{"product_id":"contemporary-issues-in-systems-science-and-engineering-isbn-9781118271865","title":"Contemporary Issues in Systems Science and Engineering","description":"Various systems science and engineering disciplines are covered and challenging new research issues in these disciplines are revealed. They will be extremely valuable for the readers to search for some new research directions and problems.\u003cbr\u003e\u003cbr\u003e \u003cul\u003e \u003cli\u003eChapters are contributed by world-renowned systems engineers\u003c\/li\u003e \u003cli\u003eChapters include discussions and conclusions\u003c\/li\u003e \u003cli\u003eReaders can grasp each event holistically without having professional expertise in the field\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eContributors xxiii\u003c\/p\u003e \u003cp\u003ePreface xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Systems Science are Engineering Methodologies 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Systems Framework For Sustainability 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAli G. Hessami, Feng Hsu, are Hamid Jahankhani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 A Unified Systems Sustainability Concept 5\u003c\/p\u003e \u003cp\u003e1.3 Sustainability Assurance: the Framework 6\u003c\/p\u003e \u003cp\u003e1.3.1 Weighted Factors Analysis 6\u003c\/p\u003e \u003cp\u003e1.3.2 the Framework 7\u003c\/p\u003e \u003cp\u003e1.3.3 the Macro Concept of a Sustainable Architecture (G4.1) 10\u003c\/p\u003e \u003cp\u003e1.3.4 the Micro Concept of a Sustainable System 11\u003c\/p\u003e \u003cp\u003e1.3.5 A Top-Down Hierarchy of a Multi-Level Sustainability Concept 12\u003c\/p\u003e \u003cp\u003e1.4 Technological Sustainability Case Study—Information Systems Security 13\u003c\/p\u003e \u003cp\u003e1.4.1 Network Security as a Business Issue 14\u003c\/p\u003e \u003cp\u003e1.4.2 the Focus of Investment on Network Security 15\u003c\/p\u003e \u003cp\u003e1.5 Conclusions 17\u003c\/p\u003e \u003cp\u003eReferences 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 System of Systems Thinking In Policy Development: Challenges are Opportunities 21\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKeith W. Hipel, Liping Fang, are Michele Bristow\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.1.1 A World in Crisis 21\u003c\/p\u003e \u003cp\u003e2.1.2 System of Systems 23\u003c\/p\u003e \u003cp\u003e2.2 Value Systems are Ethics 26\u003c\/p\u003e \u003cp\u003e2.2.1 Conflicting Value Systems 27\u003c\/p\u003e \u003cp\u003e2.2.2 Modeling Value Systems 28\u003c\/p\u003e \u003cp\u003e2.3 Complex Adaptive Systems 32\u003c\/p\u003e \u003cp\u003e2.3.1 Emergent Behavior 32\u003c\/p\u003e \u003cp\u003e2.3.2 Modeling Complex Systems 34\u003c\/p\u003e \u003cp\u003e2.4 Risk, Uncertainty, are Unpredictability 37\u003c\/p\u003e \u003cp\u003e2.4.1 Risk Management 37\u003c\/p\u003e \u003cp\u003e2.4.2 Modeling Risk are Adaptation Processes 40\u003c\/p\u003e \u003cp\u003e2.5 System of Systems Modeling are Policy Development 42\u003c\/p\u003e \u003cp\u003e2.5.1 Global Food System Model 43\u003c\/p\u003e \u003cp\u003e2.5.2 Policy Implications 51\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Systemic Yoyos: An Intuition are Playground For General Systems Research 71\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYi Lin, Yi Dongyun, are Zaiwu Gong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 71\u003c\/p\u003e \u003cp\u003e3.1.1 the Concept of General Systems 72\u003c\/p\u003e \u003cp\u003e3.1.2 A Look at the Success of Calculus-Based Theories 75\u003c\/p\u003e \u003cp\u003e3.1.3 Whole Evolution are Yoyo Fields 78\u003c\/p\u003e \u003cp\u003e3.2 Theoretical are Empirical Justifications 81\u003c\/p\u003e \u003cp\u003e3.2.1 Transitional Changes in Whole Evolutions 81\u003c\/p\u003e \u003cp\u003e3.2.2 Quantitative Infinity are Equal Quantitative Effects 83\u003c\/p\u003e \u003cp\u003e3.2.3 Fluid Circulation, Informational Infrastructure, are Human Communications 86\u003c\/p\u003e \u003cp\u003e3.3 Elementary Properties of Yoyo Fields 91\u003c\/p\u003e \u003cp\u003e3.3.1 Eddy are Meridian Fields 91\u003c\/p\u003e \u003cp\u003e3.3.2 Interactions Between Systemic Yoyos 94\u003c\/p\u003e \u003cp\u003e3.3.3 Laws on State of Motion 98\u003c\/p\u003e \u003cp\u003e3.4 Applications in Social Sciences 102\u003c\/p\u003e \u003cp\u003e3.4.1 Systemic Structures of Civilizations 102\u003c\/p\u003e \u003cp\u003e3.4.2 Systemic Structures Beneath Business Organizations 108\u003c\/p\u003e \u003cp\u003e3.4.3 Systemic Structure in Human Mind 109\u003c\/p\u003e \u003cp\u003e3.5 Applications in Economics 113\u003c\/p\u003e \u003cp\u003e3.5.1 Becker’s Rotten Kid Theorem 113\u003c\/p\u003e \u003cp\u003e3.5.2 Interindustry Wage Differentials 117\u003c\/p\u003e \u003cp\u003e3.5.3 Price Behaviors of Projects 122\u003c\/p\u003e \u003cp\u003e3.6 Applications in the Foundations of Mathematics 127\u003c\/p\u003e \u003cp\u003e3.6.1 Historical Crises in the Foundations of Mathematics 128\u003c\/p\u003e \u003cp\u003e3.6.2 Actual are Potential Infinities 131\u003c\/p\u003e \u003cp\u003e3.6.3 Vase Puzzle are the Fourth Crisis 132\u003c\/p\u003e \u003cp\u003e3.7 Applications in Extreme Weather Forecast 