1 Constraint-Based Adaptive Software Systems
中国科学英文版模板
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YuLi11.New theoretical framework for OFDM/CDMA systems with pe ak-limited nonlinearities WANG Jian，ZHANG Lin，SHAN XiuMing，R EN Yong1.Fractional Fourier domain analysis of decimation and interpolat ion MENG XiangYi，TAO Ran，WANG Yue2.A reduced state SISO iterative decoding algorithm for serially concatenated continuous phase modulation SUN JinHua，LI JianDong，JIN LiJun3.On the linear span of the p-ary cascaded GMW sequences TA NG XiaoHu4.De-interlacing technique based on total variation with spatial-t emporal smoothness constraint YIN XueMin，YUAN JianHua，LU Xia oPeng，ZOU MouYan5.Constrained total least squares algorithm for passive location based on bearing-only measurements WANG Ding，ZHANG Li，WU Ying6.Phase noise analysis of oscillators with Sylvester representation for periodic time-varying modulus matrix by regular perturbations FAN JianXing，YANG HuaZhong，WANG Hui，YAN XiaoLang，HOU ChaoHuan7.New optimal algorithm of data association for multi-passive-se nsor location system ZHOU Li，HE 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Adaptive control of discrete-time systems using multiple models
Adaptive Control of Discrete-Time Systems UsingMultiple ModelsKumpati S.Narendra,Life Fellow,IEEE,and Cheng XiangAbstract—The adaptive control of a linear time-invariant discrete-time system using multiple models is considered in this paper.Both the deterministic(noise free)case and the stochastic case when random disturbances are present are discussed.Based on the prediction errors of a finite number of fixed and adaptive identification models,a procedure is outlined for switching between a finite number of controllers to improve performance. The principal contributions of the paper are the proof of global stability of the overall system and the convergence of the tracking error signal to zero in the deterministic case and the proof of convergence of the minimum variance control puter simulation results are included to complement the theoretical results.Index Terms—Adaptive control,discrete-time,multiple models, stochastic adaptive control.I.I NTRODUCTIONT HE CONTROL of dynamical systems in the presence of large uncertainties is of great interest at the present time. Such problems arise when there are large parameter variations due to failures in the system,or due to the presence of large external disturbances.In such cases,the controller has to de-termine the specific situation that exists at any instant and take the appropriate control action.Accomplishing this rapidly,ac-curately,and in a stable fashion is the objective of control de-sign.Broadly speaking,the above problem is one of adaptive control in which,typically,controller parameters are adjusted on the basis of plant parameter estimates.However,if con-ventional adaptive control is used,experience indicates that the presence of large parameter errors will generally result in slow convergence,with large transient errors.An alternative approach which has gained a large following in recent years involves the use of multiple models to identify the unknown plant and can be considered as higher level adaptive control.At any instant,one of the models is chosen as the“best”according to a performance index,and a corresponding controller is used to control the system.Extensive simulation studies,as well as a few real applications,have demonstrated the approach to be substantially better than conventional adaptive control, provided the identification models are chosen with care,based on the past performance of the system.Manuscript received December29,1998;revised July2,1999.Recom-mended by Associate Editor,M.Polycarpou.This work was supported by the Office of Naval Research under the Contract N00014-97-1-0948.The authors are with the Center for Systems Science,Department of Electrical Engineering,Yale University,New Haven,CT06520-8267USA.Publisher Item Identifier S0018-9286(00)06320-0.The use of multiple models for identification is by no means new.In the1960s and1970s several authors including Magill [1],Lainiotis[2],and Athans et al.[3]studied Kalman filter-based models to improve the accuracy of state estimation in control problems.Numerous successful practical applications, based on these methods,were reported in the following years [4]–[7],but in all of them no switching was used,and the con-trol input was computed as a combination of those determined by the different models.Further,no stability results were pre-sented.In the context of adaptive control,switching was first pro-posed by Martensson[8].Following this,two kinds of switching schemes began to appear in the literature.In the first,known as direct switching,the choice of the next controller to be used was predetermined,and when to switch depended upon the output of the plant[9].However,it soon became evident that such schemes have little practical utility.In the second class,known as indirect switching schemes,multiple models were used both to determine when and to which controller one should switch at every instant[10]–[12]and were found to be attractive for practical applications.Many of these methods evolved from an effort to determine the minimal prior information concerning the plant needed to achieve stability.In[13]and[14]the use of multiple fixed models for robust set point control was studied, and in[15]and[16]combinations of fixed and adaptive models were introduced to achieve both stability and performance.This paper extends the ideas contained in[16]to the stochastic case when the plant is a discrete-time dynamical system.Three main reasons can be given for considering dis-crete-time systems.It is well known that most complex systems are controlled by computers which are discrete in nature,and this constitutes an obvious reason for dealing with discrete-time adaptive control.The second,and significantly more important one,is the fact that the presence of random noise can be dealt with more easily in the case of discrete-time systems.Since most practical systems have to operate in the presence of noise, the stability and performance of multiple model-based adaptive control in such contexts has to be well understood,if the theory is to find wide application in practice.Finally,our ultimate aim is to apply the proposed methodology to nonlinear systems using artificial neural networks,and this in turn,requires the use of discrete-time models.In recent years there has also been a great deal of research activity in extending the multiple model approach to the mod-eling and control of nonlinear systems.In a recent book[17], a number of articles dealing with multiple model approaches based on classical control theories,statistical methods,and fuzzy architectures have been collected together.However,0018–9286/00$10.00©2000IEEEwhile numerous interesting heuristic ideas are contained there, very few stability results are given which,in turn,can provide an analytic basis for attempting more complex problems.In contrast to this,the objective of this paper is to proceed in a systematic fashion to establish,incrementally,a mathematical framework for designing multiple model-based adaptive con-trollers for dynamical systems in stochastic environments.The principal contribution of this paper is the demonstration that in both the deterministic and stochastic cases,stability can be assured by using suitable performance indexes.That the proof of stability is significantly different from that of stochastic adaptive control based on a single model becomes quite evident on reading Section IV of the paper,where a modified Kronecker lemma plays a central role.In Section V,some of the questions that are arising in practical applications,where multiple models are used for control,are discussed briefly.Faster methods are required for switching between different models to improve performance while retaining stochastic stability.However,such questions can be addressed analytically only after the results presented in this paper are well understood.II.M ATHEMATICAL P RELIMINARIESBefore proceeding to consider the problem of adaptive con-trol using multiple models,it is essential that the reader be fa-miliar with many results that are currently known in the area of adaptive control.In particular,the essence of the proof of sta-bility in the single model case must be well understood,before the specific difficulties that arise in the multiple model case can be discussed.It is well known in adaptive control theory that the judicious choice of the parameter estimation algorithm plays an impor-tant role in the proof of stability of the overall system.In view of this,we discuss briefly in this section the recursive least squares (RLS)algorithm,which has become the preferred one in adap-tive control.Parameter Estimation:Consider theequationscalar output,measured attime(2)where is theprediction error,and,theestimateof,and based on the errorbetween the measuredvalue and the estimate,the pa-rameter estimate is updated as using(2).Different choicesof(3)where(4)The initial estimate is assumed to be knownand(8)whereimpliesthat the change in parameter value over a finite number of stagestends to zero,so that,over a finite number of steps,the param-eter vector is almost a constant.The implications of thesein the adaptive control problems are discussed in the followingsection.III.A DAPTIVE C ONTROL-D ETERMINISTIC C ASEIn this section we discuss the adaptive control problem usingmultiple models in the noise-free case.This will set the stagefor the consideration in Section IV of the problem of primaryinterest in this paper,i.e.,the stochastic adaptive control of alinear time-invariant system using multiple models.An under-standing of the many questions that arise in deterministic adap-tive control using both single and multiple models is essentialfor an appreciation of the difficulties encountered in stochasticadaptive control.NARENDRA AND XIANG:ADAPTIVE CONTROL OF DISCRETE-TIME SYSTEMS1671A.Statement of the Adaptive Control ProblemThe deterministic adaptive control problem may be stated asfollows.A linear discrete-time dynamical system is describedby the differenceequationwhere theconstantmatrixandthrough the system is known,and,,is not equal to zero.The above model maybe derived from an equivalent representation of the plant in theform(10)where(11)(12)is the unit shift operator,andsuch that theoutput of theplant asymptotically tracks a specified arbitrary bounded refer-enceoutput[Notethat],ormoreprecisely,,of modelsare used to estimate the parameters,and one of them is chosenat every instant to determine the control input.Both problemscan be stated in a unified fashion as shown below.Let of the plant be de-scribedby(13)whereand th model.If the parameters of a model are constant(i.e.,,to denote afixed model(or the general subscriptto denote an adaptive model.The identification error of thewhere.For a fixedmodelto the plant at instant models,is the adaptive control problem.In the adaptive control problem solved in[18]and[19],onlyone adaptive identification model was used and the stability ofthe overall system was established.In the same fashion,we wishto determine conditions under which all the signals in the overallsystem described by(9)and(13)will be bounded,and the con-trolerror will tend to zero.B.Choice of Multiple ModelsFor a detailed description of the choice of multiple modelsfor adaptively controlling a plant,the reader is referred to[15]and[16].In simple terms,conventional adaptive control usinga single identification model is quite efficient when the initialparametererror is small and the plant pa-rameter vector is constant or varies slowly with time.Hence,themultiple model approach becomes relevant only when either ofthese conditions is not satisfied.This is precisely the case whenthere is a fault in the system or a subsystem fails.In such casesthe parameters can vary significantly in short periods of time.Itis for such situations that the new approach is found to be par-ticularly suited.As described earlier,the models can be either fixed or adap-tive.The following four cases have been considered in the pastin the context of continuous-time systems[15],[16]:i)all models are fixed;ii)all models are adaptive;iii)()fixed models,one free running adaptive model,and one re-initialized adaptive model are used.From(13)it is seen that all the identificationmodelsin parameter space,we assume that the models cor-respondto,in parameter space.Since the parameter error of at least oneof the models must be small enough to assure stability as well asaccurate control,this implies that a very large number of modelsmay be needed.The number of models increases exponentiallywith the dimension of the parameter space.An alternative approach is to make all the models adaptive.This is computationally intensive,but assures the stability andconvergence of the adaptive scheme no matter what switchingsequence is used.However,if the plant parameter were to re-main constant for a long period of time,all the models wouldconvergeto1672IEEE TRANSACTIONS ON AUTOMA TIC CONTROL,VOL.45,NO.9,SEPTEMBER 2000space.This,in turn,would negate the use of multiple models during the subsequent performance of the system and periodic re-initialization of the models must be resorted to.Choosing a model sufficiently close to the plant,and adapting from that model,appear to be the two ingredients essential for the success of the multiple model approach.The former is achieved by choosing a set of fixed models based on the past performance of the plant.If at any instant one of them is determined to be the best,adaptation can be initiated from this model.Based on such considerations,as well as to decrease the computational effort involved,it was suggested in [16]that)is chosen,the second adaptive model is discarded and a new one is re-initializedat,,such that theoutput of the plant tracks a specified arbitrarybounded referenceoutput.Certain assumptions have to be made concerning the plant to be controlled,to determine a solution to the adaptive control problem.These are listed below.Assumptions:(A)i)thedelayin (10)lie insidethe unit circle in the complex plane (i.e.,the system is minimum phase).The above assumptions simplify the mathematics substan-tially and make the adaptive process as well as the proof of con-vergence transparent.At the end of this section,the manner in which these assumptions can be relaxed and how they affect the proof of stability are also briefly discussed.1)Proof ofStability—are known,andcan be computed from theequation(16)When(18)from which theinputcan be computed.The proof of sta-bility then consists in showing that such a procedure results in all the signals remaining boundedwhile.Proof of Stability:Let the tracking error be definedas.Using (17)and(18),can be expressed in termsofasfollows:Using the properties of the parameter estimation algorithm stated in Lemma 1,it immediately follows that both terms in the right-hand side (r.h.s.)of the inequality tend to zero sothatforsomethatforsomeNARENDRA AND XIANG:ADAPTIVE CONTROL OF DISCRETE-TIME SYSTEMS1673Sinceforsome(20)In(19),the norm of the regressionvectoror grow in an unbounded fashion.In theformer case,it directly followsthat.Ifgrows in an unbounded fashion,from(20)it is clearthat it cannot grow fasterthancarries over directly to thecaselet theis computedfromcontrollers be chosen at random at every in-stant.For any instant oftimeforall.This,in turn,results in a contradiction as inthe single model case.Hence all the signals in the system arebounded,andi)andii)Fixed Models and OneAdaptive Model:Let be the identification error of theattime(23)At every instant()is chosen,i.e.,(24)and is used as the controlinput at that instant.Tosimplify the discussion we denote the prediction errors ofthe.Proof of Stability:By Lemma1,it follows that the identifi-cation error of the adaptive model satisfies thecondition(25)For the fixedmodels,is either boundedortendstosuchthat()forallis bounded then proceeding alongthe same lines as those given for the caseofFixed Models and Two Adap-tive Models:In Case iii),if the initial parameter error of theadaptive model is large,and the parameter error of one of thefixed models is small,the system will first switch to the fixedmodel,and subsequently to the free running adaptive model,when its identification error is sufficiently small.To speed upthe adaptive process a second adaptive model is used.Its pa-rameters as well as the initial value of its performance indexare initialized at the same values as those of the fixed model inuse(as described in Section III-B).The introduction of the ad-ditional adaptive models does not adversely affect the stabilityof the overall system,and the proof of stability is similar to thatof the above caseofis known.In thiscaseis adequate to set up the identification models.Perhaps the most significant relaxation concerns Assumptioniii)about thecoefficientwas assumed to be known.However,this canbe relaxed,provided its sign and lower bound are known.Forconvenience we shall assume that the sign is positive andthat.Using this assumption,all the adaptive proceduresremain the same except that the RLS algorithm for parameterestimation is modified asfollows:where1674IEEE TRANSACTIONS ON AUTOMA TIC CONTROL,VOL.45,NO.9,SEPTEMBER2000If,then,elseth elementof,forallto be Hurwitz.Thisassumption is not needed when the desired output of thesystemis a specified constant(i.e.,set-point control),or a speci-fied periodic signal,but in such cases a modified controller hasto be used.IV.S TOCHASTIC A DAPTIVE C ONTROLThe discussions in the previous section provide the back-ground for considering the stochastic adaptive control problemusing multiple models.As in the deterministic case,a detailedunderstanding of the stochastic adaptive control problem usinga single model is a prerequisite for considering adaptive controlusing multiple models.In this section we consequently considerthe former in detail.This problem has been investigated exten-sively in the past,and for a detailed treatment of problem for-mulation as well as algorithms that have been used,the readeris referred to[18].A.Stochastic Parameter EstimationWhen an additionalinput[besides the controlinput]is present,the extended ARMAmodel(26)is a natural description of the linear system,whereare monic polynomialsin ofdegrees,respectively,andis deterministic,andhas rootseither on or inside the unit circle.In this paper we shall use theARMAX model throughout the following sections to describethe plant and further assume that the rootsofandin(26),it follows by the Bezout identity that uniquepolynomials existsatisfyingand(29)Multiplying both sides of(26)byand respectivelyas(of degree(m+d-1)),weobtain(31)where is the modified noise describedbyand(28)thatit can be shownthat-step-ahead predictionof.Comment3:Before proceeding to consider the stochasticadaptive control of the system described by(30)when the pa-rameters of the system are not known,a few comments con-cerning the nonadaptive stochastic control problem are in order.If the performance criterion to be minimizedis,it can be demonstrated that the best that the con-troller can do attime,due to the presence of noise.This,in turn,determines the con-trol input to be used at instantis greater than unity,(36)is not conve-nient for purposes of control due to the presence of terms suchas.Given thepolynomial,itfollows by the Bezout identity that uniquepolynomialsexist suchthatandNARENDRA AND XIANG:ADAPTIVE CONTROL OF DISCRETE-TIME SYSTEMS1675and(39)Let,wehaveor[ofdegree()],andas)].Letand theestimateof attime is givenby(41)We shall refer to as the predicted estimate or a prioriestimateof at time is measured attimeat time and is givenbyis thesignal of relevance in estimation and control problems,themathematical analysis is simplifiedwhen is used.Thisaccounts for the useof rather than in the regressionvectorand the a posteriori predic-tionerror can now be definedas(43)(44)where is used in the stochastic RLS algorithm to update theestimate asfollows:(45)is strictly