Verfügbarkeit: Electronic nose: algorithmic challenges

Electronic nose: algorithmic challenges:

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Bibliographische Detailangaben
Hauptverfasser:	Zhang, Lei (VerfasserIn), Tian, Fengchun (VerfasserIn), Zhang, David 1949- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Singapore Springer [2018]
Schlagworte:	Elektronische Nase Duftstoff Geruchsemission Messung Artificial Intelligence Bionic Perception Drift Compensation Electronic Nose Gas Sensing Intelligent Nose Machine Learning Machine Olfaction Odor Recognition Pattern Recognition
Online-Zugang:	Inhaltstext http://www.springer.com/ Inhaltsverzeichnis Inhaltsverzeichnis
Beschreibung:	xv, 339 Seiten Illustrationen
ISBN:	9789811321665 9811321663

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Datensatz im Suchindex

_version_	1804181706574397440
adam_text	CONTENTS PART I OVERVIEW 1 INTRODUCTION ................................................................................................. 3 1.1 BACKGROUND OF ELECTRONIC NOSE ...................................................... 3 1.2 DEVELOPMENT IN APPLICATION LEVEL ................................................. 4 1.3 DEVELOPMENT IN SYSTEM LEVEL ........................................................ 4 1.4 RELATED TECHNOLOGIES ....................................................................... 5 1.5 OUTLINE OF THE BOOK ......................................................................... 6 REFERENCES ..................................................................................................... 7 2 E-NOSE ALGORITHMS AND CHALLENGES ......................................................... 11 2.1 FEATURE EXTRACTION AND DE-NOISING ALGORITHMS ............................ 11 2.2 PATTERN RECOGNITION ALGORITHMS ....................................................... 13 2.3 DRIFT COMPENSATION ALGORITHMS ....................................................... 13 2.4 CURRENT E-NOSE CHALLENGES ............................................................. 15 2.4.1 DISCRETENESS .......................................................................... 15 2.4.2 DRIFT ..................................................................................... 16 2.4.3 DISTURBANCE ......................................................................... 16 2.5 SUMMARY ............................................................................................ 17 REFERENCES ..................................................................................................... 17 PART II E-NOSE ODOR RECOGNITION AND PREDICTION: CHALLENGE I 3 HEURISTIC AND BIO-INSPIRED NEURAL NETWORK MODEL ............................ 23 3.1 INTRODUCTION ....................................................................................... 23 3.2 PARTICLE SWARM OPTIMIZATION MODELS ............................................. 25 3.2.1 STANDARD PARTICLE SWARM OPTIMIZATION (SPSO) .............. 25 3.2.2 ADAPTIVE PARTICLE SWARM OPTIMIZATION (APSO) ............ 26 3.2.3 ATTRACTIVE AND REPULSIVE PARTICLE SWARM OPTIMIZATION (ARPSO) ...................................................... 26 VIII CONTENTS 3.2.4 DIFFUSION AND REPULSION PARTICLE SWARM OPTIMIZATION (DRPSO) .............................................................................. 27 3.2.5 BACTERIAL CHEMOTAXIS PARTICLE SWARM OPTIMIZATION (PSOBC) .............................................................................. 28 3.3 HYBRID EVOLUTIONARY ALGORITHM ........................................................ 28 3.3.1 PSO WITH COSINE MECHANISM ........................................... 28 3.3.2 ADAPTIVE GENETIC STRATEGY (AGS) .................................... 29 3.4 CONCENTRATION ESTIMATION ALGORITHM ............................................... 30 3.4.1 MULTILAYER PERCEPTRON ......................................................... 30 3.4.2 NETWORK OPTIMIZATION ....................................................... 31 3.5 EXPERIMENTS ........................................................................................ 33 3.5.1 EXPERIMENTAL SETUP ........................................................... 34 3.5.2 DATASETS ................................................................................. 34 3.5.3 CONCENTRATION ESTIMATION .................................................. 37 3.6 RESULTS AND DISCUSSION ..................................................................... 39 3.6.1 EXPERIMENTAL RESULTS ......................................................... 39 3.6.2 COMPUTATIONAL EFFICIENCY .................................................. 43 3.6.3 DISCUSSION ............................................................................ 43 3.7 SUMMARY ............................................................................................. 44 REFERENCES ...................................................................................................... 45 4 CHAOS-BASED NEURAL NETWORK OPTIMIZATION APPROACH ...................... 47 4.1 INTRODUCTION ........................................................................................ 47 4.2 MATERIALS AND METHODS ..................................................................... 48 4.2.1 ELECTRONIC NOSE .................................................................. 48 4.2.2 DATA ACQUISITION ................................................................ 49 4.2.3 BACK-PROPAGATION NEURAL NETWORK .................................... 