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528 Pages
23.5 x 15.5 x 2.54
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| Preface | p. v |
| Contributors | p. xxi |
| A Comprehensive Movement Compatibility Study for Hong Kong Chinese | p. 1 |
| Introduction | p. 1 |
| Methods | p. 4 |
| Experimental Design | p. 4 |
| Subjects | p. 4 |
| Results and Discussion | p. 4 |
| Response Preference and Mean Stereotype Strength | p. 4 |
| Reversibility | p. 7 |
| Response Time | p. 8 |
| Conclusion and Recommendations | p. 9 |
| References | p. 10 |
| A Study of Comparative Design Satisfaction Between Culture and Modern Bamboo Chair | p. 13 |
| Introduction | p. 13 |
| Methodology | p. 14 |
| Design and Fabrication | p. 14 |
| Sample Selection | p. 17 |
| Questionnaire | p. 17 |
| Results | p. 18 |
| Dimensional Appropriateness of Chair | p. 18 |
| Comfort Level of Chair | p. 21 |
| Conclusions | p. 24 |
| Dimensional Appropriateness of the Chair | p. 24 |
| Comfort Level | p. 24 |
| Aesthetic Appeal of the Modern Bamboo Chair | p. 25 |
| Recommendations | p. 25 |
| References | p. 25 |
| Factors Influencing Symbol-Training Effectiveness | p. 27 |
| Introduction | p. 27 |
| Factors Influencing Symbol-Training Effectiveness | p. 28 |
| Training Method | p. 28 |
| Other Training Factors | p. 34 |
| Experimental Design and Analysis for Symbol-Training Effectiveness Research | p. 34 |
| Conclusion | p. 36 |
| References | p. 36 |
| Multiple-Colony Ant Algorithm with Forward-Backward Scheduling Approach for Job-Shop Scheduling Problem | p. 39 |
| Introduction | p. 39 |
| Problem Definition and Graph-Based Representation | p. 40 |
| Problem Definition | p. 40 |
| Graph-Based Representation | p. 41 |
| The General Concept of ACO Algorithm | p. 42 |
| Memory Requirement for Ant and Colony | p. 43 |
| Hierarchical Cooperation in Multiple Colonies | p. 44 |
| Backward Scheduling Approach | p. 45 |
| Description of Algorithm | p. 46 |
| Initialize Pheromone and Parameter Setting | p. 47 |
| Local Improvement | p. 50 |
| Pheromone Updating | p. 51 |
| Restart Process | p. 53 |
| Experimental Results | p. 53 |
| Conclusion and Recommendation | p. 53 |
| Conclusion | p. 53 |
| Recommendation | p. 54 |
| References | p. 54 |
| Proposal of New Paradigm for Hand and Foot Controls in the Context of Spatial Compatibility Effect | p. 57 |
| Introduction | p. 57 |
| Spatial Stimulus-Response (SR) Compatibility | p. 58 |
| Research Plan and Methodology | p. 59 |
| Experiment 1: Spatial SR Compatibility Effect of Foot Controls | p. 59 |
| Design | p. 59 |
| Experiment 2: Spatial SR Compatibility Effect of Hand and Foot Controls | p. 60 |
| Design | p. 61 |
| Experiment 3: Spatial SR Compatibility Effect of Hand and Foot Controls for Stimulus and Response Arrays on Orthogonal Planes | p. 61 |
| Design | p. 62 |
| Experiment 4: Spatial SR Compatibility Effect of Hand and Foot Controls for Stimulus and Response Arrays on Parallel and Orthogonal Planes | p. 62 |
| Design | p. 63 |
| Analysis | p. 64 |
| References | p. 64 |
| Development of a Mathematical Model for Process with S-Type Quality Characteristics to a Quality Selection Problem | p. 67 |
| Introduction | p. 67 |
| Model Development | p. 69 |
| Genetic Algorithm | p. 74 |
| Genetic Representation | p. 75 |
| Population Size | p. 76 |
| Generating Initial Population | p. 76 |
| Fitness Function | p. 77 |
| Selection | p. 77 |
| Mating or Crossover | p. 77 |
| Mutation | p. 78 |
| Termination Criteria | p. 79 |
| Generic Algorithm Parameters | p. 79 |
| Numerical Example | p. 79 |
| Conclusions | p. 80 |
| References | p. 80 |