137\u003c\/p\u003e \u003cp\u003e3.7.1 V-3\u003ci\u003e𝜃 \u003c\/i\u003eGraphs: A Structural Prediction Method 137\u003c\/p\u003e \u003cp\u003e3.7.2 Digitization of Irregular Information 140\u003c\/p\u003e \u003cp\u003e3.8 Conclusions 143\u003c\/p\u003e \u003cp\u003eReferences 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Grey System: Thinking, Methods, are Models With Applications 153\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSifeng Liu, Jeffrey Y.L. Forrest, are Yingjie Yang \u003c\/i\u003e4.1 Introduction 153\u003c\/p\u003e \u003cp\u003e4.1.1 Inception are Growth of Grey System Theory 153\u003c\/p\u003e \u003cp\u003e4.1.2 Basics of Grey System 155\u003c\/p\u003e \u003cp\u003e4.2 Sequence Operators 157\u003c\/p\u003e \u003cp\u003e4.2.1 Buffer Operators 158\u003c\/p\u003e \u003cp\u003e4.2.2 Generation of Grey Sequences 160\u003c\/p\u003e \u003cp\u003e4.2.3 Exponentiality of Accumulating Generations 162\u003c\/p\u003e \u003cp\u003e4.3 Grey Incidence Analysis 163\u003c\/p\u003e \u003cp\u003e4.3.1 Grey Incidence Factors are Set of Grey Incidence Operators 163\u003c\/p\u003e \u003cp\u003e4.3.2 Degrees of Grey Incidences 164\u003c\/p\u003e \u003cp\u003e4.3.3 General Grey Incidence Models 165\u003c\/p\u003e \u003cp\u003e4.3.4 Grey Incidence Models Based on Similarity and Nearness 167\u003c\/p\u003e \u003cp\u003e4.4 Grey Cluster Evaluation Models 168\u003c\/p\u003e \u003cp\u003e4.4.1 Grey Incidence Clustering 169\u003c\/p\u003e \u003cp\u003e4.4.2 Grey Variable Weight Clustering 169\u003c\/p\u003e \u003cp\u003e4.4.3 Grey Fixed Weight Clustering 171\u003c\/p\u003e \u003cp\u003e4.4.4 Grey Evaluation Using Triangular Whitenization Functions 172\u003c\/p\u003e \u003cp\u003e4.4.5 Practical Applications 175\u003c\/p\u003e \u003cp\u003e4.5 Grey Prediction Models 176\u003c\/p\u003e \u003cp\u003e4.5.1 GM(1,1) Model 176\u003c\/p\u003e \u003cp\u003e4.5.2 Improvements on GM(1,1) Models 177\u003c\/p\u003e \u003cp\u003e4.5.3 Applicable Ranges of GM(1,1) Models 180\u003c\/p\u003e \u003cp\u003e4.5.4 Discrete Grey Models 180\u003c\/p\u003e \u003cp\u003e4.5.5 GM(r,h) Models 182\u003c\/p\u003e \u003cp\u003e4.5.6 Grey System Predictions 188\u003c\/p\u003e \u003cp\u003e4.6 Grey Models for Decision-Making 193\u003c\/p\u003e \u003cp\u003e4.6.1 Grey Target Decisions 193\u003c\/p\u003e \u003cp\u003e4.6.2 Multi-Attribute Intelligent Grey Target Decision Models 201\u003c\/p\u003e \u003cp\u003e4.7 Practical Applications 202\u003c\/p\u003e \u003cp\u003e4.7.1 To Analyze the Time Difference of Economic Indices 202\u003c\/p\u003e \u003cp\u003e4.7.2 the Evaluation of Science are Technology Park 206\u003c\/p\u003e \u003cp\u003e4.7.3 To Select the Supplier of Key Components of Large\u003c\/p\u003e \u003cp\u003eCommercial Aircrafts 209\u003c\/p\u003e \u003cp\u003e4.8 Introduction to the Software of Grey System Modeling 211\u003c\/p\u003e \u003cp\u003e4.8.1 Features are Functions 211\u003c\/p\u003e \u003cp\u003e4.8.2 Operation Guide 213\u003c\/p\u003e \u003cp\u003eAcknowledgments 220\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Building Resilience: Naval Expeditionary Command are Control 225\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChristopher Nemeth, Thomas Miller, Michael Polidoro, and C. Matthew O’Connor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 225\u003c\/p\u003e \u003cp\u003e5.2 Expeditionary Operations Command are Control 226\u003c\/p\u003e \u003cp\u003e5.2.1 Systems Acquisition 227\u003c\/p\u003e \u003cp\u003e5.3 Human-Centered System Development 228\u003c\/p\u003e \u003cp\u003e5.3.1 Envisioned World Problem 229\u003c\/p\u003e \u003cp\u003e5.3.2 Cognitive Systems Engineering 229\u003c\/p\u003e \u003cp\u003e5.3.3 Application: Navy Expeditionary Combat Command 230\u003c\/p\u003e \u003cp\u003e5.3.4 Reasonable Scientific Criteria 231\u003c\/p\u003e \u003cp\u003e5.4 Discussion 232\u003c\/p\u003e \u003cp\u003e5.4.1 Resilience Engineering 232\u003c\/p\u003e \u003cp\u003e5.4.2 the Data Hub 234\u003c\/p\u003e \u003cp\u003e5.4.3 Implementation Challenges 234\u003c\/p\u003e \u003cp\u003e5.4.4 Limitations 234\u003c\/p\u003e \u003cp\u003e5.5 Future Work 236\u003c\/p\u003e \u003cp\u003e5.5.1 Human Performance Research 236\u003c\/p\u003e \u003cp\u003e5.5.2 Transition from Qualitative Research to Design 236\u003c\/p\u003e \u003cp\u003e5.5.3 Resilience Engineering 236\u003c\/p\u003e \u003cp\u003e5.6 Conclusions 237\u003c\/p\u003e \u003cp\u003eAcknowledgments 237\u003c\/p\u003e \u003cp\u003eReferences 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Learning are Control 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Advances are Challenges On Intelligent Learning In Control Systems 243\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChing-Chih Tsai, Kao-Shing Hwang, Alan Liu, are Chia-Feng Juang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 243\u003c\/p\u003e \u003cp\u003e6.2 Reinforcement Learning 245\u003c\/p\u003e \u003cp\u003e6.2.1 Reinforcement Learning 245\u003c\/p\u003e \u003cp\u003e6.2.2 Q-Learning Algorithm 247\u003c\/p\u003e \u003cp\u003e6.2.3 Reinforcement Learning in Robots 249\u003c\/p\u003e \u003cp\u003e6.2.4 Soccer Robot Behaviors 250\u003c\/p\u003e \u003cp\u003e6.2.5 Concluding Remarks 251\u003c\/p\u003e \u003cp\u003e6.3 