Braz / Araújo / Vieira Informatics in Control, Automation and Robotics I

1. Auflage 2006
ISBN: 978-1-4020-4543-1
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 290 Seiten

This is a collection of papers presented at the 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO). The papers focus on real world applications, covering three main themes: Intelligent Control Systems, Optimization, Robotics and Automation, Signal Processing, Systems Modeling and Control. The book will interest professionals in the areas of control and robotics.

Autoren/Hrsg.

Braz, José

Araújo, Helder

Vieira, Alves

Encarnação, Bruno

Inhaltsverzeichnis

1;TABLE OF CONTENTS;5
2;PREFACE;10
3;CONFERENCE COMMITTEE;11
4;INVITED SPEAKERS;15
4.1;ROBOT-HUMAN INTERACTION;16
4.1.1;1 INTRODUCTION;16
4.1.1.1;1.1 Human Integration;17
4.1.2;2 INVASIVE NEURAL INTERFACE;18
4.1.2.1;2.1 Surgical Procedure;18
4.1.2.2;2.2 Neural Stimulation and Neural Recordings;19
4.1.3;3 NEURAL INTERACTION WITH TECHNOLOGY;20
4.1.4;4 CONCLUSIONS;21
4.2;INDUSTRIAL AND REAL WORLD APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS;24
4.2.1;1 INTRODUCTION;24
4.2.2;2 FROM NATURAL TO ARTIFICIAL;25
4.2.2.1;2.1 Back-Propagation Learning Rule and Multi- Layer Perceptron;27
4.2.2.2;2.2 Kernel Functions Based Neural Models;28
4.2.3;3 ANN BASED SOLUTIONS FOR INDUSTRIAL ENVIRONMENT;29
4.2.3.1;3.1 MLP Based Adaptive Controller;29
4.2.3.2;3.2 Kernel Functions ANN Based Image Processing for Industrial Applications;32
4.2.3.3;3.3 Bio-inspired Multiple Neural Networks Based Process Identification;36
4.2.4;4 CONCLUSION;38
4.2.5;ACKNOWLEDGEMENTS;38
4.2.6;REFERENCES;38
4.3;THE DIGITAL FACTORY;40
4.3.1;1 INTRODUCTION;40
4.3.2;2 DEFINITION;40
4.3.3;3 GLOBAL DATA BASE;41
4.3.4;4 WORKFLOW MANAGEMENT;41
4.3.5;5 SUBCONTRACTORS AND COLLABORATIVE ENDINEERING;41
4.3.6;6 DANGER AND UNRESOLVED PROBLEMS;42
4.3.7;7 SUMMARY;42
4.3.8;REFERENCES;42
4.4;WHAT'S REAL IN "REAL-TIME CONTROL SYSTEMS"?;44
4.4.1;1 INTRODUCTION;44
4.4.2;2 MODEL CHECKING;44
4.4.3;3 VISUAL FORMALISM, STATECHARTS, AND STATEMATE;44
4.4.4;4 TIMED AUTOMATA;45
4.4.5;5 REAL-TIME LOGIC, GRAPHTHEORETIC ANALYSIS, AND MODECHART;46
4.4.6;6 PROCESS ALGEBRA;46
4.4.7;7 REAL-TIME RULE-BASED DECISION SYSTEMS;47
4.4.8;8 REAL-TIME DECISION SYSTEMS;47
4.5;SUFFICIENT CONDITIONS FOR THE STABILIZABILITY OF MULTI-STATE UNCERTAIN SYSTEMS, UNDER INFORMATION CONSTRAINTS;50
4.5.1;1 INTRODUCTION;50
4.5.1.1;1.1 Main Contributions of the Paper;50
4.5.2;2 PROBLEM FORMULATION;51