| Temporal Aggregation and the Production Smoothing Model: Evidence from Electronic Parts and Components Manufacturing in Taiwan | p. 83 |
| Introduction | p. 83 |
| Literature Review | p. 84 |
| Model Specifications | p. 85 |
| Empirical Results | p. 87 |
| Tests of Production-Smoothing Hypotheses | p. 88 |
| Model Analyses | p. 88 |
| Conclusions | p. 93 |
| References | p. 94 |
| Simulations of Gear Shaving and the Tooth Contact Analysis | p. 95 |
| Introduction | p. 95 |
| Mathematical Model of the Shaving Machine | p. 96 |
| Tooth Contact Analysis of the Shaved Gear | p. 103 |
| Longitudinal Tooth Crowning Introduced by Litvin | p. 105 |
| Conclusion | p. 109 |
| References | p. 109 |
| On Aggregative Methods of Supplier Assessment | p. 111 |
| Introduction | p. 111 |
| Research Background and Motivation | p. 112 |
| The Importance of Suppliers Assessment and Selection | p. 114 |
| Alternative Techniques of Supplier Assessment | p. 116 |
| Assessment of the Quality of Supplied Products | p. 117 |
| Assessment by Adherence to Time Schedules | p. 117 |
| Supplier Assessment with Respect to the Schedule of Quantity | p. 120 |
| Aggregative Supplier Assessment | p. 123 |
| Discussion and Closing Remarks | p. 124 |
| References | p. 124 |
| Human Factors and Ergonomics for Nondestructive Testing | p. 127 |
| Introduction | p. 127 |
| Principles and Procedures | p. 128 |
| Dye Penetrant Inspection | p. 128 |
| Magnetic Particles Inspection | p. 129 |
| Ultrasonic Inspection | p. 130 |
| Eddy Current | p. 132 |
| Human Abilities and Skills Required | p. 133 |
| Perceptual and Cognitive Abilities | p. 133 |
| Physical Strength | p. 135 |
| Surface Preparation Technique | p. 135 |
| Ergonomics, Safety, and Health Problems | p. 136 |
| Illumination | p. 136 |
| Working Posture | p. 136 |
| Potential Chemical Hazards | p. 138 |
| Conclusions and Recommendations | p. 140 |
| References | p. 141 |
| A Novel Matrix Approach to Determine Makespan for Zero-Wait Batch Processes | p. 143 |
| Introduction | p. 143 |
| Batch Process | p. 144 |
| Makespan for Single-Product Batch Processing | p. 145 |
| Makespan for Multiproduct Batch Process | p. 146 |
| The Matrix Method | p. 148 |
| Application of the Matrix Method | p. 150 |
| Conclusion | p. 152 |
| References | p. 153 |
| Interactive Meta-Goal Programming: A Decision Analysis Approach for Collaborative Manufacturing | p. 155 |
| Introduction | p. 155 |
| Decision Making in Collaborative Manufacturing | p. 156 |
| Interactive Meta-Goal Programming-Based Decision Analysis Framework | p. 157 |
| Meta-Goals | p. 157 |
| Interactive Process | p. 161 |
| Interactive Meta-Goal Programming-Based Decision Analysis Workflow | p. 167 |
| Example | p. 169 |
| Conclusion | p. 171 |
| References | p. 172 |
| Nonlinear Programming Based on Particle Swarm Optimization | p. 173 |
| Introduction | p. 173 |
| Nonlinear Programming Problem | p. 174 |
| Particle Swarm Optimization | p. 174 |
| Improvement of Particle Swarm Optimization | p. 175 |
| Generation of Initial Search Positions of Particles | p. 176 |
| Modified Move Schemes of a Particle | p. 176 |
| Division of the Swarm into Two Subswarms | p. 177 |
| Secession | p. 178 |
| Multiple Stretching Technique | p. 179 |
| The Procedure of Revised PSO | p. 180 |
| Numerical Example | p. 181 |
| Conclusions | p. 183 |
| References | p. 183 |
| A Heuristic for the Capacitated Single Allocation Hub Location Problem | p. 185 |
| Introduction | p. 185 |
| Previous Related Studies | p. 186 |
| A Model | p. 187 |
| Heuristic | p. 188 |
| Determining the Number of Hubs | p. 188 |
| Selecting Hub Locations | p. 189 |