Bio-Inspired Evolutionary Learning Control 252\u003c\/p\u003e \u003cp\u003e6.3.1 Bio-Inspired Evolutionary Learning Control 252\u003c\/p\u003e \u003cp\u003e6.3.2 Bio-Inspired Evolutionary Robots 253\u003c\/p\u003e \u003cp\u003e6.4 Intelligent Learning Control Using Fuzzy Neural Networks 254\u003c\/p\u003e \u003cp\u003e6.4.1 Introduction 254\u003c\/p\u003e \u003cp\u003e6.4.2 Intelligent Learning Control Using FNNs 255\u003c\/p\u003e \u003cp\u003e6.5 Case-Based Reasoning are Learning 257\u003c\/p\u003e \u003cp\u003e6.5.1 Case-Based Reasoning Process 257\u003c\/p\u003e \u003cp\u003e6.5.2 Case Design are Reuse 257\u003c\/p\u003e \u003cp\u003e6.5.3 Hybrid Learning Method Architectures in CBR 258\u003c\/p\u003e \u003cp\u003e6.5.4 Applications in Human–Robot Interaction 259\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 260\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Adaptive Classifiers For Nonstationary Environments 265\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCesare Alippi, Giacomo Boracchi, Manuel Roveri, Gregory Ditzler, and Robi Polikar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 265\u003c\/p\u003e \u003cp\u003e7.2 Definition of the Problem 266\u003c\/p\u003e \u003cp\u003e7.3 Learning Concept Drifts 268\u003c\/p\u003e \u003cp\u003e7.4 Change Detection 272\u003c\/p\u003e \u003cp\u003e7.4.1 Change-Detection Tests: A Review 273\u003c\/p\u003e \u003cp\u003e7.4.2 Change-Detection Tests in Adaptive Classifiers 276\u003c\/p\u003e \u003cp\u003e7.5 Assessing the Performance: Figures of Merit 278\u003c\/p\u003e \u003cp\u003e7.5.1 Raw Classification Accuracy 279\u003c\/p\u003e \u003cp\u003e7.5.2 Confusion Matrix 279\u003c\/p\u003e \u003cp\u003e7.5.3 Geometric Mean 280\u003c\/p\u003e \u003cp\u003e7.5.4 Precision are Recall 280\u003c\/p\u003e \u003cp\u003e7.5.5 \u003ci\u003eF\u003c\/i\u003e-measure 281\u003c\/p\u003e \u003cp\u003e7.5.6 Receiver Operator Characteristic Curve are Area Under the Curve 281\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Modeling, Analysis, Scheduling, are Control of Cluster Tools In Semiconductor Fabrication 289\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNai Qi Wu, Mengchu Zhou, Feng Chu, are Sa¨ıd Mammar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 289\u003c\/p\u003e \u003cp\u003e8.2 Cluster Tools are Their Operations 290\u003c\/p\u003e \u003cp\u003e8.2.1 Architecture of Cluster Tools 290\u003c\/p\u003e \u003cp\u003e8.2.2 Wafer Flow Patterns 291\u003c\/p\u003e \u003cp\u003e8.2.3 Operation Requirements 294\u003c\/p\u003e \u003cp\u003e8.3 Modeling are Performance Evaluation 295\u003c\/p\u003e \u003cp\u003e8.3.1 Analysis Based on Timing Diagram Model 295\u003c\/p\u003e \u003cp\u003e8.3.2 Analysis Based on Marked Graph 296\u003c\/p\u003e \u003cp\u003e8.3.3 Analysis Based on Resource-Oriented Petri Nets 299\u003c\/p\u003e \u003cp\u003e8.3.4 Discussion 302\u003c\/p\u003e \u003cp\u003e8.4 Single Cluster Tool Scheduling 302\u003c\/p\u003e \u003cp\u003e8.4.1 Scheduling with Wafer Residency Time Constraints 302\u003c\/p\u003e \u003cp\u003e8.4.2 Scheduling with Both Wafer Residency Constraints and Activity Time Variation 305\u003c\/p\u003e \u003cp\u003e8.4.3 Scheduling with Wafer Revisiting 306\u003c\/p\u003e \u003cp\u003e8.4.4 Schedule Implementation 307\u003c\/p\u003e \u003cp\u003e8.4.5 Discussion 307\u003c\/p\u003e \u003cp\u003e8.5 Scheduling of Multi-cluster Tools 308\u003c\/p\u003e \u003cp\u003e8.5.1 Deadlock Control are Scheduling of Track Systems 308\u003c\/p\u003e \u003cp\u003e8.5.2 Schedule Optimization 309\u003c\/p\u003e \u003cp\u003e8.5.3 Discussion 311\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 311\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Design, Simulation, are Dynamic Control Of Large-Scale Manufacturing Process With Different Forms of Uncertainties 317\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHyunsoo Lee are Amarnath Banerjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 317\u003c\/p\u003e \u003cp\u003e9.1.1 Issues in Design of Large-Scale Manufacturing Processes 318\u003c\/p\u003e \u003cp\u003e9.1.2 Simulation Model for Dynamic Control 320\u003c\/p\u003e \u003cp\u003e9.2 Background are Literature Review 322\u003c\/p\u003e \u003cp\u003e9.3 Different Types of Uncertainties are FCPN-std 327\u003c\/p\u003e \u003cp\u003e9.3.1 Definition of FCPN-std 327\u003c\/p\u003e \u003cp\u003e9.3.2 Modular Design are Five-Stage Modeling Methodology 329\u003c\/p\u003e \u003cp\u003e9.3.3 Simulation Using FCPN-std 332\u003c\/p\u003e \u003cp\u003e9.4 Design of Large-Scale Manufacturing Processes 333\u003c\/p\u003e \u003cp\u003e9.5 Dynamic Control of Manufacturing Processes 335\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 339\u003c\/p\u003e \u003cp\u003eReferences 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Model Identification are Synthesis of Discrete-Event Systems 343\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMaria Paola Cabasino, Philippe Darondeau, Maria Pia Fanti, and Carla Seatzu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 343\u003c\/p\u003e \u003cp\u003e10.2 Background on Finite State Automata are Petri Nets 344\u003c\/p\u003e \u003cp\u003e10.2.1 Finite State Automata 344\u003c\/p\u003e \u003cp\u003e10.2.2 