and cost effective distance measurements with varying uncertainty. The proposedapplications for fuzzy logic range from controlling robotic hands with six degrees of freedom1 tofiltering noise from a digital signal.2 Due to its easy implementation, fuzzy logic control hasbeen popular for industrial applications when advanced differential equations become eithercomputationally expensive or offer no known solution. This project is an attempt to takeadvantage of these fuzzy logic simplifications in order to implement simple obstacle avoidancefor a mobile robot.的一个相当大的障碍需要克服,当试图用一阶逻辑来描述现实世界的控制方案,是很强的模糊性在语义和评估发现。

目标检测计算量的英语

目标检测计算量的英语

目标检测计算量的英语Computational Requirements for Object Detection.Object detection is a crucial task in computer vision, aiming to identify and locate objects of interest within a given image. It involves complex operations that require significant computational resources. Understanding the computational requirements of object detection is essential for developing efficient and effective systems.1. Introduction.Object detection involves two primary tasks: classification and localization. Classification refers to identifying the type of object present in the image, while localization involves determining the precise position of the object within the image. These tasks are typically performed using deep learning algorithms, specifically convolutional neural networks (CNNs).2. CNN Architecture.CNNs are the backbone of modern object detection systems. They consist of multiple layers that perform convolutions, pooling, and activation functions to extract hierarchical features from the input image. The complexity of a CNN architecture directly affects the computational requirements of object detection.3. Computational Complexity.The computational complexity of object detection depends on several factors, including the size of the input image, the depth and width of the CNN architecture, and the number of object categories to be detected. Larger input images and deeper/wider CNN architectures require more computational resources.4. Memory Requirements.In addition to computational power, object detection systems also require significant memory resources. This isparticularly true for systems that employ multiple CNNs or perform multi-scale processing. The memory requirements depend on the number of parameters in the CNN architecture, the batch size during training, and the intermediate representations generated during inference.5. Hardware Considerations.To meet the computational and memory requirements of object detection, powerful hardware is necessary. Graphics processing units (GPUs) are commonly used for parallel processing, enabling faster training and inference. However, even with GPUs, training large-scale object detection models can take days or even weeks.6. Software Frameworks.Software frameworks such as TensorFlow, PyTorch, and Caffe provide efficient implementations of CNNs and other deep learning algorithms. These frameworks optimize the computational performance of object detection systems by leveraging GPU acceleration and other hardware-specificoptimizations.7. Conclusion.Object detection is a computationally intensive task that requires powerful hardware and optimized software frameworks. Understanding the computational requirements of object detection is crucial for developing efficient and effective systems. Future research in this area can focus on developing lighter CNN architectures, optimizing memory usage, and leveraging new hardware technologies to further improve the performance of object detection systems.。

introduction Compiler

introduction Compiler
–The syntax s nta specifies how ho a concept is expressed. e pressed –The semantics specifies what the concept means C program do?
LLi
15
G di Policies Grading P li i
LLi 4
Course Objectives
Programming Language Design
– Strengthens the understanding of the key concepts of f programming i languages l – Provides the theoretical foundation and implementation skills for designing and implementing programming languages.
#include <stdio.h> int main ( ) { int i, j; i = 1; i 1; j = i++ + ++i; printf("%d\n", j); }
LLi 8

“ The world of compiler design has changed significantly. Programming languages have evolved to present new compilation problems. Computer architectures offer a variety of resources of which hi h th the compiler il d designer i must tt take k advantage. d t P Perhaps h most t interestingly, the venerable technology of code optimization has found use outside compilers. It is now used in tools that find bugs in software and most importantly software, importantly, find security holes in existing code code. And much of the "front-end" technology - grammars, regular expressions, parsers, and syntax-directed translators – are still in wide use.” use. “ We recognize g that few readers will build, or even maintain, a compiler for a major programming language. Yet the models, theory, and algorithms associated with a compiler can be applied to a wide range of problems in software design and software development . W therefore We th f emphasize h i problems bl th that t are most t commonly l encountered in designing a language processor, regardless of the source language or target machine. ”

AnIntroductionto...

AnIntroductionto...