| Allocating Nonhubs To Hubs | p. 189 |
| Heuristic SATLCHLP | p. 190 |
| Computational Results | p. 190 |
| Australia Post Data Set | p. 191 |
| Results | p. 191 |
| Conclusions and Suggestions for Future Research | p. 194 |
| References | p. 195 |
| Multimodal Transport: A Framework for Analysis | p. 197 |
| Introduction | p. 197 |
| Literature Review | p. 199 |
| Theoretical Framework | p. 200 |
| Research Methodology | p. 201 |
| Case Study | p. 202 |
| Railways | p. 203 |
| Road | p. 203 |
| Maritime | p. 203 |
| Multimodal | p. 204 |
| Future Directions for Research | p. 207 |
| References | p. 208 |
| Fractional Matchings of Graphs | p. 209 |
| Terminology and Notation | p. 209 |
| Basic Results on Fractional Matching | p. 210 |
| Fractional Factor-Critical Graph | p. 211 |
| Fractional Deleted Graphs | p. 215 |
| Fractional Covered Graphs | p. 217 |
| Fractional Extendable Graph | p. 220 |
| Conclusion | p. 224 |
| References | p. 224 |
| Correlation Functions for Dynamic Load Balancing of Cycle Shops | p. 227 |
| Problem Statement | p. 227 |
| Load-Balancing Systems: State of the Art | p. 229 |
| Process Plan and Resource Model | p. 230 |
| Theory of Correlation Scheduling | p. 232 |
| Two Processes Being Sent to the Plant | p. 232 |
| Three Processes Being Sent to the Plant | p. 233 |
| Generalization to n Processes | p. 233 |
| Dynamic Scheduling | p. 234 |
| Collision Functions | p. 234 |
| Scheduling Procedure | p. 235 |
| Load Balancing | p. 237 |
| Load Balancing at System Level | p. 237 |
| Load Balancing at Subsystem Level | p. 238 |
| Conclusion | p. 242 |
| References | p. 243 |
| Neural Network-Based Integral Sliding Mode Control for Nonlinear Uncertain Systems | p. 245 |
| Introduction | p. 245 |
| Sliding Mode Control | p. 245 |
| Integral Sliding Mode Control | p. 246 |
| Radial Basis Function Neural Network Approximation | p. 246 |
| Problem Statement | p. 247 |
| New Integral Sliding Surface | p. 248 |
| Sliding Mode Control Law | p. 253 |
| Numerical Example | p. 254 |
| Conclusions | p. 256 |
| References | p. 256 |
| Decentralized Neuro-Fuzzy Control of a Class of Nonlinear Systems | p. 259 |
| Introduction | p. 259 |
| Problem Statement | p. 260 |
| Recurrent Neuro-fuzzy Networks | p. 262 |
| Design of the Decentralized Control | p. 263 |
| Control Law | p. 263 |
| Stability Analysis | p. 264 |
| Output Feedback | p. 269 |
| Experimental Results | p. 269 |
| Conclusions | p. 272 |
| References | p. 273 |
| A New Training Algorithm of Adaptive Fuzzy Control for Chaotic Dynamic Systems | p. 275 |
| Introduction | p. 275 |
| Problem Formulation | p. 277 |
| Design of AFC with PID-Type Learning Algorithm | p. 278 |
| Approximation of Fuzzy System | p. 278 |
| Design of PID-AFC | p. 279 |
| Design of PID-AFC with Bound Estimation | p. 281 |
| Simulation Results | p. 284 |
| Conclusions | p. 287 |
| References | p. 289 |
| General-Purpose Simulation Management for Satellite Navigation Signal Simulation | p. 291 |
| Introduction | p. 291 |
| The Real-Time Application Requirements | p. 292 |
| Requirements for the Simulation Architecture | p. 292 |
| Requirements of the Real-Time Calculation for the High-Fidelity Model | p. 292 |
| Requirements of the Data Communication for Different Layers | p. 293 |
| Requirements of the Real-Time Simulation Engine | p. 293 |
| A General-Purpose Architecture for Satellite Navigation Signal Simulation | p. 293 |
| General-Purpose Real-Time Distributed Simulation Managements | p. 295 |
| Experiment Design and Management Techniques | p. 295 |
| Simulation Database Techniques | p. 296 |