Petri Nets 346\u003c\/p\u003e \u003cp\u003e10.3 Identification are Synthesis of Languages are Finite State Automata 347\u003c\/p\u003e \u003cp\u003e10.4 Identification are Synthesis of Petri Nets 349\u003c\/p\u003e \u003cp\u003e10.4.1 Synthesis from Graphs 350\u003c\/p\u003e \u003cp\u003e10.4.2 Identification are Synthesis from Finite Languages Over \u003ci\u003eT \u003c\/i\u003e352\u003c\/p\u003e \u003cp\u003e10.4.3 Identification are Synthesis from Finite Languages Over \u003ci\u003eE \u003c\/i\u003e355\u003c\/p\u003e \u003cp\u003e10.4.4 Related Problems in the PN Framework 360\u003c\/p\u003e \u003cp\u003e10.5 Process Mining are Workflow Problems 361\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 363\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Human–Machine Systems Design 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Advances are Challenges In Intelligent Adaptive Interface Design 369\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMing Hou, Haibin Zhu, Mengchu Zhou, are Robert Arrabito\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 369\u003c\/p\u003e \u003cp\u003e11.2 Evolution of Interface Technologies are IAI Concept 372\u003c\/p\u003e \u003cp\u003e11.2.1 Evolution of Interface Technologies 373\u003c\/p\u003e \u003cp\u003e11.2.2 A Conceptual Framework of IAI Systems 377\u003c\/p\u003e \u003cp\u003e11.3 Challenges of IAI Design, Alternative Solutions, are Empirical Investigations 381\u003c\/p\u003e \u003cp\u003e11.3.1 Challenges of IAI Design 381\u003c\/p\u003e \u003cp\u003e11.3.2 User-Centered Design Approach 382\u003c\/p\u003e \u003cp\u003e11.3.3 Agent-Based Interface Design Approaches 383\u003c\/p\u003e \u003cp\u003e11.3.4 Analytical Methodologies 385\u003c\/p\u003e \u003cp\u003e11.3.5 Empirical Investigations 387\u003c\/p\u003e \u003cp\u003e11.4 Multiagent-Based Design are Operator–Agent Interaction 389\u003c\/p\u003e \u003cp\u003e11.4.1 AIA Concept 389\u003c\/p\u003e \u003cp\u003e11.4.2 Operator–Agent Interaction Model 391\u003c\/p\u003e \u003cp\u003e11.4.3 Difference Between Human–Human Interaction, Human–Machine Interaction, are Operator–Agent Interaction 393\u003c\/p\u003e \u003cp\u003e11.4.4 Optimization of Operator–Agent Interaction 396\u003c\/p\u003e \u003cp\u003e11.5 A Generic IAI System Architecture are AIA Components 397\u003c\/p\u003e \u003cp\u003e11.5.1 Generic IAI System Architecture 397\u003c\/p\u003e \u003cp\u003e11.5.2 AIA Structure 402\u003c\/p\u003e \u003cp\u003e11.5.3 Adaptation Processes 403\u003c\/p\u003e \u003cp\u003e11.6 An IAI are AIA Design: Case Study 405\u003c\/p\u003e \u003cp\u003e11.6.1 Interface Design Requirements for the Control of Multiple UAVs 406\u003c\/p\u003e \u003cp\u003e11.6.2 Issues 407\u003c\/p\u003e \u003cp\u003e11.6.3 How the IAI Design Method Was Used 407\u003c\/p\u003e \u003cp\u003e11.6.4 Task Network Modeling are Simulation 409\u003c\/p\u003e \u003cp\u003e11.6.5 AIA Implementation 411\u003c\/p\u003e \u003cp\u003e11.6.6 Human-in-the-Loop Experimentation 413\u003c\/p\u003e \u003cp\u003e11.6.7 AIA Evaluation 413\u003c\/p\u003e \u003cp\u003e11.6.8 Discussions are Implications 413\u003c\/p\u003e \u003cp\u003e11.7 Conclusions 415\u003c\/p\u003e \u003cp\u003eAcknowledgments 417\u003c\/p\u003e \u003cp\u003eReferences 417\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Complex Adaptive System of Systems Approach to Human–Automation Interaction In Smart Grid 425\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlireza Fereidunian, Hamid Lesani, Mohammad Ali Zamani, Mohamad Amin Sharifi Kolarijani, Negar Hassanpour, are Sina Sharif Mansouri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 425\u003c\/p\u003e \u003cp\u003e12.2 Complexity in Systems Science are Engineering 426\u003c\/p\u003e \u003cp\u003e12.2.1 the Nature of Complexity 426\u003c\/p\u003e \u003cp\u003e12.2.2 Complex Systems 429\u003c\/p\u003e \u003cp\u003e12.2.3 Complexity Measures 431\u003c\/p\u003e \u003cp\u003e12.2.4 Complexity-Related Terms in Literature 433\u003c\/p\u003e \u003cp\u003e12.3 Complex Adaptive Systems 436\u003c\/p\u003e \u003cp\u003e12.3.1 What are Complex Adaptive Systems? 436\u003c\/p\u003e \u003cp\u003e12.3.2 Characteristics of Complex Adaptive Systems 437\u003c\/p\u003e \u003cp\u003e12.4 System of Systems 442\u003c\/p\u003e \u003cp\u003e12.4.1 Necessity are Definition 442\u003c\/p\u003e \u003cp\u003e12.4.2 Characteristics of System of Systems 444\u003c\/p\u003e \u003cp\u003e12.4.3 System of Systems Types 448\u003c\/p\u003e \u003cp\u003e12.4.4 A Taxonomy of Systems Family 448\u003c\/p\u003e \u003cp\u003e12.5 Complex Adaptive System of Systems 453\u003c\/p\u003e \u003cp\u003e12.6 Human–Automation Interaction 454\u003c\/p\u003e \u003cp\u003e12.6.1 Automation 454\u003c\/p\u003e \u003cp\u003e12.6.2 HAI: Where Humans Interact with Automation 455\u003c\/p\u003e \u003cp\u003e12.6.3 HAI are Function Allocation 456\u003c\/p\u003e \u003cp\u003e12.6.4 Evolution of HAI Models: Dimensions 457\u003c\/p\u003e \u003cp\u003e12.6.5 Evolution of HAI Models: Dynamism 458\u003c\/p\u003e \u003cp\u003e12.6.6 Adaptive Autonomy Implementation 460\u003c\/p\u003e \u003cp\u003e12.7 HAI in Smart Grid as a Casos 462\u003c\/p\u003e \u003cp\u003e12.7.1 Smart Grid 462\u003c\/p\u003e \u003cp\u003e12.7.2 HAI in