Human and Nature Minding Automation (eBook)

Spyros G. Tzafestas received the B.Sc. in Physics (1963) and Graduate Diploma in Electronics (1965) from Athens University, Diploma of Electrical Engineering, from Imperial College (1967), M.Sc. (Eng.) in Control from London University (1967) and Ph.D. in Systems and Control from Southampton University, England (1969). From 1969 to 1973 he was Research Leader at the Computer Science Division of the Nuclear Research Center "Demokritos", Athens. From 1973 to 1984 he was Professor of Automatic Control at the University of Patras, and from 1985 to 2006 he was Professor of Control and Robotics at the National Technical University of Athens (NTUA), Greece. Temporary visiting teaching and / or research positions include : University of Calabria, Italy (1985, 1987), University of Delft, The Netherlands (1991) and MIT, USA (1992). He is currently Director of the Institute of Communication and Control Systems, and as a Professor Emeritus he is leading the Intelligent Automation Systems Research Group engaged with research carried out in ICCS-NTUA mainly in the framework of national and European projects. Dr Tzafestas is the Recipient of D.Sc. of Southampton University (1978), and Honorary Doctorates of the Technical University of Munich (Dr.-Ing. E.h., 1997) and the Ecole Centrale de Lille (Dr. Ing.-Honoris Causa, 2003). He has published 30 edited research books, 60 book chapters and over 700 Journal and Conference technical papers in the field of control, robotics and Intelligent Systems. He has been the coordinator of national and EU projects in the fields of IT, Intelligent systems, robotics, control and CIM. He is an associate editor of 15 Journals, and he was the Editor – in – Chief of the Journal of Intelligent and Robotic Systems (1988-2006) and of the Book Series "Micro processor – Based and Intelligent Systems Engineering, Kluwer (1993-2006). Presently, he is the Editor of the Springer book series on Intelligent Control and Automation Systems. He is a Fellow of IEE, now IET (London), a Life Fellow of IEEE (New York) and a Member of ASME, NYAS and the Hellenic Technical Chamber (TEE). He received the Greek Society of Writers’ Award and the Ktesibios Award from the IEEE Mediterranean Control Association (2001). Dr Tzafestas has over the years organized and / or chaired several international conferences (IEEE, IMACS, SIRES, IASTED, EUCA).

Human and Nature 
 
1 
Dedication 
5 
1 Automation, Humans, Nature, and Development 19
1.1 Introduction 19
1.2 The Field of Automation 20
1.3 Brief History of Control and Automation 21
1.4 The Principle of Feedback 23
1.4.1 Some Examples 24
1.5 The Humans in Automation 27
1.6 Automation in the Nature 28
1.7 Social Issues of Automation 29
1.7.1 Training and Education 30
1.7.2 Unemployment 30
1.7.3 Quality of Working Conditions 30
1.7.4 Productivity and Capital Formation 31
1.7.5 Advantages 31
1.7.6 Disadvantages 32
1.8 Human Development and Modernization 32
1.8.1 Human Development Components 33
1.8.2 Modernization 34
1.8.3 Human Development Index 36
1.8.4 Life Expectancy, Literacy and Standard of Living 37
1.8.5 Human Development Report 38
2 Human Factors in Automation (I): Building Blocks, Scope, and a First Set of Factors 40
2.1 Introduction 40
2.2 The Human Factors Field: Building Blocks and Scope 41
2.2.1 Building Blocks 41
2.2.2 The Human Features 42
2.2.3 Human–Automation Relation 42
2.2.4 Automation 42
2.2.5 Goals and Scope of the Human Factors Field 43
2.3 Human Factors in Automation System Design and Development 44
2.3.1 General Issues 44
2.3.2 Developmental Elements 45
2.3.3 System Development Concepts 46
2.4 The Workload Factor in Automation 47
2.5 Three Key Human Factors in Automation 48
2.5.1 Allocation of Function 48
2.5.2 Stimulus–Response Compatibility 49
2.5.3 Internal Model of the Operator 49
2.6 The Operator Reliance Factor 50
3 Human Factors in Automation (II): Psychological, Physical Strength, Human Error and Human Values Factors 52
3.1 Introduction 52
3.2 Psychological Factors 53
3.2.1 Job Satisfaction 53
3.2.2 Job Stress 54
3.2.3 A Psychosocial Stress Model 55
3.3 Physical Strength 55
3.4 Human Bias 56
3.5 Human Error 57
3.5.1 Skill-Based Error-Shaping Factors 59
3.5.2 Rule-Based Error-Shaping Factors 59
3.5.3 Knowledge-Based Error Shaping Factors 59
3.6 Human Values and Human Rights 60