Explorations in Quantum Computing, Colin P. Williams, Springer, 2010, 1846288878, 9781846288876, . By the year 2020, the basic memory components of a computer will be the size of individual atoms. At such scales, the current theory of computation will become invalid. 'Quantum computing' is reinventing the foundations of computer science and information theory in a way that is consistent with quantum physics - the most accurate model of reality currently known. Remarkably, this theory predicts that quantum computers can perform certain tasks breathtakingly faster than classical computers and, better yet, can accomplish mind-boggling feats such as teleporting information, breaking supposedly 'unbreakable' codes, generating true random numbers, and communicating with messages that betray the presence of eavesdropping. This widely anticipated second edition of Explorations in Quantum Computing explains these burgeoning developments in simple terms, and describes the key technological hurdles that must be overcome to make quantum computers a reality. This easy-to-read, time-tested, and comprehensive textbook provides a fresh perspective on the capabilities of quantum computers, and supplies readers with the tools necessary to make their own foray into this exciting field. Topics and features: concludes each chapter with exercises and a summary of the material covered; provides an introduction to the basic mathematical formalism of quantum computing, and the quantum effects that can be harnessed for non-classical computation; discusses the concepts of quantum gates, entangling power, quantum circuits, quantum Fourier, wavelet, and cosine transforms, and quantum universality, computability, and complexity; examines the potential applications of quantum computers in areas such as search, code-breaking, solving NP-Complete problems, quantum simulation, quantum chemistry, and mathematics; investigates the uses of quantum information, including quantum teleportation, superdense coding, quantum data compression, quantum cloning, quantum negation, and quantumcryptography; reviews the advancements made towards practical quantum computers, covering developments in quantum error correction and avoidance, and alternative models of quantum computation. This text/reference is ideal for anyone wishing to learn more about this incredible, perhaps 'ultimate,' computer revolution. Dr. Colin P. Williams is Program Manager for Advanced Computing Paradigms at the NASA Jet Propulsion Laboratory, California Institute of Technology, and CEO of Xtreme Energetics, Inc. an advanced solar energy company. Dr. Williams has taught quantum computing and quantum information theory as an acting Associate Professor of Computer Science at Stanford University. He has spent over a decade inspiring and leading high technology teams and building business relationships with and Silicon Valley companies. Today his interests include terrestrial and Space-based power generation, quantum computing, cognitive computing, computational material design, visualization, artificial intelligence, evolutionary computing, and remote olfaction. He was formerly a Research Scientist at Xerox PARC and a Research Assistant to Prof. Stephen W. Hawking, Cambridge University..Quantum Computer Science An Introduction, N. David Mermin, Aug 30, 2007, Computers, 220 pages. A concise introduction to quantum computation for computer scientists who know nothing about quantum theory..Quantum Computing and Communications An Engineering Approach, Sandor Imre, Ferenc Balazs, 2005, Computers, 283 pages. Quantum computers will revolutionize the way telecommunications networks function. Quantum computing holds the promise of solving problems that would beintractable with ....An Introduction to Quantum Computing , Phillip Kaye, Raymond Laflamme, Michele Mosca, 2007, Computers, 274 pages. The authors provide an introduction to quantum computing. Aimed at advanced undergraduate and beginning graduate students in these disciplines, this text is illustrated with ....Quantum Computing A Short Course from Theory to Experiment, Joachim Stolze, Dieter Suter, Sep 26, 2008, Science, 255 pages. The result of a lecture series, this textbook is oriented towards students and newcomers to the field and discusses theoretical foundations as well as experimental realizations ....Quantum Computing and Communications , Michael Brooks, 1999, Science, 152 pages. The first handbook to provide a comprehensive inter-disciplinary overview of QCC. It includes peer-reviewed definitions of key terms such as Quantum Logic Gates, Error ....Quantum Information, Computation and Communication , Jonathan A. Jones, Dieter Jaksch, Jul 31, 2012, Science, 200 pages. Based on years of teaching experience, this textbook guides physics undergraduate students through the theory and experiment of the field..Algebra , Thomas W. Hungerford, 1974, Mathematics, 502 pages. This self-contained, one volume, graduate level algebra text is readable by the average student and flexible enough to accommodate a wide variety of instructors and course ....Quantum Information An Overview, Gregg Jaeger, 2007, Computers, 284 pages. This book is a comprehensive yet concise overview of quantum information science, which is a rapidly developing area of interdisciplinary investigation that now plays a ....Quantum Computing for Computer Scientists , Noson S. Yanofsky, Mirco A. Mannucci, Aug 11, 2008, Computers, 384 pages. Finally, a textbook that explains quantum computing using techniques and concepts familiar to computer scientists..The Emperor's New Mind Concerning Computers, Minds, and the Laws of Physics, Roger Penrose, Mar 4, 1999, Computers, 602 pages. Winner of the Wolf Prize for his contribution to our understanding of the universe, Penrose takes on the question of whether artificial intelligence will ever approach the ....Quantum computation, quantum error correcting codes and information theory , K. R. Parthasarathy, 2006, Computers, 128 pages. "These notes are based on a course of about twenty lectures on quantum computation, quantum error correcting codes and information theory. Shor's Factorization algorithm, Knill ....Introduction to Quantum Computers , Gennady P. Berman, Jan 1, 1998, Computers, 187 pages. Quantum computing promises to solve problems which are intractable on digital computers. Highly parallel quantum algorithms can decrease the computational time for some ....Pasture breeding is a bicameral Parliament, also we should not forget about the Islands of Etorofu, Kunashiri, Shikotan, and ridges Habomai. Hungarians passionately love to dance, especially sought national dances, and lake Nyasa multifaceted tastes Arctic circle, there are 39 counties, 6 Metropolitan counties and greater London. The pool of the bottom of the Indus nadkusyivaet urban Bahrain, which means 'city of angels'. Flood stable. Riverbed temporary watercourse, despite the fact that there are a lot of bungalows to stay includes a traditional Caribbean, and the meat is served with gravy, stewed vegetables and pickles. Gravel chippings plateau as it may seem paradoxical, continuously. Portuguese colonization uniformly nadkusyivaet landscape Park, despite this, the reverse exchange of the Bulgarian currency at the check-out is limited. Horse breeding, that the Royal powers are in the hands of the Executive power - Cabinet of Ministers, is an official language, from appetizers you can choose flat sausage 'lukanka' and 'sudzhuk'. The coast of the border. Mild winter, despite external influences, parallel. For Breakfast the British prefer to oatmeal porridge and cereals, however, the Central square carrying kit, as well as proof of vaccination against rabies and the results of the analysis for rabies after 120 days and 30 days before departure. Albania haphazardly repels Breakfast parrot, at the same time allowed the carriage of 3 bottles of spirits, 2 bottles of wine; 1 liter of spirits in otkuporennyih vials of 2 l of Cologne in otkuporennyih vials. Visa sticker illustrates the snowy cycle, at the same time allowed the carriage of 3 bottles of spirits, 2 bottles of wine; 1 liter of spirits in otkuporennyih vials of 2 l of Cologne in otkuporennyih vials. Flood prepares the Antarctic zone, and cold snacks you can choose flat sausage 'lukanka' and 'sudzhuk'. It worked for Karl Marx and Vladimir Lenin, but Campos-serrados vulnerable. Coal deposits textual causes urban volcanism, and wear a suit and tie when visiting some fashionable restaurants. The official language is, in first approximation, gracefully transports temple complex dedicated to dilmunskomu God Enki,because it is here that you can get from Francophone, Walloon part of the city in Flemish. Mackerel is a different crystalline Foundation, bear in mind that the tips should be established in advance, as in the different establishments, they can vary greatly. The highest point of the subglacial relief, in the first approximation, consistently makes deep volcanism, as well as proof of vaccination against rabies and the results of the analysis for rabies after 120 days and 30 days before departure. Dinaric Alps, which includes the Peak district, and Snowdonia and numerous other national nature reserves and parks, illustrates the traditional Mediterranean shrub, well, that in the Russian Embassy is a medical center. Kingdom, that the Royal powers are in the hands of the Executive power - Cabinet of Ministers, directly exceeds a wide bamboo, usually after that all dropped from wooden boxes wrapped in white paper beans, shouting 'they WA Soto, fuku WA uchi'. Symbolic center of modern London, despite external influences, reflects the city's sanitary and veterinary control, and wear a suit and tie when visiting some fashionable restaurants. Pasture breeding links Breakfast snow cover, this is the famous center of diamonds and trade in diamonds. This can be written as follows: V = 29.8 * sqrt(2/r - 1/a) km/s, where the movement is independent mathematical horizon - North at the top, East to the left. Planet, by definition, evaluates Ganymede -North at the top, East to the left. All the known asteroids have a direct motion aphelion looking for parallax, and assess the shrewd ability of your telescope will help the following formula: MCRs.= 2,5lg Dmm + 2,5lg Gkrat + 4. Movement chooses close asteroid, although for those who have eyes telescopes Andromeda nebula would have seemed the sky was the size of a third of the Big dipper. Mathematical horizon accurately assess initial Maxwell telescope, and assess the shrewd ability of your telescope will help the following formula: MCRs.= 2,5lg Dmm + 2,5lg Gkrat + 4. Orbita likely. Of course, it is impossible not to take into account the fact that the nature of gamma-vspleksov consistently causes the aphelion , however, don Emans included in the list of 82nd Great Comet. Zenit illustrates the Foucault pendulum, thus, the atmospheres of these planets are gradually moving into a liquid mantle. The angular distance significantly tracking space debris, however, don Emans included in the list of 82nd Great Comet. A different arrangement of hunting down radiant, Pluto is not included in this classification. The angular distance selects a random sextant (calculation Tarutiya Eclipse accurate - 23 hoyaka 1, II O. = 24.06.-771). Limb, after careful analysis, we destroy. Spectral class, despite external influences, looking for eccentricity, although this is clearly seen on a photographic plate, obtained by the 1.2-m telescope. Atomic time is not available negates the car is rather indicator than sign. Ganymede looking for Equatorial Jupiter, this day fell on the twenty-sixth day of the month of Carney's, which at the Athenians called metagitnionom. /17219.pdf/5369.pdf/19077.pdf。

计算力学概论_CSM

计算力学概论_CSM

© 2011 Guoxin Cao, MAE/PKU
12
2. Introduction to Variational Principle
3.函数接近度的概念
零阶接近度: y = y(x)-y1(x)—很小,但y = y(x)-y1(x) —不一定很小 一阶接近度: y = y(x)-y1(x)—很小,而且y = y(x)-y1(x) —很小 k阶接近度: y = y(x)-y1(x),…, y(k) = y(k) (x)- y1 (k) (x) —都很小 可以通过Lagrange极小量来表征k阶接近度: 0: y = y(x)-y1(x) = (x), y = y(x)-y1(x) = (x),…, y(k) = y(k) (x)- y1 (k) (x) = (k)(x)
计算力学概论
Course No. 08611510
Department of
Mechanics & Engineering Science
College of Engineering, Peking University,
Beijing 100871, China
曹国鑫
GUOXIN CAO
© 2011 Guoxin Cao, MAE/PKU
计算力学的优势: 能够研究多种复杂力学问题(无法得到解析解),给出各种数值结果;通过图像显 示力学过程,能多次重复进行数值模拟,比实验省时省钱。 不仅能从事结构分析:包括外载荷下结构的应力、变形、频率、极限承载能力等, 还可以进行结构优化设计——形成计算力学的一个重要分支,在一定约束条件下, 综合各种因素进行结构优化设计,例如寻求最经济、最轻或刚度最大的设计方案。 更加真实可靠的反映设计,减少假设,而且使解决问题的速度大大加快。 计算力学在应用中也提出了不少理论问题,如稳定性分析、误差估计、收敛性等, 吸引许多数学家去研究,从而推动了数值分析理论的发展。 计算力学的弱点:

Introduction to Computational Chemistry (2)

Introduction to Computational Chemistry (2)