51 4.2.4 MUTATIVE SCALE CHAOTIC SEQUENCE OPTIMIZATION ............ 53 4.2.5 STANDARD PARTICLE SWARM OPTIMIZATION (SPSO) ............... 54 4.2.6 PARAMETER SETTINGS .............................................................. 55 4.2.7 ON-LINE USAGE ..................................................................... 56 4.3 RESULTS AND DISCUSSION ..................................................................... 56 4.4 SUMMARY ............................................................................................. 58 REFERENCES ...................................................................................................... 59 5 MULTILAYER PERCEPTRON-BASED CONCENTRATION ESTIMATION ................... 61 5.1 INTRODUCTION ........................................................................................ 61 5.2 E-NOSE SYSTEMS AND DATA ACQUISITION ........................................... 62 5.2.1 LOW-COST ELECTRONIC NOSE SYSTEM ..................................... 62 5.2.2 EXPERIMENTAL SETUP ........................................................... 63 5.2.3 DESCRIPTION OF DATASET ....................................................... 65 5.3 MLP-BASED QUANTIZATION MODELS .................................................... 65 5.3.1 SINGLE MULTI-INPUT MULTI-OUTPUT (SMIMO) MODEL .... 65 CONTENTS IX 5.3.2 MULTIPLE MULTI-INPUT SINGLE-OUTPUT (MMISO) MODEL .... 69 5.3.3 MODEL OPTIMIZATION ........................................................... 71 5.4 RESULTS AND DISCUSSION .................................................................... 73 5.5 SUMMARY ............................................................................................ 76 REFERENCES ...................................................................................................... 76 6 DISCRIMINATIVE SUPPORT VECTOR MACHINE-BASED ODOR CLASSIFICATION ................................................................................................. 79 6.1 INTRODUCTION ....................................................................................... 79 6.2 CLASSIFICATION METHODOLOGIES ........................................................... 81 6.2.1 EUCLIDEAN DISTANCE TO CENTROIDS (EDC) .......................... 81 6.2.2 SIMPLIFIED FUZZY ARTMAP NETWORK (SEAM) .............. 81 6.2.3 MULTILAYER PERCEPTRON NEURAL NETWORK (MLP) .............. 82 6.2.4 FISHER LINEAR DISCRIMINANT ANALYSIS (FLDA) ................. 82 6.2.5 SUPPORT VECTOR MACHINE (SVM) ...................................... 83 6.3 EXPERIMENTS ....................................................................................... 84 6.3.1 EXPERIMENTAL SETUP ........................................................... 84 6.3.2 DATASET ................................................................................ 85 6.3.3 MULTI-CLASS DISCRIMINATION ............................................... 85 6.3.4 DATA ANALYSIS ....................................................................... 87 6.4 RESULTS AND DISCUSSION .................................................................... 87 6.4.1 EXPERIMENTAL RESULTS ......................................................... 87 6.4.2 DISCUSSION ............................................................................ 91 6.5 SUMMARY ............................................................................................ 92 REFERENCES ..................................................................................................... 92 7 LOCAL KERNEL DISCRIMINANT ANALYSIS-BASED ODOR RECOGNITION .......... 95 7.1 INTRODUCTION ....................................................................................... 95 7.2 RELATED WORK ..................................................................................... 97 7.2.1 PCA ..................................................................................... 97 7.2.2 KPCA ................................................................................... 97 7.2.3 LDA ..................................................................................... 98 7.3 THE PROPOSED APPROACH .................................................................. 99 7.3.1 NDA FRAMEWORK ................................................................ 99 7.3.2 THE KPCA PLUS NDA ALGORITHM (KNDA) ................... 101 7.3.3 MULTI-CLASS RECOGNITION .................................................... 102 7.4 EXPERIMENTS ....................................................................................... 105 7.4.1 E-NOSE SYSTEM .................................................................... 105 7.4.2 DATASET ................................................................................ 106 7.5 RESULTS AND DISCUSSION .................................................................... 107 7.5.1 CONTRIBUTION RATE ANALYSIS ............................................... 107 7.5.2 COMPARISONS ....................................................................... 107 7.5.3 COMPUTATIONAL EFFICIENCY .................................................. ILL X CONTENTS 7.6 SUMMARY ............................................................................................. 112 REFERENCES ...................................................................................................... 112 8 ENSEMBLE OF CLASSIFIERS FOR ROBUST RECOGNITION .................................. 115 8.1 INTRODUCTION ........................................................................................ 