Concepts NREC模块与功能
软件具体模块名称及功能简介如下：
离心/斜流压气机设计点及非设计点平均流线性能预测程序:COMPAL
叶片泵设计点及非设计点平均流线性能预测程序:PUMPAL
风机/风扇设计点及非设计点平均流线性能预测程序:FANPAL
径流涡轮设计及性能预测程序：RITAL
轴流压气机/涡轮设计点及非设计点平均流线性能预测程序:AXIAL
三维流道和叶片几何设计，快速交互式流场分析和通流计算程序:AxCent·
从其它三维CAD软件的叶轮数据输入接口：CADTranslator·
快速设计级CFD程序:PushbuttonCFD
自动FEA前后处理程序及解算程序:PushbuttonFEA
高温涡轮气冷叶片设计分析系统:CTAADS
多学科自动优化接口程序:TurboOptII
转子动力学及轴承分析软件:DyRoBeS·
叶轮零件整体数控加工自动数控编程软件：MAX-PAC
Concepts NREC软件用户群
ConceptsNREC公司业务遍布世界各地，客户数量超过400家，包括知名的制造厂商、政府科研部门、工程协会、研究所和高校等。
应用行业包括航空发动机、燃气轮机、汽轮机、火箭涡轮泵、涡轮增压器、压缩机、透平膨胀机、能量回收装置、各种叶片泵和风机等产品领域，产品类型可包括径流、斜流、轴流或组合结构，单级或多级设计。
Concepts NREC中国用户
自1993年进入中国以来，目前国内软件用户已经超过80家，涵盖压缩/气机、涡轮增压器、风机/鼓风机、透平膨胀机、叶片泵、汽轮机、机床厂、叶轮专业加工单位等领域。
如沈鼓、金通灵、重通、开山、杭氧、开封空分、宁波博格华纳、上海霍尼韦尔、湖南天雁、山东富源、无锡威孚、莱恩电泵等领域内的知名单位。2100433B
Concepts NREC造价信息
Concepts NREC是世界上唯一一个集设计、分析、加工于一体的研发平台，可用于各种叶轮机械包括压缩机、涡轮增压器、膨胀机、叶片泵等产品。软件集成了Concepts NREC公司50多年的工程设计经验。主要功能包括：
a.总体方案、一维方案设计
b.三维详细设计和全三元流CFD分析
c.有限元应力和振动分析
d.轴承设计和转子动力学分析
e.多学科多目标优化设计软件f.直纹面侧刃加工、自由曲面点加工和闭式叶轮整体铣削专业软件
Concepts NREC是世界上最著名的叶轮机械专业服务公司（以下简称CN公司）。全世界唯一的既开发和推广叶轮机械设计/加工专用（CAE/CAM）软件，同时也提供叶轮机械样机开发和性能测试的全方位高端服务公司，当前员工总数130人。
公司前身源于美国麻省理工学院的3位科学家1956年成立的北方研究工程公司（NREC）和美国工程院院士DaveJapikse博士于1980年成立的ConceptsETI公司。公司分支机构和服务体系遍布全球各个主要工业国家。
2000年，集成两家公司原软件为全新的AgileEngineeringDesignSystem（AEDS）敏捷工程设计系统，致力于为业界提供“敏捷设计”和“敏捷制造”为宗旨的透平机械研发一体化解决方案。
CN具有一支经验十分丰富的专家队伍，当前公司专家团队曾在诸多著名大公司和研究机构承担过重要型号或产品研发，包括：GE、NASA、Honeywell、Pratt&Whitney、DR、IR、RR、SolarTurbines、Hamilton、Lycoming、Williams、ARL、AEDC、Flowsever等等。
数十年研发持续积累、强大的专家队伍、全球客户不断反馈是CN工程咨询和软件开发技术能力的核心知识库。
CN还具备非常先进的样机试制和试验台位等硬件条件，能够快速实现从先进设计到高精密制造以及性能试验的完整研发过程。每年承担诸多美国SBIR,STTR科研项目。公司每年在ASME等学术会议上发表诸多研究成果论文。
Concepts NREC模块与功能常见问题
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由于人脸识别技术所使用的是常规通用设备，价格均在一般用户可接受的范围之内，与其它生物识别技术相比，人脸识别产品具有很高的性能价格比。概括地说，人脸识别技术是一种高精度、易于使用、稳定性高、难仿冒、性价...