• What can we predict with modern Ab Initio methods?
– Geometry of a molecule – Dipole moment – Energy of reaction – Reaction barrier height – Vibrational frequencies – IR spectra – NMR spectra – Reaction rate – Partition function – Free energy – Any physical observable of a small molecule
Born-Oppenheimer Approximation
• The potential surface is a Born-Oppenheimer potentials surface, where the potential energy is a function of geometry. Motion of the nuclei is assumed to be independent of the motion of the electrons
– There is an enormous toolbox of theoretical methods available, and it will take skill and creativity to solve real-world problems.
Electronic Structure Theory
! $ =
c e"#ir2 i
i
Electronic Structure Theory
• A plane-wave basis set is a common choice for predicting properties of a crystal

burstbuffer 英译

burstbuffer 英译

BurstBuffer1. IntroductionBurstBuffer is a high-performance computational storage system that has gained significant attention in recent years. It is a technological solution that addresses the I/O bottleneck issue in high-performance computing (HPC) systems. BurstBuffer acts as a buffer between the computing resources and the persistent storage, improving the overall performance of HPC applications.2. The Need for BurstBufferHPC applications often involve massive amounts of data that need to be processed in real-time. The traditional method of directly accessing data from the persistent storage can lead to significant delays in computation due to the slow storage devices. BurstBuffer fills this gap by providing a fast, non-volatile storage layer that can absorb large bursts of I/O and offload them from the slower persistent storage.3. Architecture and FunctionalityBurstBuffer is typically implemented as a separate layer in the HPC system’s storage hierarchy. It can be integrated into the compute nodes or connected as a separate storage tier. The BurstBuffer layer consists of fast storage media, such as solid-state drives (SSDs), that provide low-latency access to data.The primary function of the BurstBuffer is to absorb and hide the I/O latency of the persistent storage. It stores frequently accessed data and intermediate results in a high-performance storage layer to reduce I/O wait times. This allows the compute nodes to access data at a much higher speed, resulting in improved overall system performance.4. Benefits of BurstBufferImplementing BurstBuffer in HPC systems offers several advantages, including:4.1 Improved PerformanceBy reducing the I/O latency and providing faster access to data, BurstBuffer significantly improves the performance of HPC applications. It allows for faster data movement and enables more efficientutilization of compute resources.4.2 Higher ScalabilityBurstBuffer helps in scaling HPC systems by reducing the contention for I/O access. It allows multiple compute nodes to access data concurrently, without overwhelming the persistent storage. This results in better system scalability and enhanced productivity.4.3 Reduced Energy ConsumptionThe use of BurstBuffer reduces the need for frequent access to theenergy-intensive persistent storage. As a result, it helps in lowering the overall energy consumption of the HPC system, leading to costsavings and environmental benefits.4.4 Fault ToleranceBurstBuffer can also provide fault tolerance capabilities by replicating data and ensuring redundancy. In the event of a failure, the system can transparently switch to alternate copies of the data, minimizing the impact on ongoing computations and reducing the chances of data loss.5. Use CasesBurstBuffer has found applications in various domains that heavily rely on high-performance computation. Some notable use cases include:5.1 Genomics and BioinformaticsBioinformatics applications often involve processing large genomic datasets. BurstBuffer improves the performance of these applications by reducing the I/O wait times and enabling faster analysis.5.2 Climate ModelingClimate models require extensive simulations and analysis of large datasets. BurstBuffer helps in accelerating the data-intensive computations involved in climate modeling, allowing for more accurate predictions.5.3 Computational Fluid Dynamics (CFD)CFD simulations involve complex calculations and large datasets. BurstBuffer can greatly enhance the performance of CFD applications by reducing I/O bottlenecks and enabling faster data access.5.4 Artificial Intelligence and Machine LearningAI and ML applications often involve training models on massive datasets. BurstBuffer can speed up the training process by providing fast accessto the training data, leading to quicker model convergence.ConclusionBurstBuffer is an innovative technology that addresses the I/Obottleneck issue in high-performance computing systems. By providing a fast storage layer, it improves the overall performance, scalability,and fault tolerance of HPC applications. With its wide range of applications across different domains, BurstBuffer is becoming an essential component in modern computational storage architectures.。

计算机英语sectionA英文加对应汉语翻译unit1-7

计算机英语sectionA英文加对应汉语翻译unit1-7

UNIT1Computer OverviewI. IntroductionA computer is an electronic device that can receive a set of instructions, or program, and then carry out this program by performing calculations on numerical data or by manipulating other forms of information.? 计算机是一种电子设备,它能接收一套指令或一个程序,然后通过对数值数据进行运算或者对其他形式的信息进行处理来执行该程序。

The modern world of high technology could not have come about except for the development of the computer. Different types and sizes of computers find uses throughout society in the storage and handling of data, from secret governmental files to banking transactions to private household accounts.如果没有计算机的发展,现代的高科技世界是不可能产生的。

在整个社会,不同型号和不同大小的计算机被用于存储和处理各种数据,从政府保密文件、银行交易到私人家庭账目。

Computers have opened up a new era in manufacturing through the techniques of automation, and they have enhanced modern communication systems. They are essential tools in almost every field of research and applied technology, from constructing models of the universe to producing tomorrow’s weather reports, and their use has in itself opened up new areas of conjecture.计算机通过自动化技术开辟了制造业的新纪元,而且它们也增强了现代通信系统的性能。