115 8.2 DATA ACQUISITION ................................................................................. 117 8.2.1 ELECTRONIC NOSE SYSTEM .................................................... 117 8.2.2 EXPERIMENTAL SETUP ........................................................... 117 8.2.3 E-NOSE DATA ....................................................................... 118 8.3 METHODS ............................................................................................... 119 8.3.1 KPCA-BASED FEATURE EXTRACTION ...................................... 119 8.3.2 BASE CLASSIFIERS .................................................................. 121 8.3.3 MULTI-CLASS ISVMEN ......................................................... 122 8.4 RESULTS AND DISCUSSION ..................................................................... 126 8.5 SUMMARY ............................................................................................. 129 REFERENCES ...................................................................................................... 130 PART III E-NOSE DRIFT COMPENSATION: CHALLENGE II 9 CHAOTIC TIME SERIES-BASED SENSOR DRIFT PREDICTION .......................... 135 9.1 INTRODUCTION ........................................................................................ 135 9.2 DATA ...................................................................................................... 136 9.2.1 LONG-TERM SENSOR DATA .................................................... 136 9.2.2 DISCRETE FOURIER TRANSFORM (DFT)-BASED FEATURE EXTRACTION ............................................................................ 137 9.3 CHAOTIC TIME SERIES PREDICTION ....................................................... 139 9.3.1 PHASE SPACE RECONSTRUCTION ............................................. 139 9.3.2 PREDICTION MODEL ................................................................ 140 9.4 RESULTS ................................................................................................. 142 9.4.1 CHAOTIC CHARACTERISTIC ANALYSIS ........................................ 142 9.4.2 DRIFT PREDICTION .................................................................. 143 9.5 SUMMARY ............................................................................................ 145 REFERENCES ...................................................................................................... 145 10 DOMAIN ADAPTATION GUIDED DRIFT COMPENSATION ............................... 147 10.1 INTRODUCTION ........................................................................................ 147 10.2 RELATED WORK ..................................................................................... 149 10.2.1 DRIFT COMPENSATION ........................................................... 149 10.2.2 EXTREME LEARNING MACHINE ............................................... 150 10.3 DOMAIN ADAPTATION EXTREME LEARNING MACHINE .......................... 151 10.3.1 SOURCE DOMAIN ADAPTATION ELM (DAELM-S) .............. 151 10.3.2 TARGET DOMAIN ADAPTATION ELM (DAELM-T) ................ 155 CONTENTS XI 10.4 EXPERIMENTS ....................................................................................... 159 10.4.1 DESCRIPTION OF DATA ........................................................... 159 10.4.2 EXPERIMENTAL SETUP ........................................................... 163 10.5 RESULTS AND DISCUSSION .................................................................... 163 10.6 SUMMARY ............................................................................................ 169 REFERENCES ..................................................................................................... 169 11 DOMAIN REGULARIZED SUBSPACE PROJECTION METHOD ............................ 173 11.1 INTRODUCTION ....................................................................................... 173 11.2 RELATED WORK ..................................................................................... 174 11.2.1 EXISTING DRIFT COMPENSATION APPROACHES ........................ 174 11.2.2 EXISTING SUBSPACE PROJECTION ALGORITHMS ........................ 176 11.3 DOMAIN REGULARIZED COMPONENT ANALYSIS (DRCA) ................... 176 11.3.1 MATHEMATICAL NOTATIONS .................................................... 176 11.3.2 PROBLEM FORMULATION ........................................................ 177 11.3.3 MODEL OPTIMIZATION ........................................................... 179 11.3.4 REMARKS ON DRCA ........................................................... 181 11.4 EXPERIMENTS ....................................................................................... 181 11.4.1 EXPERIMENT ON BENCHMARK SENSOR DRIFT DATA ................ 181 11.4.2 EXPERIMENT ON E-NOSE DATA WITH DRIFT AND SHIFT ........... 185 11.4.3 PARAMETER SENSITIVITY ANALYSIS ........................................ 188 11.4.4 DISCUSSION ........................................................................... 188 11.5 SUMMARY ............................................................................................ 190 REFERENCES ..................................................................................................... 190 12 CROSS-DOMAIN SUBSPACE LEARNING APPROACH ...................................... 193 12.1 INTRODUCTION ....................................................................................... 193 12.2 RELATED WORK ..................................................................................... 