top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Automatic modulation classification : principles, algorithms, and applications / / Zhechen Zhu and Asoke K. Nand
Automatic modulation classification : principles, algorithms, and applications / / Zhechen Zhu and Asoke K. Nand
Autore Zhu Zhechen
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (194 p.)
Disciplina 621.3815/36
Soggetto topico Modulation (Electronics)
ISBN 1-118-90650-0
1-118-90652-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Title Page; Copyright Page; Contents; About the Authors; Preface; List of Abbreviations; List of Symbols; Chapter 1 Introduction; 1.1 Background; 1.2 Applications ofAMC; 1.2.1 Military Applications; 1.2.2 Civilian Applications; 1.3 Field Overview and Book Scope; 1.4 Modulation and Communication System Basics; 1.4.1 Analogue Systems and Modulations; 1.4.2 Digital Systems and Modulations; 1.4.3 Received Signal with Channel Effects; 1.5 Conclusion; References; Chapter 2 Signal Models for Modulation Classification; 2.1 Introduction; 2.2 Signal Model inAWGNChannel
2.2.1 Signal Distribution of I-Q Segments2.2.2 Signal Distribution of Signal Phase; 2.2.3 Signal Distribution of Signal Magnitude; 2.3 Signal Models in Fading Channel; 2.4 Signal Models in Non-Gaussian Channel; 2.4.1 Middleton ́s Class A Model; 2.4.2 Symmetric Alpha Stable Model; 2.4.3 Gaussian Mixture Model; 2.5 Conclusion; References; Chapter 3 Likelihood-based Classifiers; 3.1 Introduction; 3.2 Maximum Likelihood Classifiers; 3.2.1 Likelihood Function inAWGNChannels; 3.2.2 Likelihood Function in Fading Channels; 3.2.3 Likelihood Function in Non-Gaussian Noise Channels
3.2.4 Maximum Likelihood Classification Decision Making3.3 Likelihood Ratio Test for Unknown Channel Parameters; 3.3.1 Average Likelihood Ratio Test; 3.3.2 Generalized Likelihood Ratio Test; 3.3.3 Hybrid Likelihood Ratio Test; 3.4 Complexity Reduction; 3.4.1 Discrete Likelihood Ratio Test and Lookup Table; 3.4.2 Minimum Distance Likelihood Function; 3.4.3 Non-Parametric Likelihood Function; 3.5 Conclusion; References; Chapter 4 Distribution Test-based Classifier; 4.1 Introduction; 4.2 Kolmogorov-Smirnov Test Classifier; 4.2.1 The KS Test for Goodness of Fit
4.2.2 One-sample KS Test Classifier4.2.3 Two-sample KS Test Classifier; 4.2.4 Phase Difference Classifier; 4.3 Cramer-Von Mises Test Classifier; 4.4 Anderson-Darling Test Classifier; 4.5 Optimized Distribution Sampling Test Classifier; 4.5.1 Sampling Location Optimization; 4.5.2 Distribution Sampling; 4.5.3 Classification Decision Metrics; 4.5.4 Modulation Classification Decision Making; 4.6 Conclusion; References; Chapter 5 Modulation Classification Features; 5.1 Introduction; 5.2 Signal Spectral-based Features; 5.2.1 Signal Spectral-based Features; 5.2.2 Spectral-based Features Specialities
5.2.3 Spectral-based Features Decision Making5.2.4 Decision Threshold Optimization; 5.3 Wavelet Transform-based Features; 5.4 High-order Statistics-based Features; 5.4.1 High-order Moment-based Features; 5.4.2 High-order Cumulant-based Features; 5.5 Cyclostationary Analysis-based Features; 5.6 Conclusion; References; Chapter 6 Machine Learning for Modulation Classification; 6.1 Introduction; 6.2 K-Nearest Neighbour Classifier; 6.2.1 Reference Feature Space; 6.2.2 Distance Definition; 6.2.3 K-Nearest Neighbour Decision; 6.3 Support Vector Machine Classifier
6.4 Logistic Regression for Feature Combination
Record Nr. UNINA-9910132305003321
Zhu Zhechen  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Automatic modulation classification : principles, algorithms, and applications / / Zhechen Zhu and Asoke K. Nand
Automatic modulation classification : principles, algorithms, and applications / / Zhechen Zhu and Asoke K. Nand
Autore Zhu Zhechen
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (194 p.)
Disciplina 621.3815/36
Soggetto topico Modulation (Electronics)
ISBN 1-118-90650-0
1-118-90652-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Title Page; Copyright Page; Contents; About the Authors; Preface; List of Abbreviations; List of Symbols; Chapter 1 Introduction; 1.1 Background; 1.2 Applications ofAMC; 1.2.1 Military Applications; 1.2.2 Civilian Applications; 1.3 Field Overview and Book Scope; 1.4 Modulation and Communication System Basics; 1.4.1 Analogue Systems and Modulations; 1.4.2 Digital Systems and Modulations; 1.4.3 Received Signal with Channel Effects; 1.5 Conclusion; References; Chapter 2 Signal Models for Modulation Classification; 2.1 Introduction; 2.2 Signal Model inAWGNChannel
2.2.1 Signal Distribution of I-Q Segments2.2.2 Signal Distribution of Signal Phase; 2.2.3 Signal Distribution of Signal Magnitude; 2.3 Signal Models in Fading Channel; 2.4 Signal Models in Non-Gaussian Channel; 2.4.1 Middleton ́s Class A Model; 2.4.2 Symmetric Alpha Stable Model; 2.4.3 Gaussian Mixture Model; 2.5 Conclusion; References; Chapter 3 Likelihood-based Classifiers; 3.1 Introduction; 3.2 Maximum Likelihood Classifiers; 3.2.1 Likelihood Function inAWGNChannels; 3.2.2 Likelihood Function in Fading Channels; 3.2.3 Likelihood Function in Non-Gaussian Noise Channels
3.2.4 Maximum Likelihood Classification Decision Making3.3 Likelihood Ratio Test for Unknown Channel Parameters; 3.3.1 Average Likelihood Ratio Test; 3.3.2 Generalized Likelihood Ratio Test; 3.3.3 Hybrid Likelihood Ratio Test; 3.4 Complexity Reduction; 3.4.1 Discrete Likelihood Ratio Test and Lookup Table; 3.4.2 Minimum Distance Likelihood Function; 3.4.3 Non-Parametric Likelihood Function; 3.5 Conclusion; References; Chapter 4 Distribution Test-based Classifier; 4.1 Introduction; 4.2 Kolmogorov-Smirnov Test Classifier; 4.2.1 The KS Test for Goodness of Fit