4.5.2.1;2.1 Description of Uncertainty in the Plant;51
4.5.2.2;2.2 Statistical Description of a(k);52
4.5.2.3;2.3 Functional Structure of the Feedback Interconnection;52
4.5.2.4;2.4 Problem Statement and M-th Moment Stability;52
4.5.2.5;2.5 Motivation for our Definition of Stochastic Link and Further Comments on the Information Pattern;52
4.5.3;3 SUFFICIENCY CONDITIONS FOR THE ROBUST STABILIZATION OF FIRST ORDER LINEAR SYSTEMS;54
4.5.3.1;3.1 The Deterministic Case;55
4.5.3.2;3.2 Suficient Condition for the Stochastic Case;56
4.5.4;4 SUFFICIENT CONDITIONS FOR A CLASS OF SYSTEMS OF ORDER HIGHER THAN ONE;57
4.5.4.1;4.1 Description of the Nominal Plant and Equivalent Representations;57
4.5.4.2;4.2 Description of Uncertainty and Robust Stability;59
4.5.4.3;4.3 Feedback Structure and Channel Usage Assumptions;59
4.5.4.4;4.4 Construction of a Stabilizing Feedback Scheme;60
4.5.4.5;4.5 Suf.ciency for the Deterministic/Time-InvariantCase;60
4.5.4.6;4.6 Suf.ciency for the StochasticCase;61
4.5.4.7;4.7 Suf.ciency for the case ¯za = 0;62
4.5.4.8;4.8 Solving the Allocation Problem for a Class of Stochastic Systems;62
4.5.5;ACKNOWLEDGEMENTS;63
4.5.6;REFERENCES;63
5;Part 1 Intelligent Control Systems and Optimization;65
5.1;DEVICE INTEGRATION INTO AUTOMATION SYSTEMS WITH CONFIGURABLE DEVICE HANDLER;66
5.1.1;1 INTRODUCTION;66
5.1.2;2 DEVICE HANDLER TYPES;67
5.1.2.1;2.2 Configurable Device Handler;68
5.1.2.2;2.1 Device Specific Handler;68
5.1.2.3;2.3 Related Work;68
5.1.3;3 CDH DEVICE INTEGRATION;69
5.1.4;4 OFFLINE CONFIGURATION;69
5.1.4.1;4.1 Physical Line;70
5.1.4.2;4.2 Device Variables;70
5.1.4.3;4.3 Telegrams;70
5.1.4.4;4.4 Sequences;70
5.1.4.5;4.5 Global Conditions;71
5.1.5;5 ON-LINE USAGE;71
5.1.6;6 FURTHER DEVELOPMENT;72
5.1.6.1;6.1 Calculation Capability;72
5.1.6.2;6.2 Automatic Detection of Devices;72
5.1.6.3;6.3 Multi-Line Connection;72
5.1.6.4;6.4 Binary Protocols;72
5.1.6.5;6.5 Capability Profiles;73
5.1.7;7 CONCLUSION;73
5.1.8;REFERENCES;73
5.2;NON LINEAR SPECTRAL SDP METHOD FOR BMI-CONSTRAINED PROBLEMS : APPLICATIONS TO CONTROL DESIGN;74
5.2.1;1 INTRODUCTION;74
5.2.2;2 NONLINEAR SPECTRAL SDP METHOD;75
5.2.2.1;2.1 General Outline;75
5.2.2.2;2.2 The mechanism of the Algorithm;77
5.2.3;3 DERIVATIVES OF SP FUNCTIONS;77
5.2.3.1;3.1 Second Derivatives;78
5.2.3.2;3.2 Multiplier Update Rule;80
5.2.3.3;3.3 Solving the Subproblem - Implementational Issues;80
5.2.4;4 NUMERICAL EXAMPLES;81
5.2.4.1;4.1 Static Output Feedback Controller Synthesis;81