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就是很多个IGBT集成在一起
-
民建消防工程，消防报警系统图纸要求模块均设置在独立的模块箱中，可以直接计算 包括模块在内的 模块箱价格。套用模块箱 安装的定额。甲方不计模块箱的安装，只给主材,是不合理的 。
Concepts NREC模块与功能文献
英文标准名称： Industrial systems,installations and equipment and industrial products-Structuring principles and reference designations-Part 4:Discussion of concepts
发布日期 IssuanceDate： 2005-3-3
实施日期 ExecuteDate： 2005-8-1
首次发布日期 FirstIssuance Date： 1985-4-18
标准状态 StandardState： 现行
复审确认日期 ReviewAffirmance Date：
计划编号 Plan No： 20030927-T-524
代替国标号 ReplacedStandard：
被代替国标号 ReplacedStandard:
废止时间 RevocatoryDate：
采用国际标准号 AdoptedInternational Standard No： IEC 61346-4:1998
采标名称 AdoptedInternational Standard Name：
采用程度 ApplicationDegree： IDT
采用国际标准 AdoptedInternational Standard： IEC
国际标准分类号(ICS)： 29.020
中国标准分类号(CCS)： K04
标准类别 StandardSort： 基础
标准页码 Number ofPages: 18
标准价格(元) Price(￥)： 13
主管部门 Governor： 国家标准化管理委员会
归口单位 TechnicalCommittees： 全国电气信息结构、文件编制和图形符号标准化技术委员会
起草单位 DraftingCommittee：2100433B
Contents
part one Foundations
chapter one Models and Concepts of Life and Intelligence 3
The Mechanics of Life and Thought 4
Stochastic Adaptation: Is Anything Ever Really Random"para" label-module="para">
The “Two Great Stochastic Systems” 12
The Game of Life: Emergence in Complex Systems 16
The Game of Life 17
Emergence 18
Cellular Automata and the Edge of Chaos 20
Artificial Life in Computer Programs 26
Intelligence: Good Minds in People and Machines 30
Intelligence in People: The Boring Criterion 30
Intelligence in Machines: The Turing Criterion 32
chapter two Symbols, Connections, and Optimization by Trial and Error 35
Symbols in Trees and Networks 36
Problem Solving and Optimization 48
A Super-Simple Optimization Problem 49
Three Spaces of Optimization 51
Fitness Landscapes 52
High-Dimensional Cognitive Space and Word Meanings 55
Two Factors of Complexity: NK Landscapes 60
Combinatorial Optimization 64
Binary Optimization 67
Random and Greedy Searches 71
Hill Climbing 72
Simulated Annealing 73
Binary and Gray Coding 74
Step Sizes and Granularity 75
Optimizing with Real Numbers 77
Summary 78
chapter three On Our Nonexistence as Entities: The Social Organism 81
Views of Evolution 82
Gaia: The Living Earth 83
Differential Selection 86
Our Microscopic Masters"para" label-module="para">
Looking for the Right Zoom Angle 92
Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94
Accomplishments of the Social Insects 98
Optimizing with Simulated Ants: Computational Swarm Intelligence 105
Staying Together but Not Colliding: Flocks, Herds, and Schools 109
Robot Societies 115
Shallow Understanding 125
Agency 129
Summary 131
chapter four Evolutionary Computation Theory and Paradigms 133
Introduction 134
Evolutionary Computation History 134
The Four Areas of Evolutionary Computation 135
Genetic Algorithms 135
Evolutionary Programming 139
Evolution Strategies 140
Genetic Programming 141
Toward Unification 141
Evolutionary Computation Overview 142
EC Paradigm Attributes 142
Implementation 143
Genetic Algorithms 146
An Overview 146
A Simple GA Example Problem 147
A Review of GA Operations 152
Schemata and the Schema Theorem 159
Final Comments on Genetic Algorithms 163
Evolutionary Programming 164
The Evolutionary Programming Procedure 165
Finite State Machine Evolution 166
Function Optimization 169
Final Comments 171
Evolution Strategies 172
Mutation 172
Recombination 174
Selection 175
Genetic Programming 179
Summary 185
chapter five Humans—Actual, Imagined, and Implied 187
Studying Minds 188
The Fall of the Behaviorist Empire 193
The Cognitive Revolution 195