4 Human–Machine Interaction in Automation (I): Basic Concepts and Devices 64
4.1 Introduction 64
4.2 Applications of Human–Machine Interactive Systems 65
4.3 Methodologies for the Design of Human–Machine Interaction Systems 67
4.4 Keys and Keyboards 68
4.4.1 Keyboard Layout 68
4.5 Pointing Devices 70
4.5.1 Touch Screens 70
4.5.2 Light Pens 71
4.5.3 Graphic Tablets 71
4.5.4 Track Balls 71
4.5.5 Mouse 72
4.5.6 Joysticks 72
4.5.7 Selection of the Input Device 72
4.6 Screen Design 73
4.6.1 Screen Density Reduction Methods 74
4.6.2 Information Grouping and Highlighting 74
4.6.3 Spatial Relationships Among Screen Elements 75
4.7 Work Station Design 75
4.7.1 Physical Layout Factors 76
4.7.2 Work Method Factors 76
4.7.3 Video Display Terminal Factors 77
5 Human–Machine Interaction in Automation (II): Advanced Concepts and Interfaces 78
5.1 Introduction 78
5.2 Graphical User Interfaces 79
5.2.1 General Issues 79
5.2.2 Design Components of Graphical Interfaces 80
5.2.3 Windowing Systems 80
5.2.4 Components of Windowing Systems 81
5.3 Types and Design Features of Visual Displays 82
5.3.1 Visual Display Types 82
5.3.2 Further Design Features of Visual Displays 83
5.4 Intelligent Human–Machine Interfaces 85
5.5 Natural Language Human–Machine Interfaces 88
5.6 Multi-Modal Human–Machine Interfaces 89
5.7 Graphical Interfaces for Knowledge-Based Systems 91
5.7.1 End-User Interfaces 92
5.7.2 Graphical Interfaces for the Knowledge Engineer 92
5.8 Force Sensing Tactile Based Human–Machine Interfaces 93
5.9 Human–Machine Interaction via Virtual Environments 94
5.10 Human–Machine Interfaces in Computer-Aided Design 96
6 Supervisory and Distributed Control in Automation 99
6.1 Introduction 99
6.2 Supervisory Control Architectures 101
6.2.1 Evolution of Supervisory Control 101
6.2.2 Rasmussen's Architecture 102
6.2.3 Sheridan's Architecture 104
6.2.4 Meystel's Nested Architecture 108
6.3 Task Analysis and Task Allocation in Automation 109
6.4 Distributed Control Architectures 113
6.4.1 Historical Remarks 113
6.4.2 Hierarchical Distributed Systems 114
6.4.3 Distributed Control and System Segmentation 117
6.5 Discrete Event Supervisory Control 118
6.6 Behavior-Based Architectures 119
6.6.1 Subsumption Architecture 120
6.6.2 Motor Schemas Architecture 121
6.7 Discussion 123
7 Implications of Industry, Automation, and Human Activity to Nature 125
7.1 Introduction 125
7.1.1 The Concepts of Waste and Pollution Control 126
7.2 Industrial Contaminants 127
7.2.1 Organic Compounds 127
7.2.2 Metals and Inorganic Nonmetals 131
7.3 Impact of Industrial Activity on the Nature 133
7.3.1 Air Pollution 133
7.3.2 The Earth's Carbon Cycle and Balance 135
7.3.3 Global Warming, Ozone Hole, Acid Rain and Urban Smog 136
7.3.3.1 Global Warming and Greenhouse Effect 136
7.3.3.2 Ozone Hole 138
7.3.3.3 Acid Rain 139
7.3.3.4 Urban Smog 140
7.3.4 Solid Waste Disposal 141
7.3.5 Water Pollution 142
7.4 Energy Consumption and Natural Resources Depletion 142
7.5 Three Major Problems of the Globe Caused by Human Activity 144
7.6 Environmental Impact: Classification by Human Activity Type 145
8 Human-Minding Automation 148
8.1 Introduction 148
8.2 System-Minding Design Approach 149
8.3 Human-Minding Automation System Design Approach 150
8.4 Human-Minding Interface Design in Automation Systems 152
8.4.1 User–Needs Analysis 152
8.4.2 Task Analysis 153
8.4.3 Situation Analysis and Function Allocation 153
8.5 The Human Resource Problem in Automation 155
8.5.1 Allocation of System Development Resources 155
8.5.2 Investment in Human Resources 157
8.5.3 Innovation and Technology Transfer 157
8.6 Integrating Decision Aiding and Decision Training in Human-Minding Automation 158
8.6.1 Novice 159
8.6.2 Expert 159
8.7 International Safety Standards for Automation Systems 161
8.8 Overlapping Circles Representation of Human Minding Automation Systems 164
9 Nature-Minding Industrial Activity and Automation 166
9.1 Introduction 166
9.2 Life-Cycle and Environmental Impact Assessments 167
9.2.1 Life-Cycle Assessment 167
9.2.2 Environmental Impact Assessment 172
9.3 Nature-Minding Design 173
9.4 Pollution Control Planning 175
9.5 Natural Resources-Energy Conservation and ResidualsManagement 177
9.5.1 Water Conservation 177
9.5.2 Energy Conservation 178
9.5.3 Residuals Management 178
9.6 Fugitive Emissions Control and Public Pollution Control Programs 180