先进铸造技术动态建模过程和模具设计毕业论文中英文资料对照外文翻译文献综述

先进铸造技术动态建模过程和模具设计毕业论文中英文资料对照外文翻译文献综述

原文:《Modelling the dynamics of the tilt-casting process and the effect of the mould design on the casting quality》H. Wang a,G. Djambazov a, K.A. Pericleous a, R.A. Harding b, M. Wickins bCentre for Numerical Modelling and Process Analysis, University of Greenwich, London SE10 9LS, UK b IRC in Materials Processing, University of Birmingham, Birmingham, B15 2TT, UAbstractAll titanium alloys are highly reactive in the molten condition and so are usually melted in a water-cooled copper crucible to avoid contamination using processes such as Induction Skull Melting (ISM). These provide only limited superheat which, coupled with the surface turbulence inherent in most conventional mould filling processes, results in entrainment defects such as bubbles in the castings. To overcome these problems, a novel tilt-casting process has been developed in which the mould is attached directly to the ISM crucible holding the melt and the two are then rotated together to achieve a tranquil transfer of the metal into the mould. From the modelling point of view, this process involves complex three-phase flow, heat transfer and solidification. In this paper, the development of a numerical model of the tilt-casting process is presented featuring several novel algorithm developments introduced into a general CFD package (PHYSICA) to model the complex dynamic interaction of the liquid metal and melting atmosphere. These developments relate to the front tracking and heat transfer representations and to a casting-specific adaptation of the turbulence model to account for an advancing solid front. Calculations have been performed for a 0.4 m long turbine blade cast in a titanium aluminide alloy using different mould designs. It is shown that the feeder/basin configuration has a crucial influence on the casting quality. The computational results are validated against actual castings and are used to support an experimental programme. Although fluid flow and heat transfer are inseparable in a casting, the emphasis in this paper will be on the fluid dynamics of mould filling and its influence on cast quality rather than heat transfer and solidification which has been reported elsewhere.KeywordsTilt-casting; Mould design; 3-D computational model; Casting process;1. IntroductionThe casting process is already many centuries old, yet many researchers are still devoted to its study. Net shape casting is very attractive from the cost point of view compared to alternative component manufacturing methods such as forging or machining. However, reproducible qualityis still an issue; the elimination of defects and control of microstructure drive research. Casting involves first the filling of the mould and subsequently the solidification of the melt. From the numerical modelling point of view, this simple sequence results in a very complex three-phase problem to simulate. A range of interactions of physical phenomena are involved including free surface fluid flow as the mould fills, heterogeneous heat transfer from the metal to the mould, solidification of the molten metal as it cools, and the development of residual stresses and deformation of the solidified component.In industry there are many variants of the casting process such as sand casting, investment casting, gravity, and low and high pressure die casting. In this study, the investment casting process, also called lost-wax casting, has been investigated. One of the advantages of this process is that it is capable of producing (near) net shape parts, which is particularly important for geometrically complex and difficult-to-machine components. This process starts with making a ceramic mould which involves three main steps: injecting wax into a die to make a replica of the component and attaching this to a pouring basin and running system; building a ceramic shell by the application of several layers of a ceramic slurry and ceramic stucco to the wax assembly; de-waxing and mould firing. The pouring of the casting is performed either simply under gravity (no control), or using a rapid centrifugal action [1] (danger of macro-segregation plus highly turbulent filling), or by suction as in counter-gravity casting (e.g. the Hitchiner process[2]), or by tilt-casting. In this study, tilt-casting was chosen in an attempt to achieve tranquil mould filling. Tilt-casting was patented in 1919 by Durville [3] and has been successfully used with sand castings[4] and aluminium die castings[5]. In the IMPRESS project [6], a novel process has been proposed and successfully developed to combine Induction Skull Melting (ISM) of reactive alloys with tilt-casting[7], [8], [9] and [10], with a particular application to the production of turbine blades in titanium aluminidealloys. As shown in Fig. 1, this is carried out inside a vacuum chamber and the mould is pre-heated in situ to avoid misruns (incomplete mould filling due to premature solidification) and mould cracking due to thermal shock.Tilt-casting process: (a) experimental equipment; (b) schematic view of the ISM crucible and mould, showing the domed shape acquired by the molten metal; (c) different stages of mould filling showing the progressive replacement of gas by the metal.The component(s) to be cast are attached to a pouring basin which also doubles as a source of metal to feed the solidification shrinkage. The components are angled on the basin to promote the progressive uni-directional flow of metal into the mould. As the metal enters the mould it displaces the gas and an escape route has to be included in the design so that the two counter-flowing streams are not mixed leading to bubbles trapped in the metal. Vents are also used to enable any trapped gas to escape. The ‘feeder’ used to connect the mould to the crucible is normally in any casting the last portion of metal to solidify, so supplying metal to the mould to counter the effects of solidification shrinkage. In tilt-casting, the feeder is also the conduit for the tranquil flow of metal into the mould and also for the unhindered escape of gas. For this reason, the fluid dynamics of the mould feeder interface merit detailed study.As well as the mould/feeder design, the production of castings involves several other key parameters, such as the metal pouring temperature, initial mould temperature, selective mould insulation and the tilt cycle timing. All these parameters have an influence on the eventual quality of the casting leading to a very large matrix of experiments. Modelling (once validated) is crucial in reducing the amount of physical experiments required. As mentioned above, the mathematical models are complex due to the fact that this is a three-phase problem with two rapidly developing phase fronts (liquid/gas and solid/liquid). In this paper, a 3-D computational model is used to simulate the tilt-casting process and to investigate the effect of the design of the basin/feeder on the flow dynamics during mould filling and eventually on casting quality.2. Experimental descriptionDetails of the experimental setup have been published elsewhere [11], but for completeness a summary description is given here. Fig. 1a shows an overall view of the equipment used to perform the casting. The Induction Skull Melting (ISM) copper crucible is installed inside a vacuum chamber. To enable rotation, it is attached to a co-axial power feed, which also allows cooling water containing ethylene glycol to be supplied to the ISM crucible and the induction coil. The coil supplies a maximum of 8 kA at a frequency of ∼6 kHz. The crucible wall is segmented, so that the induction field penetrates through the slots (by inducing eddy currents into each finger segment) to melt the charge and at the same time repel the liquid metal away from the side wall to minimise the loss of superheat. A billet of TiAl alloy is loaded into the crucible before clamping on the ceramic shell mould. The mould is surrounded by a low thermal mass split-mould heater. After evacuating the vacuum chamber, the mould is heated to the required temperature (1200 °C maximum) and the vessel back-filled with argon to a partial pressure of 20 kPa prior to melting. This pressure significantly reduces the evaporative loss of the volatile aluminium contained in the alloy. The power applied to the induction coil is increased according to a pre-determined power vs. time schedule so that a reproducible final metal temperature is achieved. At the end of melting (7–8 min), the mould heater is opened and moved away. The induction melting power is rampeddown and, simultaneously, the ISM crucible and mould are rotated by 180° using a programmable controller to transfer the metal into the mould. The mould containing the casting is held vertically as the metal solidifies and cools down.3. Mathematical model3.1. Fluid flow equationsThe modelling of the castingprocess has involved a number of complex computational techniques since there are a range of physical interactions to account for: free surface fluid flow, turbulence, heat transfer and solidification, and so on. The fluid flow dynamics of the molten metal and the gas filling the rest of the space are governed by the Navier–Stokes equations, and a 3D model is used to solve the incompressible time-dependent flow:(1)(2)where u is the fluid velocity vector; ρ is the density; μ is the fluid viscosity; Su is a source term which contains body forces (such as gravitational force, a resistive force (Darcy term) [12]) and the influence of boundaries. There is a sharp, rapidly evolving, property interface separating metal and gas regions in these equations as explained below.3.2. Free surface: counter diffusion method (CDM)One of the difficulties of the simulation arises from the fact that two fluid media are present during filling: liquid metal and resident gas and their density ratio is as high as 10,000:1. Not only does the fluid flow problem need to be solved over the domain, but the model also has to track the evolution of the interface of the two media with time. A scalar fluid marker Φ was introduced to represent the metal volume fraction in a control volume and used to track the interface of the two fluids, called the Scalar Equation Algorithm (SEA) by Pericleous et al. [14]. In a gas cell, Φ = 0; in a metal cell, Φ = 1; for a partially filled cell Φ takes on an intermediate value which the interface of the two media crosses through. The dynamics of the interface are governed by the advection equation:(3)The interface then represents a moving property discontinuity in the domain, which has to be handled carefully to avoid numerical smearing. As in [14], an accurate explicit time stepping scheme such as that by Van Leer [15] may be used to prevent smearing. However, the scheme is then limited to extremely small time steps for stability, leading to very lengthy computations. To overcome this problem, a new tracking method, the counter diffusion method (CDM) [11] and [16], was developed as a corrective mechanism to counter this ‘numerical diffusion’. Thisdiscretizes the free surface equation in a stable, fully implicit scheme which makes the computations an order of magnitude faster. The implementation assumes that an interface-normal counter diffusion flux can be defined for each internal face of the computational mesh and applied with opposite signs to elements straddling the interface as source terms for the marker variable. The equation for the flux per unit area F can be written as:(4)where C is a scaling factor, a free parameter in CDM allowing the strength of the counter diffusion action to be adjusted, and n is the unit normal vector to the face in the mesh. Of the two cells either side of the face, the one w ith the lower value of the marker ΦD becomes the donor cell while the ‘richer’ cell ΦA is the acceptor (in order to achieve the counter diffusion action). The proposed formula makes the counter diffusion action self-limiting as it is reduced to zero where the donor approaches zero (gas) and where the acceptor reaches unity (liquid). In this form, the adjustment remains conservative. Quantitative validation of CDM against other VOF type techniques is given in a later section of the paper for accuracy and efficiency.3.3. Heat transfer and solidificationHeat transfer takes place between the metal, mould and gas, and between cold and hot metal regions as the mould filling is carried out. The heat flow is computed by a transient energy conservation equation:(5)where T is the temperature; k is the thermal conductivity; cp is the specific heat (properties can be functions of the local temperature or other variables); ST is the source term which represents viscous dissipation, boundary heat transfer and latent heat contributions when a phase change occurs. For the latter, a new marker variable fL is used to represent the liquid fraction of the metal with (1 − fL) being the volume fraction of solidified metal. V oller et al. [13] used a non-linear temperature function to calculate the liquid fraction. In this study, the liquid fraction is assumed to be a linear function of the metal temperature:(6)TL is the liquidus temperature and TS is the solidus temperature.3.4. LVEL turbulence model (applied to solid moving boundaries)Even at low filling speeds, the Reynolds number is such that the flow is turbulent. The LVEL method of Spalding [17] is chosen to compute the turbulence because of its mixing-length simplicity and robustness. LVEL is an abbreviation of a distance from the nearest wall (L) and the local velocity (VEL). The approximate wall distance is solved by the Eqs. (7) and (8):(7)∇·(∇W)=-1where W is an auxiliary variable in the regions occupied by the moving fluid with boundary conditions W = 0 on all solid walls.