Higher-order modeling and automated design-space exploration
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CCF公布推荐的国际学术会议和期刊目录经过3年多的工作,CCF推荐的国际学术会议和期刊目录现予公布。
本目录包括数据库、软件工程、计算机网络、计算机图形学(几何造型、多媒体、可视化、虚拟现实)、计算机体系结构、计算机科学理论、人工智能与模式识别、网络与信息安全等八个方向的国际学术会议及期刊目录和一个综合类的国际学术期刊目录,供国内高校和科研单位作为学术评价的参考依据。
目录中,刊物和会议分为A、B、C三档。
A类表示国际上极少数的顶级刊物和会议,鼓励我国学者去突破;B类是指国际上著名和非常重要的会议、刊物,代表该领域的较高水平,鼓励国内同行投稿;C类指国际上重要、为国际学术界所认可的会议和刊物。
早在2005年12月17日,CCF YOCSEF就举办了“从SCI反思中国的学术评价体制”的专题论坛,探讨为何SCI会成为衡量大学、科研机构和科学工作者学术水平的最重要的甚至是唯一的尺度,提出了如何建立中国公正合理的学术评价体制的问题。
这次论坛,在国内引起了强烈的反响。
李国杰理事长在各种场合多次呼吁要重视在顶级国际学术会议上发表论文,希望CCF拿出顶级学术会议和重要学术期刊的目录,提供给各高校和科研单位做学术水平评价的参考。
此后,CCF委托YOCSEF学术委员会组织此项工作,经过调研、分析、选择试点方向、收集整理资料、研讨、向学术界公开征集意见、报常务理事会审议、学术工作委员会再修订等过程,最终推出目前的推荐目录表。
这些分类目录每年将根据具体情况进行修订。
2010年9月附:国际学术会议及期刊目录计算机科学理论 (2)计算机体系结构 (8)计算机网络 (14)人工智能与模式识别 (19)软件工程 (28)数据库 (35)计算机图形学、几何造型、多媒体、可视化、虚拟现实等方向,不含算机视觉与模式识别 (42)网络/信息安全 (46)综合类刊物 (51)第 1 页 共 51 页中国计算机学会推荐国际学术刊物计算机科学理论一、A类序号刊物简称刊物全称出版社网址1.TALG ACM Transactions on Algorithms ACM /2.SICOMP SIAM Journal on Computing Society for Industrial andApplied Mathematics/sicomp二、B类序号刊物简称刊物全称出版社网址1.TOCL ACM Transactions on Computational Logic ACM /rmation & Computation Elsevier /locate/ic3.TIT IEEE Transactions on Information Theory IEEE /portal/site/mainsite/menuitem.81 8c0c39e85ef176fb2275875bac26c8/index.jsp?&pNa me=corp_level1&path=pubs/transactions&file=tit.x ml&xsl=generic.xsl&4.TCS Theoretical Computer Science Elsevier /locate/tcs5.Formal Aspects of Computing Springer /content/102822/6.Acta Informatica Springer /content/100460/7. MSCS Mathematical Structures in Computer Science Cambridge University /action/displayJournal?jid=MSC8.Algorithmica Springer /link.asp?id=100117putational Complexity Springer /content/101499/10.Journal of Complexity Birkhäuser Basel第 2 页 共 51 页11. JSL Journal of Symbolic Logic Association forSymbolic Logic/journals-journal.html12. APAL Annuals of Pure and Applied Logic Elsevier /wps/find/journaldescription.cws_home/505603/description#description13.Discrete Applied Mathematics Elsevier /wps/find/journaldescription.cws_home/505609/description#description14. JSC Journal of Symbolic Computation Elsevier /wps/find/journaldescription.cws_home/622902/description#description LMCSLogical Methods in Computer Science /index.php三、C类序号刊物简称刊物全称出版社网址1. IJFCS International Journal of Foundations ofComputer ScienceWorld Scientific /~ijfcs/2.Discrete Event Dynamic Systems – Theory andApplications Springer /math/applications/journal/106263.Formal Methods in System Design Springer /content/100266/4. HOSC Higher-Order and Symbolic Computation Springer /computer/foundations/journal/109905.Archive for Mathematical Logic Springer /math/journal/153第 3 页 共 51 页中国计算机学会推荐国际学术会议(计算机科学理论)一、A类序号会议简称会议全称出版社网址1. STOC ACM Symposium on Theory of Computing ACM /stoc/2. FOCS IEEE Symposium on Foundations of ComputerScienceIEEE /二、B类序号会议简称会议全称出版社网址1. LICS IEEE Symposium on Logic in Computer Science IEEE http://wwwrmatik.hu-berlin.de/lics/2. ICALP International Colloquium on Automata, Languagesand Programming EuropeanAssociation forTheoreticalComputer Science(EATCS)http://icalp09.cti.gr/index.php/Main/HomePage3. SCG ACM Symposium on Computational Geometry ACM /4. SODA ACM/SIAM Symposium on Discrete Algorithms SIAM /meetings/da07/5. SPAA ACM Symposium on Parallel Algorithms andArchitecturesACM /SPAA/ 6. CCC IEEE Conference on Computational Complexity IEEE /jrogers/Complexity/第 4 页 共 51 页第 5 页 共 51 页7. CSFW IEEE Computer Security Foundations WorkshopIEEE /CSFWweb/8. DATE IEEE/ACM Design, Automation & Test in Europe Conference IEEE/ACM /9. ISIT IEEE Symposium on Information Theory IEEE /10. CP International Conference on Principles & Practice of Constraint ProgrammingSpringer http://www.cs.mu.oz.au/cp2008/11. TACAS Tools and Algorithms for the Construction and Analysis of SystemsSpringer /~tacas2008/ 12. RTA Rewriting Techniques and Applications Springer http://rewriting.loria.fr/rta/13. TLCA Typed Lambda Calculi and Applications Springer http://www.lsv.ens-cachan.fr/rdp07/tlca.html 14. CSL Computer Science LogicSpringer/oucl/conferences/CSL05/15. MFPS Mathematical Foundations of Programming SemanticsElsevier /~mfps/ 16. TCS IFIP International Conference on Theoretical Computer Science Springer Science and Business Media http://bioinformatics.bio.disco.unimib.it/tc1/ 17. STACS International Conference on Theoretical Aspects of Computer ScienceSpringer http://www.lif.univ-mrs.fr/STACS06/ 18. MFCS Mathematical Foundations of Computer ScienceSpringerhttp://www.mfcs.sk/19. FCT International Symposium Fundamentals of Computation Theory Springer http://www.conferences.hu/fct2007/ 20.FSTTCSConference on Foundations of Software Technology and Theoretical Computer Science IARCS, the Indian Association for/第 6 页 共 51 页Research in Computing Science21.ICLPInternational Conference on Logical ProgrammingSpringerhttp://iclp08.dimi.uniud.it/三、C 类序号会议简称会议全称出版社网址1. CGOInternational Symposium on Code Generation and Optimization IEEE/ACM / 2. PEPMPartial Evaluation and Program ManipulationACM/PEPM083. CSB IEEE Computational Systems Bioinformatics Conference IEEE /4. FoSSaCS International Conference on Foundations of Software Science and Computation Structures Springer http://fossacs08.pps.jussieu.fr/5. iFM integrated Formal MethodsSpringer/ifm2007/6. APLAS Asian Symposium on Programming Languages and SystemsSpringer /~grama/APLAS2008/ 7. ATVA International Symposium on Automated Technology for Verification and AnalysisSpringer http://pswlab.kaist.ac.kr/atva2008/ 8. TAMC Theory and Applications of Models of ComputationSpringer /tamc2008/page/main.jsp 9. FORMATSInternational Conference on Formal Modeling and Analysis of Timed SystemsSpringer http://formats08.inria.fr/ 10.HSCC International Conference on Hybrid Systems: Computation and ControlACM, Springer/Springer /11.CIAA International Conference on Implementation andApplication of AutomataSpringer /spin08/ 12.SPIN International SPIN Workshop on Model CheckingSoftwareSpringer /vmcai08/Conference on Verification, Model13.VMCAI InternationalChecking, and Abstract InterpretationSpringer http://www-sop.inria.fr/oasis/FMCO/fmco08.htmlSymposium on Formal Methods for14.FMCO InternationalComponents and ObjectsConference on Formal Methods forSpringer http://discotec08.ifi.uio.no/FMOODS08/ 15.FMOODS InternationalOpen Object-based Distributed SystemsIEEE /memocontest08/International Conference on Formal16.MEMOCODE ACM/IEEEMethods and Models for Co-Design第 7 页 共 51 页中国计算机学会推荐国际学术刊物计算机体系结构一、A类序号刊物简称刊物全称出版社网址1. TOCS ACM Transactions on Computer Systems ACM /2. TOPLAS ACM Transactions on Programming Languagesand Systems ACM http:///~toplas/3. TPDS IEEE Transactions on Parallel and DistributedSystemsIEEE /tpds/4. TOC IEEE Transactions on Computers IEEE /portal/web/tc二、B类序号刊物简称刊物全称出版社网址1. TACO ACM Transactions on Architecture and CodeOptimizationACM /2. TOS ACM Transactions on Storage ACM /3. CAL IEEE Computer Architecture Letters IEEE /~tcca/4. JSA Journal of Systems Architecture Elsevier /locate/sysarc/5. JPDC Journal of Parallel and Distributed Computing Elsevier /wps/find/journaldescription.cws_home/622895/description6.Parallel Computing Elsevier /locate/parco第 8 页 共 51 页7.Performance Evaluation Elsevier /wps/find/journaldescription.cws_home/505618/description#description8. TECS ACM Transactions on Embedded ComputingSystemsACM 9. TJS The Journal of Supercomputing Springer /link.asp?id=100302三、C类序号刊物简称刊物全称出版社网址1. FGCSFuture Generation Computer Systems Elsevier /wps/find/journaldescription.cws_home/505611/description#description2.Microprocessors and Microsystems Elsevier /locate/micpro3.Design Automation for Embedded System Springer /link.asp?id=1002554.Concurrency and Computation: Practice andExperience John Wiley & Sons,Ltd/journal/117946197/grouphome/home.html第 9 页 共 51 页第 10 页 共 51 页中国计算机学会推荐国际学术会议(计算机体系结构)一、A 类序号会议简称会议全称出版社网址1. ISCA International Symposium on Computer ArchitectureACM SIGARCH, IEEE TCCA /pubs/contents/proceedings/series/isca/2. MICRO MICRO IEEE, ACM SIGMICRO/3. HPCA High-Performance Computer ArchitectureIEEE /~hpca/二、B 类序号会议简称会议全称出版社网址1. ASPLOS Architectural Support for Programming Languages and Operating SystemsACMSIGOPS/SIGARCH/SIGPLAN /pubs/contents/proceedings/serie s/asplos/2. FAST Conference on File and Storage Technologies USENIX /events/bytopic/fast.html3. PACT Parallel Architectures and Compilation Techniques IEEEACM SIGARCH/ 4. PPoPPPrinciples and Practice of Parallel Programming ACM SIGPLAN/ppopp08/5. SPAAACM Symposium on Parallelism in Algorithms and ArchitecturesACM /~spaa/2007/6. SIGMETRICS International Conference on Measurement andModeling of Computer Systems ACMSIGMETRICS/~sigmet08/7. RTSS Real-Time Systems Symposium IEEE /8. ICCD International Conference on Computer Design IEEE /9. MSST Mass Storage Systems and Technologies IEEE /2007/10. HOTCHIPS A Symposium on High Performance Chips IEEE /11. HPDC High-Performance Distributed Computing IEEE /hpdc2007/12.CLUSTER Cluster Computing IEEE /13.IPDPS International Parallel & Distributed ProcessingSymposiumIEEE 14.EuroSys EuroSys ACMhttp://www.gsd.inesc-id.pt/conference/EuroSys2007/15.ICDCS International Conference onDistributed Computing SystemsIEEE http://www.eecg.utoronto.ca/icdcs07/16.ICPP International Conference on Parallel Processing IEEE /~icpp2007/17.Euro-Par European Conference on Parallel and DistributedComputingSpringer http://europar2007.irisa.fr/18.FPGA International Symposium on Field-ProgrammableGate ArraysACM SIGDA /~kati/fpga2008/19.LCTES Conference on Language, Compiler and ToolSupport for Embedded SystemsACM SIGEBD /第 11 页 共 51 页第 12 页 共 51 页三、C 类序号会议简称会议全称出版社网址1. FPT International Conference on Field-Programmable TechnologyIEEE http://www.kameyama.ecei.tohoku.ac.jp/icfpt07/2. HiPC International Conference on High Performance ComputingIEEE, ACM SIGARCH / 3. ISCAS International Symposium on Circuits and SystemsIEEE/4. HiPEAC International Conference on High Performance and Embedded Architectures and CompilersSpringer /conference/ 5.ICPADSInternational Conference on Parallel and Distributed SystemsIEEE .au/conferences/icpads2008/?print_friendly=true6. CCGRID Cluster Computing and the GridIEEE http:// ccgrid07.lncc.br 7. GRID International Conference on Grid ComputingIEEE/ACM/8. ANCS Architectures for Networking and Communications Systems ACM/IEEE /9. CGOCode Generation and Optimization IEEE/ACM /10. HotOS Workshop on Hot Topics in Operating Systems USENIX/events/hotos03/11.CASESInternational Conference on Compilers, Architectures, and Synthesis for Embedded SystemsACM http://www.irit.fr/recherches/ARCHI/MARCH/CAS ES2007/index.htm 12. ICS International Conference on SupercomputingACM/13.CODES+ISSS International Conference on Hardware/SoftwareACM, IEEE /Codesign & System Synthesis14.RTAS Real-Time and Embedded Technology andIEEE / Applications Symposium15.EMSOFT International Conference on Embedded Software ACM /16.VEE Virtual Execution Environments ACM /17. FCCM Field-Programmable Custom Computing Machines IEEE /18.FPL Field Programmable Logic and Applications IEEE http://ce.et.tudelft.nl/FPL/19. SC International Conference for High PerformanceIEEE /Computing, Networking, Storage, and Analysis20. HotInterconnects Symposium on High-Performance Interconnects IEEE /第 13 页 共 51 页中国计算机学会推荐国际学术刊物计算机网络一、A类序号刊物简称刊物全称出版社网址IEEE /net/1. TON IEEE/ACM Transactions on Networking ACM,IEEE /dl/jrnal/jsac.html2. JSAC IEEE Journal of Selected Areas inCommunications3. TOIT ACM Transactions on Internet Technology ACM /二、B类序号刊物简称刊物全称出版社网址1. TMC IEEE Transactions on Mobile Computing IEEE /portal/web/tmc2. 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KBS Knowledge-BasedSystems ELSEVIER /locate/knosys8.Neurocomputing ELSEVIER /locate/neucom9.NCA Neural Computing & Applications Springer /content/102827/10.NPL Neural Processing Letters Springer /content/100321/11.PRL Pattern Recognition Letters ELSEVIER /locate/patrec12.PAA Pattern Analysis and Applications Springer /content/ 103609/第 21 页 共 51 页13.Connection Science Taylor & Francis /journals/tf/09540091.html14.AIM Artificial Intelligence in Medicine Elsevier /wps/product/cws_home/15.DSS Decision Support Systems Elsevier /wps/product/cws_home/16.IVC Image and Vision Computing ELSEVIER /science/journal/andApplications Springer /sgw/cda/ Vision17.MachineImageAnalysis Elsevier /wps/product/cws_home/ 18.MedicalComputing Springer /sgw/cda/ 19.NaturalComputing Springer /sgw/cda/20.Soft21.ESWA Expert Systems with Applications ELSEVIER /wps/product/cws_home/22.EAAI Engineering Applications of Artificial Intelligence ELSEVIER /wps/find/journaleditorialboard.cws_home/975/editorialboardSystems Blackwell / 23.ExpertWorld Scientific .sg/ijprai/ijprai.shtml24.IJPRAI International Journal of Pattern Recognition &Artificial Intelligence25.IJIS International Journal of Intelligent Systems Wiley InterScience /journal/36062/home?CRETRY=1&SRETRY=026.IJNS International Journal of Neural Systems World Scientific .sg/journals/ijns/27.AAI Applied Artificial Intelligence Taylor & Francis /journals/tf/08839514.html28.Cybernetics and Systems Taylor & Francis /journals/tf/01969722.htmlCommunications ELSEVIER /locate/specom29.Speechputer Speech and Language ELSEVIER /locate/csl31.WIAS Web Intelligence and Agent Systems IOS http://www.iospress.nl/site/html/15701263.html32.Fuzzy Sets and Systems ELSEVIER /wps/product/cws_home/第 22 页 共 51 页33.IEE Proceedings: Vision, Image and Signal IEEE /xpl/World Scientific .sg/ijcia/ijcia.shtml34.IJCIA International Journal of ComputationalIntelligence and ApplicationsTaylor & Francis /journals/tf/0952813X.html35.JETAI Journal of Experimental and Theoretical ArtificialIntelligence36.International Journal of Uncertainty, FuzzinessWorld Scientific /ijufks/ijufks.shtml and KBS37.IJAES International Journal of Applied Expert Systems Taylor Granham /~acc025/ijaes.html38.Artificial Life MIT Press /journal-home.tcl?issn=1064546239.AAMAS Autonomous Agents and Multi-Agent Systems Springer /content/102852/中国计算机学会推荐国际学术会议(人工智能与模式识别)一、A类序号会议简称会议全称出版社网址Morgan Kaufmann 1.IJCAI International Joint Conference on ArtificialIntelligence2.ICCV International Conference on Computer Vision IEEE /3.ICML International Conference on Machine Learning ACM /conferences/icml2007/IEEE /4.CVPR IEEE Conference on Computer Vision and PatternRecognition5.AAAI AAAI Conference on Artificial Intelligence AAAI 第 23 页 共 51 页二、B类序号会议简称会议全称出版社网址MIT Press 6.NIPS Annual Conference on Neural InformationProcessing SystemsMorgan Kaufmann /7.KR International Conference on Principles ofKnowledge Representation and ReasoningACL /8.ACL Annual Meeting of the Association forComputational LinguisticsSpringer /1.AAMAS International Joint Conference on AutonomousAgents and Multi-agent Systems2.ECCV European Conference on Computer Vision Springer http://eccv2008.inrialpes.fr/3.ECML European Conference on Machine Learning Springer 4.ECAI European Conference on Artificial Intelligence IOSPress http://www.ece.upatras.gr/ecai2008/Springer /colt2007/5.COLT Annual Conference on Computational LearningTheoryAUAI /6.UAI International Conference on Uncertainty inArtificial IntelligenceAAAI /7.ICAPS International Conference on Automated Planningand SchedulingSpringer /8.ICCBR International Conference on Case-BasedReasoning第 24 页 共 51 页第 25 页 共 51 页9. COLING International Conference on Computational LinguisticsACM / 10. ALT International Conference on Algorithmic Learning Theory Springer http://www-alg.ist.hokudai.ac.jp/~thomas/ALT07/alt 07.jhtml 11.ILPInternational Conference on Inductive Logic ProgrammingSpringer /conferences/ilp2007/ 12. ICRA IEEE International Conference on Robotics and Automation IEEE /13. CogSciCognitive Science Society Annual Conference Psychology Press /cogsci.html 14. IJCARInternational Joint Conference on Automated Reasoning15.EMNLPConference on Empirical Methods in Natural Language Processing ACL /~yarowsky/SIGDAT/emnlp06.html三、C 类序号 会议简称 会议全称出版社网址16. PRICAI Pacific Rim International Conference on Artificial IntelligenceSpringer / 17.NAACLThe Annual Conference of the North American Chapter of the Association for Computational LinguisticsNAACL /meetings/hlt-naacl07/1.ACCVAsian Conference on Computer VisionSpringerhttp://www.am.sanken.osaka-u.ac.jp/ACCV2007/2. 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ASEAnnals of Software Engineering Springer /computer/swe/journal/1048 05.Formal Aspects of Computing Springer London /content/1433-299X/6.IEE Proceedings – Software the Institution ofEngineering andTechnology/Publish/Journals/Profjourn/Proc/sen/7.Journal of Software Maintenance: Research and John Wiley & Sons, /cgi-bin/jhome/5第 28 页 共 51 页Practice Ltd3918. SPESoftware: Practice and Experience John Wiley & Sons,Ltd/cgi-bin/jhome/17529. IJSEKE International Journal on Software Engineeringand Knowledge Engineering World Scientific .sg/journals/ijseke/ijseke.shtml10. JSS Journal of Systems and Software Elsevier Science /wps/find/journaldescription.cws_home/505732/description#description11. JPL Journal of Programming Languages Chapman & Hall http://elib.cs.sfu.ca/Collections/CMPT/cs-journals/P-ChapmanHall/J-ChapmanHall-JPL.html12. SCP Science of Computer Programming Elsevier Science /wps/find/journaldescription.cws_home/505623/description#description13. 