Bandura’s Social Learning Paradigm 197
Social Psychology 199
Lewin’s Field Theory 200
Norms, Conformity, and Social Influence 202
Sociocognition 205
Simulating Social Influence 206
Paradigm Shifts in Cognitive Science 210
The Evolution of Cooperation 214
Explanatory Coherence 216
Networks in Groups 218
Culture in Theory and Practice 220
Coordination Games 223
The El Farol Problem 226
Sugarscape 229
Tesfatsion’s ACE 232
Picker’s Competing-Norms Model 233
Latané’s Dynamic Social Impact Theory 235
Boyd and Richerson’s Evolutionary Culture Model 240
Memetics 245
Memetic Algorithms 248
Cultural Algorithms 253
Convergence of Basic and Applied Research 254
Culture—and Life without It 255
Summary 258
chapter six Thinking Is Social 261
Introduction 262
Adaptation on Three Levels 263
The Adaptive Culture Model 263
Axelrod’s Culture Model 265
Experiment One: Similarity in Axelrod’s Model 267
Experiment Two: Optimization of an Arbitrary Function 268
Experiment Three: A Slightly Harder and More Interesting Function 269
Experiment Four: A Hard Function 271
Experiment Five: Parallel Constraint Satisfaction 273
Experiment Six: Symbol Processing 279
Discussion 282
Summary 284
part two The Particle Swarm and Collective Intelligence
chapter seven The Particle Swarm 287
Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288
Evaluate 288
Compare 288
Imitate 289
A Model of Binary Decision 289
Testing the Binary Algorithm with the De Jong Test Suite 297
No Free Lunch 299
Multimodality 302
Minds as Parallel Constraint Satisfaction Networks in Cultures 307
The Particle Swarm in Continuous Numbers 309
The Particle Swarm in Real-Number Space 309
Pseudocode for Particle Swarm Optimization in Continuous Numbers 313
Implementation Issues 314
An Example: Particle Swarm Optimization of Neural Net Weights 314
A Real-World Application 318
The Hybrid Particle Swarm 319
Science as Collaborative Search 320
Emergent Culture, Immergent Intelligence 323
Summary 324
chapter eight Variations and Comparisons 327
Variations of the Particle Swarm Paradigm 328
Parameter Selection 328
Controlling the Explosion 337
Particle Interactions 342
Neighborhood Topology 343
Substituting Cluster Centers for Previous Bests 347
Adding Selection to Particle Swarms 353
Comparing Inertia Weights and Constriction Factors 354
Asymmetric Initialization 357
Some Thoughts on Variations 359
Are Particle Swarms Really a Kind of Evolutionary Algorithm"para" label-module="para">
Evolution beyond Darwin 362
Selection and Self-Organization 363
Ergodicity: Where Can It Get from Here"para" label-module="para">
Convergence of Evolutionary Computation and Particle Swarms 367
Summary 368
chapter nine Applications 369
Evolving Neural Networks with Particle Swarms 370
Review of Previous Work 370
Advantages and Disadvantages of Previous Approaches 374
The Particle Swarm Optimization Implementation Used Here 376
Implementing Neural Network Evolution 377
An Example Application 379
Conclusions 381
Human Tremor Analysis 382
Data Acquisition Using Actigraphy 383
Data Preprocessing 385
Analysis with Particle Swarm Optimization 386
Summary 389
Other Applications 389
Computer Numerically Controlled Milling Optimization 389
Ingredient Mix Optimization 391
Reactive Power and Voltage Control 391
Battery Pack State-of-Charge Estimation 391
Summary 392
chapter ten Implications and Speculations 393
Introduction 394
Assertions 395
Up from Social Learning: Bandura 398
Information and Motivation 399
Vicarious versus Direct Experience 399
The Spread of Influence 400
Machine Adaptation 401