positive real;2)(50)It is worth noting that the boundedness of the conditionnumber oftheand predictionoutputsand grow at the same rate in some sense,ifthe system is minimum phase.This lemma plays an importantrole in the stability arguments.Lemma3:Subject to the same assumptions as in Lemma2,and in addition provided that the system is minimum phase,wehave the followingresults:a.s.a.s.a.s.a.s.1676IEEE TRANSACTIONS ON AUTOMA TIC CONTROL,VOL.45,NO.9,SEPTEMBER 2000B.Adaptive Minimum-Variance Control Using a Single ModelAs in the deterministic case,several assumptions have to be made in the stochastic case to obtain a solution.Assumptions:(C)1)Upper bounds on the degrees of the various polynomials are known exactly.2)The timedelayis strictly positive real.4)sothatsss (58)The Kronecker lemma given in [20]plays a central role in the proof of Theorem 1.This is stated as Lemma 4below without proof.Its importance becomes evident in the proof of Theorem 1that is given following the lemma.Lemma 4(Kronecker Lemma):Letbe asequence suchthatis Hurwitz (min-imum phase),it followsthatwhere thedesiredoutputis bounded,it follows,using (35)concerning the modifiednoise,that ,wehaveis bounded,then all results follow trivially.Hence,from now on,we assumethatas increases monotonically(i.e.,),it follows by Lemma 4that(61)which can be writtenasNARENDRA AND XIANG:ADAPTIVE CONTROL OF DISCRETE-TIME SYSTEMS1677 Using(63)and(34),it followsthatfixed models and one adaptive model orii)models are describedby(65)where,the constrained stochastic RLS algo-rithm is used independently toupdateth model be chosen at random at timeto the plant.We shall refer to the modelas and the parameters and signals corresponding to it by thesubscript.Theinput attimeadaptive identifica-tion models described above is globally convergent in the sensegiven in Theorem1by(56)–(58).Before proceeding to prove Theorem2,the following is worthnoting.Comment6:The principal analytic difficulty in proving The-orem2arises from the fact that the regressionvector in(66)for the model used at timein(65),it is seenthat in generalwhen.This isbecause depends uponthebe a sequence suchthat.Lemma5now permits the proof of Theorem2to be given.Proof:Based on the adaptive procedure,attimeHenceis aconstant,in(67)is monotonic;it re-duces to the single model case.However,the regression vec-tors of different models are no longer the same and sinceassumes values randomlyover isno longer assured to be monotonic(even thoughthe cor-responding to onemodel()grow at the same bining with thefact that for the increases monotonically withtimeFixed Models and One Adap-tive Model:Adaptive procedure:In Section IV-C-I no performanceindex was needed for switching between models since it wasshown that the adaptive procedure would converge for anyrandom switching.This is obviously no longer the case whenfixed models are present(e.g.,the controller for a fixed modelmay make the plant unstable).Hence a performance indexbased on the identification errors is chosen to determine the1678IEEE TRANSACTIONS ON AUTOMA TIC CONTROL,VOL.45,NO.9,SEPTEMBER 2000controller at any instant.If is the identification error of the,the performanceindex is definedas(70)and at every instant,themodel()is chosen,i.e.,(71)andis used as the controlinput at that instant.It is shown below that this procedure converges and that,generally,it will stop at the adaptive model.Comment 8:The performance index used in the stochastic case is seen to be different from that used in the deterministiccase.Due to the presence ofnoise,defined in (23)will tend to grow in an unbounded fashionwithis bounded for the optimal model.Theorem 3:Subject to the same assumptions as in Theorem 2,the switching and adaptation algorithmusingbe a random variable.If(a.s.thena.s.Lemma 6is just a direct result of the Stability Theorem given in [20].Lemma 7:Let,be the identification error and performanceindex of the fixed modeland,the identification error and performance index of the adaptive model.The approach used consists of showing that in general (i.e.,except one special case discussed later)there exists atime,forall(72)th model be chosen based on the perfor-mance indexes attimeHence(74)The asymptotic behavior of the switching system can now be discussed for two mutually exclusive but collectively exhaus-tive cases.In the first,we assumethatis bounded and discuss the implications of this as far as the adap-tive process is concerned.In the second case we assumethatand show that this leads to a contra-diction.is bounded,then it follows by Lemma 3thatis bounded.Then by Lemma 2and Lemma 4wehave,which are discussed as cases(1)–(3).Case(1):This corre-sponds to the case where the model is identical to the plant.Then from (74),wehaveBy previous assumptions and Lemma 2wehaveNotethatandUsing Lemma6,wehave)corresponding to the fixed model arestrictly inside the unit circle,it followsthatwas bounded and it was shown thatthe switching algorithm would generally converge to theadaptive model.However,to complete the proof we considerthe alternative situationwhere andshow that this also results in acontradiction.:By definitionof,it followsimmediatelythator the output error of the adaptive model grows more slowlythan that of the fixed model.Using(76),it followsthatTherefore theterm in(72)fordominates the other termsas.Case(2):A second possibility is that no lower boundgreater than zero existsforas in Case(1).Then there exists asubsequence,such thatasIt follows from(77)and(78)thatFixed Models and2AdaptiveModels:As in the deterministic case,the introduction of ad-ditional adaptive models does not adversely affect the stabilityof the overall system.The argument is almost the same as inthe case of(a)(b)(c)Fig.1.Desired output,noise,and modified noise.and in particular on the past history of the system and the fre-quency and the nature of its different failures.With proper loca-tion of fixed models,the performances in aircraft systems and process control systems have been found to be far superior to those using a single model.The increased use of the approach in different practical sit-uations is bringing in its wake a host of new theoretical ques-tions which remain unanswered at the present time.One of the key problems is to determine how to switch rapidly from one model to another when a fault occurs,particularly in the sto-chastic case.In the previous sections the proof of stochastic sta-bility was presented for the case when switching was based onthe performanceindex.This implies that errors at all instants are weighted equally.Such switching is not generally sufficiently rapid to cope with a time-varying environment.Hence,as in conventional adaptive control,it is desirable to include a “memory”factor.This implies that past errors are given less weight than present ones.In the deterministic case,any valueof,switching between controllers is slow,while small valuesofdepends upon the SNR and a smallvalueoffor which stability can be assured is one of theprincipal open problems in the field at the present time.In addition to the problem described above,work is also in progress to extend the results presented in the paper to linear multivariable systems,to special classes of nonlinear systems,as well as to systems with structurally different identification models.VI.S IMULATION S TUDIESIn Sections III and IV ,the convergence properties of both de-terministic and stochastic adaptive algorithms using multiple models were discussed.In this section we present results of computer simulations of adaptive control of an unknown linear。
abaqus结构分析单元类型
a b a q u s结构分析单元类型(总5页)--本页仅作为文档封面，使用时请直接删除即可----内页可以根据需求调整合适字体及大小--;this wordfile adds the code folding function which is useful to ignore rows of numbers,enjoy~;updated in , based on the wordfile "abaqus_67ef()";Syntax file for abaqus keywords ,code folding enabled;add *ANISOTROPIC *ENRICHMENT *LOW -DISPLACEMENT HYPERELASTIC;newly add /C"ElementType";delete DISPLACEMENT;delete MASS in /C2"Keywords2"/L29"abaqus_612" Nocase File Extensions = inp des dat msg/Delimiters = ~!@$%^&()_-+=|\/{}[]:;"'<> ,.//Function String = "%[ ^t]++[ps][a-z]+ [a-z0-9]+ ^(*(*)^)*{$"/Function String 1 = "%[ ^t]++[ps][a-z]+ [a-z0-9]+ ^(*(*)^)[ ^t]++$" /Member String = "^([A-Za-z0-9_:.]+^)[ ^t*&]+$S[ ^t]++[(=);,]"/Variable String = "^([A-Za-z0-9_:.]+^)[ ^t*&]+$S[ ^t]++[(=);,]"/Open Fold Strings = "*" "**""***"/Close Fold Strings = "*" "**""***"/C1"Keywords1" STYLE_KEYWORD*ACOUSTIC *ADAPTIVE *AMPLITUDE *ANISOTROPIC *ANNEAL *AQUA *ASSEMBLY *ASYMMETRIC *AXIAL *BASE *BASELINE *BEAM*BIAXIAL *BLOCKAGE *BOND *BOUNDARY *BRITTLE *BUCKLE *BUCKLING *BULK *C *CAP *CAPACITY *CAST *CAVITY *CECHARGE*CECURRENT *CENTROID *CFILM *CFLOW *CFLUX *CHANGE *CLAY *CLEARANCE*CLOAD *CO *COHESIVE *COMBINED *COMPLEX*CONCRETE *CONDUCTIVITY *CONNECTOR *CONSTRAINT *CONTACT *CONTOUR*CONTROLS *CORRELATION *COUPLED *COUPLING*CRADIATE *CREEP *CRUSHABLE *CYCLED *CYCLIC *D *DAMAGE *DAMPING*DASHPOT *DEBOND *DECHARGE *DECURRENT*DEFORMATION *DENSITY *DEPVAR *DESIGN *DETONATION *DFLOW *DFLUX*DIAGNOSTICS *DIELECTRIC *DIFFUSIVITY*DIRECT *DISPLAY *DISTRIBUTING *DISTRIBUTION *DLOAD *DRAG *DRUCKER*DSA *DSECHARGE *DSECURRENT *DSFLOW*DSFLUX *DSLOAD *DYNAMIC *EL *ELASTIC *ELCOPY *ELECTRICAL *ELEMENT*ELGEN *ELSET *EMBEDDED *EMISSIVITY*END *ENERGY *ENRICHMENT *EOS *EPJOINT *EQUATION *EULERIAN *EXPANSION *EXTREME *FABRIC *FAIL *FAILURE*FASTENER *FIELD *FILE *FILM *FILTER *FIXED *FLOW *FLUID *FOUNDATION *FRACTURE *FRAME *FREQUENCY *FRICTION*GAP *GASKET *GEL *GEOSTATIC *GLOBAL *HEADING *HEAT *HEATCAP*HOURGLASS *HYPERELASTIC *HYPERFOAM *HYPOELASTIC*HYSTERESIS *IMPEDANCE *IMPERFECTION *IMPORT *INCIDENT *INCLUDE*INCREMENTATION *INELASTIC *INERTIA*INITIAL *INSTANCE *INTEGRATED *INTERACTION *INTERFACE *ITS *JOINT*JOINTED *JOULE *KAPPA *KINEMATIC*LATENT *LOAD *LOADING *LOW *M1 *M2 *MAP *MASS *MATERIAL *MATRIX*MEMBRANE *MODAL *MODEL *MOHR *MOISTURE*MOLECULAR *MONITOR *MOTION *MPC *MULLINS *NCOPY *NFILL *NGEN *NMAP *NO *NODAL *NODE *NONSTRUCTURAL*NORMAL *NSET *ORIENTATION *ORNL *OUTPUT *PARAMETER *PART *PERIODIC *PERMEABILITY *PHYSICAL *PIEZOELECTRIC*PIPE *PLANAR *PLASTIC *POROUS *POST *POTENTIAL *PRE *PREPRINT*PRESSURE *PRESTRESS *PRINT *PSD *RADIATE*RADIATION *RANDOM *RATE *RATIOS *REBAR *REFLECTION *RELEASE*RESPONSE *RESTART *RETAINED *RIGID *ROTARY*SECTION *SELECT *SFILM *SFLOW *SHEAR *SHELL *SIMPEDANCE *SIMPLE*SLIDE *SLOAD *SOILS *SOLID *SOLUBILITY*SOLUTION *SOLVER *SORPTION *SPECIFIC *SPECTRUM *SPRING *SRADIATE*STATIC *STEADY *STEP *SUBMODEL*SUBSTRUCTURE *SURFACE *SWELLING *SYMMETRIC *SYSTEM *TEMPERATURE*TENSILE *TENSION *THERMAL *TIE *TIME*TORQUE *TRACER *TRANSFORM *TRANSPORT *TRANSVERSE *TRIAXIAL *TRS *UEL *UNDEX *UNIAXIAL *UNLOADING *USER*VARIABLE *VIEWFACTOR *VISCO *VISCOELASTIC *VISCOUS *VOID *VOLUMETRIC *WAVE *WIND-AXISYMMETRIC -DEFINITION -DISPLACEMENT -SIMULATION -SOIL -TENSION/C2"Keywords2"ACTIVATION ADDED AREA ASSEMBLE ASSEMBLY ASSIGNMENT AXIALBEHAVIOR BODY BULKCASE CAVITY CENTER CHAIN CHANGE CHARGE CLEARANCE COMPACTION COMPONENT COMPRESSION CONDITIONS CONDUCTANCECONDUCTIVITY CONSTANTS CONSTITUTIVE CONSTRAINT CONTACT CONTROL CONTROLS COPY CORRECTION COULOMB COUPLINGCRACKING CREEP CRITERIA CRITERION CYCLICDAMAGE DAMAGED DAMPING DATA DEFINED DEFINITION DELETE DENSITY DEPENDENCE DEPENDENT DERIVED DETECTIONDIFFUSION DIRECTORY DOFS DYNAMIC DYNAMICSEFFECT EIGENMODES ELASTIC ELASTICITY ELECTRICAL ELEMENT ELSET ENVELOPE EVOLUTION EXCHANGE EXCLUSIONSEXPANSIONFACTORS FAILURE FIELD FILE FLAW FLOW FLUID FLUX FOAM FORMAT FORMULATION FRACTION FREQUENCY FRICTIONGENERAL GENERATE GENERATION GRADIENTHARDENING HEAT HOLD HYPERELASTICINCLUSIONS INERTIA INFLATOR INITIATION INPUT INSTANCE INTEGRAL INTERACTION INTERFERENCE IRONLAYER LEAKOFF LENGTH LINE LINK LOAD LOCKM1 M2 MATERIAL MATRIX MEDIUM MESH METAL MIXTURE MODEL MODES MODULI MODULUS MOTIONNODAL NODE NSET NUCLEATIONORIGIN OUTPUTPAIR PARAMETER PART PARTICLE PATH PENETRATION PLASTIC PLASTICITY POINT POINTS POTENTIAL PRAGER PRINTPROPERTYRADIATION RATE RATIOS REDUCTION REFERENCE REFLECTION REGION RELIEF RESPONSE RESULTS RETENTIONSECTION SCALING SHAPE SHEAR SOLID SOLUTION SPECTRUM STABILIZATION STATE STEP STIFFENING STIFFNESS STOPSTRAIN STRESS SURFACE SWELLING SYMMETRYTABLE TECHNIQUE TEMPERATURE TENSION TEST THERMAL THICKNESS TO TORQUE TRANSFER TRANSPORTVALUE VARIABLES VARIATION VELOCITY VIEWFACTOR VISCOSITYWAVE WEIGHT/C3"ElementType" STYLE_ELEMENTAC1D2 AC1D3 AC2D3 AC2D4 AC2D4R AC2D6 AC2D8 AC3D4 AC3D6 AC3D8 AC3D8R AC3D10 AC3D15 AC3D20 ACAX3 ACAX4ACAX4R ACAX6 ACAX8 ACIN2D2 ACIN2D3 ACIN3D3 ACIN3D4 ACIN3D6 ACIN3D8 ACINAX2 ACINAX3 ASI1 ASI2 ASI2AASI2D2 ASI2D3 ASI3 ASI3A ASI3D3 ASI3D4 ASI3D6 ASI3D8 ASI4 ASI8 ASIAX2 ASIAX3B21 B21H B22 B22H B23 B23H B31 B31H B31OS B31OSH B32 B32H B32OSB32OSH B33 B33HC3D4 C3D4E C3D4H C3D4P C3D4T C3D6 C3D6E C3D6H C3D6P C3D6T C3D8 C3D8E C3D8H C3D8HT C3D8I C3D8IH C3D8PC3D8PH C3D8PHT C3D8PT C3D8R C3D8RH C3D8RHT C3D8RP C3D8RPH C3D8RPHTC3D8RPT C3D8RT C3D8T C3D10 C3D10EC3D10H C3D10I C3D10M C3D10MH C3D10MHT C3D10MP C3D10MPH C3D10MPTC3D10MT C3D15 C3D15E C3D15H C3D15VC3D15VH C3D20 C3D20E C3D20H C3D20HT C3D20P C3D20PH C3D20R C3D20REC3D20RH C3D20RHT C3D20RP C3D20RPHC3D20RT C3D20T C3D27 C3D27H C3D27R C3D27RH CAX3 CAX3E CAX3H CAX3T CAX4 CAX4E CAX4H CAX4HT CAX4ICAX4IH CAX4P CAX4PH CAX4PT CAX4R CAX4RH CAX4RHT CAX4RP CAX4RPHCAX4RPHT CAX4RPT CAX4RT CAX4T CAX6CAX6E CAX6H CAX6M CAX6MH CAX6MHT CAX6MP CAX6MPH CAX6MT CAX8 CAX8E CAX8H CAX8HT CAX8P CAX8PH CAX8RCAX8RE CAX8RH CAX8RHT CAX8RP CAX8RPH CAX8RT CAX8T CAXA4HN CAXA4N CAXA4RHN CAXA4RN CAXA8HN CAXA8NCAXA8PN CAXA8RHN CAXA8RN CAXA8RPN CCL12 CCL12H CCL18 CCL18H CCL24 CCL24H CCL24R CCL24RH CCL9 CCL9HCGAX3 CGAX3H CGAX3HT CGAX3T CGAX4 CGAX4H CGAX4HT CGAX4R CGAX4RH CGAX4RHT CGAX4RT CGAX4T CGAX6 CGAX6HCGAX6M CGAX6MH CGAX6MHT CGAX6MT CGAX8 CGAX8H CGAX8HT CGAX8R CGAX8RH CGAX8RHT CGAX8RT CGAX8T CIN3D12RCIN3D18R CIN3D8 CINAX4 CINAX5R CINPE4 CINPE5R CINPS4 CINPS5R COH2D4 COH2D4P COH3D6 COH3D6P COH3D8COH3D8P COHAX4 COHAX4P CONN2D2 CONN3D2 CPE3 CPE3E CPE3H CPE3T CPE4 CPE4E CPE4H CPE4HT CPE4I CPE4IHCPE4P CPE4PH CPE4R CPE4RH CPE4RHT CPE4RP CPE4RPH CPE4RT CPE4T CPE6 CPE6E CPE6H CPE6M CPE6MH CPE6MHTCPE6MP CPE6MPH CPE6MT CPE8 CPE8E CPE8H CPE8HT CPE8P CPE8PH CPE8RCPE8RE CPE8RH CPE8RHT CPE8RPCPE8RPH CPE8RT CPE8T CPEG3 CPEG3H CPEG3HT CPEG3T CPEG4 CPEG4H CPEG4HT CPEG4I CPEG4IH CPEG4R CPEG4RHCPEG4RHT CPEG4RT CPEG4T CPEG6 CPEG6H CPEG6M CPEG6MH CPEG6MHT CPEG6MT CPEG8 CPEG8H CPEG8HT CPEG8RCPEG8RH CPEG8RHT CPEG8T CPS3 CPS3E CPS3T CPS4 CPS4E CPS4I CPS4RCPS4RT CPS4T CPS6 CPS6E CPS6M CPS6MTCPS8 CPS8E CPS8R CPS8RE CPS8RT CPS8TDASHPOT1 DASHPOT2 DASHPOTA DC1D2 DC1D2E DC1D3 DC1D3E DC2D3 DC2D3EDC2D4 DC2D4E DC2D6 DC2D6E DC2D8DC2D8E DC3D10 DC3D10E DC3D15 DC3D15E DC3D20 DC3D20E DC3D4 DC3D4EDC3D6 DC3D6E DC3D8 DC3D8E DCAX3DCAX3E DCAX4 DCAX4E DCAX6 DCAX6E DCAX8 DCAX8E DCC1D2 DCC1D2D DCC2D4 DCC2D4D DCC3D8 DCC3D8D DCCAX2DCCAX2D DCCAX4 DCCAX4D DCOUP2D DCOUP3D DGAP DRAG2D DRAG3D DS3 DS4 DS6 DS8 DSAX1 DSAX2EC3D8R EC3D8RT ELBOW31 ELBOW31B ELBOW31C ELBOW32 EMC2D3 EMC2D4 EMC3D4 EMC3D8F2D2 F3D3 F3D4 FAX2 FLINK FRAME2D FRAME3D FC3D4 FC3D6 FC3D8GAPCYL GAPSPHER GAPUNI GAPUNIT GK2D2 GK2D2N GK3D12M GK3D12MN GK3D18 GK3D18N GK3D2 GK3D2N GK3D4LGK3D4LN GK3D6 GK3D6L GK3D6LN GK3D6N GK3D8 GK3D8N GKAX2 GKAX2N GKAX4 GKAX4N GKAX6 GKAX6N GKPE4 GKPE6GKPS4 GKPS4N GKPS6 GKPS6NHEATCAPIRS21A IRS22A ISL21A ISL22A ITSCYL ITSUNI ITT21 ITT31JOINT2D JOINT3D JOINTCLS3S LS6MASS M3D3 M3D4 M3D4R M3D6 M3D8 M3D8R M3D9 M3D9R MAX1 MAX2 MCL6 MCL9 MGAX1 MGAX2PC3D PIPE21 PIPE21H PIPE22 PIPE22H PIPE31 PIPE31H PIPE32 PIPE32HPSI24 PSI26 PSI34 PSI36Q3D4 Q3D6 Q3D8 Q3D8H Q3D8R Q3D8RH Q3D10M Q3D10MH Q3D20 Q3D20H Q3D20R Q3D20RHR2D2 R3D3 R3D4 RAX2 RB2D2 RB3D2 ROTARYIS3 S3T S3R S3RS S3RT S4 S4T S4R S4RT S4R5 S4RS S4RSW S8R S8R5 S8RT S9R5 SAX1 SAX2 SAX2T SAXA1NSAXA2N SC6R SC6RT SC8R SC8RT SFM3D3 SFM3D4 SFM3D4R SFM3D6 SFM3D8 SFM3D8R SFMAX1 SFMAX2 SFMCL6 SFMCL9SFMGAX1 SFMGAX2 SPRING1 SPRING2 SPRINGA STRI3 STRI65T2D2 T2D2E T2D2H T2D2T T2D3 T2D3E T2D3H T2D3T T3D2 T3D2E T3D2H T3D2T T3D3 T3D3E T3D3H T3D3TWARP2D3 WARP2D4。
机械工程专业英语
1-2statics 静力学，静止状态dynamics 动力学，原动力，动力特性i.e. 即,那就是mating 配合的，配套的，相连的mating surface 啮合表面，配合表面，接触面gear 齿轮，齿轮传动装置shaft 轴meshing 啮合，咬合，钩住bearing 轴承，支承lever 杠杆，手柄pulley 滑轮cam 凸轮，偏心轮，样板magnitude 大小，尺寸compose 组成journal bearing 滑动轴承，轴颈轴承squeeze 挤压，压缩squeeze out 挤压，压出flaking 薄片，表面脱落，压碎，易脱落的spall 削，打碎，剥落，脱皮，裂片，碎片intuitive 直觉的，本能的，天生的inherent 固有的，本征的surmise 推测，估计nevertheless 尽管如此inertia 惯性，惯量，惰性，不活动celestial 天体的celesital body 天梯incapable 无能力的，无用的，无资格的deformation 变形，形变，扭曲，应变deformable 可变形的，应变的acceleration 加速度resulant 组合的，总的，合力scalar 数量，标量vector 矢量，向量displacement 位移velocity 速度moment力矩momentum 动量constraint 抑制，限制，制约，约束constrain 强迫，强制，制约，约束，束缚sense 显示，方向noncoincident 不重合的，不一致的，不符合的parallel 并行的，平行的，相同的，平行线perpendicular 垂直的，垂直，正交，垂线product 产品，乘积free-body 自由体，隔离体free-body diagram 隔离体受力图，隔离体简图sketch 草图，简图，示意图，设计图drawing 绘图，制图，图样couple 力偶diagram 图表，简图，用图表示出facet 面，小平面，事情的某一方面kinematic 运动的，运动学的pair 一双，一对，一幅，成对，配合kinematic pair 运动副3-4Coupling联轴器，连接，耦合rectangular矩形的，直角的cross section截面，横断面，剖面Screw螺旋丝杆，螺钉screw driver螺丝刀，改锥blade叶片，浆片，刀片socket插座，插口，套筒wrench扳手，拧紧，扭转socket wrench套筒扳手knob节，旋钮stem杆，棒，柄torsional扭转的，扭力的torque转矩，扭矩mounted安装好的deviate偏离deviate form与、、有偏离twist使扭转clutch抓住，离合器bending弯曲度，挠曲度deflection偏转，挠度reversal颠倒，相反cold_roll冷轧forge锻造，打制key键keyway键槽adjacent领近的，接近的semipermanent半永久性的propeller螺旋桨flange凸缘，法兰hub中心部分，衬套bolt螺栓，螺杆alignment直线对准，调准rabbet插孔，缺口gearbox齿轮箱flexible柔性的flexible coupling 弹性联轴器in such a way as 以这样一种方式shock 冲击，打击Geometrical几何的Noncircular非圆形的Nonuniform不均匀的Follower 从动轮spatial空间的linkage 连杆机构prescribe规定categorize分类criterion标准planar平面的，二维的locus轨迹位置slider-crank mechanis m曲柄滑块机构arbitrary任意的Concentric同心的projection投影coplanar共面的curvature弯曲5-6blank 空白，空页，坯料jot 把，，，摘记下来，匆匆地记下来，一点，少许，小额recognition 认识，识别，辨别，承认，重视，认可vague 不明确的，含糊的，未定的，不明的discontent 不满意，不满的，不安的，令人不满perceptible 可感觉到的，能察觉的出的，明显的package 包裹，包装，捆，束wrap 包装，打包，覆盖，包围stack 烟囱，堆，垛，捆，束irritant 刺激的，有刺激性的，刺激物exhaust 用尽，排出，排气implied 暗指的，含蓄的，不言而喻的synthesis 合成，综合，结构综合optimum 最佳（的，条件，方式），最优的，最有利的comply 答应，同意，遵守，履行，根据synthesize 合成，综合，结合devise 设计，计划，发明，创造，产生prototype 原型，样机，模型机originator 创作者，发明者，创办人，发起人accomplishment 完成，实施，成就，成绩，本领，技能presentation 提出，展示，表示，表现supervisory 监督的，管理的versatile 通用的，多用途的，多方面的handicap 障碍，不利条件，缺陷，为。
命令中英文对照