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关于汽车造型设计的英文文献Developing a vehicle is an arduous process of design and evaluation, trial and error - constant improvement and adaptation. Initial design concepts go through a range of stages to bring them closer to realisation and modelling is key to evaluating a design at each stage.Modelling can take several forms. Traditionally, clay models have been used at various scales to help understand and resolve the form and proportions of a vehicle. To varying degrees, this has been supplemented, sometimes even replaced, by CAD modelling. Whilst clay is still a medium used to evaluate predominantly visual characteristics, CAD systems can additionally help evaluate other factors such as aerodynamics, impact scenarios and other physical considerationsClay ModellingClay modelling is one of the most established 3D visualisation techniques used in the automotive industry.Clay modelling is one of the oldest a nd most traditional methods used in car design. Studios are divided in their preferences relating to CAD or clay but many believe that it remains one of the best ways to visualise developing designs in three-dimensionsGM modellers use renderings, sketches and tape drawings as reference to create a 1/4 scale half model. Using a mirror in this way enables modellers to produce results more quickly. With full proportioned models, substantial time is spent balancing one side with the other.In this view it is possible to see the rig beneath the clay. In the rear wheel arch the base can be seen along with the core of light blue modelling foam.Clay has been used since the earliest stages of car design and emphasises the strong links between three-dimensional automotive styling and sculpture. Working on the form of a vehicle in clay is a very tight form of sculpture, reliant upon a expert eye and an advanced perception of form and proportion.Clay modellers work on the Holden FJ many decades ago. This practice is still common today. The modellers shown here are using, amongst other things, gauges to measure height and depth (to balance both sides) and profile guides to ensure the model corresponds to the design.Manual MethodWith the rig configured, clay is applied. Using a system of '10-lines', reference points are transferred from the drawings to the model. Clay is built up to match the profile from the drawings and is then added to fill out all the proportions.From here, designers can either rigidly follow their drawings, creating guides and templates to help develop the model from the package drawing, or they can begin to experiment and develop the form freely. The beauty of automotive styling clay is its ability to be reworked and continually adjusted. This freedom of form development is rarely matched by computer.Chevrolet designers work on a full scale Corvette model. Dynoc has been applied to give the impression of real glass and upper body paintwork. Real wheels add to the effect whilstdesigners make final adjustments to the surfacesAutomated MethodInstead of designers and modeler labouring over a clay for weeks, many car firms are now in the habit of sending a CAD model directly to a specialist milling machine. The machine can precisely mill out the form and proportions of the 3D computer design in a relatively short period of time, although humans may still be called in to finish the surfaces or make slight adjustments. Although most aspects of a design can be resolved on computer, especially with the aid of virtual reality evaluation, almost all companies will still produce a full size clay towards the end of the process. The cold light of day can produce suprie that manufacturers want to be aware of before a vehicle enters tooling and production phases.Once a vehicle is completed, one of several next steps may be taken. If the vehicle is to be shown as a concept, it might be painted and detailed but will more likely become the template for 'hard modellers' to use to create a production look-a-like with individual panels, real glass and details as well as an interior.If the vehicle is ready for production, it will usually be scanned using 3D digital equipment which will in turn create a new CAD wireframe model. This will be tweaked by CAD specialists to remove imperfections before being passed on to engineers who will begin the arduous process of creating panels, componentry, drivetrain and propulsion based on the design.Of course, if a vehicle was simply an in-house research project, as many are, it may never be seen by the public; in fact the clay may be reused in later projects. These Holden models give an idea of the processes involved and their purpose. Both vehicles are full-size clays that give an accurate representation of the proposed vehicles. Applying a neutral coloured paint and sitting the model outside in a typical working environment is about the most accurate way to assess a concept's visual impact without actually building it.The vehicle in the upper image appears to be in the later stages of development. Details such as light graphics, shut lines and Dynoc to imitate glass allow designers to quickly and effectively evaluate the model.SketchingThe earliest stage of the design process is the creation of initial concept sketches. The sketches are a relatively quick way to visualise ideas, themes and styles. In a typical design studio, a team of designers may be asked to submit initial ideas for a vehicle which will subsequently be narrowed down and further developed. Often, a dozen or so initial ideas will be evaluated, with further development of two or three before a final solution is chosen. At each stage, designers whose work is not chosen will be redeployed to assist in the development of the chosen ideas or posted to another project. Typically, one team is responsible for the interior whilst another takes charge of the exterior.As the sketches develop, more time will be spent on creating resolved an accurate views. These drawings will form the basis for a simple package and, in the later stages, will be developed alongside a scale model clay or CAD model.The interior rendering above is a hand-drawn, highly finished view of the dashboard ofthe Avantime. Following initial ideas, after resolving the view, proportions and perspective a rendering like this is produced. It is drawn lightly in pencil before colour is added using markers. This illustration uses mainly light yellows and cool greys. The metallic effect is achieved by banding white with very light blues. Additional light, shade and line definition is added with coloured pencil. Light mapping lines (as visible vertically on the driver's door) are sometimes used to help illustrate changes in form in addition to colour and tone. Image courtesy & ©Renault.This illustration of Ford's Mustang GT Coupe concept has most likely been developed from a sketch and then reworked in a programme such as Adobe Photoshop. The image is particularly powerful because of its crisp lines and blurred movement effect. Creating this effect is arduous with traditional airbrush techniques but is far more straight-forward in Photoshop. First of all, an image is scanned in and cleaned up, removing unwanted elements and adjusting the brightness and contrast. To create the airbrushed effects, a path is drawn around an area - accurately demarcating the section to be airbrushed.then, the path is made into a selection marquee, a new layer is created and the desired brush tool can be used within the specific area. This can be repeated and adjusted until the right result is achieved. Each section in turn is treated this way; this is not the only process involved, but it is the most significant.CAD ModellingComputers are now used to accelerate virtually every aspect of vehicle development. Computer aided design (CAD) modelling allows designers and engineers to resolve increasingly large amounts of a vehicle before even the first model is made.CAD - Computer Aided DesignComputers have been used in the design of cars for many years. The automotive industry has been one of the leading forces for CAD development alongside aerospace and the military. In fact, some years ago, the British military research unit - DERA - and Ford initiated a joint development programme to investigate new computer design technologies.As with all the things in the world of computers, things started big and expensive and eventually became cheaper and smaller. Although design studios may now have large CAD walls to visualise developing vehicles, it is also possible to work on the design of a car from a single PC. There are a few, core systems and programmes used in the automotive industry. In this section, we look at the key features of each ranging from specifications to usage.Key CAD Programmes1 Alias AutoStudioAlias began in the early 80's in Toronto as 'Alias Research'. In 1995, Silicon Graphics (SGI) bought Alias Research of Toronto and Wavefront Technologies of Santa Barbara forming 'Alias Wavefront'. In 2003, twenty years after its inception, the company took the name 'Alias'.Alias produces the leading automotive design software AutoStudio. AutoStudio is a programme to design and visualise full scale automotive projects. It is accompanied by a range of hardware and software products including sketching tools and advanced rendering and visualisation functions. AutoStudio customers include: BMW, Fiat, Ford, General Motors, Honda and Italdesign.What it doesAlias AutoStudio allows a user to access a range of features which assists them in everything from concept sketches to Class-A surfacing.Principle FeaturesSketchingConcept DesignCloud Data ProcessingAdvanced ModellingAdvanced Surface EvaluationVisualisationCAD IntegrationIn BriefUser InteractionA user interface that enables creativity and efficiencySketchingA complete set of tools for 2D design work tightly integrated into a 3D modelling environment2D / 3D IntegrationTake advantage of your sketching skills throughout the design process. Add details and explore ideas quickly by sketching over 3D forms before taking the time to model them.Modelling (Modeling)Industry-leading, NURBS-based surface modeller.Advanced Automotive Surfacing ToolsSurface creation tools that maintain positional, tangent or curvature continuity between surfaces - for high quality, manufacturable results.Reverse EngineeringTools for importing and configuring cloud data sets from scanners for visualising, as well as extracting feature lines and building surfaces based on cloud data.Evaluation ToolsTools to analyse and evaluate the styling and physical properties of curves and surfaces interactively, whilecreating and editing geometry.RenderingCreate photorealistic images using textures, colours, highlights, shadows, reflections and backgrounds. AnimationAnimations can be used for high quality design presentations, design analysis of mechanisms, motion and ergonomic studies, manufacturing or assembly simulation.Data IntegrationSupport for industry-standard data formats and a wide range of peripheral devices.2 Alias SurfaceStudioAlias SurfaceStudio™ is a technical surfacing product designed for the development of Class-A surfaces. It offers advanced modeling and reverse engineering tools, real-time diagnostics and scan data processing technology. SurfaceStudio is comprised of a complete suite of tools for creating surface models to meet the high levels of quality, accuracy and precision required in automotive styling.Key FeaturesCloud data processingDirect, Patch-based modellingProcedural, curve based modellingReal-time diagnostic feedbackDynamic surface evaluationCAD integrationIn BriefUser InteractionA user interface that enables creativity and efficiencySketchingA complete set of tools for 2D design work tightly integrated into a 3D modelling environmentModelling (Modeling)Industry-leading, NURBS-based surface modeller.Advanced Automotive Surfacing ToolsSurface creation tools that maintain positional, tangent or curvature continuity between surfaces - for high quality, manufacturable results.Reverse EngineeringTools for importing and configuring cloud data sets from scanners for visualising, as well as extracting feature lines and building surfaces based on cloud data.Evaluation ToolsTools to analyse and evaluate the styling and physical properties of curves and surfaces interactively, while creating and editing geometry.Data IntegrationSupport for industry-standard data formats and a wide range of peripheral devices.3 ICEM SurfICEM Surf is an industry standard in automotive A-Class surfacing. In the final stages of a vehicle design, ICEM Surf is used to clarify and fully resolve vehicle surfaces before beginning tool and die fabrication. Designers can use ICEM Surf to create extremely accurate computer visualisations, through static and dynamic renderings as well as 3D virtual reality views using stereo mode.In order to facilitate end-stage design evaluation and development, ICEM Surf can process data from digitised physical models which can in turn be modified, corrected, balanced and otherwise resolved as required.Key FeaturesIt is possible to create and modify aesthetic free-form surfaces directly and in real-time'Virtual clay modelling' can be performed by direct modelling and diagnosis of point clouds or facet data without prior surface generationDirect surface modelling is possible through stereo, shaded display and real-time renderer modesSafety analyses (Head Impact Diagnoses) allow for early detection and resolution of possible safety issues ICEM Surf is used for Class A surfaces for automotive exterior and interior design as well as consumer goods, structural surfaces (Body-in-White) and free form surfaces in tool and die design.Digitised physical model or CAD data can be used to create free-form surfaces with automated smoothing and approximationSurface models can be quickly developed from ordered or unordered (point clouds) digitised dataFully feasible, high-quality aesthetic designs can be developed quicklWith the 'Global Modelling' feature, whole detailed models can be modified in total, interactively and dynamicallyThe rendering process is independent; rendering can be computed in the background or remotely whilst work continues4 ICEM StyleStyle is ICEM's designer-orientated vehicle creation programme. Style builds on ICEM's existing expertise in the field of advanced CAD surfacing and provides the user with a more direct transition from the design stages through to advanced surface preparation and ultimately tooling.Key to the appeal of ICEM Style is the programme's abilities for real-time visualisation. "No longer arethird-party, stand-alone visualisation products required to visualise the complete design, nor a time-consuming wait for a static rendering to be generated".The nature of ICEM Style's interoperability with the down-stream ICEM Surf means that input from designers can be taken from tablet sketches, through preparatory stages and full scale models ready in anticipation of final A-Class surfacing. This simplifies the flow of work, eliminating certain transition stages which in turn saves time and translation discrepencies. ICEM Surf can then be used earlier to resolve a design and finalise production quality surfaces5 CATIACATIA is an integrated suite of Computer Aided Design (CAD), Computer Aided Engineering (CAE), and Computer Aided Manufacturing (CAM) applications for digital product definition and simulation. It is ideal for true integration of people, tools, methodologies and resources within an enterprise. CATIA provides advanced 3D Product Lifecycle Management (PLM) solutions for collaborative product development.Made up from many different elements, or 'products', CATIA can perform a range of tasks depending upon its configuration. Within the automotive industry these tasks include Class A surfacing, body-in-white template design, body-in-white fastening design and many more. Some of the main applications are listed below. Shape design talks directly to people’s heart. Successful products in the market are usually those with designs that elicit positive emotional response from their consumers. 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Interior and Exterior TrimAutomotive Interior process is handled by the whole Dassault Systemes V5 line of product, covering all aspects of automobile plastic parts conception from design to manufacturing.Some of the Available CATIA Automotive ProductsAutomotive Body-In-White Fastening 3 (ABF) is dedicated to the design of Automotive Body In White Fasteners. It supports Welding technologies and mechanical clinching, along with Adhesives, Sealers, and Mastics. From eletronics to automotive and consumer packaged goods, design plays an important part in determining product success in the market. How do you deliver aesthetically pleasing and functionally superior products? CATIA covers all product shape design needs from industrial design to Class A, enhancing designer abilities to produce any kind of complex shapesAutomotive Body in White Templates is an advanced product that uses unique skilled features to boost body in white design phase productivity. These powerful features allow body in white design teams to quickly create or modify a car body in an associative styling and engineering context. For instance, a user can create an associative shape, place welding points on it, and then assemble the two parts with unprecedented rapidity.Automotive Class A Optimizer 3 (ACO) offers extended tools to create and model aesthetic and ergonomic shapes to highest Class A quality. Powerful tools like using global surface modelingtechniques, Shape Modeling, and global feature creation methods, Global Flange, on top of the ACA product speed up the total development styling process.感谢您使用本店文档您的满意是我们的永恒的追求!(本句可删)------------------------------------------------------------------------------------------------------------。