Smart Grid as a CAS 465\u003c\/p\u003e \u003cp\u003e12.7.3 HAI in Smart Grid as an SoS 467\u003c\/p\u003e \u003cp\u003e12.8 Petri Nets for Complex Systems Modeling 467\u003c\/p\u003e \u003cp\u003e12.8.1 Definition 468\u003c\/p\u003e \u003cp\u003e12.8.2 Graph Representation of Petri Nets 468\u003c\/p\u003e \u003cp\u003e12.8.3 Transition Firing 469\u003c\/p\u003e \u003cp\u003e12.8.4 Reachability 470\u003c\/p\u003e \u003cp\u003e12.8.5 Incidence Matrix are State Equation 470\u003c\/p\u003e \u003cp\u003e12.8.6 Inhibitor Arc 470\u003c\/p\u003e \u003cp\u003e12.8.7 IF–THEN Rules by Petri Net 470\u003c\/p\u003e \u003cp\u003e12.9 Model-Based Implementation of Adaptive Autonomy 471\u003c\/p\u003e \u003cp\u003e12.9.1 the Implementation Framework 471\u003c\/p\u003e \u003cp\u003e12.9.2 Case Study: Adaptive Autonomy in Smart Grid 472\u003c\/p\u003e \u003cp\u003e12.10 Adaptive Autonomy Realization Using Petri Nets 473\u003c\/p\u003e \u003cp\u003e12.10.1 Implementation Methodology 473\u003c\/p\u003e \u003cp\u003e12.10.2 Realization of AAHPNES 475\u003c\/p\u003e \u003cp\u003e12.10.3 Results are Discussions 482\u003c\/p\u003e \u003cp\u003e12.11 Conclusions 483\u003c\/p\u003e \u003cp\u003eAcknowledgments 485\u003c\/p\u003e \u003cp\u003eReferences 485\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Virtual Training For Procedural Skills Development: Case Studies are Lessons Learnt 501\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDawei Jia, Asim Bhatti, are Saeid Nahavandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 501\u003c\/p\u003e \u003cp\u003e13.2 Related Work 502\u003c\/p\u003e \u003cp\u003e13.2.1 Background 502\u003c\/p\u003e \u003cp\u003e13.2.2 Human Side of VT System Efficacy—Issues and Concerns 503\u003c\/p\u003e \u003cp\u003e13.3 Present Study 505\u003c\/p\u003e \u003cp\u003e13.3.1 Motivation are Aims 505\u003c\/p\u003e \u003cp\u003e13.3.2 System Architecture are Human–Machine Interface 506\u003c\/p\u003e \u003cp\u003e13.3.3 Measures 508\u003c\/p\u003e \u003cp\u003e13.4 Case Study 1 509\u003c\/p\u003e \u003cp\u003e13.4.1 Method 509\u003c\/p\u003e \u003cp\u003e13.4.2 Results 511\u003c\/p\u003e \u003cp\u003e13.4.3 Discussion 515\u003c\/p\u003e \u003cp\u003e13.5 Case Study 2 516\u003c\/p\u003e \u003cp\u003e13.5.1 Method 516\u003c\/p\u003e \u003cp\u003e13.5.2 Results 519\u003c\/p\u003e \u003cp\u003e13.5.3 Discussion 524\u003c\/p\u003e \u003cp\u003e13.6 Lessons Learnt are Future Work 527\u003c\/p\u003e \u003cp\u003e13.6.1 Training Design are Method 527\u003c\/p\u003e \u003cp\u003e13.6.2 Measurement Methods 528\u003c\/p\u003e \u003cp\u003e13.6.3 Prior Experience with a Force-Reflective Haptic Interface 530\u003c\/p\u003e \u003cp\u003e13.6.4 Future Work 531\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 531\u003c\/p\u003e \u003cp\u003eReferences 532\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Computer Supported Collaborative Design: Technologies, Systems, are Applications 537\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWeiming Shen, Jean-Paul Barth\u003c\/i\u003e\u003ci\u003eés, are Junzhou Luo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 537\u003c\/p\u003e \u003cp\u003e14.2 History of Computer Supported Collaborative Design 538\u003c\/p\u003e \u003cp\u003e14.2.1 CSCD 538\u003c\/p\u003e \u003cp\u003e14.2.2 CSCD Eve: 1980s 539\u003c\/p\u003e \u003cp\u003e14.2.3 CSCD Emergence: 1990s 541\u003c\/p\u003e \u003cp\u003e14.2.4 CSCD: Today 542\u003c\/p\u003e \u003cp\u003e14.3 Methods, Techniques, are Technologies 542\u003c\/p\u003e \u003cp\u003e14.3.1 Communication, Coordination, are Cooperation 542\u003c\/p\u003e \u003cp\u003e14.3.2 Negotiation are Conflict Resolution 546\u003c\/p\u003e \u003cp\u003e14.3.3 Ontology are Semantic Integration 548\u003c\/p\u003e \u003cp\u003e14.3.4 Personal Assistance are Human–Machine Interaction 548\u003c\/p\u003e \u003cp\u003e14.3.5 Collaborative Workflows 550\u003c\/p\u003e \u003cp\u003e14.3.6 Collaborative Virtual Workspaces are Environments 552\u003c\/p\u003e \u003cp\u003e14.3.7 New Representation Schemes for Collaborative Design 552\u003c\/p\u003e \u003cp\u003e14.3.8 New Visualization Systems for Collaborative Design 553\u003c\/p\u003e \u003cp\u003e14.3.9 Product Data Management are Product Lifecycle Management Systems 553\u003c\/p\u003e \u003cp\u003e14.3.10 Security are Privacy 554\u003c\/p\u003e \u003cp\u003e14.4 Collaborative Design Systems 555\u003c\/p\u003e \u003cp\u003e14.4.1 System Architectures 555\u003c\/p\u003e \u003cp\u003e14.4.2 Web-Based\/Centralized Systems 557\u003c\/p\u003e \u003cp\u003e14.4.3 Agent-Based\/Distributed Systems 558\u003c\/p\u003e \u003cp\u003e14.4.4 Service-Oriented Systems 558\u003c\/p\u003e \u003cp\u003e14.4.5 Collaborative Design Over Supply Chain (Virtual Enterprise) 559\u003c\/p\u003e \u003cp\u003e14.5 Applications 560\u003c\/p\u003e \u003cp\u003e14.6 Research Challenges are Opportunities 561\u003c\/p\u003e \u003cp\u003e14.7 Conclusions 564\u003c\/p\u003e \u003cp\u003eReferences 564\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Support Collaboration With Roles 575\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHaibin Zhu, Mengchu Zhou, are Ming Hou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 575\u003c\/p\u003e \u003cp\u003e15.2 