一、File〈文件〉New〈新建〉Reset〈重置〉Open〈打开〉Save〈保存〉Save As〈保存为〉Save selected〈保存选择〉XRef Objects〈外部引用物体〉XRef Scenes〈外部引用场景〉Merge〈合并〉Merge Animation〈合并动画动作〉Replace〈替换〉Import〈输入〉Export〈输出〉Export Selected〈选择输出〉Archive〈存档〉Summary Info〈摘要信息〉File Properties〈文件属性〉View Image File〈显示图像文件〉History〈历史〉Exit〈退出〉二、Edit〈菜单〉Undo or Redo〈取消/重做〉Hold and fetch〈保留/引用〉Delete〈删除〉Clone〈克隆〉Select All〈全部选择〉Select None〈空出选择〉Select Invert〈反向选择〉Select By〈参考选择〉Color〈颜色选择〉Name〈名字选择〉Rectangular Region〈矩形选择〉Circular Region〈圆形选择〉Fabce Region〈连点选择〉Lasso Region〈套索选择〉Region:〈区域选择〉Window〈包含〉Crossing〈相交〉Named Selection Sets〈命名选择集〉Object Properties〈物体属性〉三、Tools〈工具〉Transform Type-In〈键盘输入变换〉Display Floater〈视窗显示浮动对话框〉Selection Floater〈选择器浮动对话框〉Light Lister〈灯光列表〉Mirror〈镜像物体〉Array〈阵列〉Align〈对齐〉Snapshot〈快照〉Spacing Tool〈间距分布工具〉Normal Align〈法线对齐〉Align Camera〈相机对齐〉Align to View〈视窗对齐〉Place Highlight〈放置高光〉Isolate Selection〈隔离选择〉Rename Objects〈物体更名〉四、Group〈群组〉Group〈群组〉Ungroup〈撤消群组〉Open〈开放组〉Close〈关闭组〉Attach〈配属〉Detach〈分离〉Explode〈分散组〉五、Views〈查看〉Undo View Change/Redo View change〈取消/重做视窗变化〉Save Active View/Restore Active View〈保存/还原当前视窗〉Viewport Configuration〈视窗配置〉Grids〈栅格〉Show Home Grid〈显示栅格命令〉Activate Home Grid〈活跃原始栅格命令〉Activate Grid Object〈活跃栅格物体命令〉Activate Grid to View〈栅格及视窗对齐命令〉Viewport Background〈视窗背景〉Update Background Image〈更新背景〉Reset Background Transform〈重置背景变换〉Show Transform Gizmo〈显示变换坐标系〉Show Ghosting〈显示重橡〉Show Key Times〈显示时间键〉Shade Selected〈选择亮显〉Show Dependencies〈显示关联物体〉Match Camera to View〈相机与视窗匹配〉Add Default Lights To Scene〈增加场景缺省灯光〉Redraw All Views〈重画所有视窗〉Activate All Maps〈显示所有贴图〉Deactivate All Maps〈关闭显示所有贴图〉Update During Spinner Drag〈微调时实时显示〉Adaptive Degradation Toggle〈绑定适应消隐〉Expert Mode〈专家模式〉六、Create〈创建〉Standard Primitives〈标准图元〉Box〈立方体〉Cone〈圆锥体〉Sphere〈球体〉GeoSphere〈三角面片球体〉Cylinder〈圆柱体〉Tube〈管状体〉Torus〈圆环体〉Pyramid〈角锥体〉Plane〈平面〉Teapot〈茶壶〉Extended Primitives〈扩展图元〉Hedra〈多面体〉Torus Knot〈环面纽结体〉Chamfer Box〈斜切立方体〉Chamfer Cylinder〈斜切圆柱体〉Oil Tank〈桶状体〉Capsule〈角囊体〉Spindle〈纺锤体〉L-Extrusion〈L形体按钮〉Gengon〈导角棱柱〉C-Extrusion〈C形体按钮〉RingWave〈环状波〉Hose〈软管体〉Prism〈三棱柱〉Shapes〈形状〉Line〈线条〉Text〈文字〉Arc〈弧〉Circle〈圆〉Donut〈圆环〉Ellipse〈椭圆〉Helix〈螺旋线〉NGon〈多边形〉Rectangle〈矩形〉Section〈截面〉Star〈星型〉Lights〈灯光〉Target Spotlight〈目标聚光灯〉Free Spotlight〈自由聚光灯〉Target Directional Light〈目标平行光〉Directional Light〈平行光〉Omni Light〈泛光灯〉Skylight〈天光〉Target Point Light〈目标指向点光源〉Free Point Light〈自由点光源〉Target Area Light〈指向面光源〉IES Sky〈IES天光〉IES Sun〈IES阳光〉SuNLIGHT System and Daylight〈太阳光及日光系统〉Camera〈相机〉Free Camera〈自由相机〉Target Camera〈目标相机〉Particles〈粒子系统〉Blizzard〈暴风雪系统〉PArray〈粒子阵列系统〉PCloud〈粒子云系统〉Snow〈雪花系统〉Spray〈喷溅系统〉Super Spray〈超级喷射系统〉七、Modifiers〈修改器〉Selection Modifiers〈选择修改器〉Mesh Select〈网格选择修改器〉Poly Select〈多边形选择修改器〉Patch Select〈面片选择修改器〉Spline Select〈样条选择修改器〉V olume Select〈体积选择修改器〉FFD Select〈自由变形选择修改器〉NURBS Surface Select〈NURBS表面选择修改器〉Patch/Spline Editing〈面片/样条线修改器〉：Edit Patch〈面片修改器〉Edit Spline〈样条线修改器〉Cross Section〈截面相交修改器〉Surface〈表面生成修改器〉Delete Patch〈删除面片修改器〉Delete Spline〈删除样条线修改器〉Lathe〈车床修改器〉Normalize Spline〈规格化样条线修改器〉Fillet/Chamfer〈圆切及斜切修改器〉Trim/Extend〈修剪及延伸修改器〉Mesh Editing〈表面编辑〉Cap Holes〈顶端洞口编辑器〉Delete Mesh〈编辑网格物体编辑器〉Edit Normals〈编辑法线编辑器〉Extrude〈挤压编辑器〉Face Extrude〈面拉伸编辑器〉Normal〈法线编辑器〉Optimize〈优化编辑器〉Smooth〈平滑编辑器〉STL Check〈STL检查编辑器〉Symmetry〈对称编辑器〉Tessellate〈镶嵌编辑器〉Vertex Paint〈顶点着色编辑器〉Vertex Weld〈顶点焊接编辑器〉Animation Modifiers〈动画编辑器〉Skin〈皮肤编辑器〉Morpher〈变体编辑器〉Flex〈伸缩编辑器〉Melt〈熔化编辑器〉Linked XForm〈连结参考变换编辑器〉Patch Deform〈面片变形编辑器〉Path Deform〈路径变形编辑器〉Surf Deform〈表面变形编辑器〉* Surf Deform〈空间变形编辑器〉UV Coordinates〈贴图轴坐标系〉UVW Map〈UVW贴图编辑器〉UVW Xform〈UVW贴图参考变换编辑器〉Unwrap UVW〈展开贴图编辑器〉Camera Map〈相机贴图编辑器〉* Camera Map〈环境相机贴图编辑器〉Cache Tools〈捕捉工具〉Point Cache〈点捕捉编辑器〉Subdivision Surfaces〈表面细分〉MeshSmooth〈表面平滑编辑器〉HSDS Modifier〈分级细分编辑器〉Free Form Deformers〈自由变形工具〉FFD 2×2×2/FFD 3×3×3/FFD 4×4×4〈自由变形工具2×2×2/3×3×3/4×4×4〉FFD Box/FFD Cylinder〈盒体和圆柱体自由变形工具〉Parametric Deformers〈参数变形工具〉Bend〈弯曲〉Taper〈锥形化〉Twist〈扭曲〉Noise〈噪声〉Stretch〈缩放〉Squeeze〈压榨〉Push〈推挤〉Relax〈松弛〉Ripple〈波纹〉Wave〈波浪〉Skew〈倾斜〉Slice〈切片〉Spherify〈球形扭曲〉Affect Region〈面域影响〉Lattice〈栅格〉Mirror〈镜像〉Displace〈置换〉XForm〈参考变换〉Preserve〈保持〉Surface〈表面编辑〉Material〈材质变换〉Material By Element〈元素材质变换〉Disp Approx〈近似表面替换〉NURBS Editing〈NURBS面编辑〉NURBS Surface Select〈NURBS表面选择〉Surf Deform〈表面变形编辑器〉Disp Approx〈近似表面替换〉Radiosity Modifiers〈光能传递修改器〉Subdivide〈细分〉* Subdivide〈超级细分〉八、Character〈角色人物〉Create Character〈创建角色〉Destroy Character〈删除角色〉Lock/Unlock〈锁住与解锁〉Insert Character〈插入角色〉Save Character〈保存角色〉Bone Tools〈骨骼工具〉Set Skin Pose〈调整皮肤姿势〉Assume Skin Pose〈还原姿势〉Skin Pose Mode〈表面姿势模式〉九、Animation〈动画〉IK Solvers〈反向动力学〉HI Solver〈非历史性控制器〉HD Solver〈历史性控制器〉IK Limb Solver〈反向动力学肢体控制器〉SplineIK Solver〈样条反向动力控制器〉Constraints〈约束〉Attachment Constraint〈附件约束〉Surface Constraint〈表面约束〉Path Constraint〈路径约束〉Position Constraint〈位置约束〉Link Constraint〈连结约束〉LookAt Constraint〈视觉跟随约束〉Orientation Constraint〈方位约束〉Transform Constraint〈变换控制〉Link Constraint〈连接约束〉Position/Rotation/Scale〈PRS控制器〉Transform Script〈变换控制脚本〉Position Controllers〈位置控制器〉Audio〈音频控制器〉Bezier〈贝塞尔曲线控制器〉Expression〈表达式控制器〉Linear〈线性控制器〉Motion Capture〈动作捕捉〉Noise〈燥波控制器〉Quatermion(TC〈TCB控制器〉Reactor〈反应器〉Spring〈弹力控制器〉Script〈脚本控制器〉XYZ〈XYZ位置控制器〉Attachment Constraint〈附件约束〉Path Constraint〈路径约束〉Position Constraint〈位置约束〉Surface Constraint〈表面约束〉Rotation Controllers〈旋转控制器〉注：该命令工十一个子菜单。
电信领域常用的英文缩略语
电信领域常用的英文缩略语16QAM 16-State Quadrature Amplitude Modulation 16状态正交幅度调制2G Second Generation 第二代移动通信3G Third Generation 第三代移动通信3GPP Third Generation Partnership Project 第三代协作项目组织4G Fourth Generation 第四代移动通信AA/D Analog / Digital 模拟/数字A-F Account-Function 计费功能AAA Authentication Authorization Account 验证、授权和计费AAL ATM Adaptation Layer ATM适配层ACF Admission ConFirm 接入确认ACL Access Control List 访问控制列表ACM Address Complete Message 地址全消息AD ADvertisement 广告ADM Add Drop Multiplexer 分插复用器ADSL Asymmetric Digital Subscriber Line 不对称数字用户线AG Access Gateway 接入网关AGCF Access Gateway Control Function 接入网关控制功能AH Authentication Header 认证头AIS Alarm Indication Signal 告警指示信号AKA Authentication and Key Agreement 认证和密钥协商协议ALG Application Level Gateway 应用层网关ALS Automatic Laser Shutdown 自动激光关断AMC Adaptive Modulation and Coding 自适应调制和编码AMR Adaptive Multi Rate 自适应多速率AN Access Network 接入网ANM ANswer Message 应答消息ANSI American National Standard Institute 美国国家标准协会AON Active Optical Network 有源光网络API Application Programming Interface 应用编程接口APM Application Transport Mechanism 应用传输机制APON A TM Passive Optical Network A TM无源光网络APR Automatic Power Reduction 自动功率降低ARIB Association of Radio Industries and Businesses 日本无线电产业协会ARJ Admission ReJect 接入拒绝ARPU Average Revenue Per User 平均用户贡献度ARQ Admission ReQuest 接入请求ARQ Automatic Repeat Request 自动请求重传AS Application Server 应用服务器AS-F Application Server-Function 应用服务器功能ASN.1 Abstract Syntax Notation one 抽象语法记法1ASON Automatic Switched Optical Network 自动交换光网络ASP Application Service Provider 应用服务提供商ASP Abstract Service Primitive 抽象业务原语ASTN Automatic Switched Transport Network 自动交换传输网A T Access Terminal 接入终端ATIS Alliance for Telecommunications Industry Solutions 电信业解决方案联盟ATM Asynchronous Transfer Mode 异步传输模式AUC AUthentication Center 鉴权中心BB-INAP Broadband-Intelligent Network Application Protocol 宽带智能网应用协议B-ISDN Broadband-Integrated Services Digital Network 宽带综合业务数字网B-ISUP Broadband-ISDN User Part 宽带ISDN用户部分B-SCE Broadband-Service Create Environment 宽带业务生成环境B-SCP Broadband-Service Control Point 宽带业务控制点B-SDP Broadband-Service Data Point 宽带业务数据点B-SMS Broadband-Service Manage System 宽带业务管理系统B-SSP Broadband-Service Switch Point 宽带业务交换点B-VPN Broadband Virtual Private Network 宽带虚拟专用网络B3G Beyond 3G 超3GBA Border Agent 边界代理BAS Broadband Access Server 宽带接入服务器BCF Bandwidth ConFirm 带宽确认BCF Bearer Control Function 承载控制功能BCMCS Broadcast and Multicast Service 广播和组播业务BCTP Bearer Control Tunneling Protocol 承载控制隧道协议BCU Bearer Control Unit 承载控制单元BcN Broadband Convergence Network 宽带融合网络BER Basic Encoding Rules 基本编码规则BG Border Gateway 边界网关BGCF Breakout Gateway Call Function 出口网关控制功能BGF Border Gateway Function 边界网关功能BGP Border Gateway Protocol 边界网关协议BHCA Busy Hour Call Attempts 忙时试呼次数BICC Bearer Independent Call Control 与承载无关的呼叫控制BICSCN Bearer Independent Circuit Switching Core Network 与承载无关的电路交换网络BIP Broadband Intelligent Peripheral 宽带智能外设BIS Bump In the Stack 栈内凸块BIWF Bearer InterWorking Function 承载互通功能BMAC Basic Media Access Control 基本媒体接入控制BMF Bearer Media Function 承载媒体功能BNC Backbone Network Connection 骨干网连接BNF Backus-Nayr Format Backus-Nayr形式BPON Broadband Passive Optical Network 宽带无源光网络BRAS Broadband Remote Access Server 宽带远程接入服务器BRJ Bandwidth ReJect 带宽拒绝BRQ Bandwidth Request 带宽请求BSC Base Station Controller 基站控制器BTS Base Transceiver Station 基站收发信台CC/S Client/Server 客户端/服务器CAC Connection Admission Control 连接允许控制CAMEL Customized Applications for Mobile Network 移动网增强逻辑的客户化应用CAP CAMEL Application Part CAMEL应用部分CAP Carrierless Amplitude and Phase Modulation 无载波幅相调制CATV CAble TeleVision 有线电视CBC Call Bearer Control 呼叫承载控制CBR Constant Bit Rate 固定比特率CCAMP Common Control and Measurement Plane 通用控制和测量平面CCI Connect Control Interface 连接控制接口CCM Call Control Management 呼叫控制管理CCNR Call Completion on No Reply 未应答的呼叫完成CCSA China Communications Standards Association 中国通信标准化协会CCU Call Control Unit 呼叫控制单元CCXML Call Control Extensible Markup Language 呼叫控制可扩展标识语言CDM Code Division Multiplexing 码分复用CDMA Code Division Multiple Access 码分多址接入CDR Call Detail Record 呼叫详细记录CDRS Call Detail Record Server 呼叫详细记录服务器CELP Code Excited Linear Prediction 码本激励线性预测CGI Common Gateway Interface 公共网关接口CIC Circuit Identification Code 电路识别码CIDR Classless Inter Domain Routing 无类域间路由选择CIF Common Intermediate Format 公共中间格式CIR Committed Information Rate 承诺信息速率CJK China Japan Korea 中日韩合作组织CLI Command Line Interface 命令行接口CLIP Calling Line Identification Presentation 主叫号码识别显示CLIR Calling Line Identification Restriction 主叫号码识别限制CLP Cell Loss Priority 信元丢失优先级CM Cable Modem 电缆调制解调器CMIP Common Management Information Protocol 通用管理信息协议CMISE Common Management Information Service Element 公用管理信息业务单元CMN Call Mediation Node 呼叫协调节点CN Core Network 核心网COPS Common Open Policy Service 公共开放策略业务CORBA Common Object Request Broker Architecture 公共对象请求代理结构CoS Class of Service 业务分类CPE Customer Premises Equipment 用户终端设备CPN Customer Premises Network 用户驻地网CPL Call Processing Language 呼叫处理语言CPS Character Per Second 每秒字符数CR-LDP Constraint-Based Routing Label Distribution Protocol 基于路由受限的标签分发协议CS Circuit Switched 电路交换CS-1 Capability Set 1 能力集1CSA Carrier Service Area 载波服务区CSCF Call Session Control Function 呼叫会话控制功能CSF Call Service Function 呼叫业务功能CSI Circuit Switched Interworking 电路交换域互通CSS Customer Service System 客户服务系统CSSNP Circuit-Switched Service Notification Protocol 电路交换业务通知协议CWDM Coarse Wave Division Multiplexer 稀疏波分复用CWTS China Wireless Telecommunication Standards 中国无线通信标准研究组DD/A Digital/Analog 数字/模拟DBA Dynamic Bandwidth Assignment 动态带宽分配DCF Disengage ConFirm 终止确认DCM Distributed Call and Connection Management 分布式呼叫和连接管理DCN Data Communication Network 数据通信网DCS Digital Cross-connect System 数字交叉连接DDN Digital Data Network 数字数据网DDoS Distributed Denial of Service 分布式拒绝服务DDRP Domain to Domain Routing Protocol 域到域路由协议DECT Digital Enhanced Cordless Telecommunication 数字增强型无绳通信DFE Decision Feedback Equalizer 判决反馈均衡器DFT Discrete Fourier Transform 离散傅里叶变换DHCP Dynamic Host Configuration Protocol 动态主机配置协议DiffServ Differentiated Service 区分服务DMT Discrete Multi-Tone 离散多音频DNS Domain Name Service 域名服务DNS-ALG DNS- Application Level Gateway 域名服务器-应用层代理网关DOPRA Distributed Object-oriented Programmable Real-time Architecture 分布式面向对象可编程实时构架DoS Denial of Service 拒绝服务DP Detection Point 检测点DRJ Disengage ReJect 终止拒绝DRQ Disengage ReQuest 终止请求DS-CDMA Direct Sequence-Code Division Multiple Access 直扩码分多址DSC Downlink Shared Channel 下行链路共享信道DSCP Differentiated Services Code Point DiffServ代码点DSL Digital Subscriber Line 数字用户线DSLAM Digital Subscriber Line Access Multiplexer 数字用户线接入复用器DSMP Data Service Management Platform 数据业务管理平台DSP Digital Signal Processor 数字信号处理器DSS1 Digital Subscriber Signaling No1 1号数字用户信令DSS2 Digital Subscriber Signaling No.2 2号数字用户信令DSTM Dual Stack Transition Mechanism 双协议栈过渡机制DTE Data Terminal Equipment 数据终端设备DTMF Dual Tone Multi Frequency 双音多频DU Distribution Unit 分配单元DVC Data V oice Conflux 数据语音合线DWDM Dense Wavelength Division Multiplexing 密集波分复用DXC Digital Cross Connection 数字交叉连接EE-NNI Exterior-Network Network Interface 外部网络-网络接口EAS Erisson Application Server 爱立信应用服务器ECC Embedded Control Channel 嵌入式控制信道EDFA Erbium Doped Fiber Amplifier 掺铒光纤放大器EDGE Enhanced Data rates for Global Evolution GSM演进增强数据速率EDSL Ethernet Digital Subscriber Line 以太网数字用户线EFM Ethernet in the First Mile 第一英里以太网EFMA Ethernet in the First Mile Alliance 第一英里以太网联盟EIR Equipment Identity Register 设备识别寄存器EIR Excessive Information Rate 额外信息速率EIRP Effective Isotropic Radiation Power 全向有效辐射功率EMF Element Management Function 单元管理功能EML Element Management Layer 网元管理层EMS Element Management System 网元管理系统ENUM E.164 NUMber and DNS E.164号码和域名系统EoVDSL Ethernet over VDSL 基于以太网技术的VDSLEPON Ethernet Passive Optical Network 以太网无源光网络ESCON Enterprise Systems Connection 企业系统互联ESE Expandable Switching Exchange 开放式可编程交换平台ESN Electric Sequence Number 电子序列号ESP Encapsulating Security Payload 封装安全载荷ETSI European Telecommunications Standards Institution 欧洲电信标准化委员会EUDCH Enhanced Uplink Dedicated Channel 增强的上行链路专用信道FF-SCH Forward-Supplemental Channel 前向辅助信道FCS Fast Cell Selection 快速蜂窝选择FCS Frame Check Sequence 帧检验序列FDD Frequency Division Duplex 频分双工FDDI Fiber Distributed Data Interface 光纤分布式数据接口FDM Frequency Division Multiplexing 频分复用FDMA Frequency Division Multiple Access 频分多址FE Fast Ethernet 快速以太网FFT Fast Fourier Transform 快速傅立叶变换FG NGN NGN Focus Group 下一代网络专题组FICON Fiber Connection 光纤互联FISU Fill-In Signal Unit 填充信号单元FITL Fiber In The Loop 环路光纤FMC Fixed Mobile Convergence 固定通信与移动通信融合FPBN Future Packet-Based Networks 未来分组网FPLMTS Future Public Land Mobile Telecommunication System 未来公众陆地移动通信系统FR Frame Relay 帧中继FSAN Full Service Access Networks 全业务接入网FSTP Fast Spanning Tree Protocol 快速生成树协议FTP File Transfer Protocol 文件传输协议FTTB Fiber To The Building 光纤到大楼FTTC Fiber To The Cabinet 光纤到接线柜FTTC Fiber To The Curb 光纤到路边FTTH Fiber To The Home 光纤到户FTTO Fiber To The Office 光纤到办公室GGE Gigabit Ethernet 千兆比特以太网GEM GPON Encapsulation Method GPON封装方法GEPON Gigabit Ethernet Passive Optical Network 千兆比特以太网无源光网络GERAN GSM/EDGE Radio Access Network GSM/EDGE无线接入网GFP Generic Framing Procedure 通用成帧规程GGSN Gateway GPRS Supporting Node GPRS网关支持节点GII Global Information Infrastructure 全球信息基础设施GMII Gigabit Media Independent Interface 千兆比特媒体无关接口GMSC Gateway Mobile Switching Center 网关移动交换中心GMPLS Generalized Multiple Protocol Label Switching 通用多协议标签交换GPON Gigabit Passive Optical Network 千兆比特无源光网络GPRS General Packet Radio Service 通用分组无线业务GRE Generic Routing Encapsulation 通用路由封装GSM Global System for Mobile Communication 全球移动通信系统GSN GPRS Supporting Nodes GPRS支持节点GSN Gateway Service Node 网关服务节点GSTN General Switched Telephone Network 普通电话交换网GTC GPON Transmission Convergence GPON传输汇聚层GTP GPRS Tunneling Protocol GPRS隧道协议GTT Globe Text Telephone 全球文本电话HHARQ Hybrid Automatic Repeat Request 混合自动请求重传HDLC High-level Data Link Control 高级数据链路控制HDR High Data Rate 高数据速率HDSL High Bit Rate Digital Subscriber Line 高比特率数字用户线HEC Header Error Check 帧头错误检验HFC Hybrid Fiber Coaxial 混合光纤同轴电缆网HLR Home Location Register 归属位置寄存器HS-DPCCH High Speed-Dedicated Physical Control CHannel 高速专用物理控制信道HS-DSCH High Speed-Downlink Shared CHannel 高速下行共享信道HS-SCCH High Speed-Shared Control CHannel 高速共享控制信道HSDPA High Speed Downlink Packet Access 高速下行链路数据分组接入HSPA High Speed Packet Access 高速数据分组接入HSS Home Subscriber Server 归属用户服务器HSUPA High Speed Uplink Packet Access 高速上行链路数据分组接入HTTP Hypertext Transfer Protocol 超文本传输协议II-CSCF Interrogating CSCF 查询CSCFI-NNI Inter-Network Network Interface 内部网络-网络接口IACK Information Request ACKnowledgement 信息请求确认IAD Integrated Access Device 综合接入设备IADMS Integrated Access Device Management System 综合接入设备管理系统IAM Initial Address Message 初始地址消息IANA Internet Assigned Numbers Authority 因特网编号分配部门IBCF Internet Border Control Function 因特网边界点控制功能ICMP Internet Control Message Protocol 因特网控制报文协议ICP Internet Content Provider 因特网内容提供商ICV Integrity Check Value 完整性校验值ICW Internet Call Waiting 因特网呼叫等待IDC Internet Data Center 因特网数据中心IEEE Institute of Electrical and Electronics Engineers 电子电气工程师协会IETF Internet Engineering Task Force 互联网工程任务组IKE Internet Key Exchange Internet密钥交换IM Instant Message 即时消息IM IP Multimedia IP多媒体IM-SSF IP Multimedia-Service Switching Function IP多媒体业务交换功能IMEI International Mobile Equipment Identifier 国际移动设备识别码IMS IP Multimedia Subsystem IP多媒体子系统IMTC International Multimedia Teleconferencing Consortium 国际多媒体电视会议联合会IN Intelligent Network 智能网INAP Intelligent Network Application Protocol 智能网应用协议INAK Information Request Negative AcKnowledgement 信息查询否认INES Intelligent Network Entrance System 智能网网关系统IntServ Integrated Service 综合服务ION Intelligent Optical Network 智能光网络IOS Interactive Operating System 交互式操作系统IP Internet Protocol 因特网协议IP Intelligent Peripheral 智能外设IP-CAN IP Connectivity Access Network IP接入网络IPBCP IP Bearer Control Protocol IP承载控制协议IPCC International PacketComm Consortium 国际分组通信论坛IPDC IP Device Control IP设备控制IPSec IP Security IP安全IPTV IP TeleVision IP电视IPX Internetwork Packet Exchange 网间分组交换IRQ Information ReQuest 信息请求IRR Information Request Response 信息请求响应IS-IS Intermediate System to Intermediate System Routing Protocol 中间系统到中间系统的路由选择协议ISC International Softswitch Consortium 国际软交换论坛ISC Internal Service Control 因特网业务控制ISCE Integrated Service Creation Environment 综合业务生成环境ISCP Integrated Service Control Point 综合业务控制点ISDN Integrated Services Digital Network 综合业务数字网ISDP Integrated Service Data Point 综合业务数据点ISIM IP Multimedia Service Identity Module IP多媒体业务身份模块ISMAP Integrated Service Management Access Point 综合业务管理接入点ISMP Integrated Service Management Point 综合业务管理点ISN Interface Service Node 接口服务节点ISO International Organization for Standardization 国际标准化组织ISP Internet Service Provider 因特网服务提供商ISSP Integrated Service Switching Point 综合业务交换点ISUP ISDN User Part 综合业务数字网用户部分IT Information Technology 信息技术ITU International Telecommunications Union 国际电信联盟ITU-T ITU Telecommunication Standardization Sector 国际电信联盟电信标准化组织IUA ISDN User Adaptation Layer ISDN用户适配层协议IVR Interactive V oice Response 交互式语音应答IWF InterWorking Function 互通功能JJ2EE Java 2 Platform Enterprise Edition Java 2平台企业版JAIN Java APIs for Integrated Networks 综合网络的Java APIsJRG Joint Rapporteur Group 课题报告联合起草小组LL2TP Layer2 Tunneling Protocol 第二层隧道协议LAC Link Access Control 链路接入控制LAN Local Area Network 局域网LAPS Link Access Protocol-SDH SDH链路接入协议LCAS Link Capacity Adjustment Scheme 链路容量调整机制LCR Low Chip Rate 低码片速率LDAP Lightweight Directory Access Protocol 轻量级目录访问协议LDP Label Distribution Protocol 标签分发协议LE Local Exchange 本地交换机LLID Logical Link Identification 逻辑链路标识LMDS Local Multipoint Distribution Services 本地多点分配业务LMP Link Management Protocol 链路管理协议LMT Local Maintenance Terminal 本地维护终端LRM Link Resource Manager 链路资源管理器LS Local Switch 本地交换局LSP Label Switch Path 标签交换路径LSSU Link Statues Signal Unit 链路状态信号单元LSW LAN SWitch 局域网交换机LT Line Terminal 线路终端MM2UA MTP 2 User Adaptation Layer MTP第二级用户适配层M3UA MTP 3 User Adaptation Layer MTP第三级用户适配层M2PA MTP 2 Peer-to-peer Adaptation Layer MTP第二级对等适配层MAC Medium Access Control 媒体接入控制MAI Multiple Access Interference 多址干扰MAN Metropolitan Area Network 城域网MAP Mobile Application Part 移动应用部分MBMS Multimedia Broadcast and Multicast Service 多媒体广播和组播MC Multi-point Controller 多点控制器MC-CDMA Multiple Carrier-Code Division Multiple Access 多载波码分多址MCF Media Control Function 媒体控制功能MCS Multimedia Communication Server 多媒体通信服务器MCS Multimedia Communication System 多媒体通信系统MCU Multi-point Control Unit 多点控制单元MDCP Media Device Control Protocol 媒体设备控制协议MEID Mobile Equipment Identifier 移动设备标识MFI Multiple Frame Indicator 复帧指示器MG Media Gateway 媒体网关MGC Media Gateway Controller 媒体网关控制器MGC-F Media Gateway Control-Function 媒体网关控制功能MGCF Media Gateway Control Function 媒体网关控制功能MGCP Media Gateway Control Protocol 媒体网关控制协议MG-F Media Gateway-Function 媒体网关功能MGU Media Gateway Unit 媒体网关单元MIME Multi-purpose Internet Mail Extension 多用途因特网邮件扩展MIMO Multiple Input Multiple Output 多输入多输出MIP Mobile IP 移动IPML-PPP Multi-Link Point to Point Protocol 多链路点对点协议MM Mobility Management 移动性管理MMDS Multi-channel Multi-point Distribution Services 多通道多点分配业务MML Man-Machine Language 人机语言MMS Multimedia Message Service 多媒体消息服务MMSF Media Mapping/Switching Function 媒体映射/交换功能MMUSIC Multiparty Multimedia Session Control 多方多媒体会话控制MP Multi-point Processor 多点处理器MPCP Multi-point Control Protocol 多点控制协议MPEG Moving Picture Expert Group 活动图象专家组MPLS Multi-Protocol Label Switching 多协议标签交换MRF Media Resource Function 媒体资源功能MRFC Media Resource Function Controller 媒体资源功能控制器MRFP Media Resource Function Processor 媒体资源功能处理器MRS Multimedia Resource Server 多媒体资源服务器MS Mobile Station 移动台MS Media Server 媒体服务器MS-F Media Server-Function 媒体服务器功能MSC Mobile Switching Center 移动交换中心MSID Mobile Station Identifier 移动台标识符MSP Multiplex Section Protection 复用段保护MSTP Multi-Service Transmission Platform 多业务传输平台MSU Message Signal Unit 消息信号单元MTA Message Transfer Agent 消息传输代理MTP Message Transport Part 消息传输部分MTU Maximum Transfer Unit 最大传输单元MUD Multiple User Detection 多用户检测NN-ISDN Narrowband-ISDN 窄带ISDNN-ISUP Narrowband-ISDN User Part 窄带ISDN用户部分NAPT Network Address Port Translation 网络地址端口转换NAS Network Access Server 网络接入服务器NASS Network Attachment Sub-System 网络附着子系统NA T Network Address Translation 网络地址转换NAT/PT Network Address Translation/Protocol Translation 网络地址转换/协议转换NE Network Element 网元NEL Network Element Layer 网元层NGI Next Generation Internet 下一代因特网NGN Next Generation Network 下一代网络NII National Information Infrastructure 国家信息基础设施NMI Network Management Interface 网络管理接口NMI-A Network Management Interface-A 网络管理接口ANMI-T Network Management Interface-T 网络管理接口TNMS Network Management System 网络管理系统NP Network Performance 网络性能NRT-VBR Non Real Time-Variable Bit Rate 非实时可变比特率NSAP Network Service Access Point 网络业务接入点NT Network Terminal 网络终端NTT Nippon Telegraph and Telephone Corporation 日本电话电报公司NU Network Unit 网络单元OOADM Optical Add Drop Multiplexer 光分插复用器OAM Operation Administration and Maintenance 运行、管理和维护OAM&P Operation Administration Maintenance and Provisioning 运行、管理、维护和配置OAN Optical Access Network 光接入网OBS On-line Billing System 在线计费系统ODN Optical Distribution Network 光配线网ODSI Optical Domain Service Interconnect 光域业务互连OEO Optical-Electrical-Optical Converter 光/电/光转换器OFC Optical Fiber Communications 光纤通信OFDM Orthogonal Frequency Division Multiplexing 正交频分复用OIF Optical