4.2.2 One-sample KS Test Classifier4.2.3 Two-sample KS Test Classifier; 4.2.4 Phase Difference Classifier; 4.3 Cramer-Von Mises Test Classifier; 4.4 Anderson-Darling Test Classifier; 4.5 Optimized Distribution Sampling Test Classifier; 4.5.1 Sampling Location Optimization; 4.5.2 Distribution Sampling; 4.5.3 Classification Decision Metrics; 4.5.4 Modulation Classification Decision Making; 4.6 Conclusion; References; Chapter 5 Modulation Classification Features; 5.1 Introduction; 5.2 Signal Spectral-based Features; 5.2.1 Signal Spectral-based Features; 5.2.2 Spectral-based Features Specialities
5.2.3 Spectral-based Features Decision Making5.2.4 Decision Threshold Optimization; 5.3 Wavelet Transform-based Features; 5.4 High-order Statistics-based Features; 5.4.1 High-order Moment-based Features; 5.4.2 High-order Cumulant-based Features; 5.5 Cyclostationary Analysis-based Features; 5.6 Conclusion; References; Chapter 6 Machine Learning for Modulation Classification; 6.1 Introduction; 6.2 K-Nearest Neighbour Classifier; 6.2.1 Reference Feature Space; 6.2.2 Distance Definition; 6.2.3 K-Nearest Neighbour Decision; 6.3 Support Vector Machine Classifier
6.4 Logistic Regression for Feature Combination
Record Nr. UNINA-9910807452803321
Zhu Zhechen  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
Autore Ahmed Hosameldin <1976->
Pubbl/distr/stampa Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020
Descrizione fisica 1 online resource (437 pages)
Disciplina 621.80287
Soggetto topico Machinery - Monitoring
ISBN 1-119-54464-5
1-119-54467-X
1-119-54463-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface xvii -- About the Authors xxi -- List of Abbreviations xxiii -- Part I Introduction 1 -- 1 Introduction to Machine Condition Monitoring 3 -- 1.1 Background 3 -- 1.2 Maintenance Approaches for Rotating Machines Failures 4 -- 1.2.1 Corrective Maintenance 4 -- 1.2.2 Preventive Maintenance 5 -- 1.2.2.1 Time-Based Maintenance (TBM) 5 -- 1.2.2.2 Condition-Based Maintenance (CBM) 5 -- 1.3 Applications of MCM 5 -- 1.3.1 Wind Turbines 5 -- 1.3.2 Oil and Gas 6 -- 1.3.3 Aerospace and Defence Industry 6 -- 1.3.4 Automotive 7 -- 1.3.5 Marine Engines 7 -- 1.3.6 Locomotives 7 -- 1.4 Condition Monitoring Techniques 7 -- 1.4.1 Vibration Monitoring 7 -- 1.4.2 Acoustic Emission 8 -- 1.4.3 Fusion of Vibration and Acoustic 8 -- 1.4.4 Motor Current Monitoring 8 -- 1.4.5 Oil Analysis and Lubrication Monitoring 8 -- 1.4.6 Thermography 9 -- 1.4.7 Visual Inspection 9 -- 1.4.8 Performance Monitoring 9 -- 1.4.9 Trend Monitoring 10 -- 1.5 Topic Overview and Scope of the Book 10 -- 1.6 Summary 11 -- References 11 -- 2 Principles of Rotating Machine Vibration Signals 17 -- 2.1 Introduction 17 -- 2.2 Machine Vibration Principles 17 -- 2.3 Sources of Rotating Machines Vibration Signals 20 -- 2.3.1 Rotor Mass Unbalance 21 -- 2.3.2 Misalignment 21 -- 2.3.3 Cracked Shafts 21 -- 2.3.4 Rolling Element Bearings 23 -- 2.3.5 Gears 25 -- 2.4 Types of Vibration Signals 25 -- 2.4.1 Stationary 26 -- 2.4.2 Nonstationary 26 -- 2.5 Vibration Signal Acquisition 26 -- 2.5.1 Displacement Transducers 26 -- 2.5.2 Velocity Transducers 26 -- 2.5.3 Accelerometers 27 -- 2.6 Advantages and Limitations of Vibration Signal Monitoring 27 -- 2.7 Summary 28 -- References 28 -- Part II Vibration Signal Analysis Techniques 31 -- 3 Time Domain Analysis 33 -- 3.1 Introduction 33 -- 3.1.1 Visual Inspection 33 -- 3.1.2 Features-Based Inspection 35 -- 3.2 Statistical Functions 35 -- 3.2.1 Peak Amplitude 36 -- 3.2.2 Mean Amplitude 36 -- 3.2.3 Root Mean Square Amplitude 36 -- 3.2.4 Peak-to-Peak Amplitude 36 -- 3.2.5 Crest Factor (CF) 36.
3.2.6 Variance and Standard Deviation 37 -- 3.2.7 Standard Error 37 -- 3.2.8 Zero Crossing 38 -- 3.2.9 Wavelength 39 -- 3.2.10 Willison Amplitude 39 -- 3.2.11 Slope Sign Change 39 -- 3.2.12 Impulse Factor 39 -- 3.2.13 Margin Factor 40 -- 3.2.14 Shape Factor 40 -- 3.2.15 Clearance Factor 40 -- 3.2.16 Skewness 40 -- 3.2.17 Kurtosis 40 -- 3.2.18 Higher-Order Cumulants (HOCs) 41 -- 3.2.19 Histograms 42 -- 3.2.20 Normal/Weibull Negative Log-Likelihood Value 42 -- 3.2.21 Entropy 42 -- 3.3 Time Synchronous Averaging 44 -- 3.3.1 TSA Signals 44 -- 3.3.2 Residual Signal (RES) 44 -- 3.3.2.1 NA4 44 -- 3.3.2.2 NA4* 45 -- 3.3.3 Difference Signal (DIFS) 45 -- 3.3.3.1 FM4 46 -- 3.3.3.2 M6A 46 -- 3.3.3.3 M8A 46 -- 3.4 Time Series Regressive Models 46 -- 3.4.1 AR Model 47 -- 3.4.2 MA Model 48 -- 3.4.3 ARMA Model 48 -- 3.4.4 ARIMA Model 48 -- 3.5 Filter-Based Methods 49 -- 3.5.1 Demodulation 49 -- 3.5.2 Prony Model 52 -- 3.5.3 Adaptive Noise Cancellation (ANC) 53 -- 3.6 Stochastic Parameter Techniques 54 -- 3.7 Blind Source Separation (BSS) 54 -- 3.8 Summary 55 -- References 56 -- 4 Frequency Domain Analysis 63 -- 4.1 Introduction 63 -- 4.2 Fourier Analysis 64 -- 4.2.1 Fourier Series 64 -- 4.2.2 Discrete Fourier Transform 66 -- 4.2.3 Fast Fourier Transform (FFT) 67 -- 4.3 Envelope Analysis 71 -- 4.4 Frequency Spectrum Statistical Features 73 -- 4.4.1 Arithmetic Mean 73 -- 4.4.2 Geometric Mean 73 -- 4.4.3 Matched Filter RMS 73 -- 4.4.4 The RMS of Spectral Difference 74 -- 4.4.5 The Sum of Squares Spectral Difference 74 -- 4.4.6 High-Order Spectra Techniques 74 -- 4.5 Summary 75 -- References 76 -- 5 Time-Frequency Domain Analysis 79 -- 5.1 Introduction 79 -- 5.2 Short-Time Fourier Transform (STFT) 79 -- 5.3 Wavelet Analysis 82 -- 5.3.1 Wavelet Transform (WT) 82 -- 5.3.1.1 Continuous Wavelet Transform (CWT) 83 -- 5.3.1.2 Discrete Wavelet Transform (DWT) 85 -- 5.3.2 Wavelet Packet Transform (WPT) 89 -- 5.4 Empirical Mode Decomposition (EMD) 91 -- 5.5 Hilbert-Huang Transform (HHT) 94 -- 5.6 Wigner-Ville Distribution 96.