195 12.2.1 REVIEW OF ELM .................................................................. 195 12.2.2 SUBSPACE LEARNING ............................................................. 196 12.3 THE PROPOSED CDELM METHOD ...................................................... 196 12.3.1 NOTATIONS .............................................................................. 196 12.3.2 MODEL FORMULATION ............................................................. 197 12.3.3 MODEL OPTIMIZATION ........................................................... 199 12.4 EXPERIMENTS ....................................................................................... 200 12.4.1 DATA DESCRIPTION ................................................................ 200 12.4.2 EXPERIMENTAL SETTINGS ........................................................ 201 12.4.3 SINGLE-DOMAIN SUBSPACE PROJECTION METHODS ................ 201 12.4.4 CLASSIFICATION RESULTS ......................................................... 202 12.4.5 PARAMETER SENSITIVITY ........................................................ 204 12.5 SUMMARY ............................................................................................ 206 REFERENCES ..................................................................................................... 207 XII CONTENTS 13 DOMAIN CORRECTION-BASED ADAPTIVE EXTREME LEARNING MACHINE ......................................................................................................... 209 13.1 INTRODUCTION ........................................................................................ 209 13.2 RELATED WORK ..................................................................................... 210 13.2.1 TRANSFER LEARNING ................................................................ 210 13.2.2 EXTREME LEARNING MACHINE ............................................... 211 13.3 THE PROPOSED METHOD ....................................................................... 213 13.3.1 NOTATIONS .............................................................................. 213 13.3.2 DOMAIN CORRECTION AND ADAPTIVE EXTREME LEARNING MACHINE .............................................................................. 213 13.4 EXPERIMENTS ........................................................................................ 217 13.4.1 EXPERIMENT ON BACKGROUND INTERFERENCE DATA .............. 218 13.4.2 EXPERIMENT ON SENSOR DRIFT DATA ...................................... 220 13.5 SUMMARY ............................................................................................. 221 REFERENCES ...................................................................................................... 223 14 MULTI-FEATURE SEMI-SUPERVISED LEARNING APPROACH .............................. 225 14.1 INTRODUCTION ........................................................................................ 226 14.1.1 PROBLEM STATEMENT .............................................................. 226 14.1.2 MOTIVATION ............................................................................ 226 14.2 RELATED WORK ..................................................................................... 227 14.3 MULTI-FEATURE KERNEL SEMI-SUPERVISED JOINT LEARNING MODEL ................................................................................................. 228 14.3.1 NOTATIONS .............................................................................. 228 14.3.2 MODEL ................................................................................... 229 14.3.3 OPTIMIZATION ALGORITHM .................................................... 231 14.3.4 CLASSIFICATION ....................................................................... 234 14.3.5 CONVERGENCE ....................................................................... 235 14.3.6 COMPUTATIONAL COMPLEXITY ................................................ 236 14.3.7 REMARKS ON OPTIMALITY CONDITION .................................... 237 14.4 EXPERIMENTS ON DRIFTED E-NOSE DATA ............................................. 238 14.4.1 DESCRIPTION OF DATA ........................................................... 238 14.4.2 EXPERIMENTAL SETUP ........................................................... 238 14.4.3 PARAMETER SETTING ................................................................ 239 14.4.4 COMPARED METHODS ........................................................... 239 14.4.5 RESULTS AND ANALYSIS ......................................................... 239 14.5 EXPERIMENTS ON MODULATED E-NOSE DATA ...................................... 240 14.5.1 DESCRIPTION OF DATA ........................................................... 240 14.5.2 EXPERIMENTAL SETUP ........................................................... 243 14.5.3 PARAMETER SETTING ................................................................ 243 14.5.4 RESULTS AND ANALYSIS ......................................................... 243 14.6 SUMMARY ............................................................................................. 244 REFERENCES ...................................................................................................... 244 CONTENTS XIII PART IV E-NOSE DISTURBANCE ELIMINATION: CHALLENGE III 15 PATTERN RECOGNITION-BASED INTERFERENCE REDUCTION ............................. 249 15.1 INTRODUCTION ....................................................................................... 