Benefits of Roles in Collaboration 577\u003c\/p\u003e \u003cp\u003e15.2.1 Establishing Trust in Collaboration 577\u003c\/p\u003e \u003cp\u003e15.2.2 Establishing Dynamics 578\u003c\/p\u003e \u003cp\u003e15.2.3 Facilitating Interaction 580\u003c\/p\u003e \u003cp\u003e15.2.4 Support Adaptation 582\u003c\/p\u003e \u003cp\u003e15.2.5 Information Sharing 583\u003c\/p\u003e \u003cp\u003e15.2.6 Other Benefits 585\u003c\/p\u003e \u003cp\u003e15.3 Role-Based Collaboration 585\u003c\/p\u003e \u003cp\u003e15.4 E-Cargo Model 590\u003c\/p\u003e \u003cp\u003e15.5 A Case Study with RBC are E-Cargo 592\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 595\u003c\/p\u003e \u003cp\u003eReferences 595\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Cloud are Service-Oriented Computing 599\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Control-Based Approaches to Dynamic Resource Management In Cloud Computing 601\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePengcheng Xiong, Calton Pu, Zhikui Wang, are Gueyoung Jung\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 601\u003c\/p\u003e \u003cp\u003e16.1.1 Public Cloud Computing 602\u003c\/p\u003e \u003cp\u003e16.1.2 Dynamic Resource Management: Control-Based Approaches 602\u003c\/p\u003e \u003cp\u003e16.2 Experimental Setup are Application Models 603\u003c\/p\u003e \u003cp\u003e16.2.1 Test Bed are Control Architecture for a Multi-Tier Application 604\u003c\/p\u003e \u003cp\u003e16.2.2 System Models for the Application: Open or Closed 606\u003c\/p\u003e \u003cp\u003e16.3 Dynamic Resource Allocation Through Utilization Control 607\u003c\/p\u003e \u003cp\u003e16.3.1 Design of Experiments 607\u003c\/p\u003e \u003cp\u003e16.3.2 Performance of the Application Under Control 608\u003c\/p\u003e \u003cp\u003e16.4 Performance Guarantee Through Dynamic Resource Allocation 612\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 614\u003c\/p\u003e \u003cp\u003eReferences 615\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 A Petri Net Solution to Protocol-Level Mismatches In Service Composition 619\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePengcheng Xiong, Mengchu Zhou, Calton Pu, are Yushun Fan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 619\u003c\/p\u003e \u003cp\u003e17.1.1 Interface Mismatches 621\u003c\/p\u003e \u003cp\u003e17.1.2 Protocol-Level Mismatches 622\u003c\/p\u003e \u003cp\u003e17.2 Modeling Service Interaction with Petri Nets 624\u003c\/p\u003e \u003cp\u003e17.2.1 Basic Petri Nets 624\u003c\/p\u003e \u003cp\u003e17.2.2 Model Web Service Interaction with C-Net 627\u003c\/p\u003e \u003cp\u003e17.3 Protocol-Level Mismatch Analysis 630\u003c\/p\u003e \u003cp\u003e17.3.1 Protocol-Level Mismatch Detection 630\u003c\/p\u003e \u003cp\u003e17.3.2 Core Algorithm 632\u003c\/p\u003e \u003cp\u003e17.3.3 Comprehensive Solution to Protocol-Level Mismatch 634\u003c\/p\u003e \u003cp\u003e17.4 Illustrating Examples 636\u003c\/p\u003e \u003cp\u003e17.5 Conclusions 638\u003c\/p\u003e \u003cp\u003eReferences 641\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Service-Oriented Workflow Systems 645\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWei Tan are Mengchu Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 645\u003c\/p\u003e \u003cp\u003e18.2 Workflow in SOC: State of the Art 647\u003c\/p\u003e \u003cp\u003e18.2.1 Languages for Service Composition 647\u003c\/p\u003e \u003cp\u003e18.2.2 Automatic Service Composition 649\u003c\/p\u003e \u003cp\u003e18.2.3 Mediation-Aided Service Composition 649\u003c\/p\u003e \u003cp\u003e18.2.4 Verification of Service Workflows 650\u003c\/p\u003e \u003cp\u003e18.2.5 Decentralized Execution of Workflows 651\u003c\/p\u003e \u003cp\u003e18.3 Open Issues 652\u003c\/p\u003e \u003cp\u003e18.3.1 Social Network Meets Service Computing 652\u003c\/p\u003e \u003cp\u003e18.3.2 More Practical are Flexible Service Composition 652\u003c\/p\u003e \u003cp\u003e18.3.3 Workflow as a Service 653\u003c\/p\u003e \u003cp\u003e18.3.4 Novel Applications 654\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 656\u003c\/p\u003e \u003cp\u003eReferences 657\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Sensing, Networking, are Optimization In Robotics are Manufacturing 661\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Rehabilitation Robotic Prostheses For Upper Extremity 663\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHan-Pang Huang, Yi-Hung Liu, Wei-Chen Lee, Jiun-Yih Kuan, and Tzu-Hao Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 663\u003c\/p\u003e \u003cp\u003e19.2 Rehabilitation Robot Arm are Control 664\u003c\/p\u003e \u003cp\u003e19.2.1 Mechanism Design 666\u003c\/p\u003e \u003cp\u003e19.2.2 Dynamic Model of an Individual Joint 669\u003c\/p\u003e \u003cp\u003e19.2.3 LTR-Observer-Based Individual Joint Dynamic Sliding Mode Control with Gravity Compensation 671\u003c\/p\u003e \u003cp\u003e19.2.4 Simulation of the NTU Rehabilitation Robot Arm II 676\u003c\/p\u003e \u003cp\u003e19.2.5 Experimental Results for the NTU Rehabilitation Robot Arm II 677\u003c\/p\u003e \u003cp\u003e19.3 Rehabilitation Robot Hand 678\u003c\/p\u003e \u003cp\u003e19.4 Stability of Neuroprosthesis 683\u003c\/p\u003e \u003cp\u003e19.4.1 SVDD-Based Target