5.7 Local Mean Decomposition (LMD) 98 -- 5.8 Kurtosis and Kurtograms 100 -- 5.9 Summary 105 -- References 106 -- Part III Rotating Machine Condition Monitoring Using Machine Learning 115 -- 6 Vibration-Based Condition Monitoring Using Machine Learning 117 -- 6.1 Introduction 117 -- 6.2 Overview of the Vibration-Based MCM Process 118 -- 6.2.1 Fault-Detection and -Diagnosis Problem Framework 118 -- 6.3 Learning from Vibration Data 122 -- 6.3.1 Types of Learning 123 -- 6.3.1.1 Batch vs. Online Learning 123 -- 6.3.1.2 Instance-Based vs. Model-Based Learning 123 -- 6.3.1.3 Supervised Learning vs. Unsupervised Learning 123 -- 6.3.1.4 Semi-Supervised Learning 123 -- 6.3.1.5 Reinforcement Learning 124 -- 6.3.1.6 Transfer Learning 124 -- 6.3.2 Main Challenges of Learning from Vibration Data 125 -- 6.3.2.1 The Curse of Dimensionality 125 -- 6.3.2.2 Irrelevant Features 126 -- 6.3.2.3 Environment and Operating Conditions of a Rotating Machine 126 -- 6.3.3 Preparing Vibration Data for Analysis 126 -- 6.3.3.1 Normalisation 126 -- 6.3.3.2 Dimensionality Reduction 127 -- 6.4 Summary 128 -- References 128 -- 7 Linear Subspace Learning 131 -- 7.1 Introduction 131 -- 7.2 Principal Component Analysis (PCA) 132 -- 7.2.1 PCA Using Eigenvector Decomposition 132 -- 7.2.2 PCA Using SVD 133 -- 7.2.3 Application of PCA in Machine Fault Diagnosis 134 -- 7.3 Independent Component Analysis (ICA) 137 -- 7.3.1 Minimisation of Mutual Information 138 -- 7.3.2 Maximisation of the Likelihood 138 -- 7.3.3 Application of ICA in Machine Fault Diagnosis 139 -- 7.4 Linear Discriminant Analysis (LDA) 141 -- 7.4.1 Application of LDA in Machine Fault Diagnosis 142 -- 7.5 Canonical Correlation Analysis (CCA) 143 -- 7.6 Partial Least Squares (PLS) 145 -- 7.7 Summary 146 -- References 147 -- 8 Nonlinear Subspace Learning 153 -- 8.1 Introduction 153 -- 8.2 Kernel Principal Component Analysis (KPCA) 153 -- 8.2.1 Application of KPCA in Machine Fault Diagnosis 156 -- 8.3 Isometric Feature Mapping (ISOMAP) 156 -- 8.3.1 Application of ISOMAP in Machine Fault Diagnosis 158.
8.4 Diffusion Maps (DMs) and Diffusion Distances 159 -- 8.4.1 Application of DMs in Machine Fault Diagnosis 160 -- 8.5 Laplacian Eigenmap (LE) 161 -- 8.5.1 Application of the LE in Machine Fault Diagnosis 161 -- 8.6 Local Linear Embedding (LLE) 162 -- 8.6.1 Application of LLE in Machine Fault Diagnosis 163 -- 8.7 Hessian-Based LLE 163 -- 8.7.1 Application of HLLE in Machine Fault Diagnosis 164 -- 8.8 Local Tangent Space Alignment Analysis (LTSA) 165 -- 8.8.1 Application of LTSA in Machine Fault Diagnosis 165 -- 8.9 Maximum Variance Unfolding (MVU) 166 -- 8.9.1 Application of MVU in Machine Fault Diagnosis 167 -- 8.10 Stochastic Proximity Embedding (SPE) 168 -- 8.10.1 Application of SPE in Machine Fault Diagnosis 168 -- 8.11 Summary 169 -- References 170 -- 9 Feature Selection 173 -- 9.1 Introduction 173 -- 9.2 Filter Model-Based Feature Selection 175 -- 9.2.1 Fisher Score (FS) 176 -- 9.2.2 Laplacian Score (LS) 177 -- 9.2.3 Relief and Relief-F Algorithms 178 -- 9.2.3.1 Relief Algorithm 178 -- 9.2.3.2 Relief-F Algorithm 179 -- 9.2.4 Pearson Correlation Coefficient (PCC) 180 -- 9.2.5 Information Gain (IG) and Gain Ratio (GR) 180 -- 9.2.6 Mutual Information (MI) 181 -- 9.2.7 Chi-Squared (Chi-2) 181 -- 9.2.8 Wilcoxon Ranking 181 -- 9.2.9 Application of Feature Ranking in Machine Fault Diagnosis 182 -- 9.3 Wrapper ModeĺôBased Feature Subset Selection 185 -- 9.3.1 Sequential Selection Algorithms 185 -- 9.3.2 Heuristic-Based Selection Algorithms 185 -- 9.3.2.1 Ant Colony Optimisation (ACO) 185 -- 9.3.2.2 Genetic Algorithms (GAs) and Genetic Programming 187 -- 9.3.2.3 Particle Swarm Optimisation (PSO) 188 -- 9.3.3 Application of Wrapper ModeĺôBased Feature Subset Selection in Machine Fault Diagnosis 189 -- 9.4 Embedded ModeĺôBased Feature Selection 192 -- 9.5 Summary 193 -- References 194 -- Part IV Classification Algorithms 199 -- 10 Decision Trees and Random Forests 201 -- 10.1 Introduction 201 -- 10.2 Decision Trees 202 -- 10.2.1 Univariate Splitting Criteria 204 -- 10.2.1.1 Gini Index 205.