Internetworking Forum 光因特网论坛OLS Optical Label Switching 光标签交换OLT Optical Line Terminal 光线路终端OMA Open Mobile Alliance 开放移动联盟OMC-R Operation and Maintenance Center-Radio 无线维护操作中心OMCI ONT Management and Control Interface 光网络终端管理与控制接口OMCI Operations Management Communications Interface 操作管理通信接口OMG Object Management Group 对象管理组ONLY One Number Links You 一号通ONNS Optical Network Navigation System 光网络导航系统ONU Optical Network Unit 光网络单元OPTIS Overlapped PAM Transmission with Interlocking Spectra 频谱互锁重叠的PAM传输OSA Open Service Architecture 开放的业务结构OSI Open Systems Interconnection 开放系统互连OSPF Open Shortest Path First 开放式最短路径优先OSS Operation Support Systems 运营支撑系统OSX Open Service Exchange 开放业务交换平台OTD Observation Time Difference 观察时间差OTN Optical Transport Network 光传输网络OVPN Optical Virtual Private Network 光虚拟专用网OXC Optical Cross Connect 光交叉连接PP-CSCF Proxy CSCF 代理CSCFP2MP Point to Multi-Point 点到多点PAM Pulse Amplitude Modulation 脉冲幅度调制PBN Packet Based Networks 分组网络PBS Polarization Beam Splitter 极化光束分离器PBX Private Branch eXchange 用户交换机PC Personal Computer 个人电脑PC Permanent Connection 永久性连接PCF Packet Control Function 分组控制功能实体PCM Pulse Code Modulation 脉冲编码调制PCS Physical Coding Sub-layer 物理编码子层PCS Personal Communication Service 个人通信业务PDF Policy Decision Function 策略判决功能PDN Packet Data Network 分组数据网PDP Policy Decision Point 策略决定点PDSN Packet Data Service Node 分组数据业务节点PDU Protocol Data Unit 协议数据单元PEP Policy Execution Point 策略执行点PER Packet Encoding Rules 分组编码规则PES PSTN Emulation Subsystem PSTN仿真子系统PHS Personal Hand-phone System 个人手持电话系统PI Physical Interface 物理接口PINT PSTN/Internet Internetworking PSTN与Internet的互通PLI PDU Length Indicator 协议数据单元长度指示符PLL Phase Locked Loop 锁相环PLMN Public Land Mobile Network 公用陆地移动网PLOAM Physical Layer OAM 物理层OAMPMA Physical Medium Attachment 物理媒体接入子层PMD Physical Medium Dependent 物理媒体相关子层PNNI Private Network-to-Network Interface 专用网间接口PoC Push to Talk over Cellular 无线一键通POH Path Overhead 通道开销PON Passive Optical Network 无源光网络POP Post Office Protocol 邮局协议PoS Packet over SDH SDH上的IP分组POS Passive Optical Splitter 无源光分路器POTS Plain Old Telephone Service 普通电话业务PPP Point to Point Protocol 点对点协议PPS Pre-Paid Service 预付费PR Packet Ring 分组环PRA Primary Rate Access 基群速率接入PRI Primary Rate Interface 基群速率接口PS Packet Switched 分组交换PSC Pre-paid Service Center 预付费业务中心PSE Personal Service Environment 个人业务环境PSTN Public Telephone Switched Network 公共交换电话网PTM Packet Transfer Mode 分组传输模式PTN Personal Telecommunications Number 个人通信号码PTT Push To Talk 一键通PVC Permanent Virtual Circuit 永久虚电路QQAM Quadrature Amplitude Modulation 正交幅度调制QCIF Quarter Common Intermediate Format 四分之一通用中间格式QIB Quality Indicator Bit 质量指示器位QoS Quality of Service 服务质量QPSK Quadrature Phase-Shift Keying 正交相移键控RR-SCH Reverse-Supplementary Channel 反向辅助信道R-SG Roaming Signaling Gateway 漫游信令网关RAB Reverse Activity Bit 反向激活比特RAC Resource Availability Confirm 资源可用确认RACS Resource and Admission Control Subsystem 资源与接入控制子系统RADIUS Remote Access Dial-In User Service 远程接入拨号用户业务RADSL Rate Adaptive Digital subscriber Line 速率自适应数字用户线路RAI Resource Availability Indication 资源可用指示RAN Radio Access Network 无线接入网RAS Registration Admission Status 注册、许可和状态RCF Registration ConFirm 注册确认REL Release 呼叫释放信息RFC Request For Comments 请求评论文档RG Residential Gateway 驻地网关RIP Request In Progress 请求进展RLP Radio Link Protocol 无线链路协议RLU Remote Line Unit 远端用户线单元RNC Radio Network Controller 无线网络控制器RNS Radio Network Subsystem 无线网络子系统RPR Resilient Packet Ring 弹性分组环RRJ Registration ReJect 注册拒绝RRQ Registration ReQuest 注册请求RSVP Resource Reservation Protocol 资源预留协议RSVP-TE Resource Reservation Protocol-Traffic Engineering 基于流量工程扩展的资源预留协议RTCP Real-time Transfer Control Protocol 实时传输控制协议RTP Real-time Transfer Protocol 实时传输协议RTSP Real-time Streaming Protocol 实时流媒体协议RTU Remote Terminal Unit 远方终端单元RT-VBR Real Time-Variable Bit Rate 实时可变比特率SS-CSCF Serving CSCF 服务CSCFSA Security Association 安全关联SA Smart Antenna 智能天线SACP Service Access Control Point 业务接入控制点SAD Security Association Database 安全关联数据库SAF Special Access Function 特定接入功能SAM Subscriber Application Management 用户应用管理系统SAM Subsequent Address Message 后续地址消息SC Switched Connection 交换式连接SC-F Signaling Conversion Function 信令转换功能SC-TDMA Single Carrier Time Division Multiple Access 单载波时分多址SCCP Signaling Connection control Part 信令连接控制部分SCE Service Creation Environment 业务生成环境SCEP Service Creation Environment Point 业务生成环境点SCF Service Control Function 业务控制功能SCM Sub-Carrier Multiplexing 副载波复用SCN Switched Circuit Network 电路交换网SCP Service Control Point 业务控制点SCS Service Capability Server 业务能力服务器SCTP Stream Control Transport Protocol 流控制传输协议SDF Service Data Function 业务数据功能SDH Synchronous Digital Hierarchy 同步数字系列SDL Simple Data Link 简单数据链路SDM Space Division Multiplexing 空分复用SDMA Space Division Multiple Access 空分多址SDP Service Data Point 业务数据点SDP Session Description Protocol 会话描述协议SDR Software Defined Radio 软件无线电SDSL Single Digital Subscriber Line 单线数字用户线SEP Signaling End Point 信令终结点SG Signaling Gateway 信令网关SGCP Simple Gateway Control Protocol 简单网关控制协议SGF Signaling Gateway Function 信令网关功能SGSN Serving GPRS Supporting Node GPRS服务支持节点SIB Service Independent Building Blocks 基于与业务无关的模块SIGTRAN Signaling Transport 信令传输协议SIIT Stateless IP/ICMP Translation 无状态IP/ICMP转换SIM Subscriber Identity Module 用户身份模块SIP Session Initiation Protocol 会话发起协议SIP-I SIP with Encapsulated ISUP 带有ISUP消息封装的SIP协议SIP-S SIP Servlet 应用于伺服系统的会话发起协议SIP-T Session Initiation Protocol for Telephone 应用于电话网的SIP协议SLA Service Level Agreement 服务等级协议SLF Subscriber Location Function 签约用户定位功能SLR Subscriber Location Router 用户位置路由器SMAP Service Management Access Point 业务管理接入点SMP Service Management Point 业务管理点SMS Service Management System 业务管理系统SMS Short Message Service 短消息业务SMTP Simple Mail Transfer Protocol 简单邮件传输协议SN Serving Node 服务节点SNC Sub-Network Connection 子网络连接SNCP Sub-Network Connection Protection 子网连接保护SNI Service Node Interface 业务节点接口SNMP Simple Network Management Protocol 简单网络管理协议SNR Signal to Noise Ratio 信噪比SOHO Small Office/Home Office 小办公室/家庭办公室SONET Synchronous Optical Network 光同步网SP Signaling Point 信令点SPAN Services and Protocols for Advanced Networks 高级网络的服务与协议SPC Soft Permanent Connection 软永久连接SPD Security Policy Database 安全策略数据库SPI Security Parameter Index 安全参数索引SPIRITS Service in the PSTN IN Requesting InTernet Service PSTN/IN请求因特网服务业务SPX Sequential Packet eXchange 序列分组交换SQ Sequence Indicator 序列指示器SRBP Signaling Radio Burst Protocol 信令无线突发协议SRF Specialized Resource Function 专用资源功能SRP Spatial Reuse Protocol 空间重用协议SS7 Signaling System 7 7号信令网SSF Service Switching Function 业务交换功能SSP Service Switching Point 业务交换点STC Space-Time Coding 空时编码STC Signaling Transport Converter 信令传输转换器STM Synchronous Transfer Mode 同步传输模式STM-N Synchronous Transport Module Level N 第N级同步传输模块STP Signaling Transfer Point 信令转接点STP Spanning Tree Protocol 生成树协议STS Synchronous Transport Signal 同步传输信号SUA SCCP User Adaptation Layer SCCP用户适配层TT-SG Transport Signaling Gateway 传输信令网关T2P Traffic-to-Pilot 业务到导航信道TACS Total Access Communication System 全接入通信系统TC Transmission Convergence 传输汇聚TCA Terminal Control Area 终端控制区TCAP Transaction Capabilities Application Part 事物处理应用部分TCP Transmission Control Protocol 传输控制协议TD-CDMA Time Division-Code Division Multiple Access 时分码分多址TD-SCDMA Time Division-Synchronization Code Division Multiple Access 时分同步码分多址TDD Time Division Duplex 时分双工TDM Time Division Multiplexing 时分复用TDMA Time Division Multiple Access 时分多址TE Terminal Equipment 终端设备TE Traffic Engineering 流量工程TeS Telephony Server 电话服务器TFTP Trivial File Transfer Protocol 普通文件传输协议TG Trunk Gateway 中继网关TIA Telecommunication Industry Association 电信工业协会TIPHON Telecommunications and Internet Protocol Harmonization Over Networks 透过网络的电信及网际网路通信协议TISPAN Telecommunications and Internet Converged Services and Protocols for Advanced Networking 电信和互联网融合业务及高级网络协议组TLS Transparent Local Area Network Service 透明局域网业务TML Telephone Markup Langue 电信标记语言TMN Telecommunications Management Network 电信管理网TMSC Trunk Mobile Switching Center 汇接移动交换中心TOS Type Of Service 服务类型TRAU Transcoder and Rate Adapter Unit 码型变换/速率适配器TRIP Telephony Routing over IP IP电话路由TSM TD-SCDMA System for Mobile TD-SCDMA移动通信系统TSN Trunk Service Node 中继服务节点TSP Terminal Supporting Processor 终端支持处理机TTC Telecommunication Technology Committee 日本情报通信技术委员会TTA Telecommunication Technology Association 韩国电气通信技术协会TTI Transmission Time Interval 传输时间间隔TTS Text To Speech 文本转换为语音TU Transaction User 事务用户TUP Telephone User Part 电话用户部分UUAS Universal Audio Server 通用语音服务器UBR Unspecified Bit Rate 未指定比特率UCF Unregistration ConFirm 注销确认UDP User Datagram Protocol 用户数据报协议UE User Equipment 用户设备UICC Universal Integrated Circuit Card 通用集成电路卡ULH Ultra Long Haul 超长距离传输UMS User Mobility Service 用户移动服务UMS Unified Messaging Service 统一消息业务UMTS Universal Mobile Telecommunications System 通用移动通信系统UNI User Network Interface 用户网络接口UPC Usage Parameter Control 使用参数控制UPT Universal Personal Telecommunication 通用个人通信URI Uniform Resource Identifier 统一资源标识URL Uniform Resource Locator 统一资源定位器URJ Unregistration ReJect 注销拒绝URQ Unregistration ReQuest 注销请求UBR Unspecified Bit Rate 未指定比特率USB Universal Serial Bus 通用串行总线USP Universal Signaling Point 通用信令网关UTRAN Universal Terrestrial Radio Access Network 通用地面无线接入网络VV5UA V5.2 User Adaptation Layer V5.2用户适配层VAD Voice Activity Detection 语音激活检测VC Virtual Container 虚容器VC Virtual Channel 虚拟通道VCG Virtual Concatenation Group 虚级联组VCI Virtual Channel Identifier 虚拟通道标识VCSEL Vertical Cavity Surface Emitting Lasers 垂直腔面发射激光器VDSL Very High Speed Digital Subscriber Line 甚高速数字用户线VHE Virtual Home Environment 虚拟归属环境VLAN Virtual LAN 虚拟局域网VLR Visited Location Register 拜访位置寄存器VoD Video on Demand 视频点播VoDSL Voice over DSL 在数字用户线上传输语音VoIP Voice over IP IP话音VP Virtual Path 虚拟路径VPG Virtual Path Group 虚拟路径组VPHS Virtual Private Hub Service 虚拟专用Hub业务VPI Virtual Path Identifier 虚拟路径标识VPLS Virtual Private LAN Service 虚拟专用局域网业务。
个性化自适应学习系统中的学生模型
（3）情感状态 ：学习者在学习过程中的情感状态同
样对学习效果具有较大的影响。有研究发现，当学习者
处在低落的情绪中时，会导致学习者退出任务学习。情
感因素与学习者的动机息息相关。
（4）认知能力 ：包括学习者的注意力、处理问题能
表 2 Felder-Silverman 学习风格分类
Tab.2 Learning style classification of Felder-Silverman
生信息数据，进而动态更新学生模型并且创建详细的学 师和学生没有直接的接触，导致学生的情感表现等信息
生个人资料。
无法被完全收集，所以在对学生进行诊断时一些不确定
3 学生模型的分类与比较
性就大大地增加了。而模糊模型致力于能够解决这种不
经查阅相关论文资料，本文归纳陈述以下几种常见 确定性。
的学生模型，以及各学生模型之间的特点比较，如表 3
重复，擅长于掌握新概念，能理解抽象的数学公式
智化地对知识进行转换，不喜欢复杂情况喜欢视觉表示，如图片、图表、流程图、 图像、影片和演示中的内容
言语型 (Verbal) ：擅长从文字的和口头的解释中获取信息
序列型 (Sequential) ：喜欢先学习分立的知识，善于使用记忆策略 综合型 (Global) ：喜欢先获得知识的综合视图
描述陈述性知识和简单的过程性知识的学习情况， 不便 不可或缺的作用，因其强大的功能体系，学生模型通常
习风格等，这些特征都不会因为学习过程的发生而变化， 通常在学习之前通过问卷的形式就可以得知。而动态特 征是指学生在学习时直接与系统的交互中，系统可通过
对基于本体论（Ontology-based）技术的关注越来越多， 收集的数据直接更新的特征。动态特征包括学习者的知 因其支持抽象内容和性质的呈现且方便重复使用 [7]，因 识、技巧、情感、认知因素和元认知因素等。
一种改进的强约束拓扑自适应snake模型
引 言
动态轮廓模型（ ｃｖ ｏｔ ｒ ｄ１ Ａ ｔ ｅ ｎ ｕ ｅ 也称为 ｓａ ｅ ｉ Ｃ ｏ Ｍｏ ） ｎｋ 模型，最早 由 Ｋ ｓ 在 １８ 年提出，目前该算法 已 ａｓ ｍ ９９
ｃｎｃｌｍｅ ｉ ｌｉ ｇｓ ｘ ｅｉ ｎａ ｒｓｌ ｈ ｗ ｔａ ｈ ｒｐ ｓｄ ｍｏ ｅ ｉ ｅ ｃｅｔｉ ｏｊｃ ｃｐｕｉｇ ａ ｄ ｌ ｉａ ｉ ｄｃ ｍａｅ．Ｅ ｐｒ ａ ｍｅｔｌ ｅｕｔ ｓｏ ｈｔｔｅｐｏ ｏｅ ｄ ｌ ｓ ｆ ｉ ｎ ｂｅｔ ａｔ ｎ ｎ ｓ ｉ ｎ ｒ
Ｋｅ ｒ ｓ ａｔ ｅ ｏ ｔｕ （ ａ ｅ ｍｏ ｅ； ｇ ａｕｅ ｘｒｃｉ ；ｎｅ ｓ ｅ ｅｔ ｉｔｔｐ ｌｇｃｌｒｎｆｒ ａｉｎ ｙ ｗｏｄ ： ｃｉ ｎｏ ｓ ｋ ） ｄ ｌｉ ｅ ｅｔｒ ｔ ｔ ｎ ｉ ｎｉ ｓ ａ ；ｏ ｏｏ ｉ ａ ｓ ｍ ｔ ｖｃ ｒ ｎ ｍａ ｆ ｅ ａ ｏ ｔ ｖ ｒ ｒｎ ａｔ ｏ ｏ
ｇａ ｍａｅ ｎ ｒｃｉｇ ｄ ｎｍｉ ｏｊｃ ｏ ｉ ｇ ｅｕ ｎｅ． ｉ ｔ ，ｔｅｏｉｉ ｌｉ ｇ ｎ ｎ ｋ ｕｖ ｒ ｒｙ ｉ ｇｓａｄ ｔ ｋｎ ｙ ａ ｃ ｂｅｔ ｆ ｍ ｍａ ｅｓｑ ｅｃｓ Ｆｒ ｌ ｈ ｒ ｎ ｍａｅ ａｄ ｓａ ｅｃｒｅａｅ ａ ｓｒ ｓｙ ｇａ
ｔ ｐ ｌ ｇ ｃ ｌ ｒｎ ｆ ｒ ａ ｉｎ Ｔ ｅｃ ｍｐ ｔｔ ｎ ｃ ｓ ｉ ａ ｓ ａｈ ｒ ｏ ｒ ｈ ｎ ｔ ａ ｆ ｒ ｉ ａ ｐ ｌ ｇ ｄ ｐ ｉ ｅｓ ａ ｅ ｏ ｏ ｏ ｉ ａ ａ ｓ ｏ ｔ ｍ ｔ ． ｈ ｏ ｕ ａ ｉ ｏ ｔ ｓ ｌｏ ｒ ｔ ｅ ｗｅ ａ ｔ ｉ ｎ ｌ ｏ ｏ ｏ ｙａ ａ ｔ ｎ ｋ ． ｏ ｏ ｌ ｔ ｈ ｏｏ ｇ ｔ ｖ
ｌ ｅ ． ｅ ， ｏ ｏ ｏ ｉａ ｒ ｎ ｆ ｒ ｔ ｎ ｏ ｅ ｍｏ ｅ ｓ ｉ ｌ ｍｅ ｔ ｄ ｔｒ ｕ ｈ ｖ ｒ ｘ ｓ ｌ ｔ ｇ Ｔ ｐ ｌ ｇ ｃ ｌｃ ｌ ｓ ｎ ｉ ｉ ｓ Ｔｈ ｎ ｔ ｐ ｌ ｇｃ ｌ ａ ｓ ｏ ｍａ ｉ ｆｔ ｄ ｌｉ ｍｐ ｅ ｎ ｅ ｈ ｏ ｇ ｅ ｅ ｐ ｉ ｉ ． ｏ ｏ ｏ ｉ ａ ｏ ｌ ｉ ｓ ｎ ｔ ｏ ｈ ｔ ｔｎ ｉｏ
NVIDIA Mellanox Quantum HDR 200G InfiniBand 交换机芯片数
NVIDIA MELLANOX QUANTUM HDR 200G INFINIBAND SWITCH SILICONNVIDIA® Mellanox® Quantum™ switch silicon offers 40 ports supporting HDR 200 Gb/s InfiniBand throughput per port, with a total of 16 Tb/s bidirectional throughput and 15.6 billion messages per second.Mellanox Quantum is the world’s smartest network switch that enables in-network computing through the co-design Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)™ technology. Its co-design architecture enables the usage of all active data center devices to accelerate communication frameworks, resulting in an order of magnitude improvement in application performance and enabling the highest performing server and storage system interconnect solutions for Enterprise Data Centers, Cloud Computing, High-Performance Computing, and Embedded environments.Mellanox Quantum embeds an innovative solution called SHIELD™ (Self-Healing Interconnect Enhancement for Intelligent Datacenters) that makes the fabric capable of self-healing autonomy. So, the speed at which communications can be corrected in the face of a link failure can be increased by 5000X, making it fast enough to save expensive retransmissions or absolute communications failure.Mellanox Quantum offers industry-leading integration of 160 SerDes lanes, with speed flexibility ranging from 2.5 Gb/s to 50 Gb/s per lane, making this Mellanox switch an obvious choice for OEMs that must address end-user requirements for faster and more robust applications. Network architects can utilize the reduced power and footprint, and a fully integrated PHY capable of connectivity across PCBs, backplanes, and passive and active copper/fiber cables, to deploy leading, fabric-flexible computing and storage systems with the lowest total cost of ownership.Key Features>Industry-leading switch silicon in performance, power and density>Industry-leading cut-through latency >Low-cost solution>Single-chip implementation>Fully integrated PHY>Backplane and cable support>1, 2 and 4 lanes>Up to 16 Tb/s of switching capacity>Up to 15.6 billion messagesper second>Up to 40 HDR 200 Gb/s InfiniBand ports >Collective communication acceleration >Hardware-based adaptive routing>Hardware-based congestion control >Mellanox SHARP™v2 collective offloads support streaming for Machine Learning>SHIELD-enabled self-healing technologyINFINIBAND INTERCONNECTMellanox Quantum InfiniBand devices enable industry standard networking, clustering, storage, and management protocols to seamlessly operate over a single “one-wire” converged network. Combined with the Mellanox ConnectX® family of adapters, on-the-fly fabric repurposing can be enabled for Cloud, Web 2.0, EDC and Embedded environments providing “future proofing” of fabrics independent of protocol. Mellanox Quantum enables IT managers to program and centralize their server and storage interconnect management and dramatically reduce their operations expenses by completely virtualizing their data center network.COLLECTIVE COMMUNICATION ACCELERATIONCollective communication describes communication patterns in which all members of a group of communication endpoints participate. Collective communications are commonly used in HPC protocols such as MPI and SHMEM. The Mellanox Quantum switch improves the performance of selected collective operations by processing the data as it traverses the network, eliminating the need to send data multiple times between endpoints.Mellanox Quantum also supports the aggregation of large data vectors at wire speed to enable MPI large vector reduction operations, which are crucial for machine learning applications.TELEMETRYVisibility is a critical component of an efficient network. Capturing what a network is‘thinking’ or ‘doing’ is the basis for true network automation and analytics. In particular, today’s HPC and cloud networks require fine-grained visibility into:>Network state in real-time>Dynamic workloads in virtualized and containerized environments>Advanced monitoring and instrumentation for troubleshootingMellanox Quantum is designed for maximum visibility using such features as mirroring, sFlow, congestion based mirroring, and histograms.SWITCH PRODUCT DEVELOPMENTThe Mellanox Quantum Evaluation Board (EVB) and Software Development Kit (SDK)are available to accelerate an OEM’s time to market and for running benchmark tests. These rack-mountable evaluation systems are equipped with QSFP56 interfaces for verifying InfiniBand functionality. In addition, SMA connectors are available for SerDes characterization. The Mellanox Quantum SDK provides customers the flexibility to implement InfiniBand connectivity using a single switch device.The SDK includes a robust and portable device driver with two levels of APIs, so developers can choose their level of integration. A minimal set of code is implemented in the kernelto allow for easy porting to various CPU architectures and operating systems, such asx86 and PowerPC architectures utilizing the Linux operating system. Within the SDK, the device driver and API libraries are written in standard ANSI “C” language for easy porting to additional processor architectures and operating systems. The same SDK supportsthe Mellanox SwitchX®-2, Switch-IB®, Switch-IB 2, Mellanox Spectrum®, and Mellanox Quantum switch devices. CompatibilityCPU>PowerPC, Intel x86, AMD x86, MIPS PCI Express Interface>PCIe 3.0, 2.0, and 1.1 compliant>2.5 GT/s, 5 GT/s or 8 GT/s x4link rateConnectivity>Interoperability with InfiniBand adapters and switches>Passive copper cables, fiber optics, PCB or backplanes Management & Tools>Support for Mellanox and IBTA compliant Subnet Managers (SM) >Diagnostic and debug tools>Fabric Collective Accelerator (FCA) software libraryORDERING INFORMATIONCONFIGURATIONSMellanox Quantum allows OEMs to deliver: >40-port 1U HDR 200 Gb/s InfiniBand switch >80-port 1U HDR100 100 Gb/s InfiniBand switch>Modular chassis switch with up to 800 HDR InfiniBand ports >Modular chassis switch with up to 1600 HDR100 InfiniBand portsNVIDIA MELLANOX ADVANTAGENVIDIA Mellanox is the leading supplier of industry standard InfiniBand and Ethernet network adapter silicon and cards (HCAs and NICs), switch silicon and systems,interconnect products, and driver and management software. Mellanox products have been deployed in clusters scaling to tens of thousands of nodes and are being deployed end-to-end in data centers and TOP500 systems around the world.SpecificationsInfiniBand>IBTA Specification 1.4 compliant >10, 20, 40, 56, 100 or 200 Gb/s per 4X port>Integrated SMA/GSA>Hardware-based congestion control>256 to 4 KB MTU >9 virtual lanes:8 data +1 managementI/O Specifications>SPI Flash interface, I 2C>IEEE 1149.1/1149.6 boundary scan JTAG>LED driver I/Os>General purpose I/Os >55 x 55 mm HFCBGALearn more at /products/infiniband-switches-ic/quantum© 2020 Mellanox Technologies. All rights reserved. NVIDIA, the NVIDIA logo, Mellanox, Mellanox Quantum, Mellanox Spectrum, SwitchX, SwitchIB, ConnectX, Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) and SHIELD are trademarks and/or registered。
自适应均衡rls算法仿真流程
自适应均衡rls算法仿真流程Reinforcement learning is a type of machine learning algorithm that allows an agent to learn how to make decisions by trial and error. It is particularly useful in situations where a clear set of rules cannot be defined, and the agent must learn from its interactions with the environment. 自适应均衡rls算法是一种基于强化学习的算法。
它允许代理通过不断尝试来学习如何做出决策。
在无法定义明确规则的情况下，强化学习尤其有用，代理必须从与环境的互动中学习。
One of the key challenges in using reinforcement learning algorithms is finding the right balance between exploration and exploitation. Exploration refers to the agent's willingness to try out new actions in order to discover more about the environment and potentially find better strategies. Exploitation, on the other hand, involves choosing actions that the agent already knows to be effective based on its past experiences. 在使用强化学习算法时面临的一个关键挑战是在探索和开发之间找到合适的平衡。
基于通感一体化技术的自适应调制方