第28卷㊀第3期2024年3月㊀电㊀机㊀与㊀控㊀制㊀学㊀报Electri c ㊀Machines ㊀and ㊀Control㊀Vol.28No.3Mar.2024㊀㊀㊀㊀㊀㊀基于特征模型的永磁同步直线电机自适应控制曹阳,㊀郭健(南京理工大学自动化学院,江苏南京210094)摘㊀要:为了解决永磁同步直线电机系统的参数不确定性㊁建模不确定性及饱和非线性等问题,提出一种基于特征模型的自适应控制器㊂依据特征模型理论描述永磁同步直线电机系统,采用自适应和鲁棒控制方法设计控制器㊂建立永磁同步直线电机的特征模型,并给出具体建立步骤,使得控制器设计变得简单,易于工程实现㊂通过设计参数自适应律对系统未知特征参数进行估计,可实现对系统模型的精确补偿,同时在控制器中添加带有误差积分的鲁棒控制项,提高系统对不确定参数及未知干扰的鲁棒性㊂此外,由于饱和特性的存在,导致控制器产生windup 问题,给系统的控制性能和稳定性造成不利影响㊂因此,该控制器中还带有抗饱和控制项,能够提升系统的抗饱和能力㊂最后,通过对比实验验证了所提控制器的有效性㊂关键词:永磁同步直线电机;参数不确定性;建模不确定性;饱和非线性;特征模型;自适应控制;抗饱和DOI :10.15938/j.emc.2024.03.013中图分类号:TM351文献标志码:A文章编号:1007-449X(2024)03-0131-10㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀收稿日期:2022-07-04基金项目:国家自然科学基金(61673219)作者简介:曹㊀阳(1993 ),男,博士研究生,研究方向为电机系统分析与控制;郭㊀健(1974 ),男,博士,教授,博士生导师,研究方向为智能系统与智能控制㊁机器人系统㊁高精度电机控制等㊂通信作者:郭㊀健Adaptive control of permanent magnet synchronous linear motorbased on characteristic modelCAO Yang,㊀GUO Jian(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)Abstract :To address the problems of parameter uncertainty,modeling uncertainty and saturation nonlin-earity in the permanent magnet synchronous linear motor system,an adaptive controller based on charac-teristic model was proposed.A characteristic model was used to describe the permanent magnet synchro-nous linear motor system,and the controller was designed using adaptive and robust control methods.The characteristic model was established based on the system dynamics and parameters,and the specific steps were presented.This simplifies the controller design and facilitates the engineering implementation.An online parameter adaptation law was employed to estimate the unknown characteristic parameters of the system and achieve accurate compensation for the system model.Furthermore,an integral-type robust control term was incorporated into the controller,which improves the robustness of the system against un-certain parameters and unknown disturbances.In addition,the saturation nonlinearity leads to the windup problem in the controller,which has adverse effects on the control performance and stability of the sys-tem.Therefore,an anti-windup control scheme was devised for the controller,which can enhance the an-ti-saturation ability of the system.Finally,comparative experiments with other control methods were con-ducted to verify effectiveness of the proposed controller.Keywords:permanent magnet synchronous linear motor;friction nonlinearity;saturation nonlinearity;ar-mature mass variation;characteristic model;adaptive control;anti-windup0㊀引㊀言相比于旋转同步电机,永磁同步直线电机(per-manent magnet synchronous linear motor,PMSLM)具有更高的推力密度和更快的动态响应,特别适用于对速度和精度要求较高的场合,已被广泛应用在高精密加工㊁轨道交通传输等现代工业领域[1-2]㊂但是由于采用直接驱动方式,PMSLM控制系统对参数摄动及扰动等因素变得更加敏感[3],这会严重影响系统的控制性能㊂因此,保证PMSLM系统的高精度跟踪性能与抗扰动能力十分重要,对提高机床加工精度㊁提升交通传输效率具有重要的意义㊂针对PMSLM系统的高精度跟踪问题,国内外已有众多学者对其进行了研究㊂文献[4]设计了一种带模型参考自适应观测器的预测电流控制策略,经过实验验证该控制策略可以实现对速度进行在线准确辨识,进而提高电流的跟踪性能㊂文献[5]利用扩张状态观测器和非线性状态误差反馈对PMSLM的自抗扰控制器进行优化,提高了系统的动态响应性能和抗干扰能力㊂文献[6]提出一种基于周期性扰动学习的自适应滑模控制方法,采用滑模控制确保PMSLM系统对不确定性因素具有较强的鲁棒性㊂文献[7]在系统模型反馈线性化的基础上,将Hɕ鲁棒控制方法与D-K迭代法相结合,提高了系统对不确定性因素影响的抑制能力㊂姚斌等[8]提出一种自适应鲁棒控制方法,所开发的控制器成功应用在多种控制系统中[9-11]㊂为了解决非光滑饱和非线性的影响,文献[12]构造了一种新的近似饱和模型,该模型能够以任意规定的精度平滑地逼近实际饱和㊂此外,通过添加积分器技术,使得控制器可以消除与表面误差和边界层误差有关的耦合项㊂但是该方法在控制器的设计中需要对虚拟控制量重复微分,如果系统模型阶数高,会增加设计的复杂性㊂文献[13]提出一种考虑LuGre 摩擦的自适应鲁棒控制方法,针对陀螺框架伺服系统未知惯量和阻尼系数㊁LuGre摩擦参数不确定性及未知外部干扰上界,设计参数更新律对其进行估计,该控制律提高了系统的跟踪精度并通过仿真结果验证了所提方法的有效性㊂但该方法需要被控对象的精确数学模型,另外估计的未知参数过多,多个自适应参数需要反复调试,增加了实际应用时的难度㊂自适应鲁棒控制可以估计系统未知参数,但如果系统模型复杂㊁未知参数多㊁某些状态不可测时,控制器的设计将面临巨大挑战㊂针对这些问题,吴宏鑫院士等[14-15]提出特征建模的思想,特征模型一般用一阶或二阶差分方程/微分方程来描述,有关信息都压缩到几个特征参数中,并不丢失原有的信息㊂特征模型建立的形式比原对象动力学方程简单,为实际复杂系统的建模问题提供了一条途径㊂文献[16]基于永磁同步电机的特征模型,设计一个以非线性黄金分割自适应控制为主的控制方案㊂通过安排过渡过程和特征模型参数的在线辨识,该控制方案实现了控制器参数的在线自适应调节㊂文献[17]将特征建模方法推广到具有惯性变化的齿轮传动伺服系统中,设计了一个自适应二阶离散终端滑模控制器,并实现了有限时间有界性㊂然而上述基于特征模型所设计的控制器没有进行抗饱和(anti-windup)研究㊂windup现象是指由于被控对象的输入限制,使得被控对象的实际输入与控制器的输出不等,引起系统闭环响应变差(如超调变大,调节时间变长,甚至使系统失去稳定)的现象㊂实际的PMSLM是个物理限制系统,转速控制器的输出必须限定在一定的范围内,使得实际电机的控制输入量不能大于一个预先设定值㊂当控制器输出受到饱和限制时,特别是含有积分项的控制信号仍然增加时,就会出现windup现象,使实际闭环系统的性能下降,因此对PMSLM系统设计抗饱和控制是有必要的[18-19]㊂基于上述分析,针对PMSLM系统存在的参数不确定性㊁建模不确定性及饱和非线性等问题,提出一种基于特征模型的抗饱和自适应鲁棒控制器(an-ti-windup adaptive robust control based on characteris-tic model,AARC)㊂利用特征模型简化PMSLM系统的描述,并对其进行验证㊂然后,设计一种基于参数投影的自适应律,实现对系统模型的在线补偿㊂同时,将系统的不确定参数和未知干扰视为集总的干231电㊀机㊀与㊀控㊀制㊀学㊀报㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第28卷㊀扰项,引入误差积分的鲁棒控制项进行抑制㊂此外,为了解决积分环节可能引起的windup 现象,加入抗饱和控制项,提高系统的抗饱和能力㊂最后,基于Lyapunov 函数证明闭环系统的稳定性,并通过实验验证所提控制器的有效性和鲁棒性㊂1㊀PMSLM 的特征建模与验证1.1㊀PMSLM 模型PMSLM 的运动方程为m d y d t =3π2τn p i q [ψf+(L d -L q )i d ]-F fric (y )㊂(1)式中:m 为等效质量;ψf 为磁链;y 为动子速度;i d ㊁i q 分别为d㊁q 轴电流;τ为极距;n p 为极对数;L d ㊁L q 分别为d㊁q 轴电感;F fric (y )为摩擦力㊂由式(1)可得y ㊃㊃=1.5πn p mτ[ψf i ㊃q +(L d -L q )(i ㊃d i q +i ㊃q i d )]- F fric y㊃m y㊂(2)设PMSLM 的采样周期为T ,将式(2)离散化可得㊀y (k +1)-2y (k )+y (k -1)T 2=[1.5πmTτn p ψf +1.5n p (L d -L q )i d (k )mTτ]i q (k )-[1.5πmTτn p ψf +1.5n p (L d -L q )i d (k )mTτ]i q (k -1)+1.5πn p (L d -L q )i q (k )mTτ[i d (k )-i d (k -1)]-1mT F firc (y (k )-y (k -1))y ㊂(3)在式(3)两边同乘T 2,可以重新写为y (k +1)=[1.5πmτn p ψfT +1.5n p (L d -L q )i d (k )Tmτ]i q (k )+[2-1m F firc T v ]y (k )+[1m F firc T v-1]y (k -1)+[1.5n p (L d -L q )i d (k )T mτ-1.5πmτn p ψfT ]i q (k -1)+1.5πn p (L d -L q )i q (k )Tmτˑ[i d (k )-i d (k -1)]=β1(k )i q (k )+α1(k )y (k )+α2(k )y (k -1)+Δ(k )㊂(4)式中:y (k )为系统输出;i q (k )为系统输入;α1㊁α2㊁β1为系统的特征参数,定义为:α1(k )=[2-1m F firc Tv];α2(k )=[1m F firc Tv -1];β1(k )=[1.5πmτn p ψf T +1.5n p (L d -L q )i d (k )T mτ]㊂üþýïïïïïïï(5)Δ(k )表示集总未知非线性函数,包括建模误差和未知扰动,定义为Δ(k )=[1.5n p (L d -L q )i d (k )Tmτ-1.5πmτn p ψfT ]i q (k -1)+1.5πn p (L d -L q )i q (k )Tmτˑ[i d (k )-i d (k -1)]㊂(6)通过式(4)可以看出,特征模型是将模型结构的模型不确定性和参数摄动等不确定信息压缩成几个未知的特征参数,使其与实际模型等价而不是近似㊂使用特征建模不仅能简化控制器设计,而且更利于工程应用㊂1.2㊀特征模型验证特征模型验证过程如图1所示㊂首先,分别给予PMSLM 系统和特征模型相同的输入信号u ㊂然后,采样PMSLM 的输入输出信号,采用传统投影梯算法[16]在线辨识特征参数,并计算特征模型输出㊂最后,通过比较特征模型输出y ^与PMSLM 系统输出y ,得到误差e 0㊂将输入设为1sin(2.09t )A 的正弦信号,并且设PMSLM 的采样频率为80μs㊂特征模型验证结果如图2所示㊂实验结果表明,在相同的控制输入作用下,特性模型输出与实际系统输出的误差很小,说明特征模型可以很好地描述PMSLM 系统的输入输出特征,可以利用该特征模型来设计控制器㊂331第3期曹㊀阳等:基于特征模型的永磁同步直线电机自适应控制图1㊀特征模型验证Fig.1㊀Verification block diagram of characteristicmodel图2㊀特征模型验证结果Fig.2㊀Verification results of characteristic model2㊀非线性自适应控制器设计2.1㊀自适应控制设计针对PMSLM 系统中存在的参数不确定㊁饱和非线性以及外界干扰,设计基于特征模型的自适应鲁棒控制律,对系统的不确定性和干扰进行估计和补偿,实现PMSLM 的速度跟踪控制㊂设计的自适应控制结构如图3所示,控制器包括模型补偿项u a ㊁线性反馈项u s1㊁积分鲁棒控制律u s2和抗饱和控制律k cw η,i qmax =0.03㊁i qmin =-0.03为饱和限制上下界㊂图3㊀自适应抗饱和控制结构框图Fig.3㊀Structure diagram of adaptive anti-windupcontroller将特征模型写成如下二阶时变辨识模型:y (k +1)=φ(k )T θ(k )㊂(7)式中:φ(k )=[y (k )y (k -1)u (k )]T ;θ(k )=[α1(k )α2(k )β1(k )]T ㊂在下面的部分中,㊃j 表示向量㊃的第j 个分量,并且针对2个向量的运算 < 是根据向量的相应元素来执行的㊂用θ^表示θ的估计值,θ~表示估计误差(θ~=θ^-θ)㊂结合式(7),一种不连续投影可以定义为proj θ^j {㊃j }=0,if θ^j =θj max and㊃j >0;0,if θ^j =θj min and㊃j <0;㊃j ,otherwise㊂ìîíïïïï(8)式中:j =1,2,3;proj θ^j{㊃j }可以保证估计参数在有界凸闭集D s 内㊂为保证参数估计值的有界性,设计未知参数估计自适应律为:θn (k )=θ^(k -1)+Γτλ+φT(k -1)φ(k -1);θ^(k )=proj θ^(θn(k ))㊂}(9)式中:Γ>0,λ>0为待设计的可调参数;τ为待合成的自适应函数;θ^(k )为系统参数θ(k )的估计值,利用基于不连续投影的参数自适应律可以估计出未知的特征参数α1㊁α2㊁β1㊂特征模型式(4)可被重写为y (k +1)=[α^1(k )-α~1(k )]y (k )+[α^2(k )-α~2(k )]y (k -1)+[β^1(k )-β~1(k )]u (k )+β1η(k )+Δ(k )㊂(10)式中α~1(k )=α^1(k )-α1(k ),α~2(k )=α^2(k )-α2(k ),β~1(k )=β^1(k )-β1(k )为辨识误差㊂所以式(10)可以改写为431电㊀机㊀与㊀控㊀制㊀学㊀报㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第28卷㊀y(k+1)=α^1(k)y(k)+α^2(k)y(k-1)+β^1(k)u(k)+β1η+Δ(k)-θ~(k)φ(k)㊂(11)其中θ~(k)φ(k)=α~1(k)y(k)+α~2(k)y(k)+β~1(k)u(k)表示模型估计误差㊂假设1:从工程实践中可知,对于稳定对象,参数不确定性和不确定非线性的程度已知,即θɪΩθ {θ:θminɤθɤθmax};ΔɪΩd {Δ:|Δ(k)-Δ(k-1)|ɤδd(k)}㊂}(12)式中:θmin=[θ1min, ,θ3min]T;θmax=[θ1max, ,θ3max]T;δd是已知的㊂控制目标是设计自适应控制器使得系统的输出y(k)跟踪期望输出y d(k),定义跟踪误差函数为e(k)=y(k)-y d(k)㊂(13)定义s(k)为s(k)=e(k)-k1e(k-1)㊂(14)其中0<k1<1为待设计的可调参数㊂所以有s(k+1)=e(k+1)-k1e(k)㊂(15)自适应抗饱和控制律可以设计为:u(k)=1β^1(k)[u a(k)+u s1(k)+u s2(k)];u a(k)=-α^1(k)y(k)-α^2(k)y(k-1)+ y d(k+1)+k1e(k)-k cwη;u s1(k)=k s s(k);u s2(k)=-E1(k)㊂üþýïïïïïïïï(16)式中:k cwȡ β1 max为抗饱和反馈增益;|k s|<1是待设计的可调参数;E1(k)表达式为E1(k)=E1(k-1)+k s k2s(k-1)+βsat(s(k-1))㊂(17)式中:k2>0为可调系数;sat(㊃)为饱和函数㊂设计参数自适应律τ=s(k)φ(k-1),将式(9)改写为:θn(k)=θ^(k-1)+Γs(k)φ(k-1)λ+φT(k-1)φ(k-1);θ^(k)=projθ^(θn(k))㊂üþýïïï(18) 2.2㊀稳定性分析定理1:对于特征模型式(10)所描述的PMSLM,所有信号都是有界的㊂采用自适应控制律式(16)和参数更新规律式(18),能使闭环系统的跟踪误差渐近收敛至0㊂证明:将式(16)代入式(10)中,并结合式(18)可得s(k+1)=[y(k+1)-y d(k+1)]-k1e(k)=α^1(k)y(k)+α^2(k)y(k-1)+β^1(k)u(k)-α~1(k)y(k)-α~2(k)y(k-1)-β~1(k)u(k)+Δ(k)=-θ~T(k)φ(k)+β1η(k)-k cwη(k)+k s s(k)-E1(k)+Δ(k)㊂(19)取k cwȡ β1 max,然后对式(19)进行差分可得s(k+1)-s(k)=-(θ~T(k)φ(k)-θ~T(k-1)φ(k-1))+k s(s(k)-s(k-1))-(E1(k)-E1(k-1))+Δ(k)-Δ(k-1)㊂(20)考虑到采样周期很小,通过线性外推法预测可知s(k+1)=2s(k)-s(k-1)㊂(21)构建Lyapunov函数为V(k)=s(k)λ+φT(k-1)φ(k-1)+θ~(k) 2Γ㊂(22)首先考虑式(22)的第2项,根据投影参数自适应律式(18)可得θ~(k) 2ɤ θn(k)-θ(k) 2= θ~(k-1) 2+2Γs(k)φT(k-1)θ~(k-1)λ+ φ(k-1)Tφ(k-1) +(Γs(k))2 φ(k-1) 2(λ+ φ(k-1) 2)2ɤ2Γs(k)φT(k-1)θ~(k-1)λ+ φ(k-1) 2+Γ2s2(k)λ+ φ(k-1) 2+ θ~(k-1) 2㊂(23)将式(16)㊁式(20)和式(21)代入式(23)可得 θ~(k) 2- θ~(k-1) 2ɤ2Γs(k)[-(s(k)-s(k-1))+k s(s(k-1)-s(k-2))]λ+ φ(k-1) 2+ 2Γs(k)[-θ~T(k-2)φ(k-2)+k s k2s(k-1)-βsign(s(k-1))]λ+ φ(k-1) 2+531第3期曹㊀阳等:基于特征模型的永磁同步直线电机自适应控制2Γs (k )[(Δ(k -1)-Δ(k -2)]λ+ φ(k -1) 2+Γ2s 2(k )λ+ φ(k -1) 2㊂(24)选取βȡ| θM φmax +δd |,进一步可得 θ~(k ) 2- θ~(k -1) 2ɤ2Γs (k )(k s -1)(s (k )-s (k -1))+2Γk s k 2s (k )s (k -1)λ+ φ(k -1) 2+Γ2s 2(k )λ+ φ(k -1) 2㊂(25)引理1[20]:(Young 不等式)假设a ㊁b 为非负实数,P >1,1p +1q =1,那么ab ɤa p p +b pq ,当且仅当a p=b q时,等号成立㊂根据引理1可得:2s (k )s (k -1)ɤ s (k ) 2+ s (k -1) 2; θ~(k ) 2- θ~(k -1) 2ɤ-Γ(3-3k s -k s k 2)s 2(k )λ+ φ(k -1) 2+Γ(k s +k s k 2-1)s 2(k -1)λ+ φT (k -1) 2㊂üþýïïïïïï(26)对Lyapunov 函数式(22)进行差分,并联立式(26)可得ΔV (k )=V (k )-V (k -1)ɤs 2(k )λ+ φT (k -1) 2-s 2(k -1)λ+ φT (k -2) 2+-(3-3k s -k s k 2)s 2(k )λ+ φ(k -1) 2+(k s +k s k 2-1)s 2(k -1)λ+ φT (k -1) 2+Γs 2(k )λ+ φT (k -1) 2ɤ-(2-3k s -k s k 2-Γ)s 2(k )λ+ φT (k -1) 2+(k s +k s k 2-1)s 2(k -1)λ+ φT (k -1) 2-s 2(k -1)λ+ φT (k -2) 2ɤ-As 2(k )-Bs 2(k -1)㊂(27)式中:A =2-3k s -k s k 2-Γλ+ φT (k -1) 2;B =1λ+ φT (k -2) 2-1-k s -k s k 2λ+ φT (k -1) 2㊂通过选取合适的参数k s ㊁k 2㊁Γ㊁λ使得A >0,B >0㊂根据式(27),对Δ(k )从1到k 求和可得ðki =1[As 2(k )+Bs 2(k -1)]ɤV (1)-V (k )ɤV (1)㊂(28)当k ңɕ时,As 2(k )+Bs 2(k -1)ȡ0,由于φ(k ) 有界,可知lim k ңɕ|s (k )|=0㊂(29)根据式(29)可知,∃N ,当k >N 时,有|s (k )|ɤ0㊂(30)由式(15)可得|e (k )|ɤ|k 1||e (k -1)|+|s (k )|ɤ|k 1|k -N|e (N )|+|k 1|k -N -1|s (N +1)|+ +s (k )ɤ|k 1|k -N|e (N )|+0㊂(31)因为|k s |<1,所以有lim k ңɕsup |e (k )|=0㊂(32)3㊀实验结果比较为了说明上述方法的可行性和有效性,在实验室建立一个验证平台如图4所示,PMSLM 的基本参数列于表1㊂该平台由MOSFET 三相逆变桥㊁磁栅尺㊁相电流采样电路㊁TMS320F28062(DSP)及外围电路㊁IR2181S 驱动电路㊁系统电源电路组成㊂此外,为了模拟不同的工作条件,对直线电机的动子进行了调整㊂通过直接在动子上安装标准化铁块,准确地改变其质量m ,以模拟不同的惯性效应㊂图4㊀PMSLM 实验平台Fig.4㊀PMSLM experimental platform631电㊀机㊀与㊀控㊀制㊀学㊀报㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第28卷㊀表1㊀PMSLM 的基本参数Table 1㊀Parameters of PMSLM㊀㊀参数数值极对数n p7极距τ/mm(180ʎ)12d 轴电感L d /mH 8q 轴电感L q /mH 8永磁体磁链ψf /Wb0.61PMSLM 矢量控制系统框架如图5所示㊂它由PMSLM㊁空间矢量脉宽调制(space vector pulse widthmodulation,SVPWM)模块㊁Park 和Clark 坐标变换㊁电压源逆变器㊁电流调节器和速度控制器组成㊂本文设计一种速度控制器,电流控制器采用PI 控制㊂图5㊀矢量控制总体结构框图Fig.5㊀Overall structure diagram of vector control为了验证所提控制器的可行性和有效性,本文对以下3种控制器进行比较㊂1)AARC㊂本文设计的抗饱和自适应鲁棒控制器参数设置如下:k 1=0.15,k 2=0.0006,k s =0.1,β=0.04,k cw =0.1,Γ=0.05,λ=0.995,θ^(0)=[1.9,-0.9,0.00001]T ㊂2)抗饱和自适应控制器(anti-windup adaptivecontrol based on characteristic model,AAC)㊂未添加鲁棒项u s2的抗饱和自适应控制器,其他参数与AARC 一致㊂3)抗饱和PID 控制器(anti-windup proportional-integral-differential,APID)㊂控制器的增益设置为k p =150,k i =1,k d =0,k cw =0.1㊂此外,将使用跟踪误差的最大值㊁平均值和标准差来衡量每个控制算法的质量,定义如下:1)最大跟踪误差的绝对值为M e =max i =1, ,N{|e (i )|}㊂(33)2)平均跟踪误差定义为μ=1N ðNi =1|e (i )|㊂(34)3)跟踪误差的标准差为δ=1N ðNi =1[|e (i )|-μ]2㊂(35)其中N 是所记录的数字信号的个数㊂首先将给定速度设置为y d =0.56sin(3.14t)m/s㊂系统跟踪结果如图6所示,性能指标如表2所示㊂从这些实验结果可以看出,所提出的AARC 控制器在瞬态和最终跟踪误差方面优于其他两种控制器,因为AARC 采用了基于参数自适应的补偿和鲁棒控制项,可以同时处理参数和未建模不确定性㊂虽然AAC 中也包含参数自适应,但对于建模的不确定性和未知扰动的抑制效果不佳㊂通过表2可以看出,AARC 添加鲁棒项后各种误差指标会比AAC 小,验证了鲁棒控制项u s2的有效性㊂在3种控制器中,线性抗饱和PID 的误差指标最差,达到了AARC 的2倍以上,这说明基于非线性模型的控制器设计方法具有更大的优势㊂图6㊀无铁块情况下PMSLM 的跟踪结果Fig.6㊀Tracking results of PMSLM without iron表2㊀最后两个周期的性能指标Table 2㊀Performance indexes during the last two cycles控制方法M e /(m /s)μ/(m /s)δ/(m /s)APID 0.055420.013360.00971AAC0.026890.008100.00572AARC 0.025220.006000.00490731第3期曹㊀阳等:基于特征模型的永磁同步直线电机自适应控制为了进一步验证控制器对参数变化的自适应能力,设定了不同的动子质量来进行实验㊂给PMSLM 的动子上添加1.33kg 的铁块㊂系统跟踪结果如图7所示,表3列出了最后两个周期的性能指标㊂从图7可以看出,使用AARC 控制方法的控制系统,在面对动子质量变化时,其反应速度快,并且波动较小㊂从表3可知,APID 的最大跟踪误差没有增大,意味着APID 中存在大的积分增益对该扰动也有一定的抑制效果㊂但与上一个实验情况相比,APID 的μ和δ指标增大明显,仍然比其他2个控制器差㊂适当的参数自适应在一定程度上也可以削弱动子质量变化给系统带来的参数不确定性影响,就像AAC 那样㊂AARC 的各项误差指标是3个控制器中最好的,再次证明了该控制器的有效性㊂图7㊀铁块质量为1.33kg 时PMSLM 的跟踪结果Fig.7㊀Tracking results of PMSLM when iron massis 1.33kg表3㊀最后两个周期的性能指标Table 3㊀Performance indexes during the last two cycles控制方法M e /(m /s)μ/(m /s)δ/(m /s)APID 0.043890.015370.01061AAC0.029620.008440.00605AARC 0.025320.005980.00496最后将动子上的铁块增加到2.64kg,此时PMSLM 受到的摩擦非线性和扰动进一步增大,3个控制器的跟踪性能都有所变差㊂实验结果如图8所示,误差指标见表4㊂在这个测试用例中,APID 中的跟踪误差抖动变大,而AARC 的跟踪误差则相当平滑㊂APID 控制器表现出最差的跟踪性能,最大跟踪误差为0.094,表明APID 在该跟踪任务中已经达到了其局限性㊂另外,即使在增大动子质量情况下,所提出的AARC 控制器仍然可以对模型进行补偿并衰减未建模的扰动,从而在所有比较的控制器中达到最好的跟踪性能㊂图8㊀铁块质量增加到2.64kg 情况下PMSLM 的跟踪结果Fig.8㊀Tracking results of PMSLM when the mass ofiron is increased to 2.64kg 表4㊀最后两个周期的性能指标Table 4㊀Performance indexes during the last two cycles控制方法M e /(m /s)μ/(m /s)δ/(m /s)APID 0.093700.027090.01934AAC0.034620.008410.00643AARC 0.028870.005860.005054㊀结㊀论本文针对PMSLM 系统提出一种基于特征模型的自适应控制方法,该方法能够有效地解决PMSLM 系统的参数不确定性㊁建模误差和外部干扰等问题㊂首先利用二阶变差分方程对PMSLM 系统进行简化831电㊀机㊀与㊀控㊀制㊀学㊀报㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第28卷㊀建模,然后设计了一种基于特征模型的自适应控制器,仅利用系统的输入和输出信号,实现了对PMSLM系统的精确速度跟踪控制㊂为了提高系统的鲁棒性和抗饱和能力,还引入了鲁棒补偿项和抗饱和控制项,并严格证明了闭环系统的稳定性㊂最后,通过实验结果验证了所提控制方法的有效性㊂本文控制器的参数是固定的,需要通过反复调试来确认㊂当实验条件和环境发生改变时,可能导致参数不一定是最优的㊂因此,在未来工作中将考虑进一步研究控制器参数的自动调整技术[21],采用自学习的方法来替代控制器中参数的人工调整部分㊂参考文献:[1]㊀龚夕霞,李焱鑫,卢琴芬.模块化永磁直线同步电机考虑制造公差的推力鲁棒性优化[J].电工技术学报,2024,39(2):465.GONG Xixia,LI Yanxin,LU Qinfen.Thrust robustness optimiza-tion of modular permanent magnet linear synchronous motor ac-counting for manufacture tolerance[J].Transactions of China Electrotechnical Society,2024,39(2):465.[2]㊀张春雷,张辉,叶佩青.高霍尔位置检测精度的圆筒型永磁同步直线电机设计[J].电工技术学报,2022,37(10):2481.ZHANG Chunlei,ZHANG Hui,YE Peiqing.Design of tubular permanent magnet synchronous linear motor by reliability-based ro-bust design optimization[J].Transactions of China Electrotechni-cal Society,2022,37(10):2481.[3]㊀缪仲翠,苏乙,张磊,等.梯形Halbach交替极无铁心永磁同步直线电机特性分析与优化设计[J].电机与控制学报, 2024,28(1):164.MIAO Zhongcui,SU Yi,ZHANG Lei,et al.Characteristic analy-sis and 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多相流模型经验谈多相流的介绍:Currentlytherearetwoapproachesforthenumericalcalculationofmultiphaseflows:theEuler-La grangeapproachandtheEuler-Eulerapproach.TheEuler-LagrangeApproach:TheLagrangiandiscretephasemodelinFLUENTfollowstheEuler-Lagr angeapproach,thisapproachisinappropriateforthemodelingofliquid-liquidmixtures,fluidizedbeds,oranyapplicationwhMIteration,theparticlesourcetermsarerecalculated.LengthScale:controlstheintegrationtimestepsizeusedtointegratetheequationsofmotionfort heparticle.Asmaller valuefortheLengthScaleincreasestheaccuracyofthetrajectoryandheat/masstransfercalculat ionsforthediscretephase.LengthScalefactor:AlargervaluefortheStepLengthFactordecreasesthediscretephaseintegrat iontimestep.颗粒积分方法:numerics叶中trackingscheme选项1)implicitusesanimplicitEulerintegrationofEquation23.2-1whichisunconditionallystablefor allparticlerelaxationtimes.2)trapezoidalusesasemi-implicittrapezoidalintegration.(梯形积分)3)analyticusesananalyticalintegrationofEquation23.2-1wheretheforcesareheldconstantdurin gtheintegration.4)runge-kuttafacilitatesa5thorderRungeKuttaschemederivedbyCashandKarp[47]. Youcaneitherchooseasingletrackingscheme,orswitchbetweenhigherorderandlowerordertracki ngschemesusingan12FluidFlowTimeSteptoinjecttheparticles,orwhetheryoupreferaParticleTimeStepSizeindepend entofthefluidflowtimestep.Withthelatteroption,youcanusetheDiscretePhaseModelincombinationwithchangesin thetimestepforthecontinuousequations,asitisdonewhenusingadaptiveflowtimestepping.随机轨道模型的参数:numberoftries:AninputofzerotellsFLUENTtocomputetheparticletrajectorybasedonthemeancon tinuousphasevelocityfield(Equation23.2-1),ignoringtheeffectsofturbulenceontheparticletrajectories.Aninput of1orgreatertellsFLUENTtoincludeturbulentvelocityfluctuationsintheparticleforcebalanceasinEquation23.2 -20.Ifyouwantthecharacteristiclifetimeoftheeddytoberandom(Equation23.2-32),enabletheRando mEddyLifetimeoption.YouwillgenerallynotneedtochangetheTimeScaleConstant(CLinEquation23.2-23)fromitsdefaul tvalueof0.15,unlessyouareusingtheReynoldsStressturbulencemodel(RSM),inwhichcaseavalueof0.3isrecomm ended.液滴颗粒碰撞与破碎碰撞:破碎:有两种模型,TAB模型适合低韦伯数射流雾化以及低速射流进入标态空气中的情况。