249 15.2 MATERIALS AND METHODS .................................................................... 251 15.2.1 EXPERIMENTAL PLATFORM ...................................................... 251 15.2.2 SENSOR SIGNAL PREPROCESSING ............................................. 251 15.2.3 E-NOSE DATA PREPARATION .................................................... 251 15.2.4 FEATURE SELECTION OF ABNORMAL ODOR ............................... 253 15.2.5 GENETIC CROSSOVER OPERATOR FOR SOLUTION OF UNEVEN FEATURES ................................................................................ 254 15.2.6 DESCRIPTION OF LEARNER- 1 UNDER MULTI-CLASS CONDITION .............................................................................. 255 15.2.7 DESCRIPTION OF LEAMER-2 UNDER BINARY CLASSIFICATION CONDITION .............................................................................. 257 15.2.8 ADAPTIVE COUNTERACTION MODEL ........................................ 257 15.3 RESULTS AND DISCUSSION ................................................................... 259 15.3.1 RECOGNITION ACCURACY OF LEARNER- 1 AND LEAMER-2 .... 259 15.3.2 ABNORMAL ODOR COUNTERACTION: CASE STUDY ................... 260 15.3.3 DISCUSSION ........................................................................... 262 15.4 SUMMARY ............................................................................................ 262 REFERENCES ..................................................................................................... 263 16 PATTERN MISMATCH GUIDED INTERFERENCE ELIMINATION ........................... 265 16.1 INTRODUCTION ....................................................................................... 265 16.2 DATA ACQUISITION ................................................................................ 267 16.3 PROPOSED PMIE METHOD .................................................................. 267 16.3.1 MAIN IDEA ........................................................................... 267 16.3.2 PATTERN MISMATCH-BASED INTERFERENCE ELIMINATION (PMIE) ................................................................................ 268 16.4 RESULTS AND DISCUSSION .................................................................... 272 16.4.1 DATA PREPROCESSING ............................................................. 272 16.4.2 PMIE TRAINING ON DATASET 1 ............................................. 272 16.4.3 THRESHOLD ANALYSIS BASED ON DATASET 2 ........................... 273 16.4.4 INTERFERENCE DISCRIMINATION ON DATASET 3 ......................... 273 16.4.5 PMIE-BASED INTERFERENCE ELIMINATION RESULT ................. 276 16.5 SUMMARY ............................................................................................ 277 REFERENCES ..................................................................................................... 277 17 SELF-EXPRESSION-BASED ABNORMAL ODOR DETECTION ............................... 279 17.1 INTRODUCTION ....................................................................................... 279 17.1.1 BACKGROUND ......................................................................... 279 17.1.2 PROBLEM STATEMENT ............................................................. 280 17.1.3 MOTIVATION ........................................................................... 280 XIV CONTENTS 17.2 RELATED WORK ...................................................................................... 282 17.3 SELF-EXPRESSION MODEL (SEM) ......................................................... 284 17.3.1 MODEL FORMULATION .............................................................. 284 17.3.2 ALGORITHM ............................................................................ 286 17.4 EXTREME LEARNING MACHINE-BASED SELF-EXPRESSION MODEL (SE 2 LM) ............................................................................................. 287 17.4.1 MODEL FORMULATION .............................................................. 287 17.4.2 ALGORITHM ............................................................................ 289 17.5 EXPERIMENTS ........................................................................................ 290 17.5.1 ELECTRONIC NOSE AND DATA ACQUISITION ............................. 290 17.5.2 ABNORMAL ODOR DETECTION BASED ON DATASET 1 .............. 291 17.5.3 VALIDATION OF REAL-TIME SEQUENCE ON DATASET 2 ............. 295 17.5.4 VALIDATION OF REAL-TIME SEQUENCE ON DATASET 3 ............. 295 17.6 DISCUSSION .......................................................................................... 296 17.7 SUMMARY ............................................................................................. 297 REFERENCES ...................................................................................................... 297 PART V E-NOSE DISCRETENESS CORRECTION: CHALLENGE IV 18 AFFINE CALIBRATION TRANSFER MODEL ......................................................... 301 18.1 INTRODUCTION ........................................................................................ 301 18.2 METHOD ............................................................................................... 303 18.2.1 CALIBRATION STEP .................................................................. 303 18.2.2 PREDICTION STEP ..................................................................... 307 18.3 EXPERIMENTS ........................................................................................ 