doi:10.3969/j.issn.1003-3114.2023.01.013引用格式:李本翔,向路平,胡杰,等.基于通感一体化技术的自适应调制方案[J].无线电通信技术,2023,49(1):110-117.[LI Benxiang,XIANG Luping,HU Jie,et al.Adaptive Modulation Design Assisted by Integrated Sensing and Communication [J].Radio Communications Technology,2023,49(1):110-117.]基于通感一体化技术的自适应调制方案李本翔,向路平∗,胡㊀杰,杨㊀鲲(电子科技大学信息与通信工程学院,四川成都611731)摘㊀要:通感一体化(Integrated Sensing and Communication,ISAC)技术能够通过共享频谱资源实现通信与感知功能,进一步提升频谱利用率㊂介绍了ISAC 系统模型,包括传输协议㊁传感模型和通信模型,提出了一种基于ISAC 技术的自适应调制(Adaptive Modulation,AM)方案,利用匹配滤波从回波中提取车辆距离信息,采用深度强化学习(DeepReinforcement Learning,DRL)算法,自适应选择下一个时刻的调制模式㊂减少了导频信息㊁提升了信道容量,并且省去信道预测过程,减少了计算资源消耗㊂仿真结果表明,采用深度强化学习自适应选择下一时刻调制模式提升了误码率约束下的最大信道容量,并且相比于传统通信,吞吐量有较大的提升㊂关键词:通感一体化;6G 移动通信;车载网;自适应调制;深度强化学习中图分类号:TN919.23㊀㊀㊀文献标志码:A㊀㊀㊀开放科学(资源服务)标识码(OSID):文章编号:1003-3114(2023)01-0110-08Adaptive Modulation Design Assisted by IntegratedSensing and CommunicationLI Benxiang,XIANG Luping ∗,HU Jie,YANG Kun(School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)Abstract :Integrated Sensing and Communication (ISAC)achieves communication and sensing functions by sharing spectrumresources,improving spectrum utilization.This paper introduces an ISAC system model,including transmission protocols,sensingmodels and communication models,and proposes an ISAC-assisted Adaptive Modulation (AM)scheme with a Deep reinforcementLearning (DRL)approach where the modulation mode for the next slot is selected based on vehicle distance information extracted from the echo by matched filter.In the proposed ISAC transmission protocol,the number of pilots is reduced to improve the transmission ca-pacity.Additionally,the channel estimation process is eliminated to reduce computing resource consumption.Simulation results show that the maximum channel capacity under Bit Error Rate (BER)constraint is increased by applying DRL for modulation mode selec-tion,and the throughput is significantly increased compared to traditional communication.Keywords :integrated sensing and communication;6G mobile communication;vehicle network;adaptive modulation;deep rein-forcement learning收稿日期:2022-10-15基金项目:国家重点研发计划(2021YFB2900200);国家自然科学基金重点项目(62132004)Foundation Item :National Key Research and Development Program of China (2021YFB2900200);Key Program of National Natural Science Foundationof China (62132004)0 引言随着无线通信技术的发展,联网设备的数量急剧增加,产生了许多新的通信场景和需求[1-2],例如车联网(Vehicle-to-Everything,V2X)㊁物联网(Inter-net of Things,IoT)等㊂未来6G 承载多种智能应用的V2X 系统不仅对通信性能有着较高的要求[3],还要完成一定的感知任务㊂然而,随着通信系统的载波频段不断提升,已经和雷达感知的频段相近,这会对雷达感知造成干扰,同时雷达也会影响通信性能㊂而通感一体化技术(Integrated Sensing and Communi-cation,ISAC)是解决这一问题的关键,并且如今对于V2X系统中ISAC技术的研究已经获得了学术界和工业界的广泛关注[4]㊂传统的通信和雷达系统使用不同的正交频带并进行独立设计[5]㊂然而随着大规模天线技术发展和毫米波技术的应用,通信和雷达系统的性能都得到了大幅提升,并且可以共用一些硬件设备和频谱资源,例如大规模天线雷达和大规模天线通信[6]㊂此外,在载波频率达到毫米波频段时,雷达系统与通信系统的信道特性和信号处理方法十分相似[7]㊂正是由于这些相似性,具有感知和雷达集成增益的ISAC被认为是一种很有前景的技术㊂例如,在基于ISAC的V2X网络中,路边单元(Roadside Unit, RSU)通过利用从车辆上反射的ISAC回波信号来预测车辆的位置,从而提供更好的通信服务[8]㊂发射机可以通过多种方式利用回波中的隐藏信息提高通信性能,包括自适应调制(Adaptive Modulation,AM)㊁波束成形和自适应编码等㊂众所周知,AM是一种基于信道估计来实现最优容量的简单且有效的方法[9]㊂通常,发射机从上行导频信号中获取信道信息,并根据误码率(Bit Error Rate,BER)选择调制方案来提升通信性能㊂例如,文献[10]提出了一种自适应编码和调制(Adaptive Coding and Modulation,ACM)技术,该技术根据来自上行导频反馈的飞机之间的距离信息确定调制和编码方式㊂同时,文献[11]使用深度强化学习(Deep Reinforcement Learning,DRL)根据导频反馈的历史信道状态信息(Channel State Information, CSI)预测调制模式㊂然而,在ISAC系统中,发射机可以直接从雷达回波信号中获得信道信息,无需导频交互的过程㊂如何根据反射的回波做出决策对ISAC系统来说是一个重要的挑战㊂通常,这个过程被分为两个步骤:①从回波中估计反射体的位置和速度信息并由此估计信道状态;②提升各种通信技术[5,12-15]㊂文献[12]使用匹配滤波估计用户的位置和速度,实时调整车辆的波束宽度,以此来覆盖整个车辆㊂同样,在匹配滤波之后,也可以利用扩展卡尔曼滤波(Extended Kalman Filter,EKF)[13]㊁因子图[14]等方法实现波束预测㊂此外,数据驱动技术也与ISAC 系统相结合用来适应复杂的时变环境[15],例如文献[5]根据估计的信道状态信息采用深度神经网络(Deep Neural Networks,DNNs)进行波束预测㊂这些研究已经证明了ISAC系统的优越性㊂然而, ISAC系统中自适应调制方案的设计还存在空缺,因此本文主要考虑基于ISAC系统AM的实现,并与传统通信进行比较㊂本文提出了一种ISAC系统传输协议,能够基于回波预测下一个时刻的调制模式㊂相比于传统通信减少了导频开销,提升了信道容量,并且减少了信道预测过程带来的计算资源消耗㊂为了实现所提出的框架,采用DRL算法来实现AM,根据车辆距离预测下一时刻的调制模式,在保证满足误码率约束同时,最大化通信容量㊂具体来说,RSU从回波中提取车辆的距离信息,并且将历史距离作为DRL状态输入,下一时刻调制模式作为DRL动作输出㊂1㊀系统建模如图1所示,本文考虑了V2X场景下基于ISAC的多输入多输出(Multiple-input Multiple-output,MIMO)系统,一个配备了两组均匀线性阵列天线(Uniform Linear Array,ULA)RSU为一辆车提供服务㊂其中,RSU包含N t根发射天线和N r根接收天线㊂通过多天线,RSU能够向车辆发射下行ISAC信号并接收反射回波㊂图1㊀基于ISAC的系统通信模型Fig.1㊀ISAC-assisted communication system1.1㊀传输协议如图2(a)所示,RSU与车辆之间的传输数据流被划分为不同的时隙㊂在传统通信中的AM策略依赖于车辆的上行导频来获得CSI从而做出决策[10-11],而在车辆高速移动的V2X网络下,信道状态时刻变化,频繁的导频交互会导致通信资源的浪费,也会导致信道估计的滞后㊂ISAC辅助的传输协议可以有效地解决这个问题㊂如图2(b)所示,在本文提出的基于ISAC的传输协议中,发射机连续发送ISAC信号用于下行通信和感知㊂具体来说,ISAC系统将每个时隙分为两个阶段:①信号传输和回波接收;②信号处理㊂例如,在第一阶段,RSU根据上个时隙预测的调制模式传输ISAC信号并接收回波信号㊂在第二阶段,RSU 首先从回波信号中提取车辆的距离信息,然后根据距离直接预测下一个时隙的调制模式㊂因此,由于舍去了上下行导频信号,ISAC系统下的AM相比于传统通信能较大程度的提升系统容量,并且省去了信道预测的过程,一定程度上减少了计算资源的消耗㊂(a)传统AM(b)ISAC-AM图2㊀传统AM和基于ISAC的AM的传输协议比较Fig.2㊀Comparison of transmission protocols between traditional AM and ISAC-based AM1.2㊀感知模型在车辆运动过程中,RSU可以使用ISAC信号感知车辆的位置㊂假设t时刻RSU传输给车辆的信息为s(t),所以RSU发送的下行信号表示为:s^(t)=w s(t),(1)式中,w=㊀p a(θ)ɪC N tˑ1表示波束成形向量,其中,p表示RSU传输功率,a(θ)=㊀1N t[1,e-jπcosθ, ,e-jπ(N t-1)cosθ]TɪC N tˑ1表示发送方向向量㊂RSU通过天线接收车辆反射的ISAC回波㊂因为光速足够快,本文假设车辆的位置在一个传输时隙中保持不变㊂所以反射的回波可以表示为:r(t)=Gβb(θ)a H(θ)s^(t-τ)e j2πυt+z(t),(2)式中,r(t)ɪC N rˑ1㊂G=㊀N t N r,θ㊁τ㊁υ分别表示RSU和车辆之间的天线增益㊁角度,时延和多普勒频移㊂β=ξ2cτ表示车辆表面反射系数,其中,c表示光速,ξ表示雷达散射截面(Radar Cross Section,RCS)㊂b(θ)=㊀1N r[1,e-jπcosθ, ,e-jπ(N r-1)cosθ]TɪC N rˑ1表示接收方向向量,b(θ)和a(θ)是反向的关系㊂z(t)~N(0,N0)表示高斯白噪声㊂RSU在接收到车辆反射回波后,采用匹配滤波的方法获得信号的时延和多普勒频移,由此估计车辆的距离和速度㊂匹配滤波如下所示:E(τ,υ)=ʏΔT e0b H(θ)r(t)s(t-τ)e j2πυt d t,(3)式中,ΔT e表示ISAC回波信号的持续时间㊂根据时延τn和多普勒频移υn,车辆的距离d n和速度μn可以表示为:d=τ2ˑc,(4)μ=υcf c sinθ,(5)式中,f c为载波频率㊂1.3㊀通信模型装有单天线的车辆在t时刻接收到由RSU发送的下行信息可以表示为:y(t)=G^H a H(θ)s^(t)e j2πυt+μ(t),(6)式中,G^=㊀N t表示RSU发送天线增益,H n表示大尺度路径损耗,μ(t)~CN(0,N1)表示在汽车接收端方差为N1的复高斯噪声㊂众所周知,在毫米波通信系统中[16],视距信道(Line of Sight,LoS)主导着信号传输㊂为不失一般性,本文假设车辆在开阔的道路如高速公路上行驶,RSU和汽车之间的H n可以用friis方程[17]表示如下:H2=λ2(4π)2d2,(7)式中,λ=cf c表示载波波长㊂用h表示RSU到车辆的等效信道:h=G^H a H(θ)㊂(8)基于式(6)和式(8),车辆接收信号的SNR 可以表示为:γ=|h w |2N 1㊂(9)㊀㊀假设RSU 使用多进制正交幅度调制(Multiple Quadrature Amplitude Modulation,MQAM),并且每个调制符号被传输的概率都一样㊂根据文献[18],传输系统容量C ∗可以被上界和下界约束为:C low ɤC ∗ɤC upper ,(10)式中,上下边界C low 和C upper 可以表示为:C low=lb M -1Mˑðx iɪχlb 1+(M -1)ˑ12[exp -h 24(M -1)ðx j ɪχx i -x j2()],C upper =lb M -1Mˑðx iɪχlb 1+(M -1)ˑ12[exp -h 2N 1(M -1)ðx j ɪχx i-x j2()],(11)式中,M 表示RSU 选择的调制方式,χ表示调制星座点的集合,其中,x i 和x j 表示在集合中的任何一对调制符号㊂并且根据文献[18],C low 和C upper 是渐进紧的㊂因此,使用C upper 作为C 去衡量系统的最大容量,可以描述为[19]:C =lb M -1Mˑðx iɪχlb 1+(M -1)exp ˑ12[-γM -1ðx j ɪχx i-x j2()]㊂(12)㊀㊀此外,假设每个星座点的最近邻数量均为4,则误码率可以表示为[20]:ρ=4lb MF㊀3ˑγM -1(),(13)式中,函数F (x )表示如下:F (x )=ʏ+ɕxe -0.5t2㊀2πd t ㊂(14)㊀㊀在式(12)~(13)的基础上,可以建立一个优化问题,在保证误码率满足要求的同时提高通信速率:max MC ,(15)s.t.ρɤρ0,(16)式中,ρ0为给定瞬时误码率上界㊂2 DRL 算法设计本节基于文献[21]提出了一种基于DRL 的AM 算法,DRL 智能体会根据状态选择具体的调制模式,这个过程可以被建模为一个马尔可夫决策过程(Markov Decision Process,MDP )㊂由于车辆在V2X 网络中的状态不断变化,基本的RL 算法的Q 表不能管理无限连续的状态空间,而DRL 使用DNN 建立Q 表,然后通过更新DNN 的权重来更新Q 表[22],可以较好地适应大规模动态环境[23]㊂如图3所示,本文采用经验重放和固定目标网络策略来加速训练过程[24]㊂图3㊀DRL 结构Fig.3㊀DRL construction㊀㊀如图3所示,经验重放回放是将每一时刻的元组{s t ,a t ,r t ,s t +1}存储在记忆空间O ,并且随机从中抽取B 个样本{s j ,a j ,r j ,s j +1},j ɪ[1,2, ,B ]进行训练来破坏连续传输的元组之间的相关性㊂固定目标网络即定期更新目标Q^网络的权值,以加速训练㊂DNN 的损失函数表示为:L (θ+)=(y target j-Q (s j ,a j ;θ+))2,(17)式中,y target j和Q (s j ,a j ;θ+)分别是在DRL 智能体在时隙j 选择动作a j 时Q 表的目标输出和实际输出,因此L (θ+)表示为预测误差㊂ytargetj可以表示为:y targetj=r j +γmax aᶄ㊀Q ^(s j +1,aᶄ;θ-),(18)式中,s j ,a j ,r j 分别表示j 时隙的状态㊁选择的动作和相应的奖励㊂Q ^(s j +1,aᶄ;θ-)表示在j +1时隙考虑动作aᶄ目标Q 表的输出㊂基于所提出的ISAC 传输协议,DRL 网络的输入为汽车当前距离d t 和前k 个时刻的距离{d t -1,d t -2, ,d t -k },输出为预测的下一个时隙调制模式㊂因此,对DRL 的状态空间㊁动作空间㊁即时奖励定义如下㊂状态空间㊀即所有可能的状态集合㊂具体时刻t 的状态由(k +1)个车辆距RSU 的距离组成㊂可以描述为:s t ={d t ,d t -1, ,d t -k }㊂(19)㊀㊀动作空间㊀包括所有可能选择的调制模式,如下所示:A ={M 1,M 2, ,M P },(20)在时隙j 选择的动作a j ɪA ㊂即时奖励㊀为了在保证最佳的通信速率和质量,即时奖励被设计为:r t =-lg 1-ρt +1-ρ0ρt +1(),ρt +1>ρ0C t +1,else{,(21)式中,C t +1和ρt +1可分别用式(12)~(13)计算㊂ρ0为最大瞬时误码率㊂该算法在约束ρt +1<ρ0下使C t +1最大化,来实现下一时隙调制模式的预测,并由此解决式(15)~(16)中描述的优化问题㊂DRL 具体实现如算法1所示㊂算法1㊀DRL 算法输入:存储空间O ,奖励衰减γ,学习速率l ,样本数量B ,初始化:分别用随机权值θ+和θ-初始化Q 网络和目标Q^网络 1.㊀for episode =1,E do 2.㊀初始化状态s 13.㊀for i =1,I do 4.根据贪婪因子随机选择动作为随机值㊀㊀㊀或者最大Q 值对应动作,即a i =argmax a Q (s i ,a ;θ+)5.执行动作a i ,得到奖励r i 和下一个状态s i +16.将(s i ,a i ,r i ,s i +1)存储到O7.随机在存储空间采样B 个元组(s j ,a j ,r j ,s j +1)8.计算y target j =r j +γmax a ᶄQ ^(s j +1,aᶄ;θ-),并跟据预测误差对Q 网络的权值θ+进行梯度下降更新,预测误差计算如式(17)9.每隔J 步更新目标网络Q^=Q 10.㊀end for 11.end for3 仿真结果本节利用一些数值结果来评估所提算法的有效性㊂在所考虑的V2X 系统中,N 0=N 1=-50dBm㊂使用笛卡尔坐标系来表示RSU 与车辆之间的空间关系,RSU 定义在[0m,0m],车辆坐标为[X ,Y ]㊂为不失一般性,设置Y 为30m㊂此外,假设车辆的初始速度μ0为23m/s,车辆从道路左边界[-150m,30m]驶向右边界[150m,30m],加速度设置为a ~N (0,5m /s 2)㊂此外,假定发射机支持6种调制模式:0㊁4QAM㊁8QAM㊁16QAM㊁32QAM㊁64QAM,模式0意味着发射机继续传输4QAM 信号仅进行感知㊂并且将输入的距离信息进行归一化处理,设k =5㊂其他仿真参数见表1㊂表1㊀仿真参数Tab.1㊀Simulation parameters参数值天线增益G /dB 0载波频率f c /GHz5一个时隙符号数N 1024传输功率p 0/dB 21误码率阈值ρ01ˑ10-4信号时隙周期/ms 20㊀㊀本文使用如下基线来评价系统的性能:传统导频训练㊀考虑文献[9]中使用的传统通信方案,它从导频交互中得到过时的CSI㊂本文直接使用此时刻h t 作为下一时刻h t +1来选择调制模式,其中导频开销假定为8%[25]㊂理想模式㊀根据完美CSI 选择给定瞬时BER 约束下最优调制模式㊂DRL 算法㊀它建立在本文提出的考虑历史距离的ISAC 系统上㊂DRL 中的DNN 由一个包含(k +1)个神经元的输入层,3个分别包含200㊁100和40个神经元的全连接隐藏层和一个包含6个神经元的输出层组成㊂此外,对DRL 的一些参数进行设置,例如存储大小O ㊁奖励衰减γ㊁学习速率l ,样本数量B ,更新间隔J 分别设置为5000㊁0.2㊁0.005㊁256㊁100,并且训练迭代次数E ˑI =1000ˑ1000㊂自回归(Auto Regressive,AR)㊀本文采用基于预测的AR 算法,并将其运用到提出的ISAC 自适应调制协议中,从而与本文提出的DRL 算法进行进一步对比㊂即发射机通过回波估计信道状态,然后使用AR 预测下一时刻信道状态,基于预测的信道状态选择调制模式㊂本文使用burg 方法来估计AR 模型的系数㊂图4展示了平均吞吐量(bit /s)和BER 的对比㊂由图4(a)可以看出,由于导频符号占据一部分信息符号,传统方法的平均吞吐量最低㊂AR㊁理想㊁DRL 方法的平均吞吐量接近,证明了ISAC 系统确实能够提高通信速率㊂图4(b)展示了模式选择临界点BER 的比较,可以看出DRL 可以满足瞬时BER 的约束,保证了信号传输的可靠性㊂(a )吞吐量比较(b )BER 比较图4㊀不同方法下吞吐量和BER 的比较(μ0=23m /s )Fig.4㊀Comparison of throughput and average BERversus different method (μ0=23m /s )图5为车辆运动过程中RSU 在模式切换临界点附近模式选择的比较㊂由图5可知,传统方法使用的过时的CSI,所以具有滞后性,而基于回波的ISAC 策略可以较为准确地预测调制方案㊂图5㊀不同方法下模式选择随时间变化Fig.5㊀Mode selection versus time for different methods4 结束语本文考虑了ISAC 系统下的自适应调制方案设计,在V2X 网络中RSU 根据车辆的位置提供不同调制模式来提升通信性能㊂在该场景下,RSU 接收到车辆反射的回波信号后,通过匹配滤波估计车辆的距离和速度㊂为了在保证通信质量的情况下最大化容量,RSU 根据当前车辆的距离,采用DRL 算法预测下一时隙的调制模式㊂仿真结果表明,本文采用的基于ISAC 的DRL 算法能够准确地预测调制模式,相较于传统通信在保证误码率的情况下,通信容量有较大的提升,并且具有较好的鲁棒性㊂此外,本文仅考虑了视距信道,在今后的工作中可以考虑在有非视距信道影响下的自适应调制问题㊂参考文献[1]㊀SWAMY S N,KOTA S R.An Empirical Study on SystemLevel Aspects of Internet of Things (IoT)[J].IEEE Access,2020,8:188082-188134.[2]㊀WYMEERSCH H,SECO-GRANADOS G,DESTINO G,et al.5G mmWave Positioning for Vehicular Networks[J].IEEE Wireless Communications,2017,24(6):80-86. 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[22]ZHAO Z,BU S,ZHAO T,et al.On the Design of Com-putation Offloading in Fog Radio Access Networks[J].IEEE Transactions on Vehicular Technology,2019,68(7):7136-7149.[23]MNIH V,KAVUKCUOGLU K,SILVER D,et al.Human-level Control Through Deep Reinforcement Learning[J].Nature,2015,518(7540):529-533.[24]MASHHADI S,GHIASI N,FARAHMAND S,et al.DeepReinforcement Learning Based Adaptive Modulation withOutdated CSI[J].IEEE Communications Letters,2021,25(10):3291-3295.[25]ZHAO Z,CHENG X,WEN M,et al.Channel EstimationSchemes for IEEE802.11p Standard[J].IEEE Intell-igentTransportation Systems Magazine,2013,5(4):38-49.作者简介:㊀㊀李本翔㊀硕士研究生㊂主要研究方向:通感一体化等㊂㊀㊀(∗通信作者)向路平㊀博士,讲师㊂主要研究方向:信道编码㊁通感一体化系统和数能同传等㊂㊀㊀胡㊀杰㊀博士,正高级研究员,博士生导师㊂主要研究方向:无线通信与组网中的物理层设计和资源分配㊁无线数据与能量一体化传输关键技术等㊂㊀㊀杨㊀鲲㊀博士,电子科技大学国家特聘教授,数能网络实验室主任,欧洲科学院院士MAE㊂主要研究方向:无线通信和网络㊁未来网络技术和移动边缘计算等㊂。
Computer Methods in Applied Mechanics and Engineering
Topological clustering for water distribution systems analysis Environmental Modelling & Software , In Press, Corrected Proof ,Available online 15 February 2011Lina Perelman, Avi OstfeldShow preview | Related articles | Related reference work articlesPurchase $ 19.95 716 HyphArea —Automated analysis of spatiotemporal fungal patterns Original Research ArticleJournal of Plant Physiology , Volume 168, Issue 1, 1January 2011, Pages 72-78Tobias Baum, Aura Navarro-Quezada, Wolfgang Knogge, Dimitar Douchkov, Patrick Schweizer, Udo Seiffert Close preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractIn phytopathology quantitative measurements are rarely used to assess crop plant disease symptoms. Instead, a qualitative valuation by eye is often themethod of choice. In order to close the gap between subjective humaninspection and objective quantitative results, the development of an automated analysis system that is capable of recognizing and characterizing the growth patterns of fungal hyphae in micrograph images was developed. This system should enable the efficient screening of different host –pathogen combinations (e.g., barley —Blumeria graminis , barley —Rhynchosporium secalis ) usingdifferent microscopy technologies (e.g., bright field, fluorescence). An image segmentation algorithm was developed for gray-scale image data thatachieved good results with several microscope imaging protocols.Furthermore, adaptability towards different host –pathogen systems wasobtained by using a classification that is based on a genetic algorithm. Thedeveloped software system was named HyphArea , since the quantification of the area covered by a hyphal colony is the basic task and prerequisite for all further morphological and statistical analyses in this context. By means of atypical use case the utilization and basic properties of HyphArea could bePurchase $ 41.95demonstrated. It was possible to detect statistically significant differences between the growth of an R. secalis wild-type strain and a virulence mutant. Article Outline Introduction Material and methodsExperimental set-upThe host and pathogensFluorescence image acquisition protocolBright field image acquisition protocolSegmentation of hyphal coloniesAchieving adaptive behaviorImage corpusResultsR. secalis infected material and segmentationB. graminis infected material and segmentationDiscussionAcknowledgementsReferences717 Automated generation of contrapuntal musical compositions using probabilistic logic inDerive Original Research ArticleMathematics and Computers in Simulation , Volume 80, Issue 6, February 2010, Pages 1200-1211Gabriel Aguilera, José Luis Galán, Rafael Madrid, Antonio Manuel Martínez, Yolanda Padilla, Pedro Rodríguez Close preview | Related articles | Related reference work articles Abstract | Figures/Tables | ReferencesAbstractIn this work, we present a new application developed in Derive 6 to compose Purchase$ 31.50counterpoint for a given melody (―cantus firmus‖). The result isnon-deterministic, so different counterpoints can be generated for a fixed melody, all of them obeying classical rules of counterpoint. In the case where the counterpoint cannot be generated in a first step, backtracking techniques have been implemented in order to improve the likelihood of obtaining a result. The contrapuntal rules are specified in Derive using probabilistic rules of a probabilistic logic, and the result can be generated for both voices (above and below) of first species counterpoint.The main goal of this work is not to obtain a ―professional‖ counterpoint generator but to show an application of a probabilistic logic using a CAS tool. Thus, the algorithm developed does not take into account stylistic melodic characteristics of species counterpoint, but rather focuses on the harmonic aspect.The work developed can be summarized in the following steps:(1) Development of a probabilistic algorithm in order to obtain anon-deterministic counterpoint for a given melody.(2) Implementation of the algorithm in Derive 6 using probabilistic Logic.(3) Implementation in Java of a program to deal with the input (―cantus firmus‖) and with the output (counterpoint) through inter-communication with the module developed in D ERIVE. This program also allows users to listen to the result obtained.Article Outline1. Introduction1.1. Historical background1.2. ―Cantus Firmus‖ and counterpoint1.3. Work developed1.4. D ERIVE 6 and J AVA2. Description of the algorithm2.1. The process2.2. Rules2.3.Example2.4. Backtracking3. Description of the environment3.1. Menu bar3.2. Real time modifications3.3. Inter-communication with DERIVE3.4. Playing the composition4. Results4.1. Example 1: Above voice against ―Cantus firmus‖4.2. Example 2: Below voice against ―Cantus firmus‖4.3. Example 3: Above and below voices against ―Cantus firmus‖5. Conclusions and future workReferences718 Study of pharmaceutical samples by NIR chemical-image and multivariate analysis OriginalResearch ArticleTrAC Trends in Analytical Chemistry , Volume 27, Issue 8, September 2008, Pages 696-713José Manuel Amigo, Jordi Cruz, Manel Bautista, Santiago Maspoch, Jordi Coello, Marcelo BlancoClose preview | Related articles | Related reference work articles Abstract | Figures/Tables | ReferencesAbstractNear-infrared spectroscopy chemical imaging (NIR-CI) is a powerful tool for Purchase$ 31.50providing a great deal of information on pharmaceutical samples, since the NIR spectrum can be measured for each pixel of the image over a wide range of wavelengths.Joining NIR-CI with chemometric algorithms (e.g., Principal Component Analysis, PCA) and using correlation coefficients, cluster analysis, classical least-square regression (CLS) and multivariate curve resolution-alternating least squares (MCR-ALS) are of increasing interest, due to the great amount of information that can be extracted from one image. Despite this, investigation of their potential usefulness must be done to establish their benefits and potential limitations.We explored the possibilities of different algorithms in the global study (qualitative and quantitative information) of homogeneity in pharmaceutical samples that may confirm different stages in a blending process. For this purpose, we studied four examples, involving four binary mixtures in different concentrations.In this way, we studied the benefits and the drawbacks of PCA, cluster analysis (K-means and Fuzzy C-means clustering) and correlation coefficients for qualitative purposes and CLS and MCR-ALS for quantitative purposes.We present new possibilities in cluster analysis and MCR-ALS in image analysis, and we introduce and test new BACRA software for mapping correlation-coefficient surfaces.Article Outline1. Introduction2. Structure of hyperspectral data3. Preprocessing the hyperspectral image4. Techniques for exploratory analysis4.1. Principal Component Analysis (PCA)4.2. Cluster analysis4.2.1. K-means algorithm4.2.2. Fuzzy C-means algorithm4.2.3. Number of clusters4.2.3.1. Silhouette index4.2.3.2. Partition Entropy index4.3. Similarity using correlation coefficients5. Techniques for estimating analyte concentration in each pixel 5.1. Classical Least Squares5.2. Multivariate Curve Resolution-Alternating Least Squares5.3. Augmented MCR-ALS for homogeneous samples6. Experimental and data treatment6.1. Reagents and instruments6.2. Experimental6.3. Data treatment7. Results and discussion7.1. PCA analysis7.2. Cluster analysis7.2.1. K-means results for heterogeneous samples7.2.2. FCM results7.3. Correlation-coefficient maps–BACRA results7.4. CLS results7.5. MCR-ALS and augmented-MCR-ALS results8. Conclusions and perspectivesAcknowledgementsReferences719Validation and automatic test generation on UMLmodels: the AGATHA approach Original Research ArticleElectronic Notes in Theoretical Computer Science, Volume66, Issue 2, December 2002, Pages 33-49David Lugato, Céline Bigot, Yannick ValotClose preview | Related articles | Related reference work articlesAbstract | ReferencesAbstractThe related economic goals of test generation are quite important for softwareindustry. Manufacturers ever seeking to increase their productivity need toavoid malfunctions at the time of system specification: the later the defaultsare detected, the greater the cost is. Consequently, the development oftechniques and tools able to efficiently support engineers who are in charge of elaborating the specification constitutes a major challenge whose falloutconcerns not only sectors of critical applications but also all those where poor conception could be extremely harmful to the brand image of a product.This article describes the design and implementation of a set of tools allowingsoftware developers to validate UML (the Unified Modeling Language)specifications. This toolset belongs to the AGATHA environment, which is anautomated test generator, developed at CEA/LIST.The AGATHA toolset is designed to validate specifications of communicatingconcurrent units described using an EIOLTS formalism (Extended InputOutput Labeled Transition System). The goal of the work described in thispaper is to provide an interface between UML and an EIOLTS formalism givingthe possibility to use AGATHA on UML specifications.In this paper we describe first the translation of UML models into the EIOLTS formalism, and the translation of the results of the behavior analysis, providedby AGATHA, back into UML. Then we present the AGATHA toolset; wePurchase$ 35.95particularly focus on how AGATHA overcomes several problems of combinatorial explosion. We expose the concept of symbolic calculus and detection of redundant paths, which are the main principles of AGATHA's kernel. This kernel properly computes all the symbolic behaviors of a systemspecified in EIOLTS and automatically generates tests by way of constraintsolving. Eventually we apply our method to an example and explain