5.2.4.2;4.2 Miscellaneous Examples;82
5.2.5;5 CONCLUSION;84
5.2.6;REFERENCES;84
5.3;A STOCHASTIC OFF LINE PLANNER OF OPTIMAL DYNAMIC MOTIONS FOR ROBOTIC MANIPULATORS;86
5.3.1;1 INTRODUCTION;86
5.3.2;2 PROBLEM STATEMENT;87
5.3.3;3 REFORMULATION OF THE PROBLEM;88
5.3.3.1;3.1 Scaling;88
5.3.4;4 STRATEGY OF RESOLUTION;90
5.3.5;5 NUMERICAL RESULTS;91
5.3.6;6 CONCLUSION;91
5.3.7;REFERENCES;92
5.3.8;ACKNOWLEDGEMENTS;92
5.4;FUZZY MODEL BASED CONTROL APPLIED TO IMAGE-BASED VISUAL SERVOING;94
5.4.1;1 INTRODUCTION;94
5.4.2;2 IMAGE-BASED VISUAL SERVOING;94
5.4.2.1;2.1 Modeling the Image-Based Visual Servoing System;95
5.4.2.2;2.2 Controlling the Image-Based Visual Servoing System;95
5.4.3;3 PROBLEM STATEMENT;95
5.4.4;4 INVERSE FUZZY MODELING;96
5.4.4.1;4.1 Fuzzy Modeling;96
5.4.4.2;4.2 Inverse Modeling;97
5.4.5;5 FUZZY COMPENSATION OF STEADY-STATE ERRORS;97
5.4.5.1;5.1 Derivation of Fuzzy Compensation;97
5.4.6;6 EXPERIMENTAL SETUP;98
5.4.6.1;6.1 Vision System;98
5.4.6.2;6.2 Robotic Manipulator System;99
5.4.6.3;6.3 Systems Integration;99
5.4.7;7 RESULTS;99
5.4.7.1;7.1 Inverse Fuzzy Modeling;99
5.4.7.2;7.2 Control Results;100
5.4.8;8 CONCLUSIONS;101
5.4.9;ACKNOWLEDGEMENTS;101
5.4.10;REFERENCES;101
5.5;AN EVOLUTIONARY APPROACH TO NONLINEAR DISCRETE-TIME OPTIMAL CONTROL WITH TERMINAL CONSTRAINTS;102
5.5.1;1 INTRODUCTION;102
5.5.2;2 OPTIMAL CONTROL OF NONLINEAR DISCRETE TIME DYNAMICAL SYSTEMS;103
5.5.3;3 DISCRETE VELOCITY DIRECTION PROGRAMMING FOR MAXIMUM RANGE WITH GRAVITY AND THRUST;105
5.5.4;4 NECESSARY CONDITIONS FOR ANOPTIMUM;107
5.5.5;5 AN EVOLUTIONARY APPROACH TO OPTIMAL CONTROL;108
5.5.6;6 CONCLUSIONS;109
5.5.7;REFERENCES;110
5.6;A DISTURBANCE COMPENSATION CONTROL FOR AN ACTIVE MAGNETIC BEARING SYSTEM BY A MULTIPLE FXLMS ALGORITHM;112
5.6.1;1 INTRODUCTION;112
5.6.2;2 SYSTEM MODEL;113
5.6.3;3 CONTROLLER DESIGN;114
5.6.4;4 EXPERIMENTS;116
5.6.5;5 CONCLUSION;117
5.6.6;ACKNOWLEDGEMENTS;117
5.6.7;REFERENCES;117
5.7;AN INTELLIGENT RECOMMENDATION SYSTEM BASED ON FUZZY LOGIC;118
5.7.1;1 INTRODUCTION;118
5.7.2;2 RECOMMENDATION SYSTEM;118
5.7.2.1;2.1 System Architecture;118
5.7.2.2;2.2 Information Description;119
5.7.2.3;2.3 Application of Fuzzy Logic Control Theory in a Recommendation System;119
5.7.3;3 FUZZY RECOMMENDATION;119
5.7.3.1;3.1 Fuzzy Information Database;119
5.7.3.2;3.2 Similarity Matching;120
5.7.3.3;3.3 Filtering & Ranking;120
5.7.3.4;3.4 Profiling Agent;120
5.7.4;4 EXAMPLE;121
5.7.4.1;4.1 Similarity Matching and Preference Learning Example;121