10.2.1.2 Information Gain 206 -- 10.2.1.3 Distance Measure 207 -- 10.2.1.4 Orthogonal Criterion (ORT) 207 -- 10.2.2 Multivariate Splitting Criteria 207 -- 10.2.3 Tree-Pruning Methods 208 -- 10.2.3.1 Error-Complexity Pruning 208 -- 10.2.3.2 Minimum-Error Pruning 209 -- 10.2.3.3 Reduced-Error Pruning 209 -- 10.2.3.4 Critical-Value Pruning 210 -- 10.2.3.5 Pessimistic Pruning 210 -- 10.2.3.6 Minimum Description Length (MDL) Pruning 210 -- 10.2.4 Decision Tree Inducers 211 -- 10.2.4.1 CART 211 -- 10.2.4.2 ID3 211 -- 10.2.4.3 C4.5 211 -- 10.2.4.4 CHAID 212 -- 10.3 Decision Forests 212 -- 10.4 Application of Decision Trees/Forests in Machine Fault Diagnosis 213 -- 10.5 Summary 217 -- References 217 -- 11 Probabilistic Classification Methods 225 -- 11.1 Introduction 225 -- 11.2 Hidden Markov Model 225 -- 11.2.1 Application of Hidden Markov Models in Machine Fault Diagnosis 228 -- 11.3 Logistic Regression Model 230 -- 11.3.1 Logistic Regression Regularisation 232 -- 11.3.2 Multinomial Logistic Regression Model (MLR) 232 -- 11.3.3 Application of Logistic Regression in Machine Fault Diagnosis 233 -- 11.4 Summary 234 -- References 235 -- 12 Artificial Neural Networks (ANNs) 239 -- 12.1 Introduction 239 -- 12.2 Neural Network Basic Principles 240 -- 12.2.1 The Multilayer Perceptron 241 -- 12.2.2 The Radial Basis Function Network 243 -- 12.2.3 The Kohonen Network 244 -- 12.3 Application of Artificial Neural Networks in Machine Fault Diagnosis 245 -- 12.4 Summary 253 -- References 254 -- 13 Support Vector Machines (SVMs) 259 -- 13.1 Introduction 259 -- 13.2 Multiclass SVMs 262 -- 13.3 Selection of Kernel Parameters 263 -- 13.4 Application of SVMs in Machine Fault Diagnosis 263 -- 13.5 Summary 274 -- References 274 -- 14 Deep Learning 279 -- 14.1 Introduction 279 -- 14.2 Autoencoders 280 -- 14.3 Convolutional Neural Networks (CNNs) 283 -- 14.4 Deep Belief Networks (DBNs) 284 -- 14.5 Recurrent Neural Networks (RNNs) 285 -- 14.6 Overview of Deep Learning in MCM 286 -- 14.6.1 Application of AE-based DNNs in Machine Fault Diagnosis 286.
14.6.2 Application of CNNs in Machine Fault Diagnosis 292 -- 14.6.3 Application of DBNs in Machine Fault Diagnosis 296 -- 14.6.4 Application of RNNs in Machine Fault Diagnosis 298 -- 14.7 Summary 299 -- References 301 -- 15 Classification Algorithm Validation 307 -- 15.1 Introduction 307 -- 15.2 The Hold-Out Technique 308 -- 15.2.1 Three-Way Data Split 309 -- 15.3 Random Subsampling 309 -- 15.4 K-Fold Cross-Validation 310 -- 15.5 Leave-One-Out Cross-Validation 311 -- 15.6 Bootstrapping 311 -- 15.7 Overall Classification Accuracy 312 -- 15.8 Confusion Matrix 313 -- 15.9 Recall and Precision 314 -- 15.10 ROC Graphs 315 -- 15.11 Summary 317 -- References 318 -- Part V New Fault Diagnosis Frameworks Designed for MCM 321 -- 16 Compressive Sampling and Subspace Learning (CS-SL) 323 -- 16.1 Introduction 323 -- 16.2 Compressive Sampling for Vibration-Based MCM 325 -- 16.2.1 Compressive Sampling Basics 325 -- 16.2.2 CS for Sparse Frequency Representation 328 -- 16.2.3 CS for Sparse Time-Frequency Representation 329 -- 16.3 Overview of CS in Machine Condition Monitoring 330 -- 16.3.1 Compressed Sensed Data Followed by Complete Data Construction 330 -- 16.3.2 Compressed Sensed Data Followed by Incomplete Data Construction 331 -- 16.3.3 Compressed Sensed Data as the Input of a Classifier 332 -- 16.3.4 Compressed Sensed Data Followed by Feature Learning 333 -- 16.4 Compressive Sampling and Feature Ranking (CS-FR) 333 -- 16.4.1 Implementations 334 -- 16.4.1.1 CS-LS 336 -- 16.4.1.2 CS-FS 336 -- 16.4.1.3 CS-Relief-F 337 -- 16.4.1.4 CS-PCC 338 -- 16.4.1.5 CS-Chi-2 338 -- 16.5 CS and Linear Subspace Learning-Based Framework for Fault Diagnosis 339 -- 16.5.1 Implementations 339 -- 16.5.1.1 CS-PCA 339 -- 16.5.1.2 CS-LDA 340 -- 16.5.1.3 CS-CPDC 341 -- 16.6 CS and Nonlinear Subspace Learning-Based Framework for Fault Diagnosis 343 -- 16.6.1 Implementations 344 -- 16.6.1.1 CS-KPCA 344 -- 16.6.1.2 CS-KLDA 345 -- 16.6.1.3 CS-CMDS 346 -- 16.6.1.4 CS-SPE 346 -- 16.7 Applications 348 -- 16.7.1 Case Study 1 348.
16.7.1.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 350 -- 16.7.1.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 352 -- 16.7.2 Case Study 2 354 -- 16.7.2.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 354 -- 16.7.2.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 355 -- 16.8 Discussion 355 -- References 357 -- 17 Compressive Sampling and Deep Neural Network (CS-DNN) 361 -- 17.1 Introduction 361 -- 17.2 Related Work 361 -- 17.3 CS-SAE-DNN 362 -- 17.3.1 Compressed Measurements Generation 362 -- 17.3.2 CS Model Testing Using the Flip Test 363 -- 17.3.3 DNN-Based Unsupervised Sparse Overcomplete Feature Learning 363 -- 17.3.4 Supervised Fine Tuning 367 -- 17.4 Applications 367 -- 17.4.1 Case Study 1 367 -- 17.4.2 Case Study 2 372 -- 17.5 Discussion 375 -- References 375 -- 18 Conclusion 379 -- 18.1 Introduction 379 -- 18.2 Summary and Conclusion 380 -- Appendix Machinery Vibration Data Resources and Analysis Algorithms 389 -- References 394 -- Index 395.