Learning or Adaptation"para" label-module="para">
Cellular Automata 403
Down from Culture 405
Soft Computing 408
Interaction within Small Groups: Group Polarization 409
Informational and Normative Social Influence 411
Self-Esteem 412
Self-Attribution and Social Illusion 414
Summary 419
chapter eleven And in Conclusion . . . 421
Appendix A Statistics for Swarmers 429
Appendix B Genetic Algorithm Implementation 451
Glossary 457
References 475
Index 4972100433B
part one Foundations
chapter one Models and Concepts of Life and Intelligence 3
The Mechanics of Life and Thought 4
Stochastic Adaptation: Is Anything Ever Really Random"para" label-module="para">
The “Two Great Stochastic Systems” 12
The Game of Life: Emergence in Complex Systems 16
The Game of Life 17
Emergence 18
Cellular Automata and the Edge of Chaos 20
Artificial Life in Computer Programs 26
Intelligence: Good Minds in People and Machines 30
Intelligence in People: The Boring Criterion 30
Intelligence in Machines: The Turing Criterion 32
chapter two Symbols, Connections, and Optimization by Trial and Error 35
Symbols in Trees and Networks 36
Problem Solving and Optimization 48
A Super-Simple Optimization Problem 49
Three Spaces of Optimization 51
Fitness Landscapes 52
High-Dimensional Cognitive Space and Word Meanings 55
Two Factors of Complexity: NK Landscapes 60
Combinatorial Optimization 64
Binary Optimization 67
Random and Greedy Searches 71
Hill Climbing 72
Simulated Annealing 73
Binary and Gray Coding 74
Step Sizes and Granularity 75
Optimizing with Real Numbers 77
Summary 78
chapter three On Our Nonexistence as Entities: The Social Organism 81
Views of Evolution 82
Gaia: The Living Earth 83
Differential Selection 86
Our Microscopic Masters"para" label-module="para">
Looking for the Right Zoom Angle 92
Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94
Accomplishments of the Social Insects 98
Optimizing with Simulated Ants: Computational Swarm Intelligence 105
Staying Together but Not Colliding: Flocks, Herds, and Schools 109
Robot Societies 115
Shallow Understanding 125
Agency 129
Summary 131
chapter four Evolutionary Computation Theory and Paradigms 133
Introduction 134
Evolutionary Computation History 134
The Four Areas of Evolutionary Computation 135
Genetic Algorithms 135
Evolutionary Programming 139
Evolution Strategies 140
Genetic Programming 141
Toward Unification 141
Evolutionary Computation Overview 142
EC Paradigm Attributes 142
Implementation 143
Genetic Algorithms 146
An Overview 146
A Simple GA Example Problem 147
A Review of GA Operations 152
Schemata and the Schema Theorem 159
Final Comments on Genetic Algorithms 163
Evolutionary Programming 164
The Evolutionary Programming Procedure 165
Finite State Machine Evolution 166
Function Optimization 169
Final Comments 171
Evolution Strategies 172
Mutation 172
Recombination 174
Selection 175
Genetic Programming 179
Summary 185
chapter five Humans—Actual, Imagined, and Implied 187
Studying Minds 188
The Fall of the Behaviorist Empire 193
The Cognitive Revolution 195
Bandura’s Social Learning Paradigm 197
Social Psychology 199
Lewin’s Field Theory 200
Norms, Conformity, and Social Influence 202
Sociocognition 205
Simulating Social Influence 206
Paradigm Shifts in Cognitive Science 210
The Evolution of Cooperation 214
Explanatory Coherence 216
Networks in Groups 218
Culture in Theory and Practice 220
Coordination Games 223
The El Farol Problem 226
Sugarscape 229
Tesfatsion’s ACE 232
Picker’s Competing-Norms Model 233
Latané’s Dynamic Social Impact Theory 235