thedifferent results that are computed.720Text mining techniques for patent analysis OriginalResearch ArticleInformation Processing & Management,Volume 43, Issue5, September 2007, Pages 1216-1247Yuen-Hsien Tseng, Chi-Jen Lin, Yu-I LinClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractPatent documents contain important research results. However, they arelengthy and rich in technical terminology such that it takes a lot of humanefforts for analyses. Automatic tools for assisting patent engineers or decisionmakers in patent analysis are in great demand. This paper describes a seriesof text mining techniques that conforms to the analytical process used bypatent analysts. These techniques include text segmentation, summaryextraction, feature selection, term association, cluster generation, topicidentification, and information mapping. The issues of efficiency andeffectiveness are considered in the design of these techniques. Someimportant features of the proposed methodology include a rigorous approachto verify the usefulness of segment extracts as the document surrogates, acorpus- and dictionary-free algorithm for keyphrase extraction, an efficientco-word analysis method that can be applied to large volume of patents, andan automatic procedure to create generic cluster titles for ease of resultPurchase$ 31.50interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a real-world patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches. Article Outline1. Introduction2. A general methodology3. Technique details3.1. Text segmentation3.2. Text summarization3.3. Stopwords and stemming3.4. Keyword and phrase extraction3.5. Term association3.6. Topic clustering3.6.1. Document clustering3.6.2. Term clustering followed by document categorization3.6.3. Multi-stage clustering3.6.4. Cluster title generation3.6.5. Mapping cluster titles to categories3.7. Topic mapping4. Technique evaluation4.1. Text segmentation4.2. Text summarization 4.2.1. Feature selection4.2.2. Experiment results4.2.3. Findings4.3. Key term extraction and association4.4. Cluster title generation4.5. Mapping cluster titles to categories5. Application example5.1. The NSC patent set5.2. Text mining processing5.3. Topic mapping5.4. Topic analysis comparison6. Discussions7. Conclusions and future workAcknowledgementsAppendix A. AppendixAppendix B. AppendixReferences721Incremental bipartite drawing problem OriginalResearch ArticleComputers & Operations Research, Volume 28, Issue 13,November 2001, Pages 1287-1298Rafael Martí, Vicente EstruchClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractLayout strategies that strive to preserve perspective from earlier drawings arecalled incremental. In this paper we study the incremental arc crossingminimization problem for bipartite graphs. We develop a greedy randomizedPurchase$ 31.50adaptive search procedure (GRASP) for this problem. We have also developed a branch-and-bound algorithm in order to compute the relative gap to the optimal solution of the GRASP approach. Computational experiments are performed with 450 graph instances to first study the effect of changes in grasp search parameters and then to test the efficiency of the proposed procedure.Scope and purposeMany information systems require graphs to be drawn so that these systems are easy to interpret and understand. Graphs are commonly used as a basic modeling tool in areas such as project management, production scheduling, line balancing, business process reengineering, and software visualization. Graph drawing addresses the problem of constructing geometric representations of graphs. Although the perception of how good a graph is in conveying information is fairly subjective, the goal of limiting the number of arc crossings is a well-admitted criterion for a good drawing. Incremental graph drawing constructions are motivated by the need to support the interactive updates performed by the user. In this situation, it is helpful to preserve a―mental picture‖ of the layout of a graph over successive drawings. It would not be very intuitive or effective for a user to have a drawing tool in which after a slight modification of the current graph, the resulting drawing appears very different from the previous one. Therefore, generating incrementally stable layouts is important in a variety of settings. Since ―real-world‖ graphs tend to be large, an automated procedure to deal with the arc crossing minimization problem in the context of incremental strategies is desirable. In this article, we develop a procedure to minimize arc crossings that is fast and capable of dealing with large graphs, restricting our attention to bipartite graphs.Article Outline1. Introduction2. Branch-and-bound approach3. GRASP approach3.1. Construction phase3.2. Improvement phase4. Computational experiments5. ConclusionsReferencesVitae722 Applications of vibrational spectroscopy to the analysis of novel coatings Original Research ArticleProgress in Organic Coatings , Volume 41, Issue 4, May2001, Pages 254-260A. J. Vreugdenhil, M. S. Donley, N. T. Grebasch, R. J.Passinault Close preview | Related articles |Related reference work articles Abstract | Figures/Tables | ReferencesAbstractPrecise analysis is essential to the development of novel coating technologiesand to the systematic modification of metal surfaces. Ideally, these analysistechniques will provide molecular information and will be sensitive to changesin the chemical environment of interfacial species. Many of the techniquesavailable in the field of vibrational spectroscopy demonstrate some of thesecharacteristics. Examples of the ways in which FT-IR spectroscopy are appliedto the investigation of coatings at AFRL will be described.Attenuated total reflectance (ATR), specular reflectance and photoacousticspectroscopy (PAS) are important techniques for the analysis of surfaces.Purchase $ 31.50Both ATR and PAS can be used to provide depth-profiling information crucialfor the study of coatings. Recent developments in digital signal processing(DSP) and step scan interferometry which have dramatically improved the reliability and ease of use of PAS for depth analysis will be discussed. Article Outline 1. Introduction1.1. ATR theory1.2. PAS theory1.3. IR microscopy2. Experimental2.1. Instrumentation2.2. Samples3. Results and discussion3.1. IR microscopy3.2. Photoacoustic spectroscopy3.3. Variable angle ATR4. ConclusionsReferences 723 Affective disorders in children and adolescents: addressing unmet need in primary caresettings Review ArticleBiological Psychiatry , Volume 49, Issue 12, 15 June 2001, Pages 1111-1120Kenneth B. Wells, Sheryl H. Kataoka, Joan R. Asarnow Close preview | Related articles | Related reference work articlesAbstract | ReferencesAbstractAffective disorders are common among children and adolescents but mayPurchase$ 31.50often remain untreated. Primary care providers could help fill this gap because most children have primary care. Yet rates of detection and treatment for mental disorders generally are low in general health settings, owing to multiple child and family, clinician, practice, and healthcare system factors. Potential solutions may involve 1) more systematic implementation of programs that offer coverage for uninsured children; 2) tougher parity laws that offer equity in defined benefits and application of managed care strategies across physical and mental disorders; and 3) widespread implementation of quality improvement programs within primary care settings that enhancespecialty/primary care collaboration, support use of care managers to coordinate care, and provide clinician training in clinically and developmentally appropriate principles of care for affective disorders. Research is needed to support development of these solutions and evaluation of their impacts. Article Outline• Introduction• Impact and appropriate treatment of affective disorders in youths• Unmet need for child mental health care• Primary care and treatment of child mental disorders• Barriers to detection and appropriate treatment in primary care• Child characteristics• Parental characteristics• Clinician and clinician–patient relationship factors• Finding policy solutions to unmet need: coverage for the uninsured and par ity for the insured• Finding practice-based solutions: implementing quality improvement programs• Discussion• Acknowledgements • References724 Orthogonal drawings of graphs with vertex and edgelabels Original Research ArticleComputational Geometry, Volume 32, Issue 2, October2005, Pages 71-114Carla Binucci, Walter Didimo, Giuseppe Liotta, MaddalenaNonatoClose preview | Related articles | Related reference work articlesAbstract | ReferencesAbstractThis paper studies the problem of computing orthogonal drawings of graphswith labels on vertices and edges. Our research is mainly motivated bySoftware Engineering and Information Systems domains, where tools like UMLdiagrams and ER-diagrams are considered fundamental for the design ofsophisticated systems and/or complex data bases collecting enormousamount of information. A label is modeled as a rectangle of prescribed widthand height and it can be associated with either a vertex or an edge. Ourdrawing algorithms guarantee no overlaps between labels, vertices, and edgesand take advantage of the information about the set of labels to compute thegeometry of the drawing. Several additional optimization goals are taken intoaccount. Namely, the labeled drawing can be required to have either minimumtotal edge length, or minimum width, or minimum height, or minimum areaamong those preserving a given orthogonal representation. All these goalslead to NP-hard problems. We present MILP models to compute optimaldrawings with respect to the first three goals and an exact algorithm that isbased on these models to compute a labeled drawing of minimum area. Wealso present several heuristics for computing compact labeled orthogonaldrawings and experimentally validate their performances, comparing theirsolutions against the optimum.Purchase$ 31.50725 Finite element response sensitivity analysis of multi-yield-surface J 2 plasticitymodel by direct differentiation method Original Research Article Computer Methods in Applied Mechanics and Engineering ,Volume 198, Issues 30-32, 1 June2009, Pages 2272-2285Quan Gu, Joel P. Conte, AhmedElgamal, Zhaohui YangClose preview | Related articles | Relatedreference work articles Abstract | Figures/Tables | ReferencesAbstractFinite element (FE) response sensitivity analysis is an essential tool for gradient-basedoptimization methods used in varioussub-fields of civil engineering such asstructural optimization, reliability analysis,system identification, and finite element modelupdating. Furthermore, stand-alone sensitivityanalysis is invaluable for gaining insight intothe effects and relative importance of varioussystem and loading parameters on systemresponse. The direct differentiation method(DDM) is a general, accurate and efficientmethod to compute FE response sensitivitiesto FE model parameters. In this paper, theDDM-based response sensitivity analysismethodology is applied to a pressureindependent multi-yield-surface J 2 plasticityPurchase$ 35.95material model, which has been used extensively to simulate the nonlinear undrained shear behavior of cohesive soils subjected to static and dynamic loading conditions. The complete derivation of the DDM-based response sensitivity algorithm is presented. This algorithm is implemented in a general-purpose nonlinear finite element analysis program. The work presented in this paper extends significantly the framework of DDM-based response sensitivity analysis, since it enables numerous applications involving the use of the multi-yield-surface J2 plasticity material model. The new algorithm and its software implementation are validated through two application examples, in which DDM-based response sensitivities are compared with their counterparts obtained using forward finite difference (FFD) analysis. The normalized response sensitivity analysis results are then used to measure the relative importance of the soil constitutive parameters on the system response.Article Outline1. Introduction2. Constitutive formulation ofmulti-yield-surface J2 plasticity model andnumerical integration2.1. Multi-yield surfaces2.2. Flow rule2.3. Hardening law2.3.1. Active yield surface update2.3.2. Inner yield surface update3. Derivation of response sensitivity algorithm for multi-yield-surface J2 plasticity model3.1. Introduction3.2. Displacement-based FE response sensitivity analysis using DDM3.3. Stress sensitivity for multi-yield-surface J2 material plasticity model3.3.1. Parameter sensitivity of initial configuration of multi-yield-surface plasticity model3.3.2. Stress response sensitivity3.3.3. Sensitivity of hardening parameters of active and inner yield surfaces4. Application examples4.1. Three-dimensional block of clay subjected to quasi-static cyclic loading4.2. Multi-layered soil column subjected to earthquake base excitation5. ConclusionsAcknowledgementsReferencesresults 701 - 725991 articles found for: pub-date > 1999 and tak((Communications or R&D or Engineer or DSP or software or "value-added" or services) and (communication or carriers or (Communication modem) or digital or signal or processing or algorithms or product or development or TI or Freescale) and (multicore or DSP or Development or Tools or Balanced or signal or processing or modem or codec) and (DSP or algorithm or principle or WiMAX or LTE or Matlab or Simulink) and (C or C++ or DSP or FPGA or MAC or layer or protocol or design or simulation or implementation or experience or OPNET or MATLAB or modeling) and (research or field or deal or directly or engaged or Engineers or Graduates))Edit this search | Save this search | Save as search alert | RSS Feed。
自动控制专业英语词汇(DOC)
自动控制专业英语词汇〔一〕acceleration transducer 加速度传感器acceptance testing 验收测试accessibility 可与性accumulated error 积累误差AC-DC-AC frequency converter 交-直-交变频器AC <alternating current> electric drive 交流电子传动active attitude stabilization 主动姿态稳定actuator 驱动器,执行机构adaline 线性适应元adaptation layer 适应层adaptive telemeter system 适应遥测系统adjoint operator 伴有算子admissible error 容许误差aggregation matrix 集结矩阵AHP <analytic hierarchy process> 层次分析法amplifying element 放大环节analog-digital conversion 模数转换annunciator 信号器antenna pointing control 天线指向控制anti-integral windup 抗积分饱卷aperiodic decomposition 非周期分解a posteriori estimate 后验估计approximate reasoning 近似推理a priori estimate 先验估计articulated robot 关节型机器人assignment problem 配置问题,分配问题associative memory model 联想记忆模型associatron 联想机asymptotic stability 渐进稳定性attained pose drift 实际位姿漂移attitude acquisition 姿态捕获AOCS <attritude and orbit control system> 姿态轨道控制系统attitude angular velocity 姿态角速度attitude disturbance 姿态扰动attitude maneuver 姿态机动attractor 吸引子augment ability 可扩充性augmented system 增广系统automatic manual station 自动-手动操作器automaton 自动机autonomous system 自治系统backlash characteristics 间隙特性base coordinate system 基座坐标系Bayes classifier 贝叶斯分类器bearing alignment 方位对准bellows pressure gauge 波纹管压力表benefit-cost analysis 收益成本分析bilinear system 双线性系统biocybernetics 生物控制论biological feedback system 生物反馈系统black box testing approach 黑箱测试法blind search 盲目搜索block diagonalization 块对角化Boltzman machine 玻耳兹曼机bottom-up development 自下而上开辟boundary value analysis 边界值分析brainstorming method 头脑风暴法breadth-first search 广度优先搜索butterfly valve 蝶阀CAE <computer aided engineering> 计算机辅助工程CAM <computer aided manufacturing> 计算机辅助创造Camflex valve 偏心旋转阀canonical state variable 规 X 化状态变量capacitive displacement transducer 电容式位移传感器capsule pressure gauge 膜盒压力表CARD 计算机辅助研究开辟Cartesian robot 直角坐标型机器人cascade compensation 串联补偿catastrophe theory 突变论centrality 集中性chained aggregation 链式集结chaos 混沌characteristic locus 特征轨迹chemical propulsion 化学推进calrity 清晰性classical information pattern 经典信息模式classifier 分类器clinical control system 临床控制系统closed loop pole 闭环极点closed loop transfer function 闭环传递函数cluster analysis 聚类分析coarse-fine control 粗-精控制cobweb model 蛛网模型coefficient matrix 系数矩阵cognitive science 认知科学cognitron 认知机coherent system 单调关联系统combination decision 组合决策combinatorial explosion 组合爆炸combined pressure and vacuum gauge 压力真空表command pose 指令位姿companion matrix 相伴矩阵compartmental model 房室模型compatibility 相容性,兼容性compensating network 补偿网络compensation 补偿,矫正compliance 柔顺,顺应composite control 组合控制computable general equilibrium model 可计算普通均衡模型conditionally instability 条件不稳定性configuration 组态connectionism 连接机制connectivity 连接性conservative system 守恒系统consistency 一致性constraint condition 约束条件consumption function 消费函数context-free grammar 上下文无关语法continuous discrete event hybrid system simulation 连续离散事件混合系统仿真continuous duty 连续工作制control accuracy 控制精度control cabinet 控制柜controllability index 可控指数controllable canonical form 可控规 X 型[control] plant 控制对象,被控对象controlling instrument 控制仪表control moment gyro 控制力矩陀螺control control control control panel 控制屏,控制盘synchro 控制[式]自整角机system synthesis 控制系统综合time horizon 控制时程cooperative game 合作对策coordinability condition 可协调条件coordination strategy 协调策略coordinator 协调器corner frequency 转折频率costate variable 共态变量cost-effectiveness analysis 费用效益分析coupling of orbit and attitude 轨道和姿态耦合critical damping 临界阻尼critical stability 临界稳定性cross-over frequency 穿越频率,交越频率current source inverter 电流[源]型逆变器cut-off frequency 截止频率cybernetics 控制论cyclic remote control 循环遥控cylindrical robot 圆柱坐标型机器人damped oscillation 阻尼振荡damper 阻尼器damping ratio 阻尼比data acquisition 数据采集data encryption 数据加密data preprocessing 数据预处理data processor 数据处理器DC generator-motor set drive 直流发机电-电动机组传动D controller 微分控制器decentrality 分散性decentralized stochastic control 分散随机控制decision space 决策空间decision support system 决策支持系统decomposition-aggregation approach 分解集结法decoupling parameter 解耦参数deductive-inductive hybrid modeling method 演绎与归纳混合建模法delayed telemetry 延时遥测derivation tree 导出树derivative feedback 微分反馈describing function 描述函数desired value 希翼值despinner 消旋体destination 目的站detector 检出器deterministic automaton 确定性自动机deviation 偏差deviation alarm 偏差报警器DFD 数据流图diagnostic model 诊断模型diagonally dominant matrix 对角主导矩阵diaphragm pressure gauge 膜片压力表difference equation model 差分方程模型differential dynamical system 微分动力学系统differential game 微分对策differential pressure level meter 差压液位计differential pressure transmitter 差压变送器differential transformer displacement transducer 差动变压器式位移传感器differentiation element 微分环节digital filer 数字滤波器digital signal processing 数字信号处理digitization 数字化digitizer 数字化仪dimension transducer 尺度传感器direct coordination 直接协调disaggregation 解裂discoordination 失协调discrete event dynamic system 离散事件动态系统discrete system simulation language 离散系统仿真语言discriminant function 判别函数displacement vibration amplitude transducer 位移振幅传感器dissipative structure 耗散结构distributed parameter control system 分布参数控制系统distrubance 扰动disturbance compensation 扰动补偿diversity 多样性divisibility 可分性domain knowledge 领域知识dominant pole 主导极点dose-response model 剂量反应模型dual modulation telemetering system 双重调制遥测系统dual principle 对偶原理dual spin stabilization 双自旋稳定duty ratio 负载比dynamic braking 能耗制动dynamic characteristics 动态特性dynamic deviation 动态偏差dynamic error coefficient 动态误差系数dynamic exactness 动它吻合性dynamic input-output model 动态投入产出模型econometric model 计量经济模型economic cybernetics 经济控制论economic effectiveness 经济效益economic evaluation 经济评价economic index 经济指数economic indicator 经济指标eddy current thickness meter 电涡流厚度计effectiveness 有效性effectiveness theory 效益理论elasticity of demand 需求弹性electric actuator 电动执行机构electric conductance levelmeter 电导液位计electric drive control gear 电动传动控制设备electric hydraulic converter 电-液转换器electric pneumatic converter 电-气转换器electrohydraulic servo vale 电液伺服阀electromagnetic flow transducer 电磁流量传感器electronic batching scale 电子配料秤electronic belt conveyor scale 电子皮带秤electronic hopper scale 电子料斗秤elevation 仰角emergency stop 异常住手empirical distribution 经验分布endogenous variable 内生变量equilibrium growth 均衡增长equilibrium point 平衡点equivalence partitioning 等价类划分ergonomics 工效学error 误差error-correction parsing 纠错剖析estimate 估计量estimation theory 估计理论evaluation technique 评价技术event chain 事件链evolutionary system 进化系统exogenous variable 外生变量expected characteristics 希翼特性external disturbance 外扰fact base 事实failure diagnosis 故障诊断fast mode 快变模态feasibility study 