Record Nr. UNINA-9910555291703321
Ahmed Hosameldin <1976->  
Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
Autore Ahmed Hosameldin <1976->
Pubbl/distr/stampa Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020
Descrizione fisica 1 online resource (437 pages)
Disciplina 621.80287
Soggetto topico Machinery - Monitoring
ISBN 1-119-54464-5
1-119-54467-X
1-119-54463-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface xvii -- About the Authors xxi -- List of Abbreviations xxiii -- Part I Introduction 1 -- 1 Introduction to Machine Condition Monitoring 3 -- 1.1 Background 3 -- 1.2 Maintenance Approaches for Rotating Machines Failures 4 -- 1.2.1 Corrective Maintenance 4 -- 1.2.2 Preventive Maintenance 5 -- 1.2.2.1 Time-Based Maintenance (TBM) 5 -- 1.2.2.2 Condition-Based Maintenance (CBM) 5 -- 1.3 Applications of MCM 5 -- 1.3.1 Wind Turbines 5 -- 1.3.2 Oil and Gas 6 -- 1.3.3 Aerospace and Defence Industry 6 -- 1.3.4 Automotive 7 -- 1.3.5 Marine Engines 7 -- 1.3.6 Locomotives 7 -- 1.4 Condition Monitoring Techniques 7 -- 1.4.1 Vibration Monitoring 7 -- 1.4.2 Acoustic Emission 8 -- 1.4.3 Fusion of Vibration and Acoustic 8 -- 1.4.4 Motor Current Monitoring 8 -- 1.4.5 Oil Analysis and Lubrication Monitoring 8 -- 1.4.6 Thermography 9 -- 1.4.7 Visual Inspection 9 -- 1.4.8 Performance Monitoring 9 -- 1.4.9 Trend Monitoring 10 -- 1.5 Topic Overview and Scope of the Book 10 -- 1.6 Summary 11 -- References 11 -- 2 Principles of Rotating Machine Vibration Signals 17 -- 2.1 Introduction 17 -- 2.2 Machine Vibration Principles 17 -- 2.3 Sources of Rotating Machines Vibration Signals 20 -- 2.3.1 Rotor Mass Unbalance 21 -- 2.3.2 Misalignment 21 -- 2.3.3 Cracked Shafts 21 -- 2.3.4 Rolling Element Bearings 23 -- 2.3.5 Gears 25 -- 2.4 Types of Vibration Signals 25 -- 2.4.1 Stationary 26 -- 2.4.2 Nonstationary 26 -- 2.5 Vibration Signal Acquisition 26 -- 2.5.1 Displacement Transducers 26 -- 2.5.2 Velocity Transducers 26 -- 2.5.3 Accelerometers 27 -- 2.6 Advantages and Limitations of Vibration Signal Monitoring 27 -- 2.7 Summary 28 -- References 28 -- Part II Vibration Signal Analysis Techniques 31 -- 3 Time Domain Analysis 33 -- 3.1 Introduction 33 -- 3.1.1 Visual Inspection 33 -- 3.1.2 Features-Based Inspection 35 -- 3.2 Statistical Functions 35 -- 3.2.1 Peak Amplitude 36 -- 3.2.2 Mean Amplitude 36 -- 3.2.3 Root Mean Square Amplitude 36 -- 3.2.4 Peak-to-Peak Amplitude 36 -- 3.2.5 Crest Factor (CF) 36.
3.2.6 Variance and Standard Deviation 37 -- 3.2.7 Standard Error 37 -- 3.2.8 Zero Crossing 38 -- 3.2.9 Wavelength 39 -- 3.2.10 Willison Amplitude 39 -- 3.2.11 Slope Sign Change 39 -- 3.2.12 Impulse Factor 39 -- 3.2.13 Margin Factor 40 -- 3.2.14 Shape Factor 40 -- 3.2.15 Clearance Factor 40 -- 3.2.16 Skewness 40 -- 3.2.17 Kurtosis 40 -- 3.2.18 Higher-Order Cumulants (HOCs) 41 -- 3.2.19 Histograms 42 -- 3.2.20 Normal/Weibull Negative Log-Likelihood Value 42 -- 3.2.21 Entropy 42 -- 3.3 Time Synchronous Averaging 44 -- 3.3.1 TSA Signals 44 -- 3.3.2 Residual Signal (RES) 44 -- 3.3.2.1 NA4 44 -- 3.3.2.2 NA4* 45 -- 3.3.3 Difference Signal (DIFS) 45 -- 3.3.3.1 FM4 46 -- 3.3.3.2 M6A 46 -- 3.3.3.3 M8A 46 -- 3.4 Time Series Regressive Models 46 -- 3.4.1 AR Model 47 -- 3.4.2 MA Model 48 -- 3.4.3 ARMA Model 48 -- 3.4.4 ARIMA Model 48 -- 3.5 Filter-Based Methods 49 -- 3.5.1 Demodulation 49 -- 3.5.2 Prony Model 52 -- 3.5.3 Adaptive Noise Cancellation (ANC) 53 -- 3.6 Stochastic Parameter Techniques 54 -- 3.7 Blind Source Separation (BSS) 54 -- 3.8 Summary 55 -- References 56 -- 4 Frequency Domain Analysis 63 -- 4.1 Introduction 63 -- 4.2 Fourier Analysis 64 -- 4.2.1 Fourier Series 64 -- 4.2.2 Discrete Fourier Transform 66 -- 4.2.3 Fast Fourier Transform (FFT) 67 -- 4.3 Envelope Analysis 71 -- 4.4 Frequency Spectrum Statistical Features 73 -- 4.4.1 Arithmetic Mean 73 -- 4.4.2 Geometric Mean 73 -- 4.4.3 Matched Filter RMS 73 -- 4.4.4 The RMS of Spectral Difference 74 -- 4.4.5 The Sum of Squares Spectral Difference 74 -- 4.4.6 High-Order Spectra Techniques 74 -- 4.5 Summary 75 -- References 76 -- 5 Time-Frequency Domain Analysis 79 -- 5.1 Introduction 79 -- 5.2 Short-Time Fourier Transform (STFT) 79 -- 5.3 Wavelet Analysis 82 -- 5.3.1 Wavelet Transform (WT) 82 -- 5.3.1.1 Continuous Wavelet Transform (CWT) 83 -- 5.3.1.2 Discrete Wavelet Transform (DWT) 85 -- 5.3.2 Wavelet Packet Transform (WPT) 89 -- 5.4 Empirical Mode Decomposition (EMD) 91 -- 5.5 Hilbert-Huang Transform (HHT) 94 -- 5.6 Wigner-Ville Distribution 96.