Boyd and Richerson’s Evolutionary Culture Model 240
Memetics 245
Memetic Algorithms 248
Cultural Algorithms 253
Convergence of Basic and Applied Research 254
Culture—and Life without It 255
Summary 258
chapter six Thinking Is Social 261
Introduction 262
Adaptation on Three Levels 263
The Adaptive Culture Model 263
Axelrod’s Culture Model 265
Experiment One: Similarity in Axelrod’s Model 267
Experiment Two: Optimization of an Arbitrary Function 268
Experiment Three: A Slightly Harder and More Interesting Function 269
Experiment Four: A Hard Function 271
Experiment Five: Parallel Constraint Satisfaction 273
Experiment Six: Symbol Processing 279
Discussion 282
Summary 284
part two The Particle Swarm and Collective Intelligence
chapter seven The Particle Swarm 287
Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288
Evaluate 288
Compare 288
Imitate 289
A Model of Binary Decision 289
Testing the Binary Algorithm with the De Jong Test Suite 297
No Free Lunch 299
Multimodality 302
Minds as Parallel Constraint Satisfaction Networks in Cultures 307
The Particle Swarm in Continuous Numbers 309
The Particle Swarm in Real-Number Space 309
Pseudocode for Particle Swarm Optimization in Continuous Numbers 313
Implementation Issues 314
An Example: Particle Swarm Optimization of Neural Net Weights 314
A Real-World Application 318
The Hybrid Particle Swarm 319
Science as Collaborative Search 320
Emergent Culture, Immergent Intelligence 323
Summary 324
chapter eight Variations and Comparisons 327
Variations of the Particle Swarm Paradigm 328
Parameter Selection 328
Controlling the Explosion 337
Particle Interactions 342
Neighborhood Topology 343
Substituting Cluster Centers for Previous Bests 347
Adding Selection to Particle Swarms 353
Comparing Inertia Weights and Constriction Factors 354
Asymmetric Initialization 357
Some Thoughts on Variations 359
Are Particle Swarms Really a Kind of Evolutionary Algorithm"para" label-module="para">
Evolution beyond Darwin 362
Selection and Self-Organization 363
Ergodicity: Where Can It Get from Here"para" label-module="para">
Convergence of Evolutionary Computation and Particle Swarms 367
Summary 368
chapter nine Applications 369
Evolving Neural Networks with Particle Swarms 370
Review of Previous Work 370
Advantages and Disadvantages of Previous Approaches 374
The Particle Swarm Optimization Implementation Used Here 376
Implementing Neural Network Evolution 377
An Example Application 379
Conclusions 381
Human Tremor Analysis 382
Data Acquisition Using Actigraphy 383
Data Preprocessing 385
Analysis with Particle Swarm Optimization 386
Summary 389
Other Applications 389
Computer Numerically Controlled Milling Optimization 389
Ingredient Mix Optimization 391
Reactive Power and Voltage Control 391
Battery Pack State-of-Charge Estimation 391
Summary 392
chapter ten Implications and Speculations 393
Introduction 394
Assertions 395
Up from Social Learning: Bandura 398
Information and Motivation 399
Vicarious versus Direct Experience 399
The Spread of Influence 400
Machine Adaptation 401
Learning or Adaptation"para" label-module="para">
Cellular Automata 403
Down from Culture 405
Soft Computing 408
Interaction within Small Groups: Group Polarization 409
Informational and Normative Social Influence 411
Self-Esteem 412
Self-Attribution and Social Illusion 414
Summary 419
chapter eleven And in Conclusion . . . 421
Appendix A Statistics for Swarmers 429
Appendix B Genetic Algorithm Implementation 451
Glossary 457
References 475
Index 497
……2100433B
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