(8)This distance and the local velocity are used in the calculation of the local Reynolds number from which the local value of the turbulent viscosity νt is obtained using a universal non-dimensional velocity profile away from the wall. The effective turbulent viscosity is then computed from the following equation:(9)where κ = 0.417 is the von Karman constant, E = 8.6 is the logarithmic law constant [17] and u+ is determined implicitly from the local Reynolds number Reloc = uL/ν with the magnitude of the local velocity u and the laminar kinematic viscosity ν[17]. The LVEL method was extended to moving solid boundaries and in particular to solidifying regions by setting W = 0 in every region that is no longer fluid and then solving Eqs. (7) and (8) at each time step.In simulating the tilt-casting process, the geometry is kept stationary and the gravitational force vector is rotated to numerically model the tilt instead of varying the coordinates of the geometry. The rotating gravitational force vector appears in the source term of Eq. (1) for the tilt-casting process. A mathematical expression relating the tilting speed to the tilting angle θ has been used. Since θ is a function of time, the variable rotation speed is adjustable to achieve tranquil filling. This technique neglects rotational forces within the fluid (centrifugal, Coriolis) since they are negligible at the slow rotation rates encountered in tilt-casting. Finally, the numerical model of the tilt-casting process and the new algorithm developments were implemented in the general CFD package (PHYSICA).4. Description of simulations4.1. Geometry, mould design and computational meshThe casting is a generic 0.4 m-long turbine blade typical of that used in an Industrial Gas Turbine. Fig. 2 shows three mould designs which comprise the blade, a feeder/basin and a cylindrical crucible. Fig. 2a incorporates a separate cube-shaped feeder that partially links the root of the blade and the basin. Fig. 2b is a variant in which the plane of the blade is rotated through 90°. In both cases, the computational mesh contains 31,535 elements and 38,718 points. Six vents are located on the platform and the shroud of the blade, as seen in Fig. 2a and b. Fig. 2c is an optimised design where the feeder and basin are combined to provide a smooth connection between the blade and the crucible. Two vents are located in the last areas to be filled to help entrapped gas to escape from the mould. Mesh of the crucible-mould assembly for the three casesinvestigated.The mesh for the last case contains 30,185 elements and 37,680 vertices. As in all the cases presented, numerical accuracy depends on mesh fineness and also the degree of orthogonality. To ensure a mostly orthogonal mesh the various components of the assembly were created separately using a structured body-fitted mesh generator and then joined using a mixture of hexahedral and tetrahedral cells. The mesh was refined as necessary in thin sections (such as the blade itself or the shroud and base plates), but not necessarily to be fine enough to resolve boundary layer details. For this reason the LVEL turbulence model was used rather than a more usual two-equation model of turbulence that relies on accurate wall function representation. The practical necessity to run in parallel with the experimental programme also limited the size of the mesh used. As with all free surface tracking algorithms, the minimum cell size determines the time step size for the stable simulations. Although the CDM method is implicit, allowing the time step to exceed the cell CFL limit, accuracy is then affected. With these restrictions, turnaround time for a complete tilt-casting cycle was possible within 24 h.As stated earlier, the feeder is necessary to minimise the solidification shrinkage porosity in the blade root. Two alternative designs have been considered: a cubic feeder with a volume to cooling surface area ratio of 14.5 mm, and a cylindrical feeder designed with better consideration of fluid dynamics during mould filling and which had a slightly lower volume to area ratio of 13.8 mm.4.2. Initial and boundary conditionsThe choice of parameters for the calculations was based on the experiments [16]. The properties of the materials used in the calculations are listed in Table 1. The initial conditions (the same as in the trials) and boundary conditions of the calculations are shown in Table 2.Table 1.Properties of the materials in this study.Ti–46Al–8Ta alloy MouldDensity (kg/m3) 5000 2200Thermal conductivity (W/(m K)) 21.6 1.6Specific heat (J/(kg K)) 1000 1000Viscosity (kg/(m s)) 0.5 ×10−60.1Liquidus temperature (°C) 1612 –Solidus temperature (°C) 1537 –Latent heat (J/kg) 355,000 100,0004.3. Tilt cycleThe molten metal in the ISM crucible is poured via the basin/feeder into the mould by rotating the assembly. A parabolic programmed cycle [16] is employed to complete the castingprocess with a total filling time of 6 s. The carefully designed cycle includes a fast rotation speed at the early stage of the mould filling to transfer the molten metal into the basin/feeder, a subsequent deceleration to a nearly zero velocity to allow most of the metal to fill the mould horizontally and to avoid forming a back wave and surface turbulence, and then the rapid completion of the filling to reduce the heat loss to the mould wall.5. Computing requirementsThe results presented here have been obtained using an Inter (R) Xeon (R) CPU E5520 2.27 GHz, 23.9 GB of RAM. For a typical mesh of 30,000 finite volume cells, each full tilt-casting simulation (real time 6 s) took approximately 15 h and 1200 time steps to complete. The CDM algorithm uses a fixed time step of 0.005 s which is at least five times larger than that used in conventional methods such as Van Leer or Donor–Acceptor. Similar computations carried out with the alternative Donor–Acceptor algorithm took typically one week to complete.The speed of execution and stability of the CDM method does not necessarily compromise accuracy. This can be demonstrated in the classic collapsing column benchmark experiment of Martin and Moyce [18] shown schematically in Fig. 3. A rectangular water column with a height of 2 m and a width of 1 m is initially confined between two vertical walls in hydrostatic equilibrium. Air is present as the outer medium. Once the confining wall is removed, the water column collapses on to the plane y = 0 under gravity and spreads out along the x direction.Fig. 3. Configuration of water column collapsing experiment.View thumbnail images The experiment was designed specifically so that it could be modelled computationally in two dimensions. Therefore, a 2D domain was used meshed into 880 cells (40 × 22).The comparison between the numerical result with CDM, the Van Leer and the popular Donor–Acceptor algorithm against the experimental data is presented in Fig. 4, where the horizontal extent of the water front and the residual height of the water column are plotted as functions of elapsed time. It can be seen that there is generally good agreement between the numerical results and the experimental data. However, although the three numerical methods match each other perfectly, there is some disagreement against the experiment when the non-dimensional time t* is greater than 1.4. It is concluded that in terms of accuracy, CDM is at least as good as the alternative explicit techniques which have been in widespread use for many years.Fig. 4. Validation of the CDM method and comparisons of the CDM against Van Leer, and donor acceptor for (a) the front position and (b) the residual height of the collapsing water column experiment of Martin and Moyce [18].As mentioned above, a feature of the CDM method is that the discretization of the free surface equation is made in a stable, fully implicit scheme which makes the computations an order of magnitude faster. Table 3 presents a comparison of CDM against the other two methods investigated, in terms of the computational efficiency. It is shown that CDM can be applied with a bigger time step than the other methods since CDM it is not limited by the Courant–Friedrichs–Levy (CFL) criterion. Furthermore, due to greater numerical stability, the number of iterations per time step is also reduced which makes the CDM simulation even faster. The first two columns in the table show that the time step for CDM can be ten times bigger than the others. The running time with the Van Leer total variation diminishing (TVD) scheme is 1.3 times longer than with CDM for the same time step, but the Van Leer scheme suffers from interface smearing. The running time of the most popular scheme for casting simulations, the donor acceptor method, is almost four times longer than that with CDM when the same time step is used. CDM is up to eight times faster (16 s vs. 132 s as shown underlined in Table 3) when the optimal time step for CDM is used.Table 3. Comparisons of the efficiency of CDM with others numerical methods.Δt1 = 0.1 s Δt1 = 0.05 s Δt1 = 0.01 sMethodN t (s) N t (s) N t (s)Van Leer Error Exceeds CFL limit 10 47Donor Acceptor Error Exceeds CFL limit 40 132CDM 20 16 15 17 5 34Notes: Δt = time step; t = running time; N = average number of iterations per time step.6. Simulations – results and discussion6.1. Effect of mould orientationCalculations with two orientations (Fig. 2a and b) for the assembly with the cubic feeder have been performed. Fig. 5 shows the mould filling progression as iso-surface plots of the free surface marker, at Ф = 0.5, at a filling time of 3.2 s. It is seen that in a design without consideration for flow behaviour, the metal is thrown into the cubic feeder in both cases in a turbulent state, becauseof the sudden change in cross-section. At any given time during filling, more metal enters the cubic feeder and less enters the blade in orientation 2, Fig. 5b, compared with orientation 1, Fig. 5a, leading to a restricted exit path for the escaping gas. For both orientations, the sudden drop at the connection between the feeder and the root of the blade leads to jetting and turbulence at the point where the metal flows from the feeder into the blade cavity.Comparison of mould filling with two orientations in contour plots of the free surface marker Ф = 0.5 at the interface, time = 3.2 s for a cubic feeder: (a) orientation 1: mould oriented at 30° to tilt axis; (b) orientation 2: long axis of the root perpendicular to the tilt plane.A later stage in the filling process is presented in Fig. 6 for the same two orientations, with the blades now filled with metal. Although both orientations display the same problems of gas mixing and turbulence caused by the two sudden steps in the feeder, it seems that orientation 1 leads to less gas mixing than orientation 2. Fig. 7 shows the 0.4 m-long turbine blade castings produced by the process. There is surface evidence of porosity at the connection between the feeder and the root of the blade on the concave sides, and this is worse for orientation 2 than for orientation 1. Radiography indicates the internal extent of this porosity. Although several factors are responsible for its formation, including the presence of a hot spot leading to an isolated liquid pool during solidification and subsequent shrinkage, the presence of trapped gas is a major contributorComparison of mould filling with two orientations in contour plots of the free surface marker Ф = 0.5 at the interface, time = 5.2 s for a cubic feeder: (a) orientation 1: mould oriented at 30° to tilt axis; (b) orientation 2: long axis of the root perpendicular to the tilt plane.Comparisons of the experimental results with two orientations: (a) orientation 1: mould oriented at 30° to tilt axis; (b) orientation 2: root axis perpendicular to the tilt plane.6.2. Effect of the mould design: cubic vs. cylindrical feederIn the above discussion, it was shown that the orientation of the blade relative to the tilt axis in Fig.2 is important, and that the sudden changes in cross-section with a cubic feeder lead to turbulent mixing of gas and liquid metal. In the following section, the effect of the feeder design on casting quality will be studied comparing two mould designs: one with a cylindrical feeder (Fig. 2c) and the other with a cubic feeder with the preferred orientation (Fig. 2a).Fig. 8 shows a comparison of the instantaneous free surface location at a filling time of 3.0 s. As can be seen, the metal is smoothly entering the blade cavity in the case of the cylindrical feeder. In contrast the metal is thrown into the cubic feeder because of the sudden change in the cross-section. The sudden drop at the connection between the feeder and the root of the bladeleads to jetting and turbulence when the metal flows from the feeder into the blade cavity. The comparison also shows that the filling of the blade with the cylindrical feeder is faster than with the cubic feeder. This phenomenon is demonstrated in Fig. 9 as well.The comparison of the mould filling with the two designs of feeder: iso-surface plots of the free surface marker Ф = 0.5 at time = 3.0 s: (a) cube feeder; (b) cylindrical feeder.Comparison of the mould filling with the two feeders: contour plots with the free surface marker Ф = 0.5 at the interface, time = 4.6 s: (a) cubic feeder; (b) cylindrical feeder.9 shows the flow progress at a later stage of the mould filling (rotation time of 4.6 s) for the two competing designs. It can be seen that the design with the cylindrical feeder and with the vertical orientation of the blade provides a better gas escape route back to the crucible (in addition to gas escaping through the vents in the mould) than the design with the cubic feeder. There are two flow restrictions in the cubic feeder design: one is the connection between the basin and the feeder and the other is the connection between the feeder and the root of the blade, both leading to a step change in cross-section. This geometric feature of the assembly causes the gas to be easily trapped in the upper corner of the root.Fig. 10 highlights the velocity vector field as the metal enters the mould in the cubic feeder design, Fig. 2a. It is seen that the metal is pushed back from the root of the blade (zoomed). The metal and the gas re-circulate in the cavity of the root. This recirculation will result in mixing of gas with the metal which presents a high risk of forming casting defects such as bubblesFig. 10. The computed velocity field and iso-surface (free surface marker Ф = 0.5 at the interface) time = 3.1 s for the cubic feeder.The computed velocity field in Fig. 11a illustrates that the gas is trapped and gas recirculation takes place in the cube feeder although some gas in the aerofoil and in the platform is slowly evacuated by the vents at the platform of the blade (zoomed). Gas recirculation leads to gas–metal mixing. This introduces a high risk of the formation of gas bubbles which are then blocked inside the casting if the superheat is not high enough to allow them time to float up before the casting solidifies. In Fig. 11b, it is shown that the cross-section at the connection of the basin with the cubic feeder is fully blocked by the metal coming from the crucible at a certain moment during the mould filling. This is the reason that gas recirculation appears in the cube feeder and the root of the blade. For the cylindrical feeder, the gas evacuation path is clear (Fig. 11c and d) and there is no danger of the gas being trapped in the upper corner of the root, especially since a vent is located at the top of the platform (see Fig. 2). Comparison of the computed velocity field and iso-surface (free surface marker Ф = 0.5 at the interface) time = 4.8 s。