9.6.1 Fugitive Emissions Control 180
9.6.2 Public Pollution Control Programs 181
9.7 Environmental Control Regulations 182
9.7.1 General Issues 182
9.7.2 Environmental Regulations in the United States 183
9.7.3 International and European Environmental Control Regulations 185
9.7.3.1 Climate Change 186
9.7.3.2 Biodiversity 186
9.7.3.3 Environment and Health 187
9.8 The Concept of Sustainability 188
9.9 Environmental Sustainability Index 193
9.10 A Practical Guide Towards Nature-Minding Business-Automation Operation 195
9.10.1 The Four Environmental R-Rules 195
9.10.2 Four More Nature-Minding Rules 196
9.11 Nature-Minding Economic Considerations 198
9.11.1 Cost Allocation: The Polluter-Pays Principle 201
9.11.2 Environmental Standards 201
9.12 Nature-Minding Organizations 202
10 Modern Automation Systems in Practice 207
10.1 Introduction 207
10.2 Office Automation Systems 208
10.3 Automation in Railway Systems 210
10.4 Automation in Aviation Systems 214
10.4.1 Aircraft Automation 214
10.4.2 Air Traffic Control 216
10.4.3 The Free Flight Operational Concept 219
10.5 Automation in Automobile and Sea Transportation 220
10.5.1 Advanced Traveler Information Systems 220
10.5.2 Collision Avoidance and Warning Systems 221
10.5.3 Automated Highway Systems 221
10.5.4 Vision Enhancement Systems 222
10.5.5 Advanced Traffic Management Systems 222
10.5.6 Commercial Vehicle Operations 222
10.5.7 Sea Transportation 223
10.6 Robotic Automation Systems 223
10.6.1 Material Handling and Die Casting 224
10.6.2 Machine Loading and Unloading 224
10.6.3 Welding and Assembly 225
10.6.4 Machining and Inspection 226
10.6.5 Drilling, Forging and Other Fabrication Applications 226
10.6.6 Robot Social and Medical Services 227
10.6.7 Assistive Robotics 229
10.7 Automation in Intelligent Buildings 233
10.8 Automation of Intra- and Inter-Organizational Processes in CIM 234
10.8.1 Intra-Organizational Automation 235
10.8.2 Inter-Organizational Automation 236
10.9 Automation in Continuous Process Plants 238
10.10 Automation in Environmental Systems 240
10.11 Discussion on Human- and Nature-Minding Automation and Technology Applications 241
11 Mathematical Tools for Automation Systems I: Modeling and Simulation 244
11.1 Introduction 244
11.2 Deterministic Models 245
11.3 Probabilistic Models 248
11.3.1 Discrete Probability Model 249
11.3.2 Continuous Probability Model 249
11.3.3 Bayes Updating Formula 250
11.3.4 Statistics 253
11.4 Entropy Model 255
11.5 Reliability and Availability Models 256
11.5.1 Definitions and Properties 256
11.5.2 Markov Reliability Model 259
11.6 Stochastic Processes and Dynamic Models 261
11.6.1 Stochastic Processes 261
11.6.2 Stochastic Dynamic Models 263
11.7 Fuzzy Sets and Fuzzy Models 264
11.7.1 Fuzzy Sets 264
11.7.2 Fuzzy Systems 268
11.8 System Simulation 273
11.8.1 Simulation of Dynamic Systems 273
11.8.1.1 Euler Simulation Technique 273
11.8.1.2 Runge–Kutta Simulation Technique 274
11.8.2 Simulation of Probabilistic Models 275
12 Mathematical Tools for Automation Systems II: Optimization, Estimation, Decision, and Control 280
12.1 Introduction 280
12.2 System Optimization 281
12.2.1 Static Optimization 282
12.2.1.1 Theory 282
12.2.1.2 Computational Optimization Algorithms 283
12.2.2 Dynamic Optimization 287
12.2.2.1 Dynamic Programming (Bellman) 287
12.2.2.2 Calculus of Variations (Euler–Lagrange) 289
12.2.2.3 Minimum Principle (Pontryagin) 290
12.2.3 Genetic Optimization 291
12.3 Learning and Estimation 293
12.3.1 Least-Squares Parameter Estimation 293
12.3.2 Recursive Least Squares Parameter Estimation 295
12.3.3 Least Squares State Estimation: Kalman Filter 298
12.3.3.1 Discrete-Time Filter 298
12.3.3.2 State Prediction 300
12.3.3.3 Continuous Time Filter 300
12.3.4 Neural Network Learning 303
12.3.4.1 The Multilayer Perceptron 304
12.3.4.2 The Radial Basis Function (RBF) Network 305
12.4 Decision Analysis 306
12.4.1 General Issues 306
12.4.2 Decision Matrix and Average Value Operators 308
12.4.3 Fuzzy Utility Functions 309
12.5 Control 315
12.5.1 Classical Control 316
12.5.2 Modern Control 319
12.5.2.1 Model Matching and Eigenvalue Control 320
12.5.2.2 Optimal Control 322
12.5.2.3 Stochastic Control 325
12.5.2.4 Predictive Control 326
12.5.2.5 Adaptive Control 327
12.5.2.6 Robust Control 327
12.5.2.7 Intelligent Control 329
12.6 Concluding Remarks 329
References 331
Index 354