5.7.4.2;4.2 Application Scenario;121
5.7.5;5 CONCLUSION;121
5.7.6;REFERENCES;122
5.8;MODEL REFERENCE CONTROL IN INVENTORY AND SUPPLY CHAIN MANAGEMENT;124
5.8.1;1 INTRODUCTION;124
5.8.2;2 MODEL PREDICTIVE CONTROL;124
5.8.2.1;2.1 Implementing the Cost Function;125
5.8.3;3 SIMULATIONS;126
5.8.3.1;3.1 Simulator Implementation Tool;126
5.8.3.2;3.2 Inventory Control Simulations;127
5.8.3.3;3.3 Step Response Simulations;128
5.8.3.4;3.4 Simulations with a More Realistic Demand Pattern;128
5.8.4;4 CONCLUSIONS REFERENCES;129
5.9;AN LMI OPTIMIZATION APPROACH FOR GUARANTEED COST CONTROL OF SYSTEMS WITH STATE AND INPUT DELAYS;130
5.9.1;1 INTRODUCTION;130
5.9.2;2 PROBLEM STATEMENT AND DEFINITIONS;131
5.9.3;3 SOLUTION IN THE LMI FRAMEWORK;132
5.9.4;4 EXAMPLE;135
5.9.5;5 CONCLUSIONS;135
5.9.6;REFERENCES;135
5.10;USING A DISCRETE-EVENT SYSTEM FORMALISM FOR THE MULTI- AGENT CONTROL OF MANUFACTURING SYSTEMS;138
5.10.1;1 INTRODUCTION;138
5.10.2;2 THE MULTI-AGENT SYSTEMS CONTROL APPROACH;139
5.10.3;3 THE DISCRETE-EVENT MODELLING FRAMEWORK;139
5.10.4;4 THE INTERACTIONS OF A PA WITH WAS AND TAS;140
5.10.5;5 SOME EXPERIMENTAL RESULTS AND FUTURE PLANS;144
5.10.6;6 CONCLUDING REMARKS;145
5.10.7;REFERENCES;145
6;Part 2 Robotics and Automation;147
6.1;FORCE RIPPLE COMPENSATOR FOR A VECTOR CONTROLLED PM LINEAR SYNCHRONOUS MOTOR;148
6.1.1;1 INTRODUCTION;148
6.1.2;2 SIMULATION MODEL ;149
6.1.2.1;2.1 Model of LSM;149
6.1.2.2;2.2 Non-idealities of PMLSM;150
6.1.2.3;2.3 Current Controller of the Linear Motor;151
6.1.2.4;2.4 Verification of the Simulation Model;151
6.1.3;3 DISTURBANCE COMPENSATION;153
6.1.4;4 CONCLUSION;154
6.1.5;APPENDIX;154
6.1.6;REFERENCES;154
6.2;HYBRID CONTROL DESIGN FOR A ROBOT MANIPULATOR IN A SHIELD TUNNELING MACHINE;156
6.2.1;1 INTRODUCTION;156
6.2.2;2 MODELING;157
6.2.2.1;2.1 Manipulator Model;157
6.2.2.2;2.2 Environment Model;158
6.2.2.3;2.3 Hydraulic Thrust Cylinders;159
6.2.3;3 HYBRID CONTROLLER;159
6.2.3.1;3.1 Feedback Linearization;160
6.2.3.2;3.2 Transformation of Sensor Data;160
6.2.3.3;3.3 Selection Matrix;160
6.2.3.4;3.4 Position Control;161
6.2.3.5;3.5 Force Control;161
6.2.3.6;3.6 Impedance Control;161
6.2.3.7;3.7 Simulation Results;162
6.2.4;4 CONCLUSIONS;162
6.2.5;REFERENCES;163
6.3;MOCONT LOCATION MODULE: A CONTAINER LOCATION SYSTEM BASED ON DR/ DGNSS INTEGRATION;164
6.3.1;1 INTRODUCTION;164
6.3.2;2 OVERVIEW OF THE MOCONT SYSTEM;165
6.3.3;4 DR SUBSYSTEM DESIGN;167
6.3.3.1;4.1 Description of the DR Subsystem;167