| Simulation Management Techniques | p. 297 |
| System Scalability Realization | p. 298 |
| Conclusions | p. 298 |
| References | p. 299 |
| Multilayered Quality-of-Service Architecture with Cross-layer Coordination for Teleoperation System | p. 301 |
| Introduction | p. 301 |
| Network Performance Parameters Analysis | p. 302 |
| Architectural Framework | p. 303 |
| Communication Network QoS Enhancement | p. 305 |
| Network Layer QoS Optimization | p. 305 |
| Data Link Layer QoS Optimization | p. 306 |
| Resource Network QoS Enhancement | p. 307 |
| Transport Layer QoS Optimization | p. 307 |
| Presentation Layer QoS Enhancing | p. 308 |
| Session Layer QoS Supervision | p. 309 |
| Application Layer QoS | p. 309 |
| Cross-layer Coordination and Adaptation | p. 309 |
| Application Scenarios | p. 311 |
| Conclusion | p. 313 |
| References | p. 313 |
| Improvement of State Estimation for Systems with Chaotic Noise | p. 315 |
| Introduction | p. 315 |
| Improvement of Adaptive Kalman Filtering | p. 316 |
| Results | p. 319 |
| Model | p. 319 |
| Computer Simulation | p. 319 |
| Conclusion | p. 325 |
| References | p. 325 |
| Combined Sensitivity-Complementary Sensitivity Min-Max Approach for Load Disturbance-Setpoint Tradeoff Design | p. 327 |
| Introduction | p. 327 |
| Problem Formulation | p. 328 |
| PID Controller | p. 329 |
| Process Model | p. 329 |
| Design Problem Formulation | p. 329 |
| Solution to the Optimal Approximation Problem | p. 331 |
| Step Response Tuning | p. 333 |
| Disturbance Attenuation Tuning | p. 335 |
| Example | p. 336 |
| Trade-off Tuning | p. 338 |
| Conclusions | p. 342 |
| References | p. 342 |
| Nonlinear Adaptive Sliding Mode Control for a Rotary Inverted Pendulum | p. 345 |
| Introduction | p. 345 |
| Background | p. 346 |
| Mathematical Model of System | p. 346 |
| Sliding Mode Control | p. 347 |
| Adaptive Control | p. 348 |
| Sliding Mode Control: Design and Simulation | p. 348 |
| Linear Sliding Mode Control | p. 348 |
| Nonlinear Sliding Mode Control | p. 350 |
| Nonlinear Adaptive Sliding Mode Control Design and Simulation | p. 353 |
| System Parameters | p. 353 |
| Parameter Selection | p. 356 |
| Experimental Results | p. 357 |
| Conclusion | p. 359 |
| References | p. 359 |
| Robust Load Frequency Sliding Mode Control Based on Uncertainty and Disturbance Estimator | p. 361 |
| Introduction | p. 361 |
| Dynamic Model for Load Frequency Control | p. 362 |
| Model Following and UDE-Based Control Law | p. 364 |
| Design of Control | p. 364 |
| Uncertainty and Disturbance Estimation with First-Order Filter | p. 366 |
| Uncertainty and Disturbance Estimation with Second-Order Filter | p. 366 |
| Uncertainty and Disturbance Estimation with nth-Order Filter | p. 367 |
| Model Following and UDE Based LFC | p. 368 |
| Results | p. 370 |
| Conclusion | p. 373 |
| References | p. 373 |
| Robust Intelligent Motion Control for Linear Piezoelectric Ceramic Motor System Using Self-constructing Neural Network | p. 375 |
| Introduction | p. 375 |
| Problem Formulation | p. 377 |
| Robust Intelligent Motion Controller Design | p. 378 |
| Description of SCNN | p. 379 |
| Approximation of SCNN | p. 381 |
| Design of RIMC | p. 382 |
| Experimental Results | p. 384 |
| Conclusions | p. 388 |
| References | p. 390 |
| Development of Hybrid Magnetic Bearings System for Axial-Flow Blood Pump | p. 391 |
| Introduction | p. 391 |
| Design of Axial-flow Blood Pump | p. 392 |
| Principles of Magnetic Bearings | p. 394 |
| Principles of Lorentz-type Motor | p. 394 |
| Control of the HMBs System | p. 396 |
| Performance of the HMBs System | p. 397 |
| Conclusions and Future Work | p. 399 |