309 18.3.1 ELECTRONIC NOSE MODULE .................................................... 309 18.3.2 GAS DATASETS ....................................................................... 309 18.4 RESULTS AND DISCUSSION ..................................................................... 310 18.4.1 SENSOR RESPONSE CALIBRATION ............................................. 310 18.4.2 CONCENTRATION PREDICTION .................................................... 314 18.5 SUMMARY ............................................................................................. 320 REFERENCES ...................................................................................................... 320 19 INSTRUMENTAL BATCH CORRECTION ................................................................. 323 19.1 INTRODUCTION ........................................................................................ 323 19.2 MATERIALS AND METHOD ....................................................................... 325 19.2.1 SENSORS * DISCRETENESS ......................................................... 325 19.2.2 REVIEW OF THE GAT-RWLS METHOD ............................... 326 19.2.3 INSTRUMENTAL BATCH CORRECTION SCHEME .......................... 327 19.3 RESULTS AND DISCUSSION ..................................................................... 328 19.4 SUMMARY ............................................................................................. 332 REFERENCES ...................................................................................................... 333 CONTENTS XV 20 BOOK REVIEW AND FUTURE WORK .............................................................. 335 20.1 INTRODUCTION ....................................................................................... 336 20.2 FUTURE WORK ....................................................................................... 339
adam_txt	CONTENTS PART I OVERVIEW 1 INTRODUCTION . 3 1.1 BACKGROUND OF ELECTRONIC NOSE . 3 1.2 DEVELOPMENT IN APPLICATION LEVEL . 4 1.3 DEVELOPMENT IN SYSTEM LEVEL . 4 1.4 RELATED TECHNOLOGIES . 5 1.5 OUTLINE OF THE BOOK . 6 REFERENCES . 7 2 E-NOSE ALGORITHMS AND CHALLENGES . 11 2.1 FEATURE EXTRACTION AND DE-NOISING ALGORITHMS . 11 2.2 PATTERN RECOGNITION ALGORITHMS . 13 2.3 DRIFT COMPENSATION ALGORITHMS . 13 2.4 CURRENT E-NOSE CHALLENGES . 15 2.4.1 DISCRETENESS . 15 2.4.2 DRIFT . 16 2.4.3 DISTURBANCE . 16 2.5 SUMMARY . 17 REFERENCES . 17 PART II E-NOSE ODOR RECOGNITION AND PREDICTION: CHALLENGE I 3 HEURISTIC AND BIO-INSPIRED NEURAL NETWORK MODEL . 23 3.1 INTRODUCTION . 23 3.2 PARTICLE SWARM OPTIMIZATION MODELS . 25 3.2.1 STANDARD PARTICLE SWARM OPTIMIZATION (SPSO) . 25 3.2.2 ADAPTIVE PARTICLE SWARM OPTIMIZATION (APSO) . 26 3.2.3 ATTRACTIVE AND REPULSIVE PARTICLE SWARM OPTIMIZATION (ARPSO) . 26 VIII CONTENTS 3.2.4 DIFFUSION AND REPULSION PARTICLE SWARM OPTIMIZATION (DRPSO) . 27 3.2.5 BACTERIAL CHEMOTAXIS PARTICLE SWARM OPTIMIZATION (PSOBC) . 28 3.3 HYBRID EVOLUTIONARY ALGORITHM . 28 3.3.1 PSO WITH COSINE MECHANISM . 28 3.3.2 ADAPTIVE GENETIC STRATEGY (AGS) . 29 3.4 CONCENTRATION ESTIMATION ALGORITHM . 30 3.4.1 MULTILAYER PERCEPTRON . 30 3.4.2 NETWORK OPTIMIZATION . 31 3.5 EXPERIMENTS . 33 3.5.1 EXPERIMENTAL SETUP . 34 3.5.2 DATASETS . 34 3.5.3 CONCENTRATION ESTIMATION . 37 3.6 RESULTS AND DISCUSSION . 39 3.6.1 EXPERIMENTAL RESULTS . 39 3.6.2 COMPUTATIONAL EFFICIENCY . 43 3.6.3 DISCUSSION . 43 3.7 SUMMARY . 44 REFERENCES . 45 4 CHAOS-BASED NEURAL NETWORK OPTIMIZATION APPROACH . 47 4.1 INTRODUCTION . 47 4.2 MATERIALS AND METHODS . 48 4.2.1 ELECTRONIC NOSE . 48 4.2.2 DATA ACQUISITION . 49 4.2.3 BACK-PROPAGATION NEURAL NETWORK . 51 4.2.4 MUTATIVE SCALE CHAOTIC SEQUENCE OPTIMIZATION . 53 4.2.5 STANDARD PARTICLE SWARM OPTIMIZATION (SPSO) . 54 4.2.6 PARAMETER SETTINGS . 55 4.2.7 ON-LINE USAGE . 56 4.3 RESULTS AND DISCUSSION . 56 4.4 SUMMARY . 58 REFERENCES . 59 5 MULTILAYER PERCEPTRON-BASED CONCENTRATION ESTIMATION . 61 5.1 INTRODUCTION . 61 5.2 E-NOSE SYSTEMS AND DATA ACQUISITION . 62 5.2.1 LOW-COST ELECTRONIC NOSE SYSTEM . 62 5.2.2 EXPERIMENTAL SETUP . 63 5.2.3 DESCRIPTION OF DATASET . 65 5.3 MLP-BASED QUANTIZATION MODELS . 65 5.3.1 SINGLE MULTI-INPUT MULTI-OUTPUT (SMIMO) MODEL . 65 CONTENTS IX 5.3.2 MULTIPLE MULTI-INPUT SINGLE-OUTPUT (MMISO) MODEL . 69 5.3.3 MODEL OPTIMIZATION . 71 5.4 RESULTS AND DISCUSSION . 73 5.5 SUMMARY . 76 REFERENCES . 76 6 DISCRIMINATIVE SUPPORT VECTOR MACHINE-BASED ODOR CLASSIFICATION . 79 6.1 INTRODUCTION . 79 6.2 CLASSIFICATION METHODOLOGIES . 81 6.2.1 EUCLIDEAN DISTANCE TO CENTROIDS (EDC) . 81 6.2.2 SIMPLIFIED FUZZY ARTMAP NETWORK (SEAM) . 81 6.2.3 MULTILAYER PERCEPTRON NEURAL NETWORK (MLP) . 82 6.2.4 FISHER LINEAR DISCRIMINANT ANALYSIS (FLDA) . 82 6.2.5 SUPPORT VECTOR MACHINE (SVM) . 83 6.3 EXPERIMENTS . 84 6.3.1 EXPERIMENTAL SETUP . 84 6.3.2 DATASET . 85 6.3.3 MULTI-CLASS DISCRIMINATION . 85 6.3.4 DATA ANALYSIS . 87 6.4 RESULTS AND DISCUSSION . 87 6.4.1 EXPERIMENTAL RESULTS . 87 6.4.2 DISCUSSION . 91 6.5 SUMMARY . 92 REFERENCES . 92 7 LOCAL KERNEL DISCRIMINANT ANALYSIS-BASED ODOR RECOGNITION . 95 7.1 INTRODUCTION . 95 7.2 RELATED WORK . 97 7.2.1 PCA . 97 7.2.2 KPCA . 97 7.2.3 LDA . 98 7.3 THE PROPOSED APPROACH . 99 7.3.1 NDA FRAMEWORK . 99 7.3.2 THE KPCA PLUS NDA ALGORITHM (KNDA) . 101 7.3.3 MULTI-CLASS RECOGNITION . 102 7.4 EXPERIMENTS . 105 7.4.1 E-NOSE SYSTEM . 105 7.4.2 DATASET . 106 7.5 RESULTS AND DISCUSSION . 107 7.5.1 CONTRIBUTION RATE ANALYSIS . 107 7.5.2 COMPARISONS . 