5.7 Local Mean Decomposition (LMD) 98 -- 5.8 Kurtosis and Kurtograms 100 -- 5.9 Summary 105 -- References 106 -- Part III Rotating Machine Condition Monitoring Using Machine Learning 115 -- 6 Vibration-Based Condition Monitoring Using Machine Learning 117 -- 6.1 Introduction 117 -- 6.2 Overview of the Vibration-Based MCM Process 118 -- 6.2.1 Fault-Detection and -Diagnosis Problem Framework 118 -- 6.3 Learning from Vibration Data 122 -- 6.3.1 Types of Learning 123 -- 6.3.1.1 Batch vs. Online Learning 123 -- 6.3.1.2 Instance-Based vs. Model-Based Learning 123 -- 6.3.1.3 Supervised Learning vs. Unsupervised Learning 123 -- 6.3.1.4 Semi-Supervised Learning 123 -- 6.3.1.5 Reinforcement Learning 124 -- 6.3.1.6 Transfer Learning 124 -- 6.3.2 Main Challenges of Learning from Vibration Data 125 -- 6.3.2.1 The Curse of Dimensionality 125 -- 6.3.2.2 Irrelevant Features 126 -- 6.3.2.3 Environment and Operating Conditions of a Rotating Machine 126 -- 6.3.3 Preparing Vibration Data for Analysis 126 -- 6.3.3.1 Normalisation 126 -- 6.3.3.2 Dimensionality Reduction 127 -- 6.4 Summary 128 -- References 128 -- 7 Linear Subspace Learning 131 -- 7.1 Introduction 131 -- 7.2 Principal Component Analysis (PCA) 132 -- 7.2.1 PCA Using Eigenvector Decomposition 132 -- 7.2.2 PCA Using SVD 133 -- 7.2.3 Application of PCA in Machine Fault Diagnosis 134 -- 7.3 Independent Component Analysis (ICA) 137 -- 7.3.1 Minimisation of Mutual Information 138 -- 7.3.2 Maximisation of the Likelihood 138 -- 7.3.3 Application of ICA in Machine Fault Diagnosis 139 -- 7.4 Linear Discriminant Analysis (LDA) 141 -- 7.4.1 Application of LDA in Machine Fault Diagnosis 142 -- 7.5 Canonical Correlation Analysis (CCA) 143 -- 7.6 Partial Least Squares (PLS) 145 -- 7.7 Summary 146 -- References 147 -- 8 Nonlinear Subspace Learning 153 -- 8.1 Introduction 153 -- 8.2 Kernel Principal Component Analysis (KPCA) 153 -- 8.2.1 Application of KPCA in Machine Fault Diagnosis 156 -- 8.3 Isometric Feature Mapping (ISOMAP) 156 -- 8.3.1 Application of ISOMAP in Machine Fault Diagnosis 158.
8.4 Diffusion Maps (DMs) and Diffusion Distances 159 -- 8.4.1 Application of DMs in Machine Fault Diagnosis 160 -- 8.5 Laplacian Eigenmap (LE) 161 -- 8.5.1 Application of the LE in Machine Fault Diagnosis 161 -- 8.6 Local Linear Embedding (LLE) 162 -- 8.6.1 Application of LLE in Machine Fault Diagnosis 163 -- 8.7 Hessian-Based LLE 163 -- 8.7.1 Application of HLLE in Machine Fault Diagnosis 164 -- 8.8 Local Tangent Space Alignment Analysis (LTSA) 165 -- 8.8.1 Application of LTSA in Machine Fault Diagnosis 165 -- 8.9 Maximum Variance Unfolding (MVU) 166 -- 8.9.1 Application of MVU in Machine Fault Diagnosis 167 -- 8.10 Stochastic Proximity Embedding (SPE) 168 -- 8.10.1 Application of SPE in Machine Fault Diagnosis 168 -- 8.11 Summary 169 -- References 170 -- 9 Feature Selection 173 -- 9.1 Introduction 173 -- 9.2 Filter Model-Based Feature Selection 175 -- 9.2.1 Fisher Score (FS) 176 -- 9.2.2 Laplacian Score (LS) 177 -- 9.2.3 Relief and Relief-F Algorithms 178 -- 9.2.3.1 Relief Algorithm 178 -- 9.2.3.2 Relief-F Algorithm 179 -- 9.2.4 Pearson Correlation Coefficient (PCC) 180 -- 9.2.5 Information Gain (IG) and Gain Ratio (GR) 180 -- 9.2.6 Mutual Information (MI) 181 -- 9.2.7 Chi-Squared (Chi-2) 181 -- 9.2.8 Wilcoxon Ranking 181 -- 9.2.9 Application of Feature Ranking in Machine Fault Diagnosis 182 -- 9.3 Wrapper ModeĺôBased Feature Subset Selection 185 -- 9.3.1 Sequential Selection Algorithms 185 -- 9.3.2 Heuristic-Based Selection Algorithms 185 -- 9.3.2.1 Ant Colony Optimisation (ACO) 185 -- 9.3.2.2 Genetic Algorithms (GAs) and Genetic Programming 187 -- 9.3.2.3 Particle Swarm Optimisation (PSO) 188 -- 9.3.3 Application of Wrapper ModeĺôBased Feature Subset Selection in Machine Fault Diagnosis 189 -- 9.4 Embedded ModeĺôBased Feature Selection 192 -- 9.5 Summary 193 -- References 194 -- Part IV Classification Algorithms 199 -- 10 Decision Trees and Random Forests 201 -- 10.1 Introduction 201 -- 10.2 Decision Trees 202 -- 10.2.1 Univariate Splitting Criteria 204 -- 10.2.1.1 Gini Index 205.
10.2.1.2 Information Gain 206 -- 10.2.1.3 Distance Measure 207 -- 10.2.1.4 Orthogonal Criterion (ORT) 207 -- 10.2.2 Multivariate Splitting Criteria 207 -- 10.2.3 Tree-Pruning Methods 208 -- 10.2.3.1 Error-Complexity Pruning 208 -- 10.2.3.2 Minimum-Error Pruning 209 -- 10.2.3.3 Reduced-Error Pruning 209 -- 10.2.3.4 Critical-Value Pruning 210 -- 10.2.3.5 Pessimistic Pruning 210 -- 10.2.3.6 Minimum Description Length (MDL) Pruning 210 -- 10.2.4 Decision Tree Inducers 211 -- 10.2.4.1 CART 211 -- 10.2.4.2 ID3 211 -- 10.2.4.3 C4.5 211 -- 10.2.4.4 CHAID 212 -- 10.3 Decision Forests 212 -- 10.4 Application of Decision Trees/Forests in Machine Fault Diagnosis 213 -- 10.5 Summary 217 -- References 217 -- 11 Probabilistic Classification Methods 225 -- 11.1 Introduction 225 -- 11.2 Hidden Markov Model 225 -- 11.2.1 Application of Hidden Markov Models in Machine Fault Diagnosis 228 -- 11.3 Logistic Regression Model 230 -- 11.3.1 Logistic Regression Regularisation 232 -- 11.3.2 Multinomial Logistic Regression Model (MLR) 232 -- 11.3.3 Application of Logistic Regression in Machine Fault Diagnosis 233 -- 11.4 Summary 234 -- References 235 -- 12 Artificial Neural Networks (ANNs) 239 -- 12.1 Introduction 239 -- 12.2 Neural Network Basic Principles 240 -- 12.2.1 The Multilayer Perceptron 241 -- 12.2.2 The Radial Basis Function Network 243 -- 12.2.3 The Kohonen Network 244 -- 12.3 Application of Artificial Neural Networks in Machine Fault Diagnosis 245 -- 12.4 Summary 253 -- References 254 -- 13 Support Vector Machines (SVMs) 259 -- 13.1 Introduction 259 -- 13.2 Multiclass SVMs 262 -- 13.3 Selection of Kernel Parameters 263 -- 13.4 Application of SVMs in Machine Fault Diagnosis 263 -- 13.5 Summary 274 -- References 274 -- 14 Deep Learning 279 -- 14.1 Introduction 279 -- 14.2 Autoencoders 280 -- 14.3 Convolutional Neural Networks (CNNs) 283 -- 14.4 Deep Belief Networks (DBNs) 284 -- 14.5 Recurrent Neural Networks (RNNs) 285 -- 14.6 Overview of Deep Learning in MCM 286 -- 14.6.1 Application of AE-based DNNs in Machine Fault Diagnosis 286.