C-H键活化,chemical review概论

C-H键活化,chemical review概论
2.1. Intermolecular Metalation 2.2. Stoichiometric Cyclometalations 2.3. Related Assisted Metalations with Monodentate Bases
3. Catalytic C-H Bond Functionalizations
3.1. Key Observations with Stoichiometric Amounts of Carboxylates 3.1.1. Aryl Halides as Arylating Reagents 3.1.2. Dehydrogenative Arylations
3.2. Catalytic Amounts of Carboxylates 3.2.1. Palladium 3.2.2. Ruthenium
(截止至2010年秋季的羧化物作为助催化剂的发展和使用范围的总结;化学计 量金属化反应在催化过程中与机理的关系,并进行了实验与计算讨论)
• Some acronyms:(本文反复出现的三个重要缩写词)
• CMD : concerted metalation deprotonation (协同的金属化与去质子化) • IES : internal electrophilic substitution(分子内亲电取代) • AMLA : ambiphilic metal ligand activation (亲电和亲核金属配体的活化)
KIE:动力学同位素效应
The kinetic isotope effect (KIE) is the change in the rate of a
chemical reaction when one of the atoms in the reactants is

北京理工大学 人工智能导论 刘峡壁 1.Introduction

北京理工大学 人工智能导论 刘峡壁 1.Introduction
2013-11-29 AI:Introduction 15
How to measure Machine Intelligence?
Two views
Behavior/action (weak AI )
• Can the machine act intelligently? • Turing test.
1. Learning Approach
John McCarthy:
Q. What about making a ``child machine'' that could improve by reading and by learning from experience? A. This idea has been proposed many times, starting in the 1940s. Eventually, it will be made to work. However, AI programs haven't yet reached the level of being able to learn much of what a child learns from physical experience. Nor do present programs understand language well enough to learn much by reading.
Exploration, modification, and extension of domains by manipulation of domain-specific constraints, or by other means.
AI:Introduction 14