107 7.5.3 COMPUTATIONAL EFFICIENCY . ILL X CONTENTS 7.6 SUMMARY . 112 REFERENCES . 112 8 ENSEMBLE OF CLASSIFIERS FOR ROBUST RECOGNITION . 115 8.1 INTRODUCTION . 115 8.2 DATA ACQUISITION . 117 8.2.1 ELECTRONIC NOSE SYSTEM . 117 8.2.2 EXPERIMENTAL SETUP . 117 8.2.3 E-NOSE DATA . 118 8.3 METHODS . 119 8.3.1 KPCA-BASED FEATURE EXTRACTION . 119 8.3.2 BASE CLASSIFIERS . 121 8.3.3 MULTI-CLASS ISVMEN . 122 8.4 RESULTS AND DISCUSSION . 126 8.5 SUMMARY . 129 REFERENCES . 130 PART III E-NOSE DRIFT COMPENSATION: CHALLENGE II 9 CHAOTIC TIME SERIES-BASED SENSOR DRIFT PREDICTION . 135 9.1 INTRODUCTION . 135 9.2 DATA . 136 9.2.1 LONG-TERM SENSOR DATA . 136 9.2.2 DISCRETE FOURIER TRANSFORM (DFT)-BASED FEATURE EXTRACTION . 137 9.3 CHAOTIC TIME SERIES PREDICTION . 139 9.3.1 PHASE SPACE RECONSTRUCTION . 139 9.3.2 PREDICTION MODEL . 140 9.4 RESULTS . 142 9.4.1 CHAOTIC CHARACTERISTIC ANALYSIS . 142 9.4.2 DRIFT PREDICTION . 143 9.5 SUMMARY . 145 REFERENCES . 145 10 DOMAIN ADAPTATION GUIDED DRIFT COMPENSATION . 147 10.1 INTRODUCTION . 147 10.2 RELATED WORK . 149 10.2.1 DRIFT COMPENSATION . 149 10.2.2 EXTREME LEARNING MACHINE . 150 10.3 DOMAIN ADAPTATION EXTREME LEARNING MACHINE . 151 10.3.1 SOURCE DOMAIN ADAPTATION ELM (DAELM-S) . 151 10.3.2 TARGET DOMAIN ADAPTATION ELM (DAELM-T) . 155 CONTENTS XI 10.4 EXPERIMENTS . 159 10.4.1 DESCRIPTION OF DATA . 159 10.4.2 EXPERIMENTAL SETUP . 163 10.5 RESULTS AND DISCUSSION . 163 10.6 SUMMARY . 169 REFERENCES . 169 11 DOMAIN REGULARIZED SUBSPACE PROJECTION METHOD . 173 11.1 INTRODUCTION . 173 11.2 RELATED WORK . 174 11.2.1 EXISTING DRIFT COMPENSATION APPROACHES . 174 11.2.2 EXISTING SUBSPACE PROJECTION ALGORITHMS . 176 11.3 DOMAIN REGULARIZED COMPONENT ANALYSIS (DRCA) . 176 11.3.1 MATHEMATICAL NOTATIONS . 176 11.3.2 PROBLEM FORMULATION . 177 11.3.3 MODEL OPTIMIZATION . 179 11.3.4 REMARKS ON DRCA . 181 11.4 EXPERIMENTS . 181 11.4.1 EXPERIMENT ON BENCHMARK SENSOR DRIFT DATA . 181 11.4.2 EXPERIMENT ON E-NOSE DATA WITH DRIFT AND SHIFT . 185 11.4.3 PARAMETER SENSITIVITY ANALYSIS . 188 11.4.4 DISCUSSION . 188 11.5 SUMMARY . 190 REFERENCES . 190 12 CROSS-DOMAIN SUBSPACE LEARNING APPROACH . 193 12.1 INTRODUCTION . 193 12.2 RELATED WORK . 195 12.2.1 REVIEW OF ELM . 195 12.2.2 SUBSPACE LEARNING . 196 12.3 THE PROPOSED CDELM METHOD . 196 12.3.1 NOTATIONS . 196 12.3.2 MODEL FORMULATION . 197 12.3.3 MODEL OPTIMIZATION . 199 12.4 EXPERIMENTS . 200 12.4.1 DATA DESCRIPTION . 200 12.4.2 EXPERIMENTAL SETTINGS . 201 12.4.3 SINGLE-DOMAIN SUBSPACE PROJECTION METHODS . 201 12.4.4 CLASSIFICATION RESULTS . 202 12.4.5 PARAMETER SENSITIVITY . 204 12.5 SUMMARY . 206 REFERENCES . 207 XII CONTENTS 13 DOMAIN CORRECTION-BASED ADAPTIVE EXTREME LEARNING MACHINE . 209 13.1 INTRODUCTION . 209 13.2 RELATED WORK . 210 13.2.1 TRANSFER LEARNING . 210 13.2.2 EXTREME LEARNING MACHINE . 211 13.3 THE PROPOSED METHOD . 213 13.3.1 NOTATIONS . 213 13.3.2 DOMAIN CORRECTION AND ADAPTIVE EXTREME LEARNING MACHINE . 213 13.4 EXPERIMENTS . 217 13.4.1 EXPERIMENT ON BACKGROUND INTERFERENCE DATA . 218 13.4.2 EXPERIMENT ON SENSOR DRIFT DATA . 220 13.5 SUMMARY . 221 REFERENCES . 223 14 MULTI-FEATURE SEMI-SUPERVISED LEARNING APPROACH . 225 14.1 INTRODUCTION . 226 14.1.1 PROBLEM STATEMENT . 226 14.1.2 MOTIVATION . 226 14.2 RELATED WORK . 227 14.3 MULTI-FEATURE KERNEL SEMI-SUPERVISED JOINT LEARNING MODEL . 228 14.3.1 NOTATIONS . 228 14.3.2 MODEL . 229 14.3.3 OPTIMIZATION ALGORITHM . 231 14.3.4 CLASSIFICATION . 234 14.3.5 CONVERGENCE . 235 14.3.6 COMPUTATIONAL COMPLEXITY . 236 14.3.7 REMARKS ON OPTIMALITY CONDITION . 237 14.4 EXPERIMENTS ON DRIFTED E-NOSE DATA . 238 14.4.1 DESCRIPTION OF DATA . 238 14.4.2 EXPERIMENTAL SETUP . 238 14.4.3 PARAMETER SETTING . 239 14.4.4 COMPARED METHODS . 239 14.4.5 RESULTS AND ANALYSIS . 239 14.5 EXPERIMENTS ON MODULATED E-NOSE DATA . 240 14.5.1 DESCRIPTION OF DATA . 240 14.5.2 EXPERIMENTAL SETUP . 243 14.5.3 PARAMETER SETTING . 243 14.5.4 RESULTS AND ANALYSIS . 243 14.6 SUMMARY . 244 REFERENCES . 244 CONTENTS XIII PART IV E-NOSE DISTURBANCE ELIMINATION: CHALLENGE III 15 PATTERN RECOGNITION-BASED INTERFERENCE REDUCTION . 249 15.1 INTRODUCTION . 249 15.2 MATERIALS AND METHODS . 251 15.2.1 EXPERIMENTAL PLATFORM . 251 15.2.2 SENSOR SIGNAL PREPROCESSING . 251 15.2.3 E-NOSE DATA PREPARATION . 251 15.2.4 FEATURE SELECTION OF ABNORMAL ODOR . 253 15.2.5 GENETIC CROSSOVER OPERATOR FOR SOLUTION OF UNEVEN FEATURES . 254 15.2.6 DESCRIPTION OF LEARNER- 1 UNDER MULTI-CLASS CONDITION . 255 15.2.7 DESCRIPTION OF LEAMER-2 UNDER BINARY CLASSIFICATION CONDITION . 257 15.2.8 ADAPTIVE COUNTERACTION MODEL . 257 15.3 RESULTS AND DISCUSSION . 259 15.3.1 RECOGNITION ACCURACY OF LEARNER- 1 AND LEAMER-2 . 259 15.3.2 ABNORMAL ODOR COUNTERACTION: CASE STUDY . 260 15.3.3 DISCUSSION . 262 15.4 SUMMARY . 262 REFERENCES . 263 16 PATTERN MISMATCH GUIDED INTERFERENCE ELIMINATION . 265 16.1 INTRODUCTION . 265 16.2 DATA ACQUISITION . 267 16.3 PROPOSED PMIE METHOD . 267 16.3.1 MAIN IDEA . 267 16.3.2 PATTERN MISMATCH-BASED INTERFERENCE ELIMINATION (PMIE) . 268 16.4 RESULTS AND DISCUSSION . 272 16.4.1 DATA PREPROCESSING . 272 16.4.2 PMIE TRAINING ON DATASET 1 . 272 16.4.3 THRESHOLD ANALYSIS BASED ON DATASET 2 . 273 16.4.4 INTERFERENCE DISCRIMINATION ON DATASET 3 . 273 16.4.5 PMIE-BASED INTERFERENCE ELIMINATION RESULT . 276 16.5 SUMMARY . 277 REFERENCES . 277 17 SELF-EXPRESSION-BASED ABNORMAL ODOR DETECTION . 279 17.1 INTRODUCTION . 279 17.1.1 BACKGROUND . 279 17.1.2 PROBLEM STATEMENT . 280 17.1.3 MOTIVATION . 280 XIV CONTENTS 17.2 RELATED WORK . 282 17.3 SELF-EXPRESSION MODEL (SEM) . 284 17.3.1 MODEL FORMULATION . 