6.3.3.2;4.2 DR/DGNSS Integration;167
6.3.4;5 EXPERIMENTAL RESULTS;169
6.3.5;6 CONCLUSIONS;170
6.3.6;ACKNOWLEDGEMENTS;171
6.3.7;REFERENCES;171
6.4;PARTIAL VIEWS MATCHING USING A METHOD BASED ON PRINCIPAL COMPONENTS;172
6.4.1;1 INTRODUCTION;172
6.4.2;2 OVERALL DESCRIPTION OF THE METHOD;173
6.4.3;3 PRINCIPAL COMPONENTS DATABASE GENERATE;174
6.4.3.1;3.1 Virtual Partial Views Computation;174
6.4.3.2;3.2 Principal Components of VPV Computation;176
6.4.4;4 MATCHING PROCESS;176
6.4.4.1;4.1 Initial Transformation Matrix Computation;176
6.4.4.2;4.2 ICP Algorithm Application;177
6.4.5;5 EXPERIMENTAL RESULT;178
6.4.6;6 CONCLUSIONS;179
6.4.7;ACKNOWLEDGEMENTS;179
6.4.8;REFERENCES;179
6.5;TOWARDS A CONCEPTUAL FRAMEWORK-BASED ARCHITECTURE FOR UNMANNED SYSTEMS;180
6.5.1;1 INTRODUCTION;180
6.5.2;2 DESCRIPTION OF THE SOFTWARE ARCHITECTURE;181
6.5.2.1;2.1 Architectural Elements;182
6.5.2.2;2.2 Middleware-Based Framework;183
6.5.3;3 DESIGN OF AN AUTONOMOUS ENTITY;184
6.5.3.1;3.1 Structure of an AE;184
6.5.3.2;3.2 Provider of Concrete Services;185
6.5.3.3;3.3 Retrieving CSPs;186
6.5.3.4;3.4 Provision of Services;186
6.5.4;4 EXPERIMENTAL RESULTS;186
6.5.5;5 CONCLUSIONS;188
6.5.6;REFERENCES;189
6.6;A INTERPOLATION-BASED APPROACH TO MOTION GENERATION FOR HUMANOID ROBOTS;192
6.6.1;1 INTRODUCTION;192
6.6.2;2 HUMANOID ROBOT AND THE TARGET MOTIONS;192
6.6.2.1;2.1 Humanoid Robot;192
6.6.2.2;2.2 Tai Chi Chuan;192
6.6.3;3 MOTION GENERATION SYSTEM;193
6.6.3.1;3.1 Interpolation-based Motion Generation;193
6.6.3.2;3.2 Classification of the Postures in Balance Space;193
6.6.4;4 EXPERIMENT;196
6.6.4.1;4.1 Performance Results;196
6.6.4.2;4.2 Effectiveness of Balance Checker;196
6.6.5;5 CONCLUSION;197
6.6.6;ACKNOWLEDGEMENTS;197
6.6.7;REFERENCES;198
6.7;REALISTIC DYNAMIC SIMULATION OF AN INDUSTRIAL ROBOT WITH JOINT FRICTION;200
6.7.1;1 INTRODUCTION;200
6.7.2;2 FINITE ELEMENT REPRESENTATION OF THE MANIPULATOR;201
6.7.3;3 THE DRIVING SYSTEM;202
6.7.4;4 JOINT FRICTION MODEL;203
6.7.5;5 CLOSED-LOOP ROBOT MODEL;203
6.7.6;6 PERTURBATION METHOD;204
6.7.7;7 SIMULATION RESULTS;205
6.7.8;8 CONCLUSIONS;206
6.7.9;ACKNOWLEDGEMENTS;207
6.7.10;REFERENCES;207
6.8;A NEW PARADIGM FOR SHIP HULL INSPECTION USING A HOLONOMIC HOVER- CAPABLE AUV;208
6.8.1;1 INTRODUCTION AND EXISTING CAPABILITIES;208
6.8.2;2 PHYSICAL VEHICLE OVERVIEW;209
6.8.3;3 OUR APPROACH TO HULL NAVIGATION;209
6.8.3.1;3.1 Suitability of the DVL for this Task;210
6.8.3.2;3.2 Two Approaches Using “Slicing”;211