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计算机方面核心期刊计算机方面核心期刊计算机技术1.计算机学报2.软件学报3.计算机研究与发展4.自动化学报5.计算机科学6.控制理论与应用7.计算机辅助设计与图型学学报8.计算机工程与应用9.模式识别与人工智能10.控制与决策11.小型微型计算机系统12.计算机工程13.计算机应用14.信息与控制15.机器人16.中国图象图形学报.A版17.计算机应用研究18.系统仿真学报19.计算机集成制造系统-CIMS20.遥感学报21.中文信息学报22.微计算机信息23.数据采集与处理24.微型机与应用25.传感器技术26.传感技术学报28.计算机应用与软件29.微型计算机30.微电子学与计算机法律1.中国法学2.法学研究3.法学4.法学评论5.中外法学6.现代法学7.法商研究8.法律科学9. 法学家10. 政法论坛11.人民检察12. 河北法学13.法制与社会发展14.政治与法律15.环境法律评论16.比较法研究17.法学杂志18.当代法学19.人民司法20.法律适用21.法学论坛一、程序语言和软件工程权威期刊类:ACM Trans on Programming Languages & SystemsAnnals of Software EngineeringIEEE Trans on Software EngineeringJnl of Functional ProgrammingACM Trans on S/W Eng and MethodologyFormal Methods in System Design著名期刊类:The Jnl of Logic ProgrammingIEEE Procs - SoftwareJnl of Software Maintenance: Research and PracticeHigher-Order and Symbolic Computation (previously known as LISP and Symbolic Computation)Software: Practice and ExperienceJnl of Functional and Logic ProgrammingThe Constraints JournalJournal of Logic and ComputationJournal of Programming LanguagesEmpirical Software EngineeringAutomated Software EngineeringFormal Aspects of ComputingObject-Oriented SystemsTheory and Practice of Object SystemsJournal of Object-Oriented ProgrammingIEEE Transactions on ReliabilityFuture Generations Computer Systems: FGCSProgramming and Computer SoftwareScience of Computer ProgrammingJnl of Systems and SoftwareIntl Jnl on Software Engineering and Knowledge EngNew Generation ComputingSoftware Quality JournalSoftware Testing, Verification and ReliabilityComputer LanguagesRequirements Engineering JournalIEEE Software Engineering Journal其它期刊:Journal of the Interest Group in Pure and Applied LogicNotre Dame Journal of Formal LogicJournal of Computer and Software EngineeringJournal of Structured ProgrammingInternational Journal on Software Tools for Technology TransferChinese Journal of Advanced Software ResearchJournal of Computing Systems in EngineeringJournal of Symbolic LogicProject Management JournalInternational Journal of Reliability, Quality, and SafetyJournal for Applied Nonclassical LogicThe Journal of Defense Software EngineeringComputer & Control Engineering JournalJournal of Logic, Language and InformationComputer Systems Engineering JournalJournal of Automata, Languages and CombinatoricsThe C Users JournalInformation Design JournalJava Developer’s JournalC++ JournalFortran JournalJournal of Scientific ProgrammingLogic Journal of the IGPLJournal of Philosophical LogicJournal of Quality TechnologyInternational Journal of Technology ManagementSoftware Process Modeling and TechnologyJournal of Computers and TranslationJournal of C Language TranslationJournal of Electronic TestingSoftware Engineering Notes二、软件技术权威期刊类:ACM Trans on GraphicsACM Trans on Modeling & Computer SimulationComputer Aided Geometric DesignComputer-Aided DesignIEEE Trans on CAD of Integrated Circuits & SystemsIEEE Trans on Visualization and Computer GraphicsSIAM Jnl on Scientific and Statistical ComputingMultimedia SystemsPerformance EvaluationJournal of Visual Communication and Image Representation 著名期刊类:Computers & EducationACM Trans on Mathematical SoftwareHypermediaIntl Jnl of Modelling & SimulationIntl Jnl of Shape ModellingIntl Jnl on Computational Geometry & AppsSimulation & GamesSimulation & GamingVisual ComputerComputational Geometry - Theory and ApplicationsSimulationMultimedia Tools & ApplicationsIntl Jnl in Computer SimulationIntegrated Computer-Aided EngineeringInformation RetrievalComputer Graphics Forum: Jnl of the Europ As. for CGComputer & GraphicsIntl Jnl of Applied Software TechnologyJnl of Computational and Applied MathematicsMathematical and Computer ModellingMathematics and Computers in SimulationInternational Journal of Computer MathematicsSimulation Practice and TheoryThe New Review of Hypermedia & Multimedia: Apps & ResTrans of the Intl Assoc for Math and Comps in SimulnComputer Simulation: Modeling & AnalysisTrans of the Society for Computer SimulationJournal of Visual Languages and ComputingEngineering ComputationsSoftware - Concepts and ToolsJournal of Visualization and Computer AnimationThe International Journal of The Eurographics Association其它期刊类:Iranian Journal of Electrical and Computer EngineeringJournal of Digital ImagingJournal of Concurrent Engineering: Applications and ResearchDigital Technical JournalInterface Journal of New Music ResearchSPIE Journal of Electronic ImagingThe Journal of Electronic CommerceJournal of Graphics ToolsInternational Journal of Information Processing and ManagementJournal of Library AutomationThe Journal of Computer Game DesignJournal of DocumentationVirtual Prototyping JournalJournal of Computing and Information TechnologySIAM Journal on Scientific Computing.Journal of Computer Aided SurgeryJournal of Computer-Aided Molecular DesignJournal of the Virtual Reality SocietyJournal of Virtual Reality Research, Development and ApplicationsJournal of Computational and Graphical Statistics三、数据库权威期刊:ACM Trans on Database SystemsIEEE Trans on Knowledge & Data EngineeringJnl of Intell. Info Systems: Integrating AI and DB TechVLDB Intl JnlDistributed and Parallel Databases著名期刊:Data & Knowledge EngineeringInformation systemsJnl of Systems IntegrationJnl. of Data Mining & Knowledge DiscoveryIntl Jnl of Computer & Information SciencesIntl Jnl of Cooperative Information SystemsIntl Jnl of Intelligent & Cooperative Info. SystemsIntl Jnl of Geographic Information SystemsJournal of Information Processing and Cybernetics Geoinformatica Journal on Digital LibrariesJournal of the American Society for Information ScienceJournal of Intelligent Information SystemsData EngineeringKnowledge and Information SystemsAdvances in Engineering SoftwareInformation & Software TechnologyData BaseData Base ManagementDatabase and Network JournalJournal of Data WarehousingJournal of Combinatorics, Information and System Sciences International Journal of Information TechnologyTransactions of Information Processing Society of JapanSIGMOD RecordIEICE Data Engineering其它期刊:Journal of Computing Information ScienceJournal of Information Science and EngineeringEuropean Journal of Information SystemsJournal of Databases ManagementDatabase for Advances in Information SystemsData ManagementData mationGovernment Data SystemsJournal of Database AdministrationJournal of the Association for Education Data SystemsInformation Processing and ManagementJournal of Information Science: Principles and PracticeDatabase Programming and DesignScandinavian Journal of Information System。
SolidWorks Electrical Schematic DesignAutomationIntroduction:In today's rapidly evolving technological landscape, the demand for efficient and error-free design processes is higher than ever. Design automation tools have become essential for streamlining workflows and ensuring accuracy in electrical schematic design. SolidWorks Electrical Schematic Design Automation is one such tool that offers advanced features and capabilities to enhance productivity and design quality. This article will explore the benefits and features of SolidWorks Electrical Schematic Design Automation and how it revolutionizes electrical design processes.Streamlining Design Processes:SolidWorks Electrical Schematic Design Automation optimizes the design process by automating various time-consuming tasks. With its intuitive user interface and extensive component library, designers can quickly create and modify electrical schematics, reducing the overall design cycle. The software provides a vast range of intelligent symbols, real-time cross-references, and automated numbering features, eliminating the need for manual annotation and reducing errors. These capabilities ensure consistency and accuracy in schematic designs, helping designers save valuable time and effort.Efficient Collaboration:Collaboration is crucial in any design project, and SolidWorks Electrical Schematic Design Automation facilitates seamless collaboration among team members. The software allows multiple designers to work simultaneously on a project, promoting efficiency and reducing redundant efforts. Design changes made by one team member are automatically updated for others, ensuring everyone is working with the latest version.This real-time collaboration eliminates the risk of inconsistencies and minimizes the need for manual coordination, significantly improving productivity.Intelligent Data Management:SolidWorks Electrical Schematic Design Automation incorporates intelligent data management capabilities that enhance data organization and retrieval. It offers a centralized database where designers can store and manage electrical components, manufacturer information, and design rules. The software automatically associates the appropriate manufacturer data with each component, streamlining the procurement process. Designers can easily search and access required components, reducing the risk of using outdated or incorrect information. This intelligent data management system simplifies design revisions and ensures that accurate information is readily available whenever needed.Design Validation and Verification:Ensuring that a design is error-free is crucial in avoiding costly mistakes during manufacturing and installation. SolidWorks Electrical Schematic Design Automation includes built-in design validation and verification tools to detect errors and inconsistencies before they become critical issues. The software performs various checks, including electrical integrity, component compatibility, and adherence to industry standards. Any potential errors or warnings are highlighted, allowing designers to address them promptly and maintain design quality. By catching errors in the early stages, SolidWorks Electrical Schematic Design Automation helps reduce rework and avoid costly delays.Integration with SolidWorks:SolidWorks Electrical Schematic Design Automation seamlessly integrates with the SolidWorks ecosystem, allowing smooth transition from schematic design to 3D modeling and assembly. Designers can import their electrical schematics into SolidWorks Electrical 3D, where they can create accurate and realistic models of their electrical systems. This integration enables designers to visualize the physical layout of theirelectrical systems and detect any potential clashes or interferences. It also facilitates accurate Bill of Materials (BOM) generation, ensuring consistency between the schematic design and the physical product.Conclusion:SolidWorks Electrical Schematic Design Automation offers a powerful set of features to enhance electrical schematic design processes. Its ability to streamline design processes, facilitate collaboration, manage data intelligently, and provide design validation and integration with SolidWorks 3D modeling make it an indispensable tool for electrical designers. By using this software, designers can improve productivity, reduce errors, and ensure accurate and efficient electrical schematic design. SolidWorks Electrical Schematic Design Automation has become a leading solution in the electrical design industry, empowering designers to create innovative and reliable electrical systems.。
第一课设计实践可能是一个最令人兴奋的活动和履行工程师可以承担。
有一种强烈的满足感和自豪感在看到自己的创造努力结果出现在实际产品和过程中受益的人。
做设计也需要大量的特点。
the design engineer should not only have adequate technical training, but must be a person of sound judgment and wide experience, qualities which are usually acquired only after considerable time has been spent in actual professional work设计工程师不仅要有足够的技术训练,但必须是一个健全的判断力和广泛的经验,这些特质通常只能在相当长的时间后已被用在专业实际工作a start in this direction can be made with a good teacher while the student is still at the university当学生仍在大学时,一个方向的开始可以由一个好的老师做出。
however, the beginning designer must expect to get a substantial portion of this training after leaving school through further reading and study, and especially by being associated with other competent engineers.然而,开始设计师必须指望得到相当一部分这种培训学校毕业后通过深入的阅读和研究,尤其是与其他主管工程师。
the more any one engineer knows about all phrases of design, the better.任何一个工程师更了解所有词组的设计,更好的。
Computers and Information Sciences InformaticsL ife is multi-dimensional, so it mightbe surprising to realize that nearly all calculations and simulations are done using two-dimensional mathematical arrays, better known as matrices. Evenlarge-scale, three-dimensional engineering simulations, four-dimensional physics calculations, and multi-dimensional data analysis methods have been structured and optimized to work as two-dimensional matrix calculations. These computational approximations result in slower or less accurate calculations.Sandia scientists and colleagues are at the forefront of new research inalgorithms and software for applying multi-dimensional arrays, called tensors, to solve multi-dimensional problems that arise in data analysis, signals processing, image recognition and analysis, and other fields. A major roadblock to the use of these multi-dimensional techniques was the absence of any software for large, sparse tensor calculations. Sparse tensors have a majority of entries that are zero. Only the non-zero entries are usually stored. Sandia scientists developed the Tensor Toolbox for MATLAB™ (Figure 1) to address this need. The free software integrates with MATLAB™, the matrix-based high-level language andinteractive environment that enables users to perform computationally intensive tasks faster than with traditional programming languages. The Tensor Toolbox makes working with tensors in MATLAB™ as easy as working with matrices. The user need not worry about the low-level details to do complex, high-level operations, and the tool can handle very large prob-lems such as sparse tensors the size of 10,000x10,000x10,000 with a half-million nonzero entries.Sandia’s Tensor Toolbox has enabled new and more accurate analyses in multiple application domains, particularly those involving large amounts of data (“data mining”). For example, several web pages can have connecting links (Figure 2). The links in those web pages can be analyzed in a graph form with labeled edges (Figure 3), and thence stored as a sparse tensor (Figure 4). This higher-order web link analysis allows for better automatic grouping and labeling of web pages through the TOPHITS algorithm (also developed at Sandia). Another application is in bibliometric analysis using multiple linkages (authors, documents, terms), including understanding author-keyword trends. An example isSandia’s Toolbox allows scientists to solve large, sparse tensor calculations on very large data sets.Figure 1: Tensor Toolbox software package Tensor Toolbox for MATLAB™For more information:Technical Contact:Tamara G. Kolda, Ph.D.925-294-4769******************Science Matters Contact:Alan Burns, Ph.D 505-844-9642*****************Figure 2:Four web pages with connecting linksshown in Figure 5, where connections between cited authors and their publications are plotted in graph form. Similarly, national security applications of the Tensor Toolbox include temporal analysis of email exchanges, including automated discovery of conversation topics and sender/recipient roles over time.Outside of Sandia, over one thousand registered users of the Tensor Toolbox have reported diverse applications including chatroom data analysis, continuum mechanics, online monitoring of network data, acoustic signal research, chemometrics, finite element computations, studies of bird migration, statistical computations, biochemical analysis, image classification, air traffic control studies, astronomy, models of tumor growth, character animation, computer vision, brain imaging, multidimensional economics, general relativity research, modeling optical systems, physics, multilayer absorption for photovoltaics, signal processing, computational differential geometry, neuro-fuzzy networks, and video analysis.Tensor Toolbox Web Site/~tgkolda/TensorToolbox/References:Brett W. Bader, Richard A. Harshman, and Tamara G. Kolda. Temporal analysis of semantic graphs using ASALSAN . In ICDM 2007: Proceedings of the 7th IEEE International Confer-ence on Data Mining , pages 33–42, October 2007.Brett W. Bader and Tamara G. Kolda. Efficient MATLAB com-putations with sparse and factored tensors . SIAM Journal on Scientific Computing , July 2007.Brett W. Bader and Tamara G. Kolda. Algorithm 862: MAT-LAB tensor classes for fast algorithm prototyping . ACM Transactions on Mathematical Software , 32(4):635–653, December 2006.Peter A. Chew, Brett W. Bader, Tamara G. Kolda, and Ahmed Abdelali. Cross-language information retrieval using PARAFAC2. In KDD ‘07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages 143–152. ACM Press, 2007.Daniel M. Dunlavy, Tamara G. Kolda, and W. Philip Kegelmey-er. Multilinear algebra for analyzing data with multiple linkages. Technical Report SAND2006-2079, Sandia National Laboratories, April 2006.Tamara G. Kolda and Brett W. Bader. Tensor decompositions and applications. Technical Report SAND2007-6702, Sandia National Laboratories, November 2007.Tamara Kolda and Brett Bader. The TOPHITS model for higher-order web link analysis. In Workshop on Link Analy-sis, Counterterrorism and Security , 2006.Tamara G. Kolda, Brett W. Bader, and Joseph P . Kenny.Higher-order web link analysis using multilinear algebra. In ICDM 2005: Proceedings of the 5th IEEE International Confer-ence on Data Mining , pages 242–249, November 2005.Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND 2008-0486P01/2008Figure 3: The linked web pages in Figure 2 are converted to a graph with labelededges (stored as a tensor) and the link text is used in the analysis.Figure 4: The resulting tensor representation of Figure 3 is extremely sparse.This tensor is decomposed using the Tensor Toolbox.Figure 5: A graphical representation of 5000 papers (in blue), their authors (in red), and the citation and authorship connections between them.。