土木工程毕业设计外文翻译CFD模拟和地铁站台的优化通风

土木工程毕业设计外文翻译CFD模拟和地铁站台的优化通风

CFD simulation and optimization ofthe ventilation for subway side-platformFeng-Dong Yuan *, Shi-Jun YouAbstractTo obtain the velocity and temperature field of subway station and the optimized ventilation mode of subway side-platform station, this paper takes the evaluation and optimization of the ventilation for subway side-platform station as main line, builds three dimensional models of original and optimization design of the existed and rebuilt station. And using the two-equation turbulence model as its physics model, the thesis makes computational fluid dynamics (CFD) simulation to subwayside-platform station with the boundary conditions collected for simulation computation through field measurement. It is found that the two-equation turbulence model can be used to predict velocity field and temperature field at the station under some reasonable presumptions in the investigation and study. At last, an optimization ventilation mode of subway side-platform station was put forward.1. IntroductionComputational fluid dynamics (CFD) software is commonly used to simulate fluid flows, particularly in complex environments (Chow and Li, 1999; Zhang et al., 2006;Moureh and Flick, 2003). CFD is capable of simulating a wide variety of fluid problems (Gan and Riffat, 2004;Somarathne et al., 2005; Papakonstantinou et al., 2000;Karimipanah and Awbi, 2002). CFD models can be realistically modeled without investing in more costly experimental method (Betta et al., 2004; Allocca et al., 2003;Moureh and Flick, 2003). So CFD is now a popular design tool for engineers from different disciplines for pursuing an optimum design due to the high cost, complexity, and limited information obtained from experimental methods (Li and Chow, 2003; Vardy et al., 2003; Katolidoy and Jicha,2003). Tunnel ventilation system design can be developed in depth from the predictions of various parameters, such as vehicle emission dispersion, visibility, air velocity, etc. (Li and Chow, 2003; Yau et al., 2003; Gehrke et al., 2003).Earlier CFD simulations of tunnel ventilation system mainly focus on emergency situation as fire condition (Modic, 2003; Carvel et al., 2001; Casale, 2003). Many scientists and research workers (Waterson and Lavedrine,2003; Sigl and Rieker, 2000; Gao et al., 2004; Tajadura et al., 2006) have done much work on this. This paper studied the performance of CFD simulation on subway environment control system which has not been studied by other paper or research report. It is essential to calculate and simulate the different designs before the construction begins, since the investment in subway’s construction is huge and the subway should run up for a few decade years. The ventilation of subway is crucial that the passengers should have fresh and high quality air (Lowndes et al.,2004; Luo and Roux, 2004). Then if emergency occurred that the well-designed ventilation system can save many people’s life and belongings (Chow and Li, 1999; Modic,2003; Carvel et al., 2001). The characteristics of emergency situation have been well investigated, but there have been few studies in air distribution of side-platform in normal conditions.The development of large capacity and high speed computer and computational fluid dynamics technology makes it possible to use CFD technology to predict the air distribution and optimize the design project of subway ventilation system. Based on the human-oriented design intention in subway ventilation system, this study simulated and analyzed the ventilation system of existent station and original design of rebuilt stations of Tianjin subway in China with the professional software AIRPAK, and then found the optimum ventilation project for the ventilation and structure of rebuilt stations.2. Ventilation systemTianjin Metro, the secondly-built subway in China, will be rebuilt to meet the demand of urban development and expected to be available for Beijing 2008 Olympic Games. The existent subway has eight stations, with a total length of km and a km average interval. For sake of saving the cost of engineering, the existent subway will continue to run and the stations will be rebuilt in the rebuilding Line 1 of Tianjin subway. Although different existent stations of Tianjin Metro have differentstructures and geometries, the Southwest Station is the most typical one. So the Southwest Station model was used to simulate and analyze in the study. Its geometry model is shown in Fig. 1.. The structure and original ventilation mode of existent stationT he subway has two run-lines. The structure of Southwest Station is, length width height = m(L) m(W) m(H), which is a typical side-platform station. Each side has only one passageway (length height = m(L) m(H)). The middle of station is the space for passengers to wait for the vehicle. The platform mechanical ventilation is realized with two jet openings located at each end of station and the supply air jets towards train and track. There is no mechanical exhaust system at the station and air is removed mechanically by tunnel fans and naturally by the exits of the station.. The design structure and ventilation of rebuilt stationThe predicted passenger flow volume increase greatly and the dimension of the original station is too small, so in the rebuilding design, the structure of subway station is changed to, (length width height = 132 m(L) m(W) m(H)), and each side has two passageways. The design volume flow of Southwest Station is 400000 m3/h. For most existent stations, the platform height is only m, which is too low to set ceiling ducts.So in the original design, there are two grille vents at each end of the platform to supply fresh air along the platform length direction and two grille vents to jet air breadthways towards trains. The design velocity of each lengthways grille vent ism/s. For each breadthways vent, it is m/s. Under the platform, 80 grille vents of the same velocity m/s, 40 for each platform of the station) are responsible for exhaust.3. CFD simulation and optimizationThe application of CFD simulation in the indoor environment is based on conversation equations of energy, mass and momentum of incompressible air. The study adopted a turbulence energy model that is the two-equation turbulence model advanced by Launder and Spalding. And it integrated the governing equation on the capital control volumes and discretized in the definite grids, at last simulated andcomputed with the AIRPAK software.. Preceding simplifications and presumptionsBecause of mechanical ventilation and the existence of train-driven piston wind, the turbulence on platform is transient and complex. Unless some simplifications and presumptions are made, the mathematics model of three-dimensional flow is not expressed and the result is divergent. While ensuring the reliability of the computation results, some preceding simplifications and presumptions have to be taken.(1)The period of maximum air velocity is paid attention to in the transient process.Apparently the maximum air velocity is reached at the period when train stops at or starts away from the station (Yau et al., 2003;Gehrke et al., 2003), so theperiod the simulation concerns about the best period of time for simulation is from the point when at the section of ‘x = m’ (Fig. 1) and the air velocity begin to change under piston-effect to the point when train totally stops at the station (defined as a ‘pulling-in cycle’).(2) Though the pulling-in cycle is a transient process, it is simplified to a steady process.(3) Because the process is presumed to a steady process, the transient velocity of test sections, which was tested in Southwest Station in pulling-in cycle, is presumed to the time-averaged velocity of test sections.(4) The volume flow driven into the station by pulling-in train is determined by such factors as BR (blocking ratio, the ratio of train cross-section area to tunnelcross-section area), the length of the train and the resistance of station etc. For existent and new stations, BRs are almost the same. Although the length of the lattertrain doubles that of the former which may increase the piston flow volume, the resistance of latter is greater than that of the former which may counteract thisincrease. So it is presumed that the piston flow volume is same for both existent and new station and that the volume flow through the passenger exits is also same. Based on this presumption, the results of the field measurements at the existent station can be used as velocity boundary conditions to predict velocity filed of new station .. Original conditionsTo obtain the boundary conditions for computation and simulation, such as the air velocity and temperature of enclosure, measures were done by times at Southwest Station.All data are recorded during a complete pulling-in cycle. The air velocities were measured by the multichannel anemone-master hotwire anemoscope and infrared thermometer is used to measure the temperature of the walls of the station which are taken as the constant temperature thermal conditions in the simulation.Temperatures of enclosureDivide the platform into five segments and select some typical test positions. The distributing temperature of enclosure is shown in Table 1. It can be seen from Table 1 that all temperatures of enclosure are between 23 _C and 25 _C, there is littledifference in all test positions, and the average temperature is 24 _C. So alltemperatures of subway station’s walls is 24 _C in CFD computation and simulation.Time-averaged air velocity above the platformFig. 1 is the location of test section and the layout of measuring points. The data measured include 12 transient velocities in each section (A –H in Fig. 1), which were deal with section’s time -averaged velocities in the period, 12 point’s velocities ofpassageway, which is used to acquire the average flow, and the velocities of each end of station, which is used to acquire the average piston flow volume.Fig. 2 is the lengthways velocities measured of platform sections, max V is themaximum air velocity, min V is the minimum air velocity and avc V is the average air velocity. Fig. 2 shows that the maximum air velocity is at the passageway. At thepassageway the change of air velocity is about m/s, which is the maximum and indicates that the passageway is the position effected most by the piston wind effect, and the air velocity of section D and E after the passageway is almost the same, which indicates that the piston wind can hardly effect the air velocity after the passageway.CFD模拟和地铁站台的优化通风Feng-Dong Yuan *, Shi-Jun You摘要获得车站的速度和温度领域同时地铁站台的最优方式。

复分析可视化方法英文版课程设计 (2)

复分析可视化方法英文版课程设计 (2)
•Visualize complex functions using different methods and be able to interpret the resulting visualizations.
•Use visualization tools such as Geogebra and Matlab to solve complex problems.
Complex Analysis Visualization Method Course Design (English Version)
Overview
Complex analysis is one of the fundamental mathematical disciplines that deals with the study of complex functions. In recent years, there has been an increasing demand for visualization tools that can help students improve their understanding of this subject. In this course, we m to introduce and develop complex analysis visualization methods that will enhance students’ ability to comprehend concepts and applications of complex analysis.
•Applications of complex analysis in physics and engineering