6.8.3.3;3.3 Role of Low- and Mid-Level Control;211
6.8.4;4 SUMMARY;212
6.8.5;REFERENCES;212
6.9;DIMSART: A REAL TIME - DEVICE INDEPENDENT MODULAR SOFTWARE ARCHITECTURE FOR ROBOTIC AND TELEROBOTIC APPLICATIONS;214
6.9.1;1 INTRODUCTION;214
6.9.1.1;1.1 Telepresence Environment;214
6.9.1.2;1.2 Control and Software engineering Interplay;215
6.9.1.3;1.3 Existing Architectures;215
6.9.2;2 ROBOT CONTROL;215
6.9.2.1;2.1 Wave Variables Scheme as Bilateral Control Example;215
6.9.2.2;2.2 General Robot Control setup;216
6.9.2.3;2.3 Bilateral Control Scheme with DIMSART Embedded;217
6.9.3;3 ARCHITECTURE OVERVIEW;217
6.9.3.1;3.1 Modules;217
6.9.3.2;3.2 The Data Base;218
6.9.3.3;3.3 The Module Engine;219
6.9.3.4;3.4 The Frame;219
6.9.4;4 EXAMPLE;220
6.9.5;5 CONCLUDING REMARKS AND REMAINING ASPECTS;220
6.9.6;REFERENCES;221
7;Part 3 Signal Processing, Systems Modeling and Control;223
7.1;ON MODELING AND CONTROL OF DISCRETE TIMED EVENT GRAPHS WITH MULTIPLIERS USING (MIN, +) ALGEBRA;224
7.1.1;1 INTRODUCTION;224
7.1.2;2 RECURRENT EQUATIONS OF TEGM’s;225
7.1.3;3 DIOID, OPERATORIAL REPRESENTATION;225
7.1.4;4 JUST IN TIME CONTROL;227
7.1.4.1;4.1 Residuation Theory;227
7.1.4.2;4.2 Control Problem Statement;228
7.1.5;5 CONCLUSION;228
7.1.6;REFERENCES;229
7.2;MODEL PREDICTIVE CONTROL FOR HYBRID SYSTEMS UNDER A STATE PARTITION BASED MLD APPROACH ( SPMLD);230
7.2.1;1 INTRODUCTION;230
7.2.2;2 HYBRID SYSTEMS MODELING;231
7.2.2.1;2.1 Mixed Logical Dynamical Model;231
7.2.2.2;2.2 Piecewise Affine Model;231
7.2.3;3 MODEL PREDICTIVE CONTROL;231
7.2.3.1;3.1 Model Predictive Control for the MLD Systems;232
7.2.3.2;3.2 Model Predictive Control for the PWA Systems;232
7.2.4;4 MPC FOR STATE PARTITION BASED MLD ( SPMLD) FORMALISM;233
7.2.4.1;4.1 The SPMLD Formalism;233
7.2.4.2;4.2 Reformulation of the MPC Solution;233
7.2.4.3;4.3 Compared Computational Burden;234
7.2.4.4;4.4 Further Improvements of the Optimization Time;235
7.2.5;5 APPLICATION ;235
7.2.5.1;5.1 Description of the Benchmark;235
7.2.5.2;5.2 Application of MPC for the SPMLD Formalism;235
7.2.6;6 CONCLUSION;237
7.2.7;REFERENCES;237
7.3;EFFICIENT SYSTEM IDENTIFICATION FOR MODEL PREDICTIVE CONTROL WITH THE ISIAC SOFTWARE;238
7.3.1;1 INTRODUCTION;238
7.3.2;2 SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL;238
7.3.3;3 ISIAC;240
7.3.3.1;3.1 Approaches to Model Estimation;240
7.3.3.2;3.2 General Structure and Layout;241
7.3.4;4 WORKING WITH ISIAC;242
7.3.4.1;4.1 Experiment Design;242
7.3.4.2;4.2 Working with Data;242