| References | p. 399 |
| Critical Angle for Optimal Correlation Assignment to Control Memory and Computational Load Requirements in a Densely Populated Target Environment | p. 401 |
| Introduction | p. 402 |
| Critical Angle Representation | p. 402 |
| Motion Model Consideration | p. 404 |
| Implementation | p. 406 |
| Performance Parameter | p. 407 |
| Simulation Results | p. 408 |
| Conclusion | p. 412 |
| References | p. 413 |
| High-Precision Finite Difference Method Calculations of Electrostatic Potential | p. 415 |
| Introduction | p. 415 |
| Historical Development 1970-2007 | p. 415 |
| Brief Description of the Process | p. 416 |
| Construction of Order-10 Algorithm for General Mesh Points and the Definition of the grad6 Function | p. 417 |
| Properties of the grad6 Function and the Definition of the Maximum-Error Function | p. 419 |
| Comparison of Different Algorithms for the Two-Tube Zero-Gap Lens | p. 423 |
| Application to Region Construction | p. 425 |
| Dependence of Algorithm Precision upon the Set of Surrounding Points | p. 426 |
| Notes of Caution | p. 428 |
| Summary and Conclusion | p. 430 |
| p. 431 | |
| References | p. 432 |
| Newton-Tau Method | p. 433 |
| Introduction | p. 433 |
| Solving Nonlinear Fredholm Integral Equation | p. 433 |
| Formulation of the Problem | p. 433 |
| Application of the Newton Method | p. 434 |
| Application of the Tau Method | p. 435 |
| Numerical Examples | p. 436 |
| Solving a System of Nonlinear Integral Equations | p. 438 |
| Formulation of the Problem | p. 438 |
| Application of the Newton Method to SNFIE | p. 440 |
| The Tau Method Applied to (8) | p. 441 |
| Numerical Examples | p. 443 |
| Solving Nonlinear Integro-Differential Equation | p. 445 |
| Formulation of the Problem | p. 445 |
| Application of the Newton Method | p. 447 |
| Application of the Tau Method | p. 448 |
| Numerical Examples | p. 451 |
| References | p. 452 |
| Reconfigurable Hardware Implementation of the Successive Overrelaxation Method | p. 453 |
| Introduction | p. 453 |
| Description of the Algorithm | p. 455 |
| Reconfigurable Computing | p. 455 |
| Hardware Compilation | p. 456 |
| Handel-C Language | p. 457 |
| Hardware Implementation of SOR | p. 459 |
| Experimental Results | p. 462 |
| Conclusion | p. 464 |
| References | p. 464 |
| Tabu Search Algorithm Based on Strategic Oscillation for Nonlinear Minimum Spanning Tree Problems | p. 467 |
| Introduction | p. 467 |
| Problem Formulation | p. 468 |
| Summary of Tabu Search | p. 469 |
| Tabu Search Algorithm Based on Strategic Oscillation for Nonlinear MST Problems | p. 469 |
| Initial Solution | p. 470 |
| Neighborhood Structure and Local Search | p. 471 |
| Tabu List and Aspiration Criterion | p. 471 |
| Strategic Oscillation | p. 471 |
| Diversification | p. 472 |
| Numerical Experiment | p. 472 |
| Conclusion | p. 475 |
| References | p. 475 |
| Customization of Visual Lobe Measurement System for Testing the Effects of Foveal Load | p. 477 |
| Introduction | p. 477 |
| Design | p. 480 |
| Additional Features | p. 480 |
| Stimuli | p. 481 |
| Software | p. 483 |
| Apparatus | p. 483 |
| Output | p. 483 |
| Conclusion | p. 484 |
| References | p. 485 |
| Index | p. 487 |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9780387749037
ISBN-10: 0387749039
Series: Lecture Notes in Electrical Engineering
Published: 25th February 2008
Format: Hardcover
Language: English
Number of Pages: 528
Audience: General Adult
Publisher: Springer Nature B.V.
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5 x 2.54
Weight (kg): 0.84
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