智能技术是否会让人变愚蠢英语作文全文共3篇示例,供读者参考篇1Will AI Technology Make Us Dumber?As AI and automation continue advancing at a breakneck pace, I can't help but wonder – are these incredible innovations actually making us less intelligent as a society? It's a concerning question that has been on my mind a lot lately.On one hand, AI is clearly augmenting human capabilities in amazing ways. We now have digital assistants that can instantly retrieve information, solve complex equations, and even engage in natural conversations. Automation is handling tedious tasks that used to consume hours of our time and mental energy. And advanced AI systems are aiding scientific research, medical diagnoses, and so many other crucial endeavors in ways that dramatically expand the frontiers of human knowledge.Just think about how much more productive and capable we've become thanks to technologies like smartphones, search engines, word processors, and spreadsheets. Instead of spending countless hours looking up factual information or manuallyperforming calculations, we can leverage AI to make us massively more efficient. We're essentially offloading a huge portion of basic cognitive labor onto machines.So in that sense, AI could be viewed as a great liberator –freeing up our finite human brainpower to focus on higher-order thinking, creativity, and problem-solving instead of mundane tasks. We no longer have to preoccupy our minds with rote memorization when we have unfathomably vast databases of information at our fingertips.However, there's also a strong counterargument that AI may be actively atrophying our mental capabilities. With tools like spellcheck, autocomplete, and automated writing assistants, are we becoming overly reliant on AI crutches and allowing our communication skills to deteriorate? When we don't have to exercise cognitive muscles like mental math, active recall, or spatial reasoning, will those vital neural pathways weaken over time?Some concerning studies have already shown declines in human memory and attention spans that seem to correlate with our growing dependence on AI and the internet. It's becoming exponentially easier to outsource cognitive work to our devices instead of developing and exercising those skills internally.Just look at how many of us struggle with simple arithmetic or directions without pulling out our smartphones for assistance. Or how advice columnists report an increasingly common theme – partners who forget important details about each other's lives because they've stopped actively committing information to long-term memory.There's also the insidious ways AI can manipulate our thoughts and beliefs through social media virality, targeted advertising, and recommendation algorithms designed to maximize engagement instead of objectivity. We're constantly being fed personalized narratives dictated by AI models that can easily distort our worldviews in pernicious ways if we're not carefully scrutinizing the information we consume.Another disturbing trend is the proliferation of deepfake audio and video generated by AI that makes it increasingly difficult to distinguish reality from fiction. In a world where any image or recording can be realistically faked by an AI model, how can we have confidence in objective truth anymore? Misinformation campaigns powered by AI pose a massive threat to human knowledge and reasoning abilities.So while AI is undoubtedly augmenting and expanding human intelligence in certain domains, we also have to becognizant of the ways it may be actively undermining our critical thinking skills and mental autonomy. As AI capabilities continue growing more sophisticated and integrated into our lives, it's crucial that we maintain our reasoning faculties and don't become overly reliant on machines as cognitive crutches.We have to find the right balance between leveraging AI as a productivity multiplier while still exercising our own brainpower through practices like:Doing mental math and actively recalling information without tech aidsWriting, reading, and communicating substantively without relying on AI writing toolsScrutinizing information sources and developing robust media literacy skillsCultivating creativity, empathy, and interpersonal intelligence that AI can't easily replicateMost importantly, we must be intentional about developing cognitive self-regulation and thoughtful consumption of AI/tech instead of allowing ourselves to be passively shaped by the information feeds, recommendations, and digital stimuli were bombarded with.At the end of the day, I believe AI should be viewed as a supplemental tool that exponentially expands the scope of what we as humans are capable of understanding and achieving. But we have to be vigilant about not allowing AI to supplant the core elements of human intelligence, autonomy, and agency that have defined our civilization.By nurturing our skills in reasoning, communication, and good old-fashioned critical thinking, we can partner with AI in powerful synergy instead of surrendering our cognitive sovereignty. We must keep pushing the boundaries of machine intelligence while also rigorously upholding the eternal value and preservation of human intelligence. It's the only way we'll be able to steer AI's continued development in ethical directions that uplift humanity's collective flourishing.篇2Will Intelligent Technology Make Us Dumber?As technology continues its rapid advancement, one of the biggest concerns is whether all these intelligent devices and systems will end up making us less intelligent as humans. Will we become overly reliant on AI assistants, smart apps, and advanced computers to the point that our own cognitive abilitiesdeteriorate? As a student trying to prepare for the increasingly tech-driven world, this is an important question to grapple with.On one hand, the prospect of intelligent technology eroding human intelligence seems valid. We've already seen how calculators have caused a decline in mental math abilities for many. With AI writing assistants, language models that can produce passable essays and papers, and virtually unlimited information at our fingertips through search engines, there is a risk that we become mentally lazy. Why spend effort retaining knowledge when you can just outsource any thinking to a device?Moreover, some argue that intelligent tech has diminished our attention spans and ability to focus deeply. With entertaining videos, games, social media, and other digital stimuli constantly vying for our attention, we've grown accustomed to rapidly shifting our attention from one thing to the next. Prolonged periods of concentration increasingly feel like a struggle. If this trend continues, there are concerns about our capacity for analytical and creative thought processes that require sustained, focused effort over longer periods.However, we need to consider the potential upsides and opportunities that intelligent technology brings. Sure, we may beoffloading some rote memorization and basic skills to machines, but does that necessarily make us dumber? It could instead free up cognitive resources to focus on higher-order reasoning, creative endeavors, and driving innovation further. As tools get smarter, we can tackle more complex challenges.Additionally, AI language models and knowledge databases put a wealth of information at our fingertips that can enhance learning and understanding when used properly. We don't have to waste time memorizing facts and figures when we can quickly access that data, but can instead focus on comprehending context, connections, and deeper insights. With the right skillset to filter, analyze, and apply that knowledge, intelligent tech could make us smarter in more meaningful ways.Furthermore, the interactivity and personalization enabled by modern educational technology allows for more engaging, adaptive, and effective learning experiences tailored to individual needs. From AI tutors to immersive VR/AR environments, these tools have the potential to improve knowledge acquisition and retention compared to traditional instructional methods.Ultimately, whether intelligent tech makes us dumber or not comes down to how we, as imperfect humans, choose to use andadapt to these rapidly evolving capabilities. If we simply use them as mental crutches and disengage our critical thinking faculties, then yes, there is a risk of eroding our intelligence over time. However, if we thoughtfully integrate these tools as assistance aids to augment our natural abilities, they could help propel humanity's collective intelligence further.As students, we need to be discerning about building skills that intelligent machines cannot easily replicate - skills like creativity, emotional intelligence, complex reasoning, and cognitive flexibility. While offloading some routine cognitive labor to technology, we should prioritize honing the uniquely human strengths that artificial intelligence cannot yet match. This two-pronged approach of leveraging technology's capabilities where it exceeds human abilities, while doubling down on our own distinct strengths, could be the optimal path forward.Moreover, we must develop robust critical thinking abilities to circumvent the risks of misinformation, bias, and manipulation that can come with powerful AI systems. Building robust media literacy skills to scrutinize sources, separate fact from fiction, and identify skewed framing or agendas will be crucial. We cannotafford to become gullible consumers mindlessly accepting outputs from a black box.Technological progress is inevitable, and intelligent systems will only become more pervasive and powerful in years to come. This is not something to be feared, but is a new reality we must adapt to thoughtfully and strategically. By being intentional about developing the right skill sets, utilizing these tools in a supplementary fashion, and maintaining our intellectual grit, we can ensure that intelligent technology propels us toward becoming a smarter, more capable species overall.At least, that is my perspective as a student looking ahead at the technological tides reshaping our world. While the risks of mental atrophy are real, I choose to view this technological renaissance as an opportunity to redefine human intelligence itself. We may no longer need to be repositories of staid information, but can embrace roles as curators, innovators, and creative forces driving progress in partnership with intelligent systems. Our path forward is to intelligently integrate technology's exponential capabilities while cultivating the critical, creative, and uniquely human aptitudes that machines cannot replicate. With wisdom and pragmatism, we can mitigate thepitfalls and harness intelligent tech's vast potential to amplify our collective intelligence for generations to come.篇3Will Artificial Intelligence Make Humans Dumber?We live in an era of unprecedented technological advancements, where artificial intelligence (AI) is revolutionizing nearly every aspect of our lives. From virtual assistants like Siri and Alexa to self-driving cars and sophisticated language models, AI has become an integral part of our daily routines. However, amidst this technological boom, a persistent concern has emerged: Will the increasing reliance on AI lead to a decline in human intelligence and cognitive abilities?As a student navigating the ever-evolving landscape of education, I often find myself grappling with this question. On one hand, AI-powered tools and applications offer incredible convenience and efficiency, allowing us to access vast amounts of information and automate tedious tasks. On the other hand, there is a fear that our over-reliance on these technologies might erode our critical thinking and problem-solving skills, ultimately making us "dumber."To delve deeper into this dilemma, we must first understand the nature of intelligence and how it is measured. Intelligence is a multifaceted concept that encompasses various cognitive abilities, including reasoning, problem-solving, memory, and adaptability. While AI systems excel in specific domains, such as pattern recognition and data processing, human intelligence is far more nuanced and encompasses qualities like creativity, emotional intelligence, and abstract thinking.One argument against the notion that AI will make us dumber is that these technologies are designed to augment and enhance human capabilities rather than replace them entirely. AI-powered educational tools, for instance, can provide personalized learning experiences, adapt to individual learning styles, and offer real-time feedback, ultimately improving our understanding and retention of information. Additionally,AI-assisted research and data analysis can unlock new insights and accelerate scientific discovery, potentially expanding the boundaries of human knowledge.However, critics argue that our growing reliance on AI could lead to a gradual atrophy of certain cognitive skills. With AI systems handling complex calculations, retrieving information, and even generating written content, there is a risk that we maybecome overly dependent on these technologies, leading to a diminished ability to perform these tasks independently. This phenomenon, often referred to as "digital dementia," could potentially impair our problem-solving abilities and critical thinking skills.Furthermore, the constant distraction and stimulation provided by AI-powered devices and applications may contribute to shorter attention spans and impaired memory retention. The ease of accessing information through a simple voice command or search query could discourage us from actively engaging in the process of learning and retaining knowledge, ultimately hindering our cognitive development.Despite these concerns, it is crucial to recognize that technology has always been a double-edged sword, and its impact on human intelligence largely depends on how we approach and integrate it into our lives. Throughout history, major technological advancements have sparked similar fears, yet human ingenuity and adaptability have consistently prevailed.As students and lifelong learners, we must embrace a balanced approach to AI, recognizing its potential to enhance our cognitive abilities while also fostering a mindset ofcontinuous learning and intellectual curiosity. By actively engaging with AI-powered tools and utilizing them as aids rather than substitutes, we can leverage their capabilities to augment our problem-solving skills, deepen our understanding, and unlock new realms of knowledge.Moreover, it is imperative that educational institutions and curriculums adapt to this evolving landscape, emphasizing the development of essential skills like critical thinking, creativity, and emotional intelligence – areas where human intelligence currently holds a distinct advantage over AI. By striking a balance between leveraging AI's computational prowess and nurturing our unique human strengths, we can create a synergistic relationship that amplifies our collective intelligence.In conclusion, the impact of AI on human intelligence is a complex and multifaceted issue that defies a simple binary answer. While the potential risks of over-reliance on AI cannot be ignored, the true threat lies not in the technology itself but in our inability to adapt and harness its potential effectively. By embracing a growth mindset, fostering intellectual curiosity, and integrating AI into our educational and cognitive development in a thoughtful and balanced manner, we can ensure that thesetechnologies enhance rather than diminish our human intelligence.As students and lifelong learners, it is our responsibility to stay informed, think critically, and continuously challenge ourselves to grow and evolve alongside these technological advancements. Only then can we truly unlock the synergistic potential of human and artificial intelligence, paving the way for a future where our cognitive abilities are not diminished but elevated to unprecedented heights.。