14.6.2 Application of CNNs in Machine Fault Diagnosis 292 -- 14.6.3 Application of DBNs in Machine Fault Diagnosis 296 -- 14.6.4 Application of RNNs in Machine Fault Diagnosis 298 -- 14.7 Summary 299 -- References 301 -- 15 Classification Algorithm Validation 307 -- 15.1 Introduction 307 -- 15.2 The Hold-Out Technique 308 -- 15.2.1 Three-Way Data Split 309 -- 15.3 Random Subsampling 309 -- 15.4 K-Fold Cross-Validation 310 -- 15.5 Leave-One-Out Cross-Validation 311 -- 15.6 Bootstrapping 311 -- 15.7 Overall Classification Accuracy 312 -- 15.8 Confusion Matrix 313 -- 15.9 Recall and Precision 314 -- 15.10 ROC Graphs 315 -- 15.11 Summary 317 -- References 318 -- Part V New Fault Diagnosis Frameworks Designed for MCM 321 -- 16 Compressive Sampling and Subspace Learning (CS-SL) 323 -- 16.1 Introduction 323 -- 16.2 Compressive Sampling for Vibration-Based MCM 325 -- 16.2.1 Compressive Sampling Basics 325 -- 16.2.2 CS for Sparse Frequency Representation 328 -- 16.2.3 CS for Sparse Time-Frequency Representation 329 -- 16.3 Overview of CS in Machine Condition Monitoring 330 -- 16.3.1 Compressed Sensed Data Followed by Complete Data Construction 330 -- 16.3.2 Compressed Sensed Data Followed by Incomplete Data Construction 331 -- 16.3.3 Compressed Sensed Data as the Input of a Classifier 332 -- 16.3.4 Compressed Sensed Data Followed by Feature Learning 333 -- 16.4 Compressive Sampling and Feature Ranking (CS-FR) 333 -- 16.4.1 Implementations 334 -- 16.4.1.1 CS-LS 336 -- 16.4.1.2 CS-FS 336 -- 16.4.1.3 CS-Relief-F 337 -- 16.4.1.4 CS-PCC 338 -- 16.4.1.5 CS-Chi-2 338 -- 16.5 CS and Linear Subspace Learning-Based Framework for Fault Diagnosis 339 -- 16.5.1 Implementations 339 -- 16.5.1.1 CS-PCA 339 -- 16.5.1.2 CS-LDA 340 -- 16.5.1.3 CS-CPDC 341 -- 16.6 CS and Nonlinear Subspace Learning-Based Framework for Fault Diagnosis 343 -- 16.6.1 Implementations 344 -- 16.6.1.1 CS-KPCA 344 -- 16.6.1.2 CS-KLDA 345 -- 16.6.1.3 CS-CMDS 346 -- 16.6.1.4 CS-SPE 346 -- 16.7 Applications 348 -- 16.7.1 Case Study 1 348.
16.7.1.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 350 -- 16.7.1.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 352 -- 16.7.2 Case Study 2 354 -- 16.7.2.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 354 -- 16.7.2.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 355 -- 16.8 Discussion 355 -- References 357 -- 17 Compressive Sampling and Deep Neural Network (CS-DNN) 361 -- 17.1 Introduction 361 -- 17.2 Related Work 361 -- 17.3 CS-SAE-DNN 362 -- 17.3.1 Compressed Measurements Generation 362 -- 17.3.2 CS Model Testing Using the Flip Test 363 -- 17.3.3 DNN-Based Unsupervised Sparse Overcomplete Feature Learning 363 -- 17.3.4 Supervised Fine Tuning 367 -- 17.4 Applications 367 -- 17.4.1 Case Study 1 367 -- 17.4.2 Case Study 2 372 -- 17.5 Discussion 375 -- References 375 -- 18 Conclusion 379 -- 18.1 Introduction 379 -- 18.2 Summary and Conclusion 380 -- Appendix Machinery Vibration Data Resources and Analysis Algorithms 389 -- References 394 -- Index 395.
Record Nr. UNINA-9910813338803321
Ahmed Hosameldin <1976->  
Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Image segmentation : principles, techniques, and applications / / Tao Lei, Asoke K. Nandi
Image segmentation : principles, techniques, and applications / / Tao Lei, Asoke K. Nandi
Autore Lei Tao (Professor)
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2023]
Descrizione fisica 1 online resource (334 pages)
Disciplina 006.6
Soggetto topico Image segmentation
ISBN 9781119859031
9781119859000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910642950503321
Lei Tao (Professor)  
Hoboken, New Jersey : , : Wiley, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Image segmentation : principles, techniques, and applications / / Tao Lei, Asoke K. Nandi
Image segmentation : principles, techniques, and applications / / Tao Lei, Asoke K. Nandi
Autore Lei Tao (Professor)
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2023]
Descrizione fisica 1 online resource (334 pages)
Disciplina 006.6
Soggetto topico Image segmentation
ISBN 9781119859031
9781119859000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910677278503321
Lei Tao (Professor)  
Hoboken, New Jersey : , : Wiley, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Image Segmentation : Principles, Techniques, and Applications
Image Segmentation : Principles, Techniques, and Applications
Autore Lei Tao
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (334 pages)
Disciplina 621.367
Altri autori (Persone) NandiAsoke Kumar
Soggetto genere / forma Electronic books.
ISBN 9781119859031
9781119859000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910595595703321
Lei Tao  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Integrative cluster analysis in bioinformatics / / Basel Abu Jamous, Dr Rui Fa, and Prof. Asoke K. Nandi
Integrative cluster analysis in bioinformatics / / Basel Abu Jamous, Dr Rui Fa, and Prof. Asoke K. Nandi
Autore Abu Jamous Basel
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , 2015
Descrizione fisica 1 online resource (994 p.)