284 17.3.2 ALGORITHM . 286 17.4 EXTREME LEARNING MACHINE-BASED SELF-EXPRESSION MODEL (SE 2 LM) . 287 17.4.1 MODEL FORMULATION . 287 17.4.2 ALGORITHM . 289 17.5 EXPERIMENTS . 290 17.5.1 ELECTRONIC NOSE AND DATA ACQUISITION . 290 17.5.2 ABNORMAL ODOR DETECTION BASED ON DATASET 1 . 291 17.5.3 VALIDATION OF REAL-TIME SEQUENCE ON DATASET 2 . 295 17.5.4 VALIDATION OF REAL-TIME SEQUENCE ON DATASET 3 . 295 17.6 DISCUSSION . 296 17.7 SUMMARY . 297 REFERENCES . 297 PART V E-NOSE DISCRETENESS CORRECTION: CHALLENGE IV 18 AFFINE CALIBRATION TRANSFER MODEL . 301 18.1 INTRODUCTION . 301 18.2 METHOD . 303 18.2.1 CALIBRATION STEP . 303 18.2.2 PREDICTION STEP . 307 18.3 EXPERIMENTS . 309 18.3.1 ELECTRONIC NOSE MODULE . 309 18.3.2 GAS DATASETS . 309 18.4 RESULTS AND DISCUSSION . 310 18.4.1 SENSOR RESPONSE CALIBRATION . 310 18.4.2 CONCENTRATION PREDICTION . 314 18.5 SUMMARY . 320 REFERENCES . 320 19 INSTRUMENTAL BATCH CORRECTION . 323 19.1 INTRODUCTION . 323 19.2 MATERIALS AND METHOD . 325 19.2.1 SENSORS * DISCRETENESS . 325 19.2.2 REVIEW OF THE GAT-RWLS METHOD . 326 19.2.3 INSTRUMENTAL BATCH CORRECTION SCHEME . 327 19.3 RESULTS AND DISCUSSION . 328 19.4 SUMMARY . 332 REFERENCES . 333 CONTENTS XV 20 BOOK REVIEW AND FUTURE WORK . 335 20.1 INTRODUCTION . 336 20.2 FUTURE WORK . 339
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author	Zhang, Lei Tian, Fengchun Zhang, David 1949-
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author_facet	Zhang, Lei Tian, Fengchun Zhang, David 1949-
author_role	aut aut aut
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dewey-full	006.24835379 628.0287 681.754
dewey-hundreds	000 - Computer science, information, general works 600 - Technology (Applied sciences)
dewey-ones	006 - Special computer methods 628 - Sanitary engineering 681 - Precision instruments and other devices
dewey-raw	006.24835379 628.0287 681.754
dewey-search	006.24835379 628.0287 681.754
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dewey-tens	000 - Computer science, information, general works 620 - Engineering and allied operations 680 - Manufacture of products for specific uses
discipline	Handwerk und Gewerbe / Verschiedene Technologien Informatik Bauingenieurwesen Elektrotechnik / Elektronik / Nachrichtentechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik
discipline_str_mv	Handwerk und Gewerbe / Verschiedene Technologien Informatik Bauingenieurwesen Elektrotechnik / Elektronik / Nachrichtentechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik
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spelling	Zhang, Lei Verfasser aut Electronic nose: algorithmic challenges Lei Zhang, Fengchun Tian, David Zhang Singapore Springer [2018] xv, 339 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Elektronische Nase (DE-588)1067516492 gnd rswk-swf Duftstoff (DE-588)4113361-4 gnd rswk-swf Geruchsemission (DE-588)4156902-7 gnd rswk-swf Messung (DE-588)4038852-9 gnd rswk-swf Artificial Intelligence Bionic Perception Drift Compensation Electronic Nose Gas Sensing Intelligent Nose Machine Learning Machine Olfaction Odor Recognition Pattern Recognition Elektronische Nase (DE-588)1067516492 s Geruchsemission (DE-588)4156902-7 s Duftstoff (DE-588)4113361-4 s Messung (DE-588)4038852-9 s DE-604 Tian, Fengchun Verfasser aut Zhang, David 1949- Verfasser (DE-588)12099190X aut Springer Malaysia Representative Office (DE-588)1065365012 pbl Erscheint auch als Online-Ausgabe Electronic Nose: Algorithmic Challenges 1st edition 2018 Singapore : Springer Singapore, 2018 Online-Ressource X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=ad266c9db5714817a826f8f5ba59af0e&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ B:DE-101 application/pdf https://d-nb.info/1166979512/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032272392&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Zhang, Lei Tian, Fengchun Zhang, David 1949- Electronic nose: algorithmic challenges Elektronische Nase (DE-588)1067516492 gnd Duftstoff (DE-588)4113361-4 gnd Geruchsemission (DE-588)4156902-7 gnd Messung (DE-588)4038852-9 gnd
subject_GND	(DE-588)1067516492 (DE-588)4113361-4 (DE-588)4156902-7 (DE-588)4038852-9
title	Electronic nose: algorithmic challenges
title_auth	Electronic nose: algorithmic challenges
title_exact_search	Electronic nose: algorithmic challenges
title_exact_search_txtP	Electronic nose: algorithmic challenges
title_full	Electronic nose: algorithmic challenges Lei Zhang, Fengchun Tian, David Zhang
title_fullStr	Electronic nose: algorithmic challenges Lei Zhang, Fengchun Tian, David Zhang
title_full_unstemmed	Electronic nose: algorithmic challenges Lei Zhang, Fengchun Tian, David Zhang
title_short	Electronic nose: algorithmic challenges
title_sort	electronic nose algorithmic challenges
topic	Elektronische Nase (DE-588)1067516492 gnd Duftstoff (DE-588)4113361-4 gnd Geruchsemission (DE-588)4156902-7 gnd Messung (DE-588)4038852-9 gnd
topic_facet	Elektronische Nase Duftstoff Geruchsemission Messung
url	http://deposit.dnb.de/cgi-bin/dokserv?id=ad266c9db5714817a826f8f5ba59af0e&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ https://d-nb.info/1166979512/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032272392&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT zhanglei electronicnosealgorithmicchallenges AT tianfengchun electronicnosealgorithmicchallenges AT zhangdavid electronicnosealgorithmicchallenges AT springermalaysiarepresentativeoffice electronicnosealgorithmicchallenges

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