智慧水利专业英语English:Smart water management, or intelligent water resource management, is a specialized field that focuses on utilizing advanced technologies and data analytics to optimize the planning, distribution, and management of water resources. From real-time monitoring and remote sensing to predictive modeling and automated control systems, the application of smart technologies in water management allows for improved efficiency, sustainability, and resilience in the face of growing water challenges. By leveraging sensors, meters, and internet of things (IoT) devices, water managers can collect and analyze large amounts of data to make informed decisions and proactively address issues such as leak detection, demand forecasting, and infrastructure maintenance. Additionally, smart water systems can enhance water quality monitoring, flood management, and drought mitigation efforts, ultimately leading to more reliable and secure water supplies for communities and industries.中文翻译:智慧水利管理,或称智能水资源管理,是一个专门的领域,其重点在于利用先进技术和数据分析来优化水资源的规划、分配和管理。
采购专业术语中英文对照在工作场景中,采购经常会接触到各种英文和英文缩写,涉及的客户需求、采购需求、计划、订单、合同、物流包装、结算......等等,对很多采购与供应链朋友来说还是有点困难。
从互联网上收集并整理了这份采购专业相关英语日常属于,供大家收藏学习,希望对大家有所帮助。
第一部分custom made:需指定制造的产品first priority:最高的优先级别PPR( Premium price request):高于标准价格采购请求RFQ(request for quote):报价请求,询盘ESI(Early supplier involvement):供应商早期介入cost modeling:成本模型STD price:标准价格Stock-out cost:缺货成本awarded supplier:指定供应商escalate to higher level:提交上级处理cut hard order:手动下订单customer demand pull-in:客户需求提前或增加Distributor:分销商;Manufacturing:厂商;Broker:经纪商。
(紧急情况下启用,价格较高。
)EAU (Estimated annual usage):预估每年需求量line down:停产APQP(advanced product quality planning): 指产品在量产前对如何实现产品以及如何进行质量控制看展的策划活动。
PPAP(production part approval process):生产件批准程序,是指第一次生产样件时向客户提交一系列文件记录清单,如样品检测报告,FMEA,工艺流程图,控制计划,图纸等等,要提交的资料根据客户要求进行,提交后客户将确认OK后方可进行试生产阶段。
EOQ(economic ordering quantity):经济订购数量,EOQ=(A:单位时间净需求S:每次订购费用U:商品单位成本C:储存成本)EDI(electronic data interchange): 电子数据交换。
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A/D converter 模数转换器large scale 大规模abnomal signal 异常信号lever 杠杆abort 中断停止light run 空转actuator 执行器机构loading and unloading machinetools 安装拆卸机床actuator 执行器lugic 逻辑actuators and sensors 执行元件和传感器machining operations 机械加工air compressor 空压机major overhaul 大修air conditioner 空调man-machine interaction 人机对话alarm 报警mantenance manual 检修手册alternating current 交流电manual 手册amplitude 振幅,调幅manufacturing 制造加工analog input 模拟输入marvel 奇迹,惊奇analog ourput 模拟输出mean 中间平均数analog signal 模拟信号mechanization 机械化analog-to-digital 模数转换micro devices and optoelectronicssystems 微电子装置和光电子系统assembly 装配modeling and design 建模与设计asynchronization 异步module 模块组件at regular intervals 每隔一定时间motion control 运动控制automated drafting 自动绘图motor winding 电动机绕组automatic vdtage regulator 自动调压器moving heavy parts 移动重的零部件automotive systems 自动化系统negative 负极avometer 万用表nominal power 额定功率back pressure 负压numerical control or computernumerical control programming 数控或计算机数控编程back up 支持备用nut 螺母螺帽bearing BRG 轴承nut 螺母螺帽bearing BRG 轴承open circuit 开路bind 结合,粘贴open circuit 断路bit-rate 比特率opende-loop 开环blade 叶片oscillate 震荡boolean 逻辑oscilloscope 示波器breaker 断路器隔离开关other applications 其他应用calibrate 校准over current 过流cam/concave 凹凸轮over loading 过载capacitance 电容over voltage 过压capacitor 电容器overload protection 过载保护capacitors 电控器parallel 并联carbon tool steel 碳素刀具钢parallel digital signal 并行数字信号cast alloys 铸造合金phase sequence 相序cease 取消phase voltage 相电压cemented carbides 硬质合金PLC 可编程控制器central contiol room 中控室plot 曲线ceramics 陶瓷positive 正极charge indicator 充电指示器PPI(point-to-point Interface) 点对点接口chip 芯片probe 探头clamp 夹具production planning andscheduling 生产计划与安排closde-loop 闭环radix 根基closed/opened loop 闭开环reactance 电抗clutch 离合器rectifier 整流器communication 通讯电缆rectify 校正computer-aided process planning 计算机辅助工艺规划register 寄存器configuer 组态relay 继电器consde 控制台relay 继电器consumer products 消费品resist 电阻control engineering 控制工程resistor 电阻control valve 控制阀resistors 电阻器coupling cupling 连接器robotics 机器人技术crane 起重机,鹤rotary switch 转换开关current transformer CT电流互感器routing inspection 日常检查cutter 切削刀具sampling theorem 采样定理debugging 调试scale 刻度比例尺delay time 延时screw 螺杆螺丝delay time 延时器screw driver 螺丝刀design review and evaluation 设计审评与评估secector 选择器detetor 检测器探测器self-hold 自保持deviation 偏差sensor 传感器diamonds 金刚石series 串联digital clocks 数字时钟servomechanism 私服机构,随动系统digital-to-analog 数模转换servomotor 伺服电机diodes 二极管short circuit 短路direct digital transducers 直接数字传感器siemens 西门子directing current 直流电signal amplifier 信号放大器discharge 放电simulation 仿真模拟discret 离散的开关量spraying painting 喷漆distributed control system DCS集散控制系统spring 弹簧domain 领土,范围stan conrected system Y连接electrical analog signal 电器模拟信号stator 定子electric-hydraulie comtrol 电液控制step by step motor 步进电机electrode 电极storage battery 蓄电池engineering analysis 工程分析symmetrical 对称的explosion-proof motor 防爆电机synchro 同步的feedback 反馈synchro output 同步输出fixture 夹具synchronization 同步flexible manufacturing system FMS柔性制造系统system integration 系统集成fluctuate 波动telecommunication 电信frequency 频率,周率,发生次数tenperature compensation 温度补偿fusible 熔断开关the drill press 钻床gauge 仪表the grinder 磨床geometric modeling 几何建模the lathe 车床haltinstruction 停止指令the milling machine 铣床harmonic 谐波the shaper or planer 成型刨床或牛头刨床hertz 赫兹thermal couple 热电偶high-speed steel 高速钢time-lag relay 延时继电器ignitor 点火器tiny 微小的impedance 阻抗T-junction 三通impedance 阻抗tolerance 公差indicator 指示器tool and fixture design 刀具夹具设计inductive reactance 感抗transducer 传感器inertia 惯性transformer 变压器integrate 集成transistors 晶体管integrated circuit 集成电路tweezers 镊子钳子intelligent control 智能控制undergrand cable 地下电缆intergral digetal circuit 积分数字电路uninterrupted power supply UPS不间断电源intermediate pelay 串联继电器upgrade 升级interpolate 插入ventelator 通风设备interval 间隔vibration and noise control 震动和噪声控制key 键voltage transformer 电压互感器knife-switch 闸刀开关weding 焊接ladder-diagram 梯形图wire stripper 剥线钳。
H o w t o Prove H i g h e r O r d e r T h e o r e m si n F i r s t O r d e r LogicM a n f r e d K e r b e rFachbereich Informatik, Universitat K aiserslauternD-6750 K aiserslautern, Germany kerber@informatik.uni-kl.de A b s t r a c tIn this paper we are interested in using a first order theorem prover to prove theorems that are formulated in some higher order logic. Tothis end we present translations of higher or-der logics into first order logic w i t h flat sorts and equality and give a sufficient criterion for the soundness of these translations. In addi-tion translations are introduced that are sound and complete w i t h respect to L. Henkin's gen-eral model semantics. Our higher order logics are based on a restricted type structure in the sense of A. Church, they have typed function symbols and predicate symbols, but no sorts. K e y w o r d s : higher order logic, second order logic, trans-lation, morphism, soundness, completenessDie Grenzen meiner Spache bedeuten die Grenzen meiner Welt. Ludwig Wittg e nst ein,True tutus logico-philosophic us 5.61 I n t r o d u c t i o nFirst order logic is a powerful tool for expressing and proving mathematical facts. Nevertheless higher order expressions are often better suited for the representation of mathematics and in fact almost all mathematical text books rely on some higher order fragments for expres-siveness. In order to prove such theorems mechanically there are two options: either to have a theorem prover for higher order logic such as TPS [Andrews e t al., 1990] or to translate the higher order constructs into corre-sponding first order expressions and to use a first order theorem prover. As important as the first development is - which may be the way of the future - we follow the second approach because strong first order theorem provers are available today.T h e L i m i t a t i o n s o f F i r s t O r d e r L o g i cFirst order logic and the set theories of Z E R M E L O -F R A E N K E L or V O N N E U M A N N -G O D E L -B E R N A Y S have been developed for the formalization of mathematical concepts and for reasoning about them. Other ap-proaches are R U S S E L 'S ramified theory of types and C H U R C H 'S simple theory of types which formalize higherW h y a n d H o w T r a n s l a t i o nRepresenting knowledge in an adequate way - adequate with respect to the naturalness of the representation of the object - is one thing, the other thing is to have an adequate and strong form of reasoning. If one uses higherKerber137For general considerations concerning the expressive-ness of higher order logic, it is obvious that if we find a translation from higher order to first order logic, it cannot be complete in the general sense, especially since the theorem of L O W E N H E I M-S K O L E M must hold and be-cause of G O D E L'S incompleteness result. In principle such a translation must be equivalent to some set the-oretical formulation as stated in M O S T O W S K I'S isomor-phism theorem [Mostowski, 1949].R e l a t e d W o r kJ. VAN B E N T H E M and K.D O E T S [1983] give a transla-tion of a restricted higher order logic without function symbols and without higher order constants and identi-ties to a standard first order logic. They introduce the general idea of a translation, and its soundness and com-pleteness. The translation to standard first order logic leads to more complicated formulae than the translation to a sorted version, because it is necessary to relativize quantification w i t h respect to the corresponding type.Of great influence for the present paper are the trans-lation techniques of H. J.O H L B A C H [1989], who trans-lates modal logics and other non-classical logics to a context logic, where contexts are restricted higher order expressions. These contexts are translated to an order sorted first order logic.Here a translation of (almost) full higher order logic w i t h function symbols to a many sorted first order logic with equality is given. We do not need a general order sorted logic as long as we do not use a sorted higher order source logic.2 H i g h e r Order LogicIn this section we define formally a higher order logic based on C H U R C H'S simple theory of types, much of the notation is taken from [Andrews, 1986]. However, we shall write the types in a different way. For example if P is a binary predicate symbol on individuals, we write its type as (i x i — 0) instead of (ou) for better readability. Apologies to all who are familiar w i t h C H U R C H'S original notation.138 Automated ReasoningKerber 139140 Automated Reasoning5.6 R e m a r k: One might wonder why we proposed a suf-ficient criterion for the soundness of translations, whenwe have a translation that is sound and complete andhence could be used always. However in a concrete situ-ation it can be better not to translate into the full soundand complete formulae, because the search space may become too big. It would not be a good idea to addthe extensionality axioms if they are not really needed.In addition we can prevent instantiation if we translate certain constants not by an apply or if we use differentapply functions or predicates although we could use the same. On the other hand the completeness result guar-antees that we can find a translation at all. Which onewe choose may be very important for the theorem proverto find a proof. Whereas the extensionality axioms are relatively harmless, for re ally highe r orde r theorems it is necessary to add so-called comprehension axioms (com-pare [Andrews, 1986, p. 156]) in order to approximateweak semantics to strong semantics. For many theoremsthese axioms are not necessary, for the others one must choose the axioms very carefully, otherwise the first or-der theorem prover will get a search space that is toobig. It is the advantage of higher order theorem prov-ing compared to our approach, that there one does notneed these axioms (for the prize of the undecidability of unification). In the appendix we give an example of a theorem, where a comprehension axiom is necessary.6 Summary and Open ProblemsIn the sections above we introduced the basic machineryfor translating higher order formulae to first order logic.Kerber 141We introduced a sufficient criterion for the soundness of such a translation, namely that it has to be an injective quasi-homomorphism. Then we gave a complete trans-lation for the restricted higher order language.In the full version of the paper [K erber, 1990] we gen-eralized the results to logics with equality. An interest-ing and useful generalization would be to a higher order sorted logic. Then the first order logic should have a sort structure at least as powerful as that of the higher order source logic. The results should be transferable although the formal treatment can become strenuous. A c k n o w l e d g e m e n tI like to thank A X E L P R A C K L E I N for many discussions and thorough reading of a draft and J O R G SlEK MANN for his advice that resulted in numerous improvements.References[Andrews e t ai, 1990] Peter B. Andrews, Sunil Issar,Dan Nesmith, and Frank Pfenning. The TPS theo-rem proving system. In M.E. Stickel, editor, Proc. ofthe 10th CADE, pages 641-642, K aiserslautern, Ger-many, July 1990. Springer Verlag, Berlin, Germany.L N A I 449. [Andrews, 1986] Peter B. Andrews. An Introduction to Math e matical Logic and Typ e Th eory: To Truth through Proof. Academic Press, Orlando, Florida, USA, 1986. [Benthem and Doets, 1983] Johan van Benthem and Kees Doets. High e r Orde r Logic, volume I: Elements of Classical Logic of Handbook of Philosophical Logic, D. Gabbay, F. Gu e nthn er, Edts., chapter 1.4, pages 275-329. D.Reidel Publishing Company, Dodrecht, Netherlands, 1983. [Henkin, 1950] Leon Henkin. Completeness in the the-ory of types. Journal of Symbolic Logic, 15:81-91, 1950. [Kerber, 1990] Manfred K erber. How to prove higher order theorems in first order logic. SEK 1 Report SR-90-19, Fachbereich Informatik, Universitat Kaiserslau-tern, K aiserslautern, Germany, 1990. [MKRP, 1984] K arl Mark G Raph. The Markgraf K arl Refutation Procedure. Technical Report Memo-SEK I-MK-84-01, Fachbereich Informatik, Universitat K ai-serslautern, K aiserslautern, Germany, January 1984. [Mostowski, 1949] Andrzej Mostowski. An undecidable arithmetical statement. Fundam e nta Math e matica e , 36:143-164, 1949. [Ohlbach, 1989] Hans Jiirgen Ohlbach. Context logic. SEKI Report SR-89-08, Fachbereich Informatik, Uni-versitat K aiserslautern, Kaiserslautern, Germany, 1989.AppendixWe present an MK RP-proof of C A N T O R 'S theorem that the power set of a set has greater cardinality than the set itself. We use the formulation of [Andrews, 1986, p.184]. A comprehension axiom is necessary. We write t as I, oas 0, — as T, a "^4-*))" as A [I T [I T 0]], and so on.142Automated Reasoning。