EMG Pattern Estimation 685\u003c\/p\u003e \u003cp\u003e19.4.2 Nontarget EMG Pattern Filtering Scheme 686\u003c\/p\u003e \u003cp\u003e19.4.3 Illustrative Example 688\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 691\u003c\/p\u003e \u003cp\u003eReferences 692\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Accelerometer-Based Body Sensor Network (Bsn) For Medical Diagnosis Assessment are Training 699\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMing-Yih Lee, Kin Fong Lei, Wen-Yen Lin, Wann-Yun Shieh, Wen-Wei Tsai, Simon H. Fu, are Chung-Hsien Kuo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 699\u003c\/p\u003e \u003cp\u003e20.2 Body Sensor Network 700\u003c\/p\u003e \u003cp\u003e20.3 Information Retrieved from Accelerometer 702\u003c\/p\u003e \u003cp\u003e20.4 Recent Advances in Accelerometer-Based BSN 703\u003c\/p\u003e \u003cp\u003e20.4.1 Tilting Angle Identification 703\u003c\/p\u003e \u003cp\u003e20.4.2 Muscle Strength Identification 706\u003c\/p\u003e \u003cp\u003e20.4.3 Gait Performance Identification 708\u003c\/p\u003e \u003cp\u003e20.5 Applications of Accelerometer-Based BSN for Rehabilitation 711\u003c\/p\u003e \u003cp\u003e20.5.1 Human Stability Evaluation System 711\u003c\/p\u003e \u003cp\u003e20.5.2 Postural Stability Evaluation for Stroke Patients 712\u003c\/p\u003e \u003cp\u003e20.5.3 Postural Stability Training for Stroke Patients 713\u003c\/p\u003e \u003cp\u003e20.6 BSN Simulation System 715\u003c\/p\u003e \u003cp\u003e20.7 Conclusions 718\u003c\/p\u003e \u003cp\u003eReferences 719\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Telepresence Robots For Medical are Homecare Applications 725\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJun-Ming Lu are Yeh-Liang Hsu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 725\u003c\/p\u003e \u003cp\u003e21.2 Surgery, Diagnosis, are Consultation 727\u003c\/p\u003e \u003cp\u003e21.3 Rehabilitation are Therapy 728\u003c\/p\u003e \u003cp\u003e21.4 Monitoring are Assistance 728\u003c\/p\u003e \u003cp\u003e21.5 Communication 729\u003c\/p\u003e \u003cp\u003e21.6 Key Factors Contributing to the Success of Telepresence Robots 729\u003c\/p\u003e \u003cp\u003e21.6.1 Robot Factors of Acceptance 729\u003c\/p\u003e \u003cp\u003e21.6.2 Human Factors of Acceptance 731\u003c\/p\u003e \u003cp\u003e21.6.3 Summary 732\u003c\/p\u003e \u003cp\u003e21.7 Conclusions 732\u003c\/p\u003e \u003cp\u003eReferences 732\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Advances In Climbing Robots 737\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJizhong Xiao are Hongguang Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 737\u003c\/p\u003e \u003cp\u003e22.2 Technologies for Adhering to Surfaces 738\u003c\/p\u003e \u003cp\u003e22.2.1 Magnetic Adhesion 739\u003c\/p\u003e \u003cp\u003e22.2.2 Vacuum Suction Techniques 740\u003c\/p\u003e \u003cp\u003e22.2.3 Aerodynamic Attraction 744\u003c\/p\u003e \u003cp\u003e22.2.4 Grasping Grippers 748\u003c\/p\u003e \u003cp\u003e22.2.5 Bio-Mimetic Approaches Inspired by Climbing Animals 749\u003c\/p\u003e \u003cp\u003e22.2.6 Emerging Technologies for Climbing Robots 753\u003c\/p\u003e \u003cp\u003e22.3 Locomotion Techniques of Climbing Robots 755\u003c\/p\u003e \u003cp\u003e22.4 Conclusions 759\u003c\/p\u003e \u003cp\u003eAcknowledgment 760\u003c\/p\u003e \u003cp\u003eReferences 760\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Data Processing In Current 3D Robotic Perception Systems 767\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCang YE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 767\u003c\/p\u003e \u003cp\u003e23.1.1 Stereovision 767\u003c\/p\u003e \u003cp\u003e23.1.2 LIDAR 769\u003c\/p\u003e \u003cp\u003e23.1.3 Flash LIDAR Camera (FLC) 770\u003c\/p\u003e \u003cp\u003e23.2 An LIDAR-Based Terrain Mapping are Navigation System 771\u003c\/p\u003e \u003cp\u003e23.2.1 Overview of the Mapping are Navigation System 772\u003c\/p\u003e \u003cp\u003e23.2.2 Terrain Mapping 773\u003c\/p\u003e \u003cp\u003e23.2.3 Terrain Traversability Analysis 776\u003c\/p\u003e \u003cp\u003e23.2.4 PTI Histogram for Path Planning 777\u003c\/p\u003e \u003cp\u003e23.2.5 Experimental Results 779\u003c\/p\u003e \u003cp\u003e23.3 FLC-Based Systems 781\u003c\/p\u003e \u003cp\u003e23.3.1 VR-Odometry 782\u003c\/p\u003e \u003cp\u003e23.3.2 Three-Dimensional Data Segmentation 787\u003c\/p\u003e \u003cp\u003e23.4 Conclusions 791\u003c\/p\u003e \u003cp\u003eAcknowledgments 792\u003c\/p\u003e \u003cp\u003eReferences 792\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Hybrid\/Electric Vehicle Battery Manufacturing: The State-Of-The-Art 795\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eClaudia P. Arenas Guerrero, Feng Ju, Jingshan Li, Guoxian Xiao, and Stephan Biller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 795\u003c\/p\u003e \u003cp\u003e24.2 Vehicle Battery Requirements 796\u003c\/p\u003e \u003cp\u003e24.3 Hybrid, Plug-In Hybrid, are Electric Vehicle 797\u003c\/p\u003e \u003cp\u003e24.3.1 Hybrid Electric Vehicle 797\u003c\/p\u003e \u003cp\u003e24.3.2 Plug-In Hybrid Electric Vehicle 797\u003c\/p\u003e \u003cp\u003e24.3.3 Electric Vehicle 798\u003c\/p\u003e \u003cp\u003e24.4 Battery Technology Development 798\u003c\/p\u003e \u003cp\u003e24.5 Nickel-Metal Hydride Battery 799\u003c\/p\u003e \u003cp\u003e24.5.1 NiMH Battery Manufacturing 800\u003c\/p\u003e \u003cp\u003e24.5.2 NiMH Batteries in Commercial Vehicles 800\u003c\/p\u003e \u003cp\u003e24.5.3 Cost 801\u003c\/p\u003e \u003cp\u003e24.5.4 Recycling 801\u003c\/p\u003e \u003cp\u003e24.6 Lithium-Ion (Li-Ion) Battery 802\u003c\/p\u003e \u003cp\u003e24.6.1 Lithium Technology 802\u003c\/p\u003e \u003cp\u003e24.6.2 Manufacturing Processes 803\u003c\/p\u003e \u003cp\u003e24.6.3 Li-Ion Batteries in Commercial Vehicles 807\u003c\/p\u003e \u003cp\u003e24.6.4 Safety 808\u003c\/p\u003e \u003cp\u003e24.6.5 Cost 809\u003c\/p\u003e \u003cp\u003e24.6.6 Environmental Issues 809\u003c\/p\u003e \u003cp\u003e24.6.7 Recycling 809\u003c\/p\u003e \u003cp\u003e24.7 Challenges 810\u003c\/p\u003e \u003cp\u003e24.8 Conclusions 812\u003c\/p\u003e \u003cp\u003eReferences 812\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Recent Advances are Issues In Facility Location Problems 817\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFeng Chu, Zhanguo Zhu, are Sa\u003c\/i\u003e\u003ci\u003eïıd Mammar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 817\u003c\/p\u003e \u003cp\u003e25.2 A Capacitated Plant Location Problem with Multicommodity Flow 819\u003c\/p\u003e \u003cp\u003e25.2.1 Problem Description 819\u003c\/p\u003e \u003cp\u003e25.2.2 Problem Formulation 819\u003c\/p\u003e \u003cp\u003e25.3 A Multitype Transshipment Point Location Problem with Multicommodity Flow 821\u003c\/p\u003e \u003cp\u003e25.3.1 Problem Description 821\u003c\/p\u003e \u003cp\u003e25.3.2 Problem Formulation 822\u003c\/p\u003e \u003cp\u003e25.4 A Large Scale New Variant of Capacitated Clustering Problem 824\u003c\/p\u003e \u003cp\u003e25.4.1 Problem Description 824\u003c\/p\u003e \u003cp\u003e25.4.2 Problem Formulation 825\u003c\/p\u003e \u003cp\u003e25.5 A Location Problem with Selective Matching are Vehicles Assignment 826\u003c\/p\u003e \u003cp\u003e25.5.1 Problem Description 826\u003c\/p\u003e \u003cp\u003e25.5.2 Problem Formulation 826\u003c\/p\u003e \u003cp\u003e25.6 Competitive Facility Location are Design with Reactions of Competitors Already in the Market 828\u003c\/p\u003e \u003cp\u003e25.6.1 Problem Description 829\u003c\/p\u003e \u003cp\u003e25.6.2 Problem Formulation 829\u003c\/p\u003e \u003cp\u003e25.7 Conclusions are Future Research Directions 831\u003c\/p\u003e \u003cp\u003eReferences 832\u003c\/p\u003e \u003cp\u003eIndex 835\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMengChu Zhou \u003c\/b\u003eis a Distinguished Professor of Electrical and Computer Engineering at the \u003cb\u003eNew Jersey Institute of Technology\u003c\/b\u003e (\u003cb\u003eNJIT\u003c\/b\u003e), \u003cb\u003eUSA\u003c\/b\u003e. He is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, and is a fellow of IEEE, IFAC, and AAAS.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eHan-Xiong Li\u003c\/b\u003e is a Professor in the Department of Systems Science and Engineering and Engineering Management at the \u003cb\u003eCity University of Hong Kong\u003c\/b\u003e, \u003cb\u003eHK\u003c\/b\u003e. Dr. Li Serves as an Associate Editor of IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Electronics. He is a Fellow of the IEEE.\u003c\/p\u003e \u003cb\u003eMargot Weijnen\u003c\/b\u003e is a full Professor of Process and Energy Systems Engineering at \u003cb\u003eDelft University of Technology, the Netherlands\u003c\/b\u003e. She is the founding and Scientific Director of Next Generation Infrastructures and, since 2013, a member of the Netherlands Scientific Council for Government Policy. She is a founding fellow of ISEAM and European editor of the Journal of Critical Infrastructures. \u003cp\u003eThis volume provides a comprehensive overview of all important areas in systems science and engineering and poses the issues and challenges in these areas in order to deal with ever-increasingly complex systems and newly emergent applications. The topics range from discrete event systems, distributed intelligent systems, grey systems, and enterprise information systems to conflict resolution, robotics and intelligent sensing, smart grids, and system of systems approaches. Individual chapters are written by leading experts in the field.\u003cbr\u003e\u003cbr\u003eAdditional features include:\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents multiple systems science and engineering topics in-depth\u003c\/li\u003e \u003cli\u003eEach section includes detailed problems, technical issues, and solution methodologies, illustrated with a series of significant examples\u003c\/li\u003e \u003cli\u003eDiscusses important ideas for future research\u003c\/li\u003e \u003c\/ul\u003e This volume can be viewed as a handbook in this important field, as well as an important reference book for researchers and practicing engineers.","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47988982808805,"sku":"NP9781118271865","price":167.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118271865.jpg?v=1761782309","url":"https:\/\/k12savings.com\/products\/contemporary-issues-in-systems-science-and-engineering-isbn-9781118271865","provider":"K12savings","version":"1.0","type":"link"}