可行性研究feasible coordination 可行协调feasible region 可行域feature detection 特征检测feature extraction 特征抽取feedback compensation 反馈补偿feedforward path 前馈通路field bus 现场总线finite automaton 有限自动机FIP <factory information protocol> 工厂信息协议first order predicate logic 一阶谓词逻辑fixed sequence manipulator 固定顺序机械手fixed set point control 定值控制FMS <flexible manufacturing system> 柔性创造系统flow sensor/transducer 流量传感器flow transmitter 流量变送器fluctuation 涨落forced oscillation 强迫振荡formal language theory 形式语言理论formal neuron 形式神经元forward path 正向通路forward reasoning 正向推理fractal 分形体,分维体frequency converter 变频器frequency domain model reduction method 频域模型降阶法frequency response 频域响应full order observer 全阶观测器functional decomposition 功能分解FES <functional electrical stimulation> 功能电刺激functional simularity 功能相似fuzzy logic 含糊逻辑game tree 对策树gate valve 闸阀general equilibrium theory 普通均衡理论generalized least squares estimation 广义最小二乘估计generation function 生成函数geomagnetic torque 地磁力矩geometric similarity 几何相似gimbaled wheel 框架轮global asymptotic stability 全局渐进稳定性global optimum 全局最优globe valve 球形阀goal coordination method 目标协调法grammatical inference 文法判断graphic search 图搜索gravity gradient torque 重力梯度力矩group technology 成组技术guidance system 制导系统gyro drift rate 陀螺漂移率gyrostat 陀螺体Hall displacement transducer 霍尔式位移传感器hardware-in-the-loop simulation 半实物仿真harmonious deviation 和谐偏差harmonious strategy 和谐策略heuristic inference 启示式推理hidden oscillation 隐蔽振荡hierarchical chart 层次结构图hierarchical planning 递阶规划hierarchical control 递阶控制homeostasis 内稳态homomorphic model 同态系统horizontal decomposition 横向分解hormonal control 内分泌控制hydraulic step motor 液压步进马达hypercycle theory 超循环理论I controller 积分控制器identifiability 可辨识性IDSS <intelligent decision support system> 智能决策支持系统image recognition 图象识别impulse 冲量impulse function 冲击函数,脉冲函数inching 点动incompatibility principle 不相容原理incremental motion control 增量运动控制index of merit 品质因数inductive force transducer 电感式位移传感器inductive modeling method 归纳建模法industrial automation 工业自动化inertial attitude sensor 惯性姿态敏感器inertial coordinate system 惯性坐标系inertial wheel 惯性轮inference engine 推理机infinite dimensional system 无穷维系统information acquisition 信息采集infrared gas analyzer 红外线气体分析器inherent nonlinearity 固有非线性inherent regulation 固有调节initial deviation 初始偏差initiator 发起站injection attitude 入轨姿式input-output model 投入产出模型instability 不稳定性instruction level language 指令级语言integral of absolute value of error criterion 绝对误差积分准则integral of squared error criterion 平方误差积分准则integral performance criterion 积分性能准则integration instrument 积算仪器integrity 整体性intelligent terminal 智能终端interacted system 互联系统,关联系统interactive prediction approach 互联预估法,关联预估法interconnection 互联intermittent duty 断续工作制internal disturbance 内扰ISM <interpretive structure modeling> 解释结构建模法invariant embedding principle 不变嵌入原理inventory theory 库伦论inverse Nyquist diagram 逆奈奎斯特图inverter 逆变器investment decision 投资决策isomorphic model 同构模型iterative coordination 迭代协调jet propulsion 喷气推进job-lot control 分批控制joint 关节Kalman-Bucy filer 卡尔曼-布西滤波器knowledge accomodation 知识顺应knowledge acquisition 知识获取knowledge assimilation 知识同化KBMS <knowledge base management system> 知识库管理系统knowledge representation 知识表达ladder diagram 梯形图lag-lead compensation 滞后超前补偿Lagrange duality 拉格朗日对偶性Laplace transform 拉普拉斯变换large scale system 大系统lateral inhibition network 侧抑制网络least cost input 最小成本投入least squares criterion 最小二乘准则level switch 物位开关libration damping 天平动阻尼limit cycle 极限环linearization technique 线性化方法linear motion electric drive 直线运动电气传动linear motion valve 直行程阀linear programming 线性规划LQR <linear quadratic regulator problem> 线性二次调节器问题load cell 称重传感器local asymptotic stability 局部渐近稳定性local optimum 局部最优log magnitude-phase diagram 对数幅相图long term memory 长期记忆lumped parameter model 集总参数模型Lyapunov theorem of asymptotic stability 李雅普诺夫渐近稳定性定理自动控制专业英语词汇〔二〕macro-economic system 宏观经济系统magnetic dumping 磁卸载magnetoelastic weighing cell 磁致弹性称重传感器magnitude-frequency characteristic 幅频特性magnitude margin 幅值裕度magnitude scale factor 幅值比例尺manipulator 机械手man-machine coordination 人机协调manual station 手动操作器MAP <manufacturing automation protocol> 创造自动化协议marginal effectiveness 边际效益Mason's gain formula 梅森增益公式master station 主站matching criterion 匹配准则maximum likelihood estimation 最大似然估计maximum overshoot 最大超调量maximum principle 极大值原理mean-square error criterion 均方误差准则mechanism model 机理模型meta-knowledge 元知识metallurgical automation 冶金自动化minimal realization 最小实现minimum phase system 最小相位系统minimum variance estimation 最小方差估计minor loop 副回路missile-target relative movement simulator 弹体- 目标相对运动仿真器modal aggregation 模态集结modal transformation 模态变换MB <model base> 模型库model confidence 模型置信度model fidelity 模型逼真度model reference adaptive control system 模型参考适应控制系统model verification 模型验证modularization 模块化MEC <most economic control> 最经济控制motion space 可动空间MTBF <mean time between failures> 平均故障间隔时间MTTF <mean time to failures> 平均无故障时间multi-attributive utility function 多属性效用函数multicriteria 多重判据multilevel hierarchical structure 多级递阶结构multiloop control 多回路控制multi-objective decision 多目标决策multistate logic 多态逻辑multistratum hierarchical control 多段递阶控制multivariable control system 多变量控制系统myoelectric control 肌电控制Nash optimality 纳什最优性natural language generation 自然语言生成nearest-neighbor 最近邻necessity measure 必然性侧度negative feedback 负反馈neural assembly 神经集合neural network computer 神经网络计算机Nichols chart 尼科尔斯图noetic science 思维科学noncoherent system 非单调关联系统noncooperative game 非合作博弈nonequilibrium state 非平衡态nonlinear element 非线性环节nonmonotonic logic 非单调逻辑nonparametric training 非参数训练nonreversible electric drive 不可逆电气传动nonsingular perturbation 非奇妙摄动non-stationary random process 非平稳随机过程nuclear radiation levelmeter 核辐射物位计nutation sensor 章动敏感器Nyquist stability criterion 奈奎斯特稳定判据objective function 目标函数observability index 可观测指数observable canonical form 可观测规 X 型on-line assistance 在线匡助on-off control 通断控制open loop pole 开环极点operational research model 运筹学模型optic fiber tachometer 光纤式转速表optimal trajectory 最优轨迹optimization technique 最优化技术orbital rendezvous 轨道交会orbit gyrocompass 轨道陀螺罗盘orbit perturbation 轨道摄动order parameter 序参数orientation control 定向控制originator 始发站oscillating period 振荡周期output prediction method 输出预估法oval wheel flowmeter 椭圆齿轮流量计overall design 总体设计overdamping 过阻尼overlapping decomposition 交叠分解Pade approximation 帕德近似Pareto optimality 帕雷托最优性passive attitude stabilization 被动姿态稳定path repeatability 路径可重复性pattern primitive 模式基元PR <pattern recognition> 模式识别P control 比例控制器peak time 峰值时间penalty function method 罚函数法perceptron 感知器periodic duty 周期工作制perturbation theory 摄动理论pessimistic value 悲观值phase locus 相轨迹phase trajectory 相轨迹phase lead 相位超前photoelectric tachometric transducer 光电式转速传感器phrase-structure grammar 短句结构文法physical symbol system 物理符号系统piezoelectric force transducer 压电式力传感器playback robot 示教再现式机器人PLC <programmable logic controller> 可编程序逻辑控制器plug braking 反接制动plug valve 旋塞阀pneumatic actuator 气动执行机构point-to-point control 点位控制polar robot 极坐标型机器人pole assignment 极点配置pole-zero cancellation 零极点相消polynomial input 多项式输入portfolio theory 投资搭配理论pose overshoot 位姿过调量position measuring instrument 位置测量仪posentiometric displacement transducer 电位器式位移传感器positive feedback 正反馈power system automation 电力系统自动化predicate logic 谓词逻辑pressure gauge with electric contact 电接点压力表pressure transmitter 压力变送器price coordination 价格协调primal coordination 主协调primary frequency zone 主频区PCA <principal component analysis> 主成份分析法principle of turnpike 大道原理priority 优先级process-oriented simulation 面向过程的仿真production budget 生产预算production rule 产生式规则profit forecast 利润预测PERT <program evaluation and review technique> 计划评审技术program set station 程序设定操作器proportional control 比例控制proportional plus derivative controller 比例微分控制器protocol engineering 协议工程prototype 原型pseudo random sequence 伪随机序列pseudo-rate-increment control 伪速率增量控制pulse duration 脉冲持续时间pulse frequency modulation control system 脉冲调频控制系统pulse width modulation control system 脉冲调宽控制系统PWM inverter 脉宽调制逆变器pushdown automaton 下推自动机QC <quality control> 质量管理quadratic performance index 二次型性能指标qualitative physical model 定性物理模型quantized noise 量化噪声quasilinear characteristics 准线性特性queuing theory 排队论radio frequency sensor 射频敏感器ramp function 斜坡函数random disturbance 随机扰动random process 随机过程rate integrating gyro 速率积分陀螺ratio station 比值操作器reachability 可达性reaction wheel control 反作用轮控制realizability 可实现性,能实现性real time telemetry 实时遥测receptive field 感受野rectangular robot 直角坐标型机器人rectifier 整流器recursive estimation 递推估计reduced order observer 降阶观测器redundant information 冗余信息reentry control 再入控制regenerative braking 回馈制动,再生制动regional planning model 区域规划模型regulating device 调节装载regulation 调节relational algebra 关系代数relay characteristic 继电器特性remote manipulator 遥控操作器remote regulating 遥调remote set point adjuster 远程设定点调整器rendezvous and docking 交会和对接reproducibility 再现性resistance thermometer sensor 热电阻resolution principle 归结原理resource allocation 资源分配response curve 响应曲线return difference matrix 回差矩阵return ratio matrix 回比矩阵reverberation 回响reversible electric drive 可逆电气传动revolute robot 关节型机器人revolution speed transducer 转速传感器rewriting rule 重写规则rigid spacecraft dynamics 刚性航天动力学risk decision 风险分析robotics 机器人学robot programming language 机器人编程语言robust control 鲁棒控制robustness 鲁棒性roll gap measuring instrument 辊缝测量仪root locus 根轨迹roots flowmeter 腰轮流量计rotameter 浮子流量计,转子流量计rotary eccentric plug valve 偏心旋转阀rotary motion valve 角行程阀rotating transformer 旋转变压器Routh approximation method 劳思近似判据routing problem 路径问题sampled-data control system 采样控制系统sampling control system 采样控制系统saturation characteristics 饱和特性scalar Lyapunov function 标量李雅普诺夫函数SCARA <selective compliance assembly robot arm> 平面关节型机器人scenario analysis method 情景分析法scene analysis 物景分析s-domain s 域self-operated controller 自力式控制器self-organizing system 自组织系统self-reproducing system 自繁殖系统self-tuning control 自校正控制semantic network 语义网络semi-physical simulation 半实物仿真sensing element 敏感元件sensitivity analysis 灵敏度分析sensory control 感觉控制sequential decomposition 顺序分解sequential least squares estimation 序贯最小二乘估计servo control 伺服控制,随动控制servomotor 伺服马达settling time 过渡时间sextant 六分仪short term planning 短期计划short time horizon coordination 短时程协调signal detection and estimation 信号检测和估计signal reconstruction 信号重构similarity 相似性simulated interrupt 仿真中断simulation block diagram 仿真框图simulation experiment 仿真实验simulation velocity 仿真速度simulator 仿真器single axle table 单轴转台single degree of freedom gyro 单自由度陀螺single level process 单级过程single value nonlinearity 单值非线性singular attractor 奇妙吸引子singular perturbation 奇妙摄动sink 汇点slaved system 受役系统slower-than-real-time simulation 欠实时仿真slow subsystem 慢变子系统socio-cybernetics 社会控制论socioeconomic system 社会经济系统software psychology 软件心理学solar array pointing control 太阳帆板指向控制solenoid valve 电磁阀source 源点specific impulse 比冲speed control system 调速系统spin axis 自旋轴spinner 自旋体stability criterion 稳定性判据stability limit 稳定极限stabilization 镇定,稳定Stackelberg decision theory 施塔克尔贝格决策理论state equation model 状态方程模型state space description 状态空间描述static characteristics curve 静态特性曲线station accuracy 定点精度stationary random process 平稳随机过程statistical analysis 统计分析statistic pattern recognition 统计模式识别steady state deviation 稳态偏差steady state error coefficient 稳态误差系数step-by-step control 步进控制step function 阶跃函数stepwise refinement 逐步精化stochastic finite automaton 随机有限自动机strain gauge load cell 应变式称重传感器strategic function 策略函数strongly coupled system 强耦合系统subjective probability 主观频率suboptimality 次优性supervised training 监督学习supervisory computer control system 计算机监控系统sustained oscillation 自持振荡swirlmeter 旋进流量计switching point 切换点symbolic processing 符号处理synaptic plasticity 突触可塑性synergetics 协同学syntactic analysis 句法分析system assessment 系统评价systematology 系统学system homomorphism 系统同态system isomorphism 系统同构system engineering 系统工程tachometer 转速表target flow transmitter 靶式流量变送器task cycle 作业周期teaching programming 示教编程telemechanics 远动学telemetering system of frequency division type 频分遥测系统telemetry 遥测teleological system 目的系统teleology 目的论temperature transducer 温度传感器template base 模版库tensiometer X 力计texture 纹理theorem proving 定理证明therapy model 治疗模型thermocouple 热电偶thermometer 温度计thickness meter 厚度计three-axis attitude stabilization 三轴姿态稳定three state controller 三位控制器thrust vector control system 推力矢量控制系统thruster 推力器time constant 时间常数time-invariant system 定常系统,非时变系统time schedule controller 时序控制器time-sharing control 分时控制time-varying parameter 时变参数top-down testing 自上而下测试topological structure 拓扑结构TQC <total quality control> 全面质量管理tracking error 跟踪误差trade-off analysis 权衡分析transfer function matrix 传递函数矩阵transformation grammar 转换文法transient deviation 瞬态偏差transient process 过渡过程transition diagram 转移图transmissible pressure gauge 电远传压力表transmitter 变送器trend analysis 趋势分析triple modulation telemetering system 三重调制遥测系统turbine flowmeter 涡轮流量计Turing machine 图灵机two-time scale system 双时标系统ultrasonic levelmeter 超声物位计unadjustable speed electric drive 非调速电气传动.unbiased estimation 无偏估计underdamping 欠阻尼uniformly asymptotic stability 一致渐近稳定性uninterrupted duty 不间断工作制,长期工作制unit circle 单位圆unit testing 单元测试unsupervised learing 非监督学习upper level problem 上级问题urban planning 城市规划utility function 效用函数value engineering 价值工程variable gain 可变增益,可变放大系数variable structure control system 变结构控制vector Lyapunov function 向量李雅普诺夫函数velocity error coefficient 速度误差系数velocity transducer 速度传感器vertical decomposition 纵向分解vibrating wire force transducer 振弦式力传感器vibrometer 振动计viscous damping 粘性阻尼voltage source inverter 电压源型逆变器vortex precession flowmeter 旋进流量计vortex shedding flowmeter 涡街流量计WB <way base> 方法库weighing cell 称重传感器weighting factor 权因子weighting method 加权法Whittaker-Shannon sampling theorem 惠特克-香农采样定理Wiener filtering 维纳滤波work station for computer aided design 计算机辅助设计工作站w-plane w 平面zero-based budget 零基预算zero-input response 零输入响应zero-state response 零状态响应zero sum game model 零和对策模型z-transform z 变换21 / 21。
ADAPTIVE OBJET-ORIENTED OPTIMIZATION SOFTWARE SYS
专利名称：ADAPTIVE OBJET-ORIENTEDOPTIMIZATION SOFTWARE SYSTEM发明人：HALES, Lynn, B.,YNCHAUSTI, Randy, A.,FOOT, Donald, G., Jr.申请号：US1998003356申请日：19980220公开号：WO98/037465P1公开日：19980827专利内容由知识产权出版社提供摘要：The present invention relates to process control optimization systems which utilize an adaptive optimization software systems comprising goal seeking intelligent software objects; the goal seeking intelligent software objects further comprise internal software objects which include expert system objects, adaptive models objects, optimizer objects, predictor objects, sensor objects, and communication translation objects. The goal seeking intelligent software objects can be arranged in a hierarchical relationship whereby the goal seeking behavior of each intelligent software object can be modified by goal seeking intelligent software objects higher in the hierarchical structure. The goal seeking intelligent software objects can also be arranged in a relationship which representationally corresponds to the controlled process's flow of materials or data.申请人：BAKER HUGHES INCORPORATED地址：Suite 1200 3900 Essex Lane Houston, TX 77027 US国籍：US代理机构：ROWOLD, Carl, A.更多信息请下载全文后查看。
科技改变了我们的学习方式 英语作文
科技改变了我们的学习方式英语作文全文共3篇示例，供读者参考篇1Technology has transformed the way we learn in remarkable ways. As a young student, I've witnessed firsthand how advancements in technology have reshaped our educational experiences. Let me share with you how these innovations have impacted our learning journey.One of the most significant changes brought about by technology is the accessibility of information. In the past, we relied heavily on textbooks and libraries to acquire knowledge. However, today, with just a few clicks or taps on our devices, we can access a vast wealth of information from all around the world. Online educational resources, such as websites, videos, and interactive platforms, have become invaluable tools for learning. We can explore diverse topics, gain insights from experts, and even take virtual tours of historical sites or scientific laboratories, all from the comfort of our classrooms or homes.Another transformative aspect of technology in education is the introduction of interactive and engaging learning methods.Gone are the days when we passively listened to lectures or read from static textbooks. Today, we have access to a wide range of multimedia resources, including educational apps, simulations, and virtual reality experiences. These tools not only make learning more enjoyable but also provide us with hands-on experiences that deepen our understanding. For example, in science classes, we can observe chemical reactions or explore the intricacies of the human body through interactive models, making abstract concepts more tangible and easier to comprehend.Moreover, technology has facilitated collaborative learning opportunities that were once limited by physical boundaries. Through online platforms and communication tools, we can connect with students from different parts of the world, exchange ideas, and work together on projects. This exposure to diverse perspectives and cultures not only enhances our academic knowledge but also fosters essential skills such as teamwork, communication, and cultural awareness.However, it's important to recognize that technology is not a substitute for traditional teaching methods but rather a complementary tool. Our teachers play a crucial role in guiding us through the wealth of information available and helping usdevelop critical thinking skills. They provide valuable guidance, facilitate discussions, and ensure that we utilize technology in a responsible and productive manner.While technology has undoubtedly transformed our learning experiences, it also presents challenges that we must navigate. With the abundance of information available online, it's essential to develop skills in evaluating the credibility and reliability of sources. Additionally, we must strike a balance between screen time and other activities, ensuring that we maintain a healthy lifestyle and develop social skills through face-to-face interactions.In conclusion, technology has revolutionized the way we learn, offering unprecedented access to knowledge, interactive learning experiences, and opportunities for global collaboration. As young students, we are fortunate to have these resources at our fingertips, and it's our responsibility to embrace them responsibly and use them to fuel our curiosity, expand our horizons, and prepare ourselves for the ever-evolving world.