Geometric ModelingGeometric modeling is a crucial aspect of computer graphics and design,playing a significant role in various industries such as architecture, engineering, and animation. It involves creating digital representations of objects and environments using mathematical and computational techniques. Geometric modeling enables designers and engineers to visualize and analyze complex structures, simulate real-world scenarios, and create stunning visual effects. However, the process of geometric modeling comes with its own set of challenges and limitations, which require careful consideration and innovative solutions. One of the primary challenges in geometric modeling is achieving accuracy and precision in the representation of 3D objects. Designers and engineers often need to create models that closely resemble real-world objects or adhere to specific measurements and standards. This requires the use of advanced mathematical algorithms and computational methods to ensure that the digital models are geometrically accurate and free from errors. Achieving this level of precision can be time-consuming and resource-intensive, especially for complex and intricate designs. Another significant challenge in geometric modeling is the complexity of representing organic and free-form shapes. While traditional geometric shapes such as cubes, spheres, and cylinders can be easily defined using mathematical equations, organic shapes like human figures, animals, and natural landscapes pose a greater challenge. Creating realistic and detailed representations of such shapes requires the use of advanced modeling techniques such as NURBS (Non-Uniform Rational B-Splines) and subdivision surfaces, which allow for more flexible and natural-looking forms. However, mastering these techniques and achieving the desired level of realism often requires extensive skill and experience. Furthermore, geometric modeling also faces challenges related to data interoperability and compatibility. In many cases, digital models need to be shared across different softwareplatforms and integrated into larger design or simulation workflows. However, the lack of standardized file formats and data structures can lead to issues with data loss, format conversion errors, and compatibility issues. This can hinder the seamless exchange and utilization of geometric models across different software applications and industry domains, leading to inefficiencies and workflowdisruptions. In addition to technical challenges, geometric modeling also presents creative and artistic dilemmas for designers and artists. While the use of mathematical algorithms and computational methods is essential for creating accurate and precise models, it can also limit the creative freedom and artistic expression of designers. Striking a balance between technical precision andartistic creativity is often a delicate and iterative process, requiring designers to constantly refine and adjust their models to achieve the desired aesthetic and functional outcomes. Moreover, the rapid advancement of technology and the growing demand for high-fidelity visual content pose a challenge for geometric modeling. As the expectations for realism and detail in digital models continue to rise, designers and engineers are under pressure to deliver increasingly complex and sophisticated models within shorter timeframes. This necessitates the development of more efficient and automated modeling tools and techniques that can streamline the modeling process without compromising quality. Despite these challenges, the field of geometric modeling continues to evolve and innovate, driven by the relentless pursuit of realism, efficiency, and creativity. Researchers and practitioners are constantly exploring new algorithms, computational methods, and software tools to overcome the limitations oftraditional modeling approaches. From the development of machine learning algorithms for automated shape generation to the integration of virtual reality and haptic feedback for immersive modeling experiences, the future of geometric modeling holds great promise for addressing current challenges and unlocking new possibilities. In conclusion, geometric modeling is a multifaceted disciplinethat encompasses technical, creative, and practical challenges. From achieving precision and accuracy in 3D representations to addressing issues of data interoperability and artistic expression, the field of geometric modeling requires a holistic and innovative approach. By embracing technological advancements, fostering interdisciplinary collaboration, and nurturing creative talent, the industry can overcome current challenges and pave the way for groundbreaking advancements in computer graphics and design.。
机械设计制造及其自动化英文自我介绍全文共3篇示例,供读者参考篇1Hello, my name is [Your Name] and I am a mechanical design and manufacturing engineer. I have always been passionate about creating innovative solutions and have found my calling in the field of mechanical design and automation.I graduated with a Bachelor's degree in Mechanical Engineering and have since gained valuable experience working in various roles within the industry. My expertise lies in designing and manufacturing mechanical components and systems, as well as implementing automation processes to enhance efficiency and productivity.In my previous roles, I have worked on a range of projects, from designing small components for consumer electronics to complete production lines for industrial applications. 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I am confident that my skills and experience make me a valuable asset to any organization looking to innovate and excel in the field of mechanical engineering. Thank you for taking the time to learn more about me.篇3Hello, my name is Peter and I am a mechanical design and manufacturing engineer specializing in automation. I have a passion for creating and optimizing automated systems that improve efficiency and productivity in manufacturing processes.I have a strong background in mechanical engineering with a focus on designing machines and components that meet the highest standards of quality and precision. I have experience working with 3D modeling software such as SolidWorks and AutoCAD to create detailed designs that are both functional and cost-effective.In addition to my design skills, I also have a keen understanding of automation technologies such as robotics, PLC programming, and mechatronics. I have worked on projects that involve designing and integrating automated systems into manufacturing processes to streamline operations and reduce human error.One of my proudest accomplishments is a project where I designed a robotic assembly line for a client in the automotive industry. The system I created was able to assemble car partswith precision and efficiency, resulting in a significant increase in production output for the client.I am always seeking new challenges and opportunities to further develop my skills in mechanical design, manufacturing, and automation. I am confident in my ability to contribute to any team or project and I am eager to continue learning and growing in this field.Thank you for considering my profile and I look forward to the opportunity to discuss how I can contribute to your organization.。
H IGHER-ORDER MODELING AND AUTOMATED DESIGN-SPACE EXPLORATIONJ¨o rn W.JanneckEECS Department University of California at Berkeley Berkeley,CA,U.S.Ajwj@Robert Esser Department of Computer Science Adelaide UniversityAdelaide,S.A.,Australiaesser@Keywords:design space exploration,exploratory simula-tion,performance evaluation,higher-order models ABSTRACTAn important part of the design of complex systems is the evaluation of the large number of potential alterna-tive designs.Due to the number and complexity of design parameters,this design space is potentially huge and very complex.Automating part of the design exploration task can be an invaluable help infinding the optimal or near optimal settings of design parameters.The choice of the most appropriate exploration strategy depends on the nature of the parameters,such as their role in the model,the di-mensionality and structure of the design space including the number and location of local optima,etc.This paper advo-cates the use of higher-order modeling techniques to express exploration strategies.This allows users to formulate them in the same set of languages used to model the original sys-tem.Hence the set of design space exploration tools can be extended and parameterized as easily as the model itself.In this paper a higher-order modeling langage is presented.As an example a number of simple exploration tools are mod-eled and applied to a small optimization problem.1INTRODUCTIONThe design and construction of models of large and complex systems is an integral part of many design pro-cesses.The simulation of these models helps designers test whether customer requirements have been successfully cap-tured and are often used as a basis for an investigation into different solutions—an exploration of the design space.In many real-world systems,the design space of pos-sible design alternatives is very large and the process of finding an optimal,or even very good,solution based on a set of constraints can be very time consuming.In general, a design represented by a system model depends on a set of parameters.The nature of these parameters may range from simple scalars describing a delay,capacity constraint, etc.in some part of the model,to algorithms that describe alternative computations taking place inside the model,to complete’active’sub-models that serve,e.g.,as embedded strategies that control the behavior of(parts of)the model. Hence the task offinding an optimal solution,the maximum value of some cost function,is one where appropriate values for the set of design parameters must be determined.Ideally,one would employ an analytic procedure for computing the optimal parameter set for a given problem. However in many practical situations this is not possible, the only way offinding the good solution is to construct a system model based on these parameters and to simulate it.Simulating many alternative system models is costly, both in terms of the effort it takes to create these models in thefirst place and also in the time it takes to execute a large number of these models.In particular,when the design space is very large,constructing all possible models quickly becomes infeasible,and better search techniques are needed to reduce the number of alternatives to be explored.This work therefore assumes that a modeling environ-ment should support the automation of the following two tasks:•automatic creation of models based on a set of param-eters,•the creation of new parameter sets based on previous parameter sets and the results of the simulation of the models created from them.Thefirst requirement implies some form of parametric modeling language whereas the second requires the exis-tence of an automated process for design space exploration. There are many ways of automating design space explo-ration,e.g.exhaustive simulation,different kinds of search strategies(from simple hill climbing to more complex meth-ods),genetic algorithms,simulated annealing,etc.Their suitability to a particular design problem critically dependspublished in:Proceedings High-Performance Computing (HPC) 2002, A. Tentner, Ed.on the kinds of parameters and the size,dimensionality and structure of the design space.In practice,the user will need to select and,ideally,be able to modify a predefined explo-ration strategy.This paper advocates the use of higher-order model-ing languages as a simple yet powerful way to address these issues.The term higher-order modeling language as it is used here refers to modeling languages(and their associ-ated runtime environments)which are able to treat models themselves as pieces of data.Models can be created,de-stroyed and manipulated,moved through and accessed in different parts of the system and be the values of parameters and computations inside the system.There are many prac-tical uses for higher-order modeling and in this paper we will explore how it can be applied to express design space exploration strategies.Being able to formulate these strategies in the same language that is used to model the actual system enables any user to create,maintain,use,and extend a library of these strategies without any further training beyond what was necessary to do the original modeling task.This work elaborates the work presented in[5],where we described a framework for the exhaustive simulation of parametric models.It extends those results by using higher-order modelling concepts to construct a new framework pro-viding a parametric exploration template that allows an effi-cient and automatic exploration of the design-space.The remainder of this paper is structured as follows: after an overview of related work in section2we describe a simple hypothetical system and sketch ways in which its design space might be explored to optimize its performance. In section4we then shortly present a higher-order model-ing language and show how it may be used to specify the components of the exploration strategies used in the exam-ple.Finally we discuss the results and give an outlook for further work in this area in section5.2RELATED WORKAlthough there is a large body of work in the area of design-space exploration nearly all of it has been in specific domains such as embedded system design[2],hardware-software co-design[17],or control systems[13].Even though,at an abstract level,the tasks and the algorithms involved in design-space exploration tend to be similar, frameworks continue to be application domain specific. Moreover,they distinguish between the model,and the sys-tem that executes and simulates it—defining the model is very different from describing the way it is simulated,i.e. the way parameters are chosen and results are collected.There are a number of meta-modeling frameworks that would support a sufficiently expressive higher-order mod-eling language to be designed—e.g.Dome[7],GME[8],Ptolemy[3],or Moses[6].Most modeling frameworks ei-ther focus on the visual syntax(as in the case of Dome and GME),providing little or no support for formalized defini-tion of model semantics,or are primarily designed to sup-port the creation offirst-order models(as e.g.Ptolemy).In contrast,Moses is built on a simulation framework that naturally facilitates the definition of higher-order mod-eling languages.It also provides infrastructure for formally defining language semantics in addition to syntax.This is described in detail in[10]with some smaller general mod-eling examples using these techniques in[11].Finally,the inspiration for higher-order modeling tech-niques,as well as the name for them,comes from thefield of programming languages,where higher-order functions or procedures are used as a natural and powerful technique for composing functionality and for building complex systems from simpler ones(see for example[1]).3EXAMPLE APPLICATIONThe simple hypothetical system used to illustrate our approach is one that processes an input and produces ex-actly one output.The goal of the design space exploration is to minimize the latency between consumption of the input data and production of the output data.The system(and its performance)will depend on two design parameters which are real numbers in the range of0.1to0.9.1Fig.1shows the design space of our system,depicting the latency as a function of the two system parameters,P1 and P2.2The function depicted infig.1has several local minima and as a result a simple strategy such as hill climb-ing will not always be successful infinding the global min-imum.It is important to stress that in practice,a user would typically not have the luxury of knowing the structure of the design space from a convenient overviewfigure such as this. Generating afigure like this requires that the design space be exhaustively scanned and is exactly what a more sophis-ticated search strategy is designed to avoid.3Also,there may be many more parameters than the2in our example, making it more difficult to visualize the design space.In order tofind a good solution we might start by em-ploying an evolutionary algorithm,which maintains a pop-1The number and type of these parameters are chosen primarily for ease of visualizing the design space—as will become apparent later on, the proposed techniques do not require these assumptions and are in fact sigificantly more general.2In our hypothetical example,the latency is in fact directly computed from the two parameters,as follows:L=(3+sin(17P1)∗cos(23P2))(0.2+max(|P1−0.6|,|p2−0.5|))In general,obtaining thisfigure might involve very lengthy simulations of the system for a large number of possible inputs.3The abovefigure has a resolution of0.01in both parameters,i.e.it shows the results for80∗80=6400parameter settings.0.1Delayb)0.1DelayP2P1Figure1:Views of the delay of the example system. ulation of parameter settings and selects,recombines,and mutates elements in the population to obtain new elements. This method will tend to identify areas of interest,around which the better parameter settings will gravitate.Figure2 shows the parameter tuples explored by a very simple evo-lutionary algorithm afterfive generations.4In our example, the last population essentially appears in one area,however in more complex cases we might see several clusters.Having identified interesting design subspaces,we might now proceed by zooming in on them and perhaps ex-haustively simulating them with afiner resolution.Fig.3 shows the results of such an exploration using a parameter range for P1of0.45..0.65and for P2of0.4..0.6,and a res-olution of0.005for both parameters.This produced a result of the minimum delay at P1=0.6and P2=0.51,using a 4In each generation,50parameter seetings were explored,i.e.thefigure shows250parameter settings.P1P2Figure2:Explored parameter settings afterfive generations of evolutionary optimization(last population and best result highlighted).total of1600simulation runs.Alternatively,we could assume that the design space in that area will be smooth with a single optimum.In this case, we could simply start from somewhere inside our area of interest as defined by the initial evolutionary algorithm run, and use a hill climbing algorithm to improve the solutions found there.Fig.4shows the path taken by a very simple hill climbing algorithm in64steps from the point P1= 0.55,P2=0.55to the point P1=0.601,P2=0.532, with afixed step size in both parameters of0.001.In practice modelers ideally use a tool box of search strategies and will combine them as is appropriate for the model,its design space and optimization goal.They may also wish to extend the tool box by modifying,configuring, and extending existing strategies to best suit their tasks.The following section illustrates how higher-order modeling lan-guages may be useful in this context.4HIGHER-ORDER MODELS FOR DESIGN-SPACE EXPLORATIONIn this section a notation is introduced that allows models to be described implementing the design space ex-ploration tasks described in the previous section.The chosen notation is a variant of high-level Petri nets(see e.g.