7.3.4.3;4.3 Working with Models;242
7.3.4.4;4.4 Estimating and Validating Models;242
7.3.4.5;4.5 Building the Control Model;243
7.3.5;5 AN INDUSTRIAL APPLICATION: MODEL PREDICTIVE CONTROL OF A MTBE UNIT;243
7.3.6;6 CONCLUSION;244
7.3.7;REFERENCES;245
7.4; IMPROVING PERFORMANCE OF THE DECODER FOR TWO-DIMENSIONAL BARCODE SYMBOLOGY PDF417;246
7.4.1;1 INTRODUCTION;246
7.4.2;2 LOCALIZING THE DATA REGION;246
7.4.3;3 DECODING CODEWORDS FROM BAR- SPACE PATTERNS;247
7.4.4;4 EXPERIMENTAL RESULTS;248
7.4.5;5 CONCLUSION;249
7.4.6;REFERENCES;249
7.5;CONTEXT IN ROBOTIC VISION;252
7.5.1;1 INTRODUCTION;252
7.5.2;2 CONTEXT IN COMPUTER VISION;253
7.5.3;3 AN OPERATIVE DEFINITION OF CONTEXT;254
7.5.3.1;3.1 Model Set M;254
7.5.3.2;3.2 Operator Set Z;254
7.5.3.3;3.3 Contextual Changes;255
7.5.4;4 BAYESIAN CONTEXT SWITCHING;255
7.5.4.1;4.1 Opportunistic Switching;255
7.5.4.2;4.2 Context Commutation;256
7.5.4.3;4.3 A Practical Implementation;257
7.5.5;5 CONCLUSIONS;259
7.5.6;REFERENCES;259
7.6;DYNAMIC STRUCTURE CELLULAR AUTOMATA IN A FIRE SPREADING APPLICATION;260
7.6.1;1 INTRODUCTION;260
7.6.2;2 BACKGROUND;261
7.6.3;3 DSCA MODELLING;262
7.6.4;4 DSCA SIMULATION;263
7.6.5;ACTIVITY TRACKING;263
7.6.6;6 FIRE SPREADING APPLICATION;265
7.6.7;7 CONCLUSION;267
7.6.8;REFERENCES;267
7.6.9;ACKNOWLEDGEMENTS;267
7.7;SPEAKER VERIFICATION SYSTEM;268
7.7.1;1 INTRODUCTION;268
7.7.2;2 BASIC IDEA OF THE VQ-VERIFICATION;269
7.7.3;3 THE GMM BASED SPEAKER VERIFICATION;270
7.7.4;4 EXPERIMENTAL RESULTS;271
7.7.5;5 CONCLUSION;273
7.7.6;REFERENCES;273
7.8;MOMENT-LINEAR STOCHASTIC SYSTEMS;276
7.8.1;1 INTRODUCTION;276
7.8.2;2 MLSS: FORMULATION AND BASIC ANALYSIS;277
7.8.3;3 FURTHER RESULTS;279
7.8.4;4 EXAMPLE: MJLS;280
7.8.5;5 MLSS MODELS FOR NETWORK DYNAMICS: SUMMARY;282
7.8.6;REFERENCES;283
7.9;ACTIVE ACOUSTIC NOISE CONTROL IN DUCTS;286
7.9.1;1 INTRODUCTION;286
7.9.2;2 FEEDFORWARD CONTROL;287
7.9.3;3 EXPERIMENTAL SET-UP;290
7.9.4;4 IDENTIFICATION;291
7.9.5;5 EXPERIMENTAL RESULTS;291
7.9.6;6 CONCLUSIONS;293
7.9.7;REFERENCES;293
7.10;HYBRID UML COMPONENTS FOR THE DESIGN OF COMPLEX SELF-OPTIMIZING MECHATRONIC SYSTEMS;294
7.10.1;1 INTRODUCTION;294
7.10.2;2 RELATEDWORK;295
7.10.3;3 MODELING RECONFIGURATION;295
7.10.4;4 THE APPROACH;297
7.10.4.1;4.1 Hybrid UML Model;298
7.10.4.2;4.2 Hybrid Statecharts;298
7.10.4.3;4.3 Hybrid Components;298
7.10.4.4;4.4 Modular Recon.guration;299
7.10.5;5 RUN-TIME ARCHITECTURE;300
7.10.6;6 CONCLUSION AND FUTURE WORK;300
7.10.7;REFERENCES;300
8;AUTHOR INDEX;302

