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Big data and differential privacy : analysis strategies for railway track engineering
- Author / Creator
- Available as
- Online
Details
- Creator
- Nii O. Attoh-Okine
- Format
- Books
- Language
- English
- Publication
-
- Hoboken, NJ : John Wiley & Sons, Inc., 2017
- Physical Details
-
- 1 online resource (xiii, 252 pages)
- ISBNs
- 9781119229070, 9781119229063, 9781119229049, 1119229049, 1119229057, 1119229065, 9781119229056, 1119229073
- OCLC
- ocn974487431
- Includes bibliographical references and index.
- Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; Chapter 1 Introduction; 1.1 General; 1.2 Track Components; 1.3 Characteristics of Railway Track Data; 1.4 Railway Track Engineering Problems; 1.5 Wheel-Rail Interface Data; 1.5.1 Switches and Crossings; 1.6 Geometry Data; 1.7 Track Geometry Degradation Models; 1.7.1 Deterministic Models; 1.7.1.1 Linear Models; 1.7.1.2 Nonlinear Models; 1.7.2 Stochastic Models; 1.7.3 Discussion; 1.8 Rail Defect Data; 1.9 Inspection and Detection Systems; 1.10 Rail Grinding; 1.11 Traditional Data Analysis Techniques; 1.11.1 Emerging Data Analysis
- 1.12 RemarksReferences; Chapter 2 Data Analysis -- Basic Overview; 2.1 Introduction; 2.2 Exploratory Data Analysis (EDA); 2.3 Symbolic Data Analysis; 2.3.1 Building Symbolic Data; 2.3.2 Advantages of Symbolic Data; 2.4 Imputation; 2.5 Bayesian Methods and Big Data Analysis; 2.6 Remarks; References; Chapter 3 Machine Learning: A Basic Overview; 3.1 Introduction; 3.2 Supervised Learning; 3.3 Unsupervised Learning; 3.4 Semi-Supervised Learning; 3.5 Reinforcement Learning; 3.6 Data Integration; 3.7 Data Science Ontology; 3.7.1 Kernels; 3.7.1.1 General; 3.7.1.2 Learning Process
- 3.7.2 Basic Operations with Kernels3.7.3 Different Kernel Types; 3.7.4 Intuitive Example; 3.7.5 Kernel Methods; 3.7.5.1 Support Vector Machines; 3.8 Imbalanced Classification; 3.9 Model Validation; 3.9.1 Receiver Operating Characteristic (ROC) Curves; 3.9.1.1 ROC Curves; 3.10 Ensemble Methods; 3.10.1 General; 3.10.2 Bagging; 3.10.3 Boosting; 3.11 Big P and Small N (P k N); 3.11.1 Bias and Variances; 3.11.2 Multivariate Adaptive Regression Splines (MARS); 3.12 Deep Learning; 3.12.1 General; 3.12.2 Deep Belief Networks; 3.12.2.1 Restricted Boltzmann Machines (RBM)
- 3.12.2.2 Deep Belief Nets (DBN)3.12.3 Convolutional Neural Networks (CNN); 3.12.4 Granular Computing (Rough Set Theory); 3.12.5 Clustering; 3.12.5.1 Measures of Similarity or Dissimilarity; 3.12.5.2 Hierarchical Methods; 3.12.5.3 Non-Hierarchical Clustering; 3.12.5.4 k-Means Algorithm; 3.12.5.5 Expectation-Maximization (EM) Algorithms; 3.13 Data Stream Processing; 3.13.1 Methods and Analysis; 3.13.2 LogLog Counting; 3.13.3 Count-Min Sketch; 3.13.3.1 Online Support Regression; 3.14 Remarks; References; Chapter 4 Basic Foundations of Big Data; 4.1 Introduction; 4.2 Query
- 4.3 Taxonomy of Big Data Analytics in Railway Track Engineering4.4 Data Engineering; 4.5 Remarks; References; Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis; 5.1 Hilbert-Huang Transform; 5.1.1 Traditional Empirical Mode Decomposition; 5.1.1.1 Side Effect (Boundary Effect); 5.1.1.2 Example; 5.1.1.3 Stopping Criterion; 5.1.2 Ensemble Empirical Mode Decomposition (EEMD); 5.1.2.1 Post-Processing EEMD; 5.1.3 Complex Empirical Mode Decomposition (CEMD); 5.1.4 Spectral Analysis; 5.1.5 Bidimensional Empirical Mode Decomposition (BEMD); 5.1.5.1 Example