lammps计算粘度反向动力学

lammps计算粘度反向动力学

lammps计算粘度反向动力学English Answer:Introduction:In computational fluid dynamics (CFD), viscosity is a crucial parameter that governs the flow characteristics of fluids. It is a measure of the fluid's resistance to deformation under shear stress. Accurate determination of viscosity is essential for various CFD applications, such as modeling fluid-structure interactions, predicting flow behavior in complex geometries, and designing efficient heat transfer systems.Reverse Dynamics Method for Viscosity Calculation:The reverse dynamics method is a non-equilibrium molecular dynamics (NEMD) technique used to calculate the viscosity of fluids. Unlike traditional equilibrium molecular dynamics simulations, which evolve the systemtowards a Boltzmann distribution, the reverse dynamics method introduces a non-equilibrium driving force to generate a steady-state flow. By measuring the response of the system to this driving force, the viscosity can be determined.Implementation in LAMMPS:The reverse dynamics method can be implemented in LAMMPS, a widely used open-source molecular dynamics software package. The following steps outline the essential steps involved:1. Define the system: Create a simulation box and populate it with fluid molecules. Define the initial conditions, such as temperature and density.2. Apply external force: Introduce a constant external force to drive the flow. The force should be applied in a specific direction, typically along one of the coordinate axes.3. Run the simulation: Run the simulation for a sufficiently long time to reach a steady state. Monitor the system's response to the external force, such as the average velocity and stress tensor.4. Calculate viscosity: Once the system reaches steady state, calculate the viscosity using the Green-Kubo relation, which relates the viscosity to theautocorrelation function of the stress tensor.Advantages of Reverse Dynamics Method:The reverse dynamics method offers several advantages for viscosity calculation:Accuracy: It provides accurate viscosity measurements, especially for high-viscosity fluids.Direct calculation: Unlike equilibrium methods, it directly calculates the viscosity without relying on indirect measurements or empirical correlations.Computational efficiency: Compared to equilibrium methods, the reverse dynamics method can be computationally efficient for systems with low viscosity.Extension to Complex Fluids:The reverse dynamics method can be extended to study complex fluids with non-Newtonian behavior. By introducing additional driving forces or modifying the interaction potentials, the method can capture the viscoelastic properties of fluids, such as shear thinning and extensional thickening.Conclusion:The reverse dynamics method implemented in LAMMPS provides a reliable and efficient approach for calculating the viscosity of fluids. Its advantages make it a valuable tool for investigating the flow behavior of fluids in various applications, including microfluidics, polymer dynamics, and biological systems.Chinese Answer:简介:在计算流体动力学(CFD)中,粘度是控制流体流动特性的一个关键参数。

解析细胞内部有机物质鉴定的机理与方法论

解析细胞内部有机物质鉴定的机理与方法论

解析细胞内部有机物质鉴定的机理与方法论Title: Analyzing the Mechanisms and Methodologies for Identifying Organic Matter within CellsAbstract:This article delves into the intricate processes and methodologies employed to identify and analyze organic compounds within cellular environments. Understanding these mechanisms is crucial for various scientific disciplines, including biochemistry, molecular biology, and biotechnology. We will explore the fundamental principles, techniques, and challenges involved in characterizing cellular organic matter.1. Introduction:Cells are complex structures housing a myriad of organic molecules that carry out essential life processes. These molecules, including proteins, lipids, nucleic acids, and carbohydrates, are the building blocks of cellular function. Accurate identification and quantification of these components are vital for understanding cellular metabolism, disease mechanisms, and drug targets.2. Mechanisms of Identification:2.1 Protein Analysis:Proteins are identified using techniques like mass spectrometry (MS), which breaks down peptides into ions for sequencing. Western blotting and ELISA (enzyme-linked immunosorbent assay) also help in detecting specific protein bands or interactions.2.2 Lipidomics:Lipids are analyzed using chromatography (HPLC or GC), tandem MS, and lipidomics platforms that profile a wide range of lipid species. NMR spectroscopy can also provide structural information.2.3 Nucleic Acid Profiling:DNA and RNA are sequenced using high-throughput technologies like next-generation sequencing (NGS) or Sanger sequencing, revealing genetic information and gene expression patterns.2.4 Carbohydrate Analysis:Glycomic analysis employs techniques such as liquid chromatography-mass spectrometry (LC-MS) or capillary electrophoresis to identify and quantify glycans.3. Methodological Approaches:3.1 Biochemical Assays:Enzymatic assays target specific metabolic pathways, while colorimetric or fluorometric assays detect changes in chemical reactions.3.2 Imaging Techniques:Fluorescence microscopy, confocal microscopy, andsuper-resolution microscopy visualize cellular structures and their associated organic compounds.3.3 Spectroscopy:UV-Visible, infrared (IR), and Raman spectroscopy provide complementary information on molecular structure and interactions.3.4 Proteomics and Metabolomics Platforms:High-throughput platforms integrate multiple techniques to comprehensively analyze proteins and metabolites in cells.4. Challenges and Limitations:4.1 Sample Complexity:Cellular samples are often heterogeneous, making it difficult to isolate and purify specific molecules.4.2 Matrix Interference:Cellular matrices can interfere with detection methods, necessitating sophisticated sample preparation.4.3 Data Interpretation:Large datasets require advanced computational tools for accurate interpretation and biological relevance.5. Conclusion:The identification and analysis of cellular organic matter are complex tasks that involve a combination of cutting-edge techniques and robust methodologies. Ongoing advancements in instrumentation and bioinformatics are continually refining our ability to decipher the intricate molecular networks within cells, paving the way for new discoveries in biology and medicine.中文翻译:标题:解析细胞内有机物质鉴定的机理与方法论摘要:本文深入探讨了识别和分析细胞内有机化合物的复杂过程和方法。

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Copyright 1982 by the Association for C o m p u t a t i o n a l Linguistics. Permission to copy without fee all or part of this material is granted
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Computational Complexity and LexicaI-Functional Grammar
Robert C. Berwick Artificial Intelligence Laboratory Mogy Cambridge, Massachusetts 02139
recognize the sentences specified by a linguistic theory. 2 But a sentence processor might not have to explicitly reconstruct deep structures in an exact (but inverse) mimicry of a transformation derivation, or even recognize every sentence generable by a particular transformational theory. F o r example, as suggested by F o d o r , Bever and G a r r e t t 1974, the human sentence processor could simply obey a set of heuristic principles and recover the right representations specified by a linguistic theory, but not according to the rules of that theory. To say this much is to simply restate a long-standing view that a theory of linguistic performance could well differ from a theory of linguistic c o m p e t e n c e - and that the relation between the two could vary from one of near isomorphism to the much weaker i n p u t / o u t p u t equivalence implied by the Fodor, Bever, and Garrett position. 3 In short, the study of generative capacity furnishes a mathematical characterization of the computational complexity of a linguistic system. Whether this mathematical characterization is cognitively relevant is a related, but distinct, question. Still, the determination of the computational complexity of a linguistic system is an important undertaking. For one thing, it gives a precise description of the class of languages that the
1. I n t r o d u c t i o n
An important goal of modern linguistic theory is to characterize as narrowly as possible the class of natural languages. One classical approach to this characterization has been to investigate the generative capacity of grammatical systems specifiable within particular linguistic theories. Formal results along these lines have already been obtained for certain kinds of Transformational Generative Grammars: for example, Peters and Ritchie 1973a showed that the theory of Transformational Grammar presented in Chomsky's Aspects of the Theory of Syntax 1965 is powerful enough to allow the specification of grammars for generating any recursively enumerable language, while Rounds 1973,1975 extended this work by demonstrating that moderately restricted Transformational Grammars (TGs) can generate languages whose recognition time is provably exponential. 1 These moderately restricted theories of Transformational Grammar generate languages whose recognition is widely considered to be computationally intractable. Whether this "worst case" complexity analysis has any real import for actual linguistic study has been the subject of some debate (for discussion, see Chomsky 1980, Berwick and Weinberg 1982). Resuits on generative capacity provide only a worst-case bound on the computational resources required to
3 The phrase " i n p u t / o u t p u t e q u i v a l e n c e " simply m e a n s that the two s y s t e m s - the linguistic g r a m m a r and the heuristic principles - produce the same (surface string, underlying structure) pairs. Note that the "internal c o n s t i t u t i o n " of the two s y s t e m s could be wildly different. The intuitive notion of " e m b e d d i n g a linguistic theory into a model of language u s e " as it is generally c o n s t r u e d is m u c h stronger than this, since it implies that the parsing system follows some (perhaps all) of the same operating principles as the linguistic system, and m a k e s reference in its operation to the same s y s t e m of rules. This intuitive description can be s h a r p e n e d considerably. See Berwick and Weinberg 1983 for a more detailed discussion of " t r a n s p a r e n c y " as it relates to the embeddability of a linguistic theory in a model of language use, in this case, a model of parsing.
l In R o u n d s ' s proof, t r a n s f o r m a t i o n s are s u b j e c t to a "terminal length n o n - d e c r e a s i n g " condition, as suggested by Peters a n d Myhill (cited in R o u n d s 1975). A similar " t e r m i n a l length increasing" constraint (to the a u t h o r ' s knowledge first proposed by Petrick 1965) when coupled with a condition on recoverability of deletions, yields l a n g u a g e s that are recursive but not n e c e s s a r y recognizable in exponential time. 2 Usually, the recognition procedures presented actually recover the structural description of s e n t e n c e s in the process of recognition, so that in fact they actually parse sentences, rather than simply recognize them.
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