[18]),however many other higher-order modeling lan-guages are conceivable.A more comprehensive introduc-tion to the notation used here can be found in[9,11],a complete description of the higher-order modeling frame-work and the formal semantics of this notation is contained0.40.40.40.40.40.0.0.0.0.0.0.0.0.0.0.000DelayP2P1Figure 3:An exhaustive exploration of the design space around the last EA population.in [10].5In the following,it is important to note that the design-space exploration framework can itself be thought of as a system—a system to calculate an appropriate set of parame-ters of the application system being designed.It is our belief that the modeling requirements of the application system and the exploration framework are not essentially different and that it is useful to be able to express both in one and the same notation,or set of notations.Petri nets [16,18]are a notation for modeling concur-rent systems which combine a solid formal underpinning with an intuitive visual appearance.They have been ex-tended in various ways in order to make them more suitable for real-world modeling tasks,in particular with the addition of concepts such as time (e.g.[15,12,4])and composition-ality (e.g.[12,14,4].Our approach takes a high-level time Petri net formal-ism similar to the one defined in [4],and adds three features:•A concept of components that are connected to their environment using interfaces over which tokens may flow.•Components can have parameters that can be bound to any type of object including functions and compo-nents.5Themodeling framework is significantly more general than what ispresented here.It allows for arbitrary textual and visual notations to sup-port higher-order components.We will skip the details of this here for brevity.P1P 2Figure 4:Explored parameter settings of hill-climbing al-gorithm starting somewhere inside the last EA population.TestRun(P1, P2)Function: res = F(P1, P2)Compute resultFigure 5:The simple test run component.•Components are objects and can be treated as tokens residing within other components.While residing on a container place ,they can be connected to the environ-ment of that place via their interfaces.Fig.5shows the simple component described in the previous section.It has one input port named start on the left,through which it may receive tokens from its en-vironment.These tokens are added to the place (the circu-lar node),where they activate the transition (the rectangle),labeled Compute result .Upon activation the transition per-forms a computation,viz.defining a variable called res by computing the term F(P1,P2).P1and P2are param-eters of this component,and F is the function (not shown here)that computes the delay in Fig.1.The outgoing arc of this transition is labeled res ,indicating that the value of the token flowing across this arc when that transition fires is the value of the like-named variable.The arc is connected to an output port named result ,indicating that the tokens will be sent via this port to any component connected to it.For the purpose of this paper,this component is a very trivial one:in response to an incoming token,it computes a number (incidentally,it always computes the same number,as the expression only depends on the two parameters and a function,none of which change during the lifetime of this component),and sends that number to its output port.In the context of our experiment,this represents an entire sim-Figure6:A design-space explorer component.ulation of a model,and the number produced represents any ’result’(tency,resource requirements,etc.)indicating how well the model performs.4.1The design space explorerFig.6represents a more sophisticated component,the DesignSpaceExplorer.This component successively creates instances of(in this case)the TestRun compo-nent from parameter tuples,executes them,and collects and outputs the results(together with the corresponding param-eters).The set of parameter tuples to explore is passed to the DesignSpaceExplorer as a parameter named parameterSet.This set becomes the initial token set of the place labeled Parameters.Activity starts with the token entering the component via the port labeled next being placed on the associated place.As long as the Parameters place contains at least one parameter tuple,the transition labeled Next parameters is activated,and the inhibited transition Signal done is not.When the Next parameters transitionfires,it picks one of the parameter tuples,and places it onto the two places in its postset.This in turn activates the transition labeled Cre-ate run.Uponfiring,it applies the function runFactory (a parameter of the DesignSpaceExplorer)to this pa-rameter tuple.This results in a new TestRun component being instantiated,which is subsequently bound to the vari-able run.The result of thefiring is two tokens:a valueless (black or null)token that will ultimately initiate the execu-tion of the new TestRun and a token,containing the new TestRun bound to run and placed on the double-rimmed container place,labeled Simulation.This container place is the key to higher-order model-ing in this notation.As can be seen in thefigure,arcs can be connected to it in two different ways:they may be at-tached to the place itself or to one of the smaller ports on its perimeter.If an incoming arc is connected directly to the place(as is the one from the Create run transition),tokens flowing across this arc are added to the tokens already resid-ing on the place,just as they would with a’regular’Petri net place,even though in this case these tokens are themselves components.Similarly,outgoing arcs remove tokens from the container place when the corresponding transitionfires.By contrast,the arcs connected to the port symbols on the container place do not transport tokens to and from the place itself.Instead,tokens are moved into and out of the components residing on that place.Ports are matched by name against the correspondingly named input and output ports of the contained components.So in this example,after the Create run transition hasfired and placed the TestRun component onto the container place,the Start run transition becomes activated and uponfiring it produces a token at its outgoing arc.This token is sent to the start input port of the contained components,in this case the TestRun compo-nent.The arrival of this token triggers the chain of activ-ity previously described for the TestRun component in Fig.5,up to the production of a token at its result output port.This token emerges from the like-named port of the container place and is placed onto the place connected to it,activating the transition labeled Combine result and pa-rameters.When this transitionfires,not only does it pro-duce a result token(containing the parameters and the result value),it also removes the TestRun component from the Simulation container place,effectively removing it from the system.The DesignSpaceExplorer responds in a simi-Figure7:Iterating the design-space exploration process. lar fashion to every token sent to it on the next port,un-til the Parameters place becomes empty.Then the previ-ously inhibited transitionfires,and produces a token on the done output port,signaling to the environment of the DesignSpaceExplorer that its parameter tuples have been exhausted.4.2Generational design space explorationAs described above the DesignSpaceExplorer simulates the system for one set of parameter tuples,but hill climbing as well as evolutionary methods usually require a number of’generations’of parameter tuples to be explored, where each set of parameter tuples will depend on the re-sults generated by the previous generation.In the case of hill climbing,the next generation will be the neighborhood of the parameter tuple producing the best result,while evo-lutionary algorithms will select a part of the original param-eter set based on the results,and generate new individuals from those that were selected.Fig.7shows a GenerationalDSE,i.e.a compo-nent that explores the design space in a sequence of gen-erations.It takes a strategy as a parameter—a component with two input ports(nextGen and addResult)and two output ports(resultAck and dseOut).This component is initially placed on(and never removed from)the Strategy container place.The DSE container place is initially empty.The GenerationalDSE component is started by sending it a token on its start input port,which is im-mediately sent to the nextGen input port of the strategy. Whenever a strategy receives a token on this port,it may reply with a new design space explorer component6pro-duced at its dseOut output port.(It may also choose not to do anything.)As can be seen in thefigure,this token is placed directly onto the DSE container place,as well as on 6In our example,the design space explorer will be of the kind we dis-cussed previously,but in general it may be any other component that ad-heres to the general input/output contract outlined above.the place in the lower right,activating the Start new gener-ation transition.When this transitionfires,it sends a token to the next input port of the design space explorer(residing on the DSE place).This may reply by either producing a result at its result output port,or by producing a token at its done output port,indicating that its set of parameter tuples is ex-hausted.If the explorer produces a result,this is directly sent to the addResult input port of the strategy,where it may to stored for computing the next generation.It is also sent to the result output port,so that users of the GenerationalDSE component may store the result.The strategy is required to acknowledge the reception of the re-sult by emitting a token at its resultAck output port, which is sent to the next input port of the strategy and causes the next parameter tuple to be evaluated.Eventually,the design space explorer will exhaust its set of parameter tuples,and emit a token at its done out-put port.This activates the Finish generation transition,and when itfires,two things happen:it removes thefinished design space explorer from its container place(removing it from the model),and it sends a token to the next in-put port of the strategy,triggering the creation of the next design space explorer.This transition,and the connection between the dseOut port of the strategy and the DSE con-tainer place are the higher-order elements in this compo-nent,in that they treat a component as a piece of data,con-necting it to and disconnecting it from the rest of the model.4.3Simple exploration strategiesThe generational explorer in Fig.7has a parameter that defines the specific strategy component to be used.In the experiments described in section3we used a hill climb-ing strategy,an evolutionary strategy,and exhaustive explo-ration.The latter is rather straightforward,and its core func-tionality was described in[5].We will not go into the details of these strategies here, as they do not contain any higher-order elements—they es-sentially manipulate sets of parameter tuples based on re-sults produced during the exploration process.To give an impression of the(relatively modest)complexity of these components,Fig.8depicts the hill climbing strategy,which also supports dynamic adaptation of the step size(some-thing that was not used in section3).Fig.9shows the evo-lutionary strategy,which is also ratherflexible as it can be parameterized in the way it selects,mates,and mutates the individual parameter tuples.Even though these models may not appear to be self-explanatory,it is important to bear in mind that in that case of Fig.9it takes a model of seven places and six transitions (and a few short function definitions)to add aflexible evo-) dseFactory // dseFactory(pop) nextStep, // nextStep(curBestPars, preBestPars, stepSize) initStep,neighbors, // neighbors(bestPars, stepSize) initialPars,resultBetter, // resultBetter(newResOutput, oldResOutput)SimpleHillClimbingGC(Current Best ResultInitialTokens: [initStep]Previous step size Function: gen = new dse.util.CollectionGenerator([{initialPars}])Generate first unary neighborhoodnextGenGuard: step = nullTerminateresultAck addResultdseOutelse best end then rFunction: best = if resultBetter(r("output"), best("output")) Compute current best resultNew step sizebestgenstep Guard: step <> null dseFactory(neighbors(best("pars"), step))Function: dse =Create neighborhood generator beststepstep dsenextStep(best("pars"), prevBest, inStep) outStep =Function: prevBest = best("pars"),Compute next step sizeoutStepbestinStepprevBest[null]InitialTokens: InitialTokens: [null]rInitialize best resultrrFigure 8:A simple hill-climbing strategy.lutionary exploration capability to a modeling environment,that was not originally designed with this in mind.5CONCLUSIONIn this paper we illustrated how higher-order model-ing concepts can be applied to automate design-space ex-ploration.We demonstrated this with a small design explo-ration example and then went on the show how higher-order modeling techniques could be used to create the capabili-ties needed in order to perform the exploration tasks.We designed four rather simple models,two of which manipu-lated other models,with the result that a generic modeling and simulation environment was augmented with a general and flexible design-exploration capability.The flexibility required for exploring the model behav-ior for a range of parameters could have been directly im-plemented in the model.New parameter values could be sent to the model (as tokens)and stored there,rather than fixing them at the time when a model is instantiated.This implies that additional model structures need to be created to ’reinitialize’the model with new parameter values.These additional structures have nothing to do with the system that the model is supposed to represent and typically representsdseOutresultAck Current population resultsBestselPop = initialPopFunction: pop = [],Initialize population InitialTokens: [null]Guard: pop'size() >= popSizeFunction: dse = dseFactory(pop)Send new populationGuard: pop'size() < popSizeFunction: pop = pop + [mate(pick(pop, rand), pick(pop, rand))]Add new inidividualaddResultInitialize elitenextGenpop = [] for r in cropTo(pop, selPopSize)],Function: selPop = [best("pars")] + [r("pars") : Select populationthen r else best endbest = if resultBetter(r("output"), best("output")) Function: pop = if r in pop then pop else insertRes(r, pop) end, Add resultInitialTokens: [null])dseFactory // dseFactory(pop) selPopSize,popSize, mate, // mate(pars) initialPop,resultBetter, // resultBetter(newResOutput, oldResOutput)SimpleEvolutionaryStrategy(selPoppoppopbestrdserbestpoprpoppopselPopFigure 9:A simple evolutionary strategy.a non-trivial task for complex models.We believe that in modeling,as in programming lan-guages,the additional conceptual complexity of higher-order constructs is an invaluable investment that makes models simpler,more compositional,and allows the ca-pabilities of modeling environments to be easily extended.While we have integrated these constructs into a Petri net language,they could equally well be integrated into other languages,often with very little notational overhead.ACKNOWLEDGMENTSPart of this work was conducted in the Ptolemy project ()and supported in part by the MARCO/DARPA Gigascale Silicon Research Center ().Their support is grate-fully acknowledged.References[1]Harold Abelson and Gerald Jay Sussman.Structureand Interpretation of Computer Programs .MIT Press,2nd edition,1999.[2]Santosh Abraham,B.Ramakrishna Rau,and RobertSchreiber.Fast design space exploration through va-。