篇2Technology Has Changed How We LearnHi there! My name is Jamie and I'm a 10-year-old student. A lot has changed in how we learn compared to when my parents and grandparents were kids. Technology has totally transformed the way we go to school and study. Let me tell you about some of the biggest changes!One of the most obvious ways technology impacts our learning is through computers and the internet. When my parents were younger, they had to use physical books and encyclopedias to find information for school projects and homework. Nowadays, I just need to hop on the internet and I can instantly access a wealth of knowledge from all around the world at my fingertips!Sure, books are still used sometimes, but a lot of our textbooks and reading materials are actually online or digital. I remember in 2nd grade when our teacher gave us each an iPad that had all our textbooks loaded onto it. It was so convenient not having to lug around a heavy backpack full of bulky books. The digital textbooks also had interactive features like videos, quizzes, and learning games built right into them.Speaking of interactive tools, there are so many awesome educational apps, websites, and online programs available now. My math curriculum utilizes an adaptive learning software thatadjusts the lessons based on my strengths and weaknesses. If I'm struggling with a certain concept, it provides extra instruction and practice in that area. But if I seem to understand something easily, it allows me to skip ahead.We also use a lot of educational games and simulations in our science and social studies classes. For example, when learning about ecosystems, we got to explore a virtual rainforest environment and see how all the elements were interconnected. Or in history, we've done interactive games that immersed us in different time periods and historical events. It's a lot more engaging than just reading from a textbook!Our school has also implemented a "flipped classroom" model for some subjects, thanks to technology. That means instead of getting new lessons at school, we watch video lectures at home. Then during class time, we spend it working on exercises, projects, and getting personalized attention from the teacher as needed. I really like this method because I canre-watch the video lessons if I need to at my own pace.In addition to laptops and tablets, lots of other cool tech is used in our classrooms now too. We have interactive whiteboards that can display multimedia lessons and also let us interact with the content. Our science lab is equipped withadvanced tools like 3D printers, robotics kits, and even a small greenhouse with hydroponics systems. Learning isn't just meant for textbooks anymore!One major benefit of technology is that it allows for much more collaboration between students and teachers beyond just the classroom setting. We use online platforms where we can chat with teachers, submit assignments, get feedback, and work together on group projects. If I ever need extra help, I can easily schedule a video conference with my teacher in the evenings.During the COVID-19 pandemic a few years back, our schools had to move entirely to remote learning for a while. Thanks to technology, we were able to continue our education virtually through video conferencing, online lessons, and digital assignment submission. While it wasn't the same as in-person school, technology allowed our learning to continue uninterrupted.Outside of school, technology has also transformed how we learn and do homework. Instead of having to trek to the library to research for projects, I can instantly access a world's worth of information from home with a few clicks. If I need help with a tough math problem, there are tons of video tutorials andstep-by-step guides online to walk me through it.Speaking of math, we now use graphic calculators and computers to solve really complex equations and analyze data. My grandpa still has his old-school scientific calculator from when he was a kid and it looks like a antique compared to what we use! Technology allows us to focus on the higher-level concepts rather than getting bogged down by tedious calculations.Overall, technology has utterly transformed how we experience education. Learning is more interactive, personalized, collaborative and engaging than ever before. Instead of just absorbing information from lectures and books, we're actively applying knowledge through simulations, experiments, and digital tools. We can learn at our own pace with adaptive curriculums and video lessons we can revisit as needed.While technology certainly has its downsides like the potential for distraction, I feel incredibly fortunate to be able to learn in such an advanced, engaging way. I can't even imagine what antiquated educational methods my grandparents had to use! With technology, learning is finally becoming tailored to the individual student instead of taking a one-size-fits-all approach.Who knows what amazing new learning technologies will be developed by the time I get to college or become an adult?Perhaps we'll have virtual reality classrooms where we can immerse ourselves in any environment or time period for the ultimate interactive learning experience. Or maybe we'll have artificial intelligence tutors that can provide differentiated instruction perfectly suited to each student's strengths and needs.The possibilities are endless when technology and education combine. While it's radically different from how previous generations learned, I think these modern methods are extremely beneficial and effective. Education is finally catching up to the digital age we live in. As technology continues evolving, I'm excited to see what new ways of learning will emerge and how it will further transform our experiences as students. Technology has already changed so much about how we learn - and this is just the beginning!篇3Technology is Changing How We LearnHi there! My name is Emma and I am a 4th grade student. A lot has changed in how we learn at school compared to when my parents and grandparents were kids. Technology has made learning way more fun and interactive than just reading fromtextbooks and listening to teachers talk all day. Let me tell you about some of the cool ways we use technology for learning now!First up, we have these things called interactive whiteboards in every classroom instead of old chalkboards or dry erase boards. The teacher can use a special pen or just touch the board to write notes, draw pictures and diagrams, play videos, and so much more. All the pages and notes get automatically saved so we can view them again later. It's like having the world's biggest tablet right in the classroom!We also each have our own tablet computer that the school provides. On these tablets, we can access all our digital textbooks, games for practicing different skills, and sites for online lessons and activities. Instead of carrying around a heavy backpack of books, we just need our slim tablet. The tablets have special kid-friendly modes to make them easy and safe to use.For subjects like math, we have really cool apps that actually work like little video games. They start off easy to learn the basic concepts, but then get harder and harder as we master new skills. We can race against the clock, earn points and badges, and even compete against other students. It's a lot more motivating than just doing page after page of equations!In science class, we get to go on virtual field trips using VR headsets and 3D video. Just this week, we got to explore the depths of the Great Barrier Reef and observe the marine life until it felt like we were actually underwater. Last month, we virtually toured the inside of the human body. So much better than those old-fashioned plastic models and diagrams!When we are learning about history, we can just put on the VR goggles and actually walk around ancient civilizations to see what life was like back then. Or we can explore famous museums and historic sites from all over the world without even leaving our classroom. It's like having a virtual time machine!Speaking of virtual travel, we also can have video chat sessions with students from other schools in different countries and cultures. We can ask them questions, share about our own lives, play games together, and really get to know each other. It's helping us become citizens of the world at such a young age.Coding and programming skills are also a huge part of our learning now. We have kid-friendly coding apps and games that teach us logic and problem solving almost like we are playing. I've already coded my own simple computer animations and games, and I'm just in 4th grade! Students who are really into it can even join coding clubs and competitions.Of course, we still learn lots of core basics like reading, writing, and arithmetic. But even for those subjects, we often use digital workbooks, interactive storytelling apps, speech recognition for dictation practice, and all sorts of other edtech tools. Let's just say our pencils don't get a workout like they did for previous generations!One of my favorite things is that we can learn at our own pace a lot more. The apps and programs will automatically adjust for students who need extra practice or reminders on certain skills. Or they can skip ahead for kids who have already mastered those skills. No more the whole class being forced to move at the exact same speed.We also can access so many more resources for self-guided learning on topics we are passionate about. If I'm really interested in a subject like dinosaurs or space exploration, I can find kid-friendly websites, videos, programs and even full online courses all about those topics. The amount of information we can get our hands on is basically unlimited compared to just the few books our parents had as kids.Another great thing is how technology helps us get individualized support and attention, even in crowded classrooms. Some of the lessons are taught by virtual teachingassistants using AI. They can patiently re-explain concepts, provide extra examples, and always have the time to workone-on-one with every student who needs it. Pretty neat, right?Now I know what you might be thinking - doesn't all this technology make everything too easy? Won't we get lazy or miss out on struggling and working hard? That's definitely something teachers, parents, and educators debate a lot.But from my perspective, technology just makes the fundamentals of learning more engaging and memorable. Sure, we may have fun games for practicing multiplication tables. But we still have to use our brains, think critically, and master those skills. Technology enhances the process rather thanmaking it mindless.The other big criticism is too much screentime. That's why all the programs have built-in breaks, fitness activities, and timers to make sure we aren't just zoning out in front of devices all day. We still go outside for recess, participate in hands-on projects, and learn from actual human teachers for most of the core lessons. EdTech is a tool, not a replacement for the role of teachers and classrooms.So even though school looks really different now, we are still developing critical thinking abilities, creativity, social skills, andall the other important strengths. Technology is just making it more exciting, interactive, and adapted for how we learn best as the digital generation. Pretty cool if you ask me!Of course, these are just the technologies we have today in 2024. By the time I graduate from high school, or go to college or career training programs, who knows what new mind-blowing technologies will be shaping how we learn! We are truly living in the most amazing era of opportunity when it comes to lifelong learning. I can't wait to see what's next!。
基于人工智能的自适应软件京东直播应用平台模型的构建研究
基于人工智能的自适应软件京东直播应用平台模型的构建研究作者：***来源：《无线互联科技》2023年第24期摘要：在流媒体技术快速发展的背景下，用户对网络内容的需求不断提升，视频传输迎来了全新的挑战。
针对传统流媒体技术视频直播过程中忽略网络环境变化且不能根据网络变化适时调整视频传输的问题，文章设计了一种基于人工智能的码率自适应软件，通过连续时延和码率控制算法实现视频直播传输的动态调整。
仿真结果表明，基于人工智能的码率自适应软件可根据直播过程中的网络变化情况实时调整视频直播效果，有效提升用户的观看体验感。
关键词：人工智能；自适应；码率算法；直播平台中图分类号：F062.9 文献标志码：A0 引言当下，自适应视频传输技术在视频点播场景中已得到广泛应用，大部分直播平台开始通过此技术传输相应的音频数据与图像数据［1］。
相比较之下，直播平台播放视频过程中需根据弹幕内容与用户进行实时互动，对直播视频流对码率控制和低时延要求相对较高。
基于此，本文设计了一种码率自适应软件，通过连续时延和码率控制算法，有效提升用户的观看体验质量。
1 基于播放速率和跳帧控制的连续时延和码率控制算法当下，大部分直播平台侧重于静态码率的使用，并未考虑直播过程中的网络与传输条件。
在视频直播传输条件发生变化的情况下，需通过手动的方式调整视频码率，以满足用户观看需求。
虽然此种方式可解决部分问题，但会降低用户的体验质量（Quality of Experience，QoE）。
因此，为有效提升用户QoE，需根据网络条件的变化情况以及用户设备信息，对视频直播传输进行自适应管理，为用户提供一种高质量的实时视频直播流。
基于此，本文设计了一种码率自适应决策软件。
该软件基于播放速率和跳帧控制的连续时延和码率控制算法PRFDCLRC，实现视频码率的自动调整［2］。
1.1 QoE度量问题在视频点播场景中，QoE分为3个部分，即视频质量、卡顿时间、视频码率切换等。
一种基于协同演化的自适应约束多目标进化算法
一种基于协同演化的自适应约束多目标进化算法自适应约束多目标进化算法（Adaptive Constraint-based Multi-Objective Evolutionary Algorithm，简称ACMOEA）是一种通过协同演化和自适应调整约束权重的算法，能够有效处理多目标问题和约束优化问题的综合算法。
本文将介绍ACMOEA的基本原理、算法步骤以及应用实例。
第一部分：引言在现实世界中，很多问题往往具有多个优化目标和一系列约束条件，如工程设计、资源分配等。
传统的优化算法往往难以有效解决这类复杂问题。
因此，研究者们提出了多目标优化和约束优化的概念，并开发了相应的算法。
第二部分：ACMOEA的基本原理1. 多目标优化多目标优化是指在一个优化问题中存在多个相互冲突的目标函数，希望找到一组解，使得这些目标函数都能得到较好的解。
ACMOEA采用Pareto支配关系作为多目标优化的评价标准，并通过维护一个非支配解集来得到 Pareto 前沿。
2. 约束优化约束优化是指在优化问题中存在一系列约束条件，需要找到满足所有约束的最优解。
ACMOEA通过引入罚函数来处理约束问题，将其转化为无约束问题进行求解。
3. 协同演化ACMOEA中采用协同演化的方法来解决多目标和约束优化问题。
协同演化是一种通过个体间的相互作用和竞争来逐步改进整个种群的演化算法。
在ACMOEA中，不同的个体分别负责优化目标函数和约束条件，通过演化过程中的竞争和合作，逐步提高整体的优化性能。
第三部分：ACMOEA的算法步骤1. 初始化随机生成初始种群，并计算每个个体的适应度值。
2. 选择根据个体的适应度值，按照一定的选择策略选择一部分个体作为父代。
3. 变异和交叉对选择的父代进行变异和交叉操作，生成一定数量的新个体。
4. 更新非支配解集根据新生成的个体和当前的非支配解集，更新非支配解集。
5. 更新约束权重根据非支配解集中的个体，动态更新约束权重，实现自适应调整。
高铁夕发朝至列车开行与天窗设置协同优化
铰链损失的梯度
铰链损失的梯度1. 引言铰链是一种常见的连接装置，用于连接两个物体并允许它们在一个或多个方向上相对运动。
它在许多领域中都有广泛的应用，例如机械工程、建筑工程和航空航天工程等。
然而，由于各种原因，铰链可能会遭受损失，这将导致其功能的下降甚至完全失效。
本文将探讨铰链损失的梯度问题。
我们将介绍铰链的基本原理和结构，并分析导致铰链损失的主要因素。
接着，我们将研究如何通过计算和实验来评估铰链损失以及如何确定其梯度。
最后，我们将讨论一些解决铰链损失问题的方法和策略。
2. 铰链基础知识2.1 铰链类型根据其结构和功能，铰链可以分为多种类型。
常见的铰链类型包括：•钉轴铰链：由一个或多个钉轴连接两个物体。
•合页铰链：由一条长而窄的金属带连接两个物体。
•摆线铰链：由两个相互嵌合的摆线轮连接两个物体。
•滚子铰链：由滚子和凸轮构成的滚动连接装置。
2.2 铰链原理铰链的基本原理是通过允许物体在一定范围内相对运动来实现连接。
不同类型的铰链具有不同的运动范围和限制条件。
例如，钉轴铰链只允许物体在一个平面内绕钉轴旋转，而合页铰链可以使物体绕着连接线作直线运动。
3. 铰链损失因素铰链损失是指铰链功能受到影响或完全失效的情况。
以下是导致铰链损失的主要因素：3.1 磨损和疲劳长时间使用或不当使用会导致铰链磨损和疲劳，进而降低其性能。
磨损会导致接触面积减小、摩擦增大，从而使得运动不流畅；而疲劳则可能导致铰链断裂。
3.2 材料质量和制造工艺材料质量和制造工艺对于铰链的性能至关重要。
低质量的材料可能会导致铰链强度不足，而制造过程中的缺陷可能会导致铰链易损坏。
3.3 温度和湿度变化温度和湿度的变化会对铰链产生影响。
例如，高温可能导致材料膨胀，使得铰链失去原有的连接性能；而湿度变化可能导致锈蚀和腐蚀，进而损害铰链的表面质量。
3.4 外力和振动外力和振动也是导致铰链损失的常见因素。
过大的外力可能会超过铰链的承载能力，从而引起破坏；而频繁的振动可能引起松动和疲劳断裂。
空间目标自适应光学图像椭圆部件检测
第 x 卷 第 x 期 xxxx 年 x 月
文章编号 2095-1531（xxxx）x-0001-10
中国光学 Chinese Optics
Vol. x No. x xxx. xxxx
空间目标自适应光学图像椭圆部件检测
寇 鹏1,2，智帅峰1，程 耘1，刘永祥1
（1. 国防科技大学 电子科学学院, 湖南 长沙 410073； 2. 西安卫星测控中心, 陕西 西安 710600）
近年来，基于边缘连接的检测方法大大提高 了椭圆检测性能。这类方法的主要问题是如何确 定 属 于 同 一 椭 圆 的 椭 圆 弧 。 ELSD(Ellipse and Line Segment Detector) 方法通过检测 LS(Line Segments) 和对 LS 分组，充分利用了椭圆的梯度和 几何特征，可以在不调整任何参数的情况下减少 对各种类型图像的错检率[9]。文献 [10] 结合了基 于 HT 和基于边缘链接的方法的优点来检测工业 图像中的椭圆，但它们不适用于一般的椭圆检 测。文献 [11] 提出了一种弧段基于弧邻接矩阵
KOU Peng1,2，ZHI Shuai-feng1，CHENG Yun1，LIU Yong-xiang1 （1. School of Electronic Science, University of Defense Science and technology, Changsha 410073, China；
摘要：为了识别空间目标的椭圆部件，提出了一种基于自适应光学图像的椭圆检测方法。首先，利用
RL(Richardson–Lucy) 方法对自应光学图像进行复原，在此基础上，采用弧支撑线段 (ASLS, Arc-support Line Seg-
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nitions will be domain-specific, although the underlying representation of domain-specific constraints will be in a domain-independent language).Given the similarities with databases, one might construe that the problem of adaptive software is merely one of query optimization for the container domain. This is not the case. We must search the space of all possible container implementations, and for any particular implementation, we must search the space of all execution plans to find the most efficient for that (query, container implementation) pair. The space of con-tainer implementations defined by compositions of P3 components is gargantuan, and coupling it with the space of query execution plans makes searching formidable, and far more difficult than just optimizing queries for a predetermined container implementation.To minimize the combinatorial explosion we exploit semantic information embedded and structured in the CSP. The introduction of a new component into a target implementation is conditioned on the satisfaction of the constraint system. Each additional component creates additional semantic constraints. These con-straints, in conjunction with fast algorithms for computing incremental changes to a CSP yield an effective filter constraining the search [Mir90].3 Status and AvailabilityConstraint solution mechanisms are implemented directly by expressing the CSP as rules in the V enus for-ward-chaining rule language [War96a]. V enus supports the encapsulation and parameterization of rules. The incremental nature of the evaluation techniques for rule systems yields fast evaluation of small changes in the CSP. Venus will be posted on the web, making it available for research use before the end of the summer.A clean presentation of our approach to search and planning can be reviewed by visiting a sister project on using Venus as the basis of an extensible database query optimizer [War96b]. Our original research plan called for separate but concurrent development of CDL, its support structure, and Jakarta. Given the unex-pectedly rapid development of JTS, further development of CDL proper is likely to be curtailed and further development continued by integrating the key ideas directly into JTS.This research is sponsored by the Defense Advanced Research Projects Agency. Technical and contrac-tual management are provided by Rome Laboratory, USAF, under Cooperative Agreement F30602-96-2-0226.4 References[Bat92] D. Batory and S. O'Malley. “The Design and Implementation of Hierarchical Software Systems with Reusable Components”.ACM Trans. on Software Engin. and Methodology, October 1992. [Bat95] D. Batory, L. Coglianese, M. Goodwin, and S. Shafer. “Creating Reference Architectures: An Example from Avionics”,Symposium on Software Reusability, Seattle Washington, April 1995. [Bat97] D. Batory and B.J. Geraci. “Composition Validation and Subjectivity in GenV oca Generators”, IEEE Transactions on Software Engineering,February 1997.[Mir90] D. P. Miranker, D. Brant. B.J. Lofaso and D. Gadbois, “On the Performance of Lazy Matching in Production Systems”,Proceedings of the 1990 National Conference on Artificial Intelligence, (AAAI-90), July 1990, 685-692[War96a] L.B. Warshaw and D.P. Miranker, "A Case Study of the Venus Approach to Rule-Based Modularity",Conferenece on Information and Knowledge Management, (CIKM-96), 317-325. [War96b] L.B Warshaw, D.P. Miranker, and T. Wang, “A General Purpose Rule Language as the Basis ofa Query Optimizer”, TR97-19, Dept. of Computer Sciences, University of Texas at Austin, 1997.Constraint-Based Adaptive Software SystemsTao Wang, Daniel Miranker and Don BatoryDepartment of Computer SciencesUniversity of Texas at AustinAustin, Texas 78712{taowang, miranker, batory}@EDCS Contract Number: F30602-96-2-02261 IntroductionGenV oca is a domain-independent model for defining scalable families of hierarchical systems from com-ponents [Bat92-95]. A GenVoca generator is a tool that composes components to build target systems. The goal of the Jakarta Tool Suite (JTS)is to help users develop GenV oca-style component libraries and gener-ators correctly and efficiently. A promise of JTS includes the automation of engineering tasks beyond those already attributed to GenV oca generators.Once a component library has been defined, we claim there is an opportunity for describing workloads and having the system automatically synthesize a particular system implementation (i.e., component composi-tion) that has been optimized for that workload. Besides the obvious benefit to engineering productivity, our work comprises two critical advances. First, not all users of software generators are experts. Thus, an automated system containing domain knowledge may guide a user toward a good implementation and help them avoid blunders. Second, these elements of program synthesis are prerequisite to building adaptive software systems; systems that dynamically change their configuration as a function of current workload.Toward this end, we have defined an architectural description language,CDL (Component Definition Lan-guage) for GenV oca-style componentry. CDL goes beyond the syntactic structure of the components and provides an avenue for developers to declare their semantic properties. Furthermore, semantic constraints can also be expressed. As a direct result of the constraint capability of CDL we:•defined and implemented semantic design rule checking, i.e., the validation of GenV oca compositions, in terms of a constraint-satisfaction problem (CSP),•defined domain-independent algorithms for compiling CDL descriptions to label sets for the CSP, and •have results for exploiting a constraint-structured planner for synthesizing programs from components.The correctness of a GenV oca composition has always been manifest in a type grammar that defines the reference architecture of the target domain. Previously the expression of design-rules has been generalized and implemented in the form of attribute grammars [Bat97]. Though both attribute grammars and proposi-tional constraints are declarative forms of specification, the lexical scope of propositional constraints yields a tighter syntax that is able to denote a better encapsulation of semantic properties within component definitions. Also, propositional constraints map trivially to logic and thus form a basis for reasoning about component-based software.2 Constraints, Workloads, and SynthesisThe structure of the planner is succinctly described through an example. P3 is a GenV oca generator for containers that is is the first generator built using JTS. The basic operations supported by P3-containers are insert, delete and retrieve. A workload is specified by providing statistics typical of conventional database data catalogs and operation execution frequencies. Since this is our first effort toward workload specifica-tion, we are not in a position to define a general-purpose language. (In fact, we suspect that workload defi-。