Leseproben

ROBOT-HUMAN INTERACTION (p. 3)

Practical experiments with a cyborg

Kevin Warwick
Department of Cybernetics, University of Reading,
Whiteknights, Reading, RG6 6AY, UK
Abstract: This paper presents results to indicate the potential applications of a direct connection between the human nervous system and a computer network. Actual experimental results obtained from a human subject study are given, with emphasis placed on the direct interaction between the human nervous system and possible extra-sensory input.

An brief overview of the general state of neural implants is given, as well as a range of application areas considered. An overall view is also taken as to what may be possible with implant technology as a general purpose human-computer interface for the future.

1 INTRODUCTION

There are a number of ways in which biological signals can be recorded and subsequently acted upon to bring about the control or manipulation of an item of technology, (Penny et al., 2000, Roberts et al., 1999). Conversely it may be desired simply to monitor the signals occurring for either medical or scientific purposes.

In most cases, these signals are collected externally to the body and, whilst this is positive from the viewpoint of non-intrusion into the body with its potential medical side-effects such as infection, it does present enormous problems in deciphering and understanding the signals observed (Wolpaw et al., 1991, Kubler et al., 1999).

Noise can be a particular problem in this domain and indeed it can override all other signals, especially when compound/collective signals are all that can be recorded, as is invariably the case with external recordings which include neural signals.

A critical issue becomes that of selecting exactly which signals contain useful information and which are noise, and this is something which may not be reliably achieved. Additionally, when specific, targeted stimulation of the nervous system is required, this is not possible in a meaningful way for control purposes merely with external connections.

The main reason for this is the strength of signal required, which makes stimulation of unique or even small subpopulations of sensory receptor or motor unit channels unachievable by such a method.

A number of research groups have concentrated on animal (non-human) studies, and these have certainly provided results that contribute generally to the knowledge base in the field. Unfortunately actual human studies involving implants are relatively limited in number, although it could be said that research into wearable computers has provided some evidence of what can be done technically with bio-signals.

We have to be honest and say that projects which involve augmenting shoes and glasses with microcomputers (Thorp, 1997) are perhaps not directly useful for our studies, however monitoring indications of stress or alertness by this means can be helpful in that it can give us an idea of what might be subsequently achievable by means of an implant.

Of relevance here are though studies in which a miniature computer screen was fitted onto a standard pair of glasses.