Disciplina 519.5/3
Soggetto topico Bioinformatics - Mathematics
Cluster analysis
ISBN 1-118-90655-1
1-118-90654-3
1-118-90656-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Table of Contents; Title page; Preface; List of Symbols; About the Authors; Part One: Introduction; 1 Introduction to Bioinformatics; 1.1 Introduction; 1.2 The "Omics" Era; 1.3 The Scope of Bioinformatics; 1.4 What Do Information Engineers and Biologists Need to Know?; 1.5 Discussion and Summary; References; 2 Computational Methods in Bioinformatics; 2.1 Introduction; 2.2 Machine Learning and Data Mining; 2.3 Optimisation; 2.4 Image Processing: Bioimage Informatics; 2.5 Network Analysis; 2.6 Statistical Analysis; 2.7 Software Tools and Technologies; 2.8 Discussion and Summary
ReferencesPart Two: Introduction to Molecular Biology; 3 The Living Cell; 3.1 Introduction; 3.2 Prokaryotes and Eukaryotes; 3.3 Multicellularity; 3.4 Cell Components; 3.5 Discussion and Summary; References; 4 Central Dogma of Molecular Biology; 4.1 Introduction; 4.2 Central Dogma of Molecular Biology Overview; 4.3 Proteins; 4.4 DNA; 4.5 RNA; 4.6 Genes; 4.7 Transcription and Post-transcriptional Processes; 4.8 Translation and Post-translational Processes; 4.9 Discussion and Summary; References; Part Three: Data Acquisition and Pre-processing; 5 High-throughput Technologies; 5.1 Introduction
5.2 Microarrays5.3 Next-generation Sequencing (NGS); 5.4 ChIP on Microarrays and Sequencing; 5.5 Discussion and Summary; References; 6 Databases, Standards and Annotation; 6.1 Introduction; 6.2 NCBI Databases; 6.3 The EBI Databases; 6.4 Species-specific Databases; 6.5 Discussion and Summary; References; 7 Normalisation; 7.1 Introduction; 7.2 Issues Tackled by Normalisation; 7.3 Normalisation Methods; 7.4 Discussion and Summary; References; 8 Feature Selection; 8.1 Introduction; 8.2 FS and FG - Problem Definition; 8.3 Consecutive Ranking; 8.4 Individual Ranking
8.5 Principal Component Analysis8.6 Genetic Algorithms and Genetic Programming; 8.7 Discussion and Summary; References; 9 Differential Expression; 9.1 Introduction; 9.2 Fold Change; 9.3 Statistical Hypothesis Testing - Overview; 9.4 Statistical Hypothesis Testing - Methods; 9.5 Discussion and Summary; References; Part Four: Clustering Methods; 10 Clustering Forms; 10.1 Introduction; 10.2 Proximity Measures; 10.3 Clustering Families; 10.4 Clusters and Partitions; 10.5 Discussion and Summary; References; 11 Partitional Clustering; 11.1 Introduction; 11.2 k-Means and its Applications
11.3 k-Medoids and its Applications11.4 Discussion and Summary; References; 12 Hierarchical Clustering; 12.1 Introduction; 12.2 Principles; 12.3 Discussion and Summary; References; 13 Fuzzy Clustering; 13.1 Introduction; 13.2 Principles; 13.3 Discussion; References; 14 Neural Network-based Clustering; 14.1 Introduction; 14.2 Algorithms; 14.3 Discussion; References; 15 Mixture Model Clustering; 15.1 Introduction; 15.2 Finite Mixture Models; 15.3 Infinite Mixture Models; 15.4 Discussion; References; 16 Graph Clustering; 16.1 Introduction; 16.2 Basic Definitions; 16.3 Graph Clustering
16.4 Resources
Record Nr. UNINA-9910131284203321
Abu Jamous Basel  
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Integrative cluster analysis in bioinformatics / / Basel Abu Jamous, Dr Rui Fa, and Prof. Asoke K. Nandi
Integrative cluster analysis in bioinformatics / / Basel Abu Jamous, Dr Rui Fa, and Prof. Asoke K. Nandi
Autore Abu Jamous Basel
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , 2015
Descrizione fisica 1 online resource (994 p.)
Disciplina 519.5/3
Soggetto topico Bioinformatics - Mathematics
Cluster analysis
ISBN 1-118-90655-1
1-118-90654-3
1-118-90656-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Table of Contents; Title page; Preface; List of Symbols; About the Authors; Part One: Introduction; 1 Introduction to Bioinformatics; 1.1 Introduction; 1.2 The "Omics" Era; 1.3 The Scope of Bioinformatics; 1.4 What Do Information Engineers and Biologists Need to Know?; 1.5 Discussion and Summary; References; 2 Computational Methods in Bioinformatics; 2.1 Introduction; 2.2 Machine Learning and Data Mining; 2.3 Optimisation; 2.4 Image Processing: Bioimage Informatics; 2.5 Network Analysis; 2.6 Statistical Analysis; 2.7 Software Tools and Technologies; 2.8 Discussion and Summary
ReferencesPart Two: Introduction to Molecular Biology; 3 The Living Cell; 3.1 Introduction; 3.2 Prokaryotes and Eukaryotes; 3.3 Multicellularity; 3.4 Cell Components; 3.5 Discussion and Summary; References; 4 Central Dogma of Molecular Biology; 4.1 Introduction; 4.2 Central Dogma of Molecular Biology Overview; 4.3 Proteins; 4.4 DNA; 4.5 RNA; 4.6 Genes; 4.7 Transcription and Post-transcriptional Processes; 4.8 Translation and Post-translational Processes; 4.9 Discussion and Summary; References; Part Three: Data Acquisition and Pre-processing; 5 High-throughput Technologies; 5.1 Introduction
5.2 Microarrays5.3 Next-generation Sequencing (NGS); 5.4 ChIP on Microarrays and Sequencing; 5.5 Discussion and Summary; References; 6 Databases, Standards and Annotation; 6.1 Introduction; 6.2 NCBI Databases; 6.3 The EBI Databases; 6.4 Species-specific Databases; 6.5 Discussion and Summary; References; 7 Normalisation; 7.1 Introduction; 7.2 Issues Tackled by Normalisation; 7.3 Normalisation Methods; 7.4 Discussion and Summary; References; 8 Feature Selection; 8.1 Introduction; 8.2 FS and FG - Problem Definition; 8.3 Consecutive Ranking; 8.4 Individual Ranking
8.5 Principal Component Analysis8.6 Genetic Algorithms and Genetic Programming; 8.7 Discussion and Summary; References; 9 Differential Expression; 9.1 Introduction; 9.2 Fold Change; 9.3 Statistical Hypothesis Testing - Overview; 9.4 Statistical Hypothesis Testing - Methods; 9.5 Discussion and Summary; References; Part Four: Clustering Methods; 10 Clustering Forms; 10.1 Introduction; 10.2 Proximity Measures; 10.3 Clustering Families; 10.4 Clusters and Partitions; 10.5 Discussion and Summary; References; 11 Partitional Clustering; 11.1 Introduction; 11.2 k-Means and its Applications
11.3 k-Medoids and its Applications11.4 Discussion and Summary; References; 12 Hierarchical Clustering; 12.1 Introduction; 12.2 Principles; 12.3 Discussion and Summary; References; 13 Fuzzy Clustering; 13.1 Introduction; 13.2 Principles; 13.3 Discussion; References; 14 Neural Network-based Clustering; 14.1 Introduction; 14.2 Algorithms; 14.3 Discussion; References; 15 Mixture Model Clustering; 15.1 Introduction; 15.2 Finite Mixture Models; 15.3 Infinite Mixture Models; 15.4 Discussion; References; 16 Graph Clustering; 16.1 Introduction; 16.2 Basic Definitions; 16.3 Graph Clustering
16.4 Resources
Record Nr. UNINA-9910821677103321
Abu Jamous Basel  
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui