Natural computing for unsupervised learning. (2018)

• Record Type: Book
• Further Information: Note: Xiangtao Li, Ka-Chun Wong, editors.
• Editors: Li, Xiangtao; Wong, Ka-Chun
• Contents: Intro; Contents; Part I Advances in Natural Computing; 1 Detailed Modeling of CSC-STATCOM with Optimized PSO Based Controller; 1.1 Introduction; 1.2 System Configuration and Modeling of CSC-Based STATCOM; 1.3 Design of DC-Link Reactor; 1.4 Pole-Shifting Controller Design; 1.4.1 Simulation Results Using Pole-Shifting Method; 1.5 Linear Quadratic Regulator for CSC-STATCOM; 1.5.1 Algorithm; 1.5.2 Controller Design; 1.5.3 Simulation Results Using LQR Method; 1.6 AI Technique-Based Proposed Controller Design; 1.6.1 PSO-Based LQR Controller 1.6.2 Use of PSO Algorithm for Adjusting the Optimal Gain Matrix (K) in the LQR Controller-Based CSC-STATCOM1.7 Performance of Optimal LQR Controller-Based CSC-STATCOM Using AI Technique; 1.8 Conclusions; Appendix; References; 2 A Brief Review and Comparative Study of Nature-Inspired Optimization Algorithms Applied to Power System Control; 2.1 Introduction; 2.2 Power System Frequency and Voltage Control; 2.3 Literature Review on Frequency and Voltage Control Strategies; 2.4 Simulation Results and Discussion; 2.5 Conclusion; References 3 Self-Organization: A Perspective on Applications in the Internet of Things3.1 Introduction; 3.2 Self-Organizing Systems: An Overview; 3.2.1 Scalability; 3.2.2 Adaptability; 3.2.3 Emergence; 3.2.4 Resilience; 3.3 Self-Organizing Wireless Networks; 3.3.1 Self-Organization in Contemporary Cellular Networks; 3.3.2 Self-Organization for Enabling Internet of Things; 3.4 Self-Organizing M2M for IoT: Use Cases; 3.4.1 Home M2M; 3.4.2 V2V Communications; 3.4.3 Public and Infrastructure Safety; 3.5 Toward Self-Organizing Intelligent IoT; 3.6 Conclusions; References Part II Advances in Unsupervised Learning4 Applications of Unsupervised Techniques for Clustering of Audio Data; 4.1 Introduction; 4.2 Structure of Audio Data; 4.2.1 Mathematics of Audio Signals; 4.2.2 Preprocessing of Audio Data; 4.2.2.1 Framing; 4.2.2.2 Windowing; 4.2.3 Feature Extraction; 4.2.3.1 Mel Frequency Cepstral Coefficients (MFCC); 4.2.3.2 Linear Predictive Coefficients (LPC); 4.2.3.3 Short-Time Energy Function; 4.2.3.4 Zero-Crossing Rate; 4.2.3.5 Spectral Centroid; 4.3 Clustering of Audio Data; 4.3.1 Hierarchical Clustering; 4.3.2 K-means Clustering; 4.3.3 Fuzzy C-means Clustering 4.3.4 Gaussian Mixture Model Clustering4.4 Cluster Evaluations; 4.4.1 Clustering Audio Data of Small Size; 4.4.2 Clustering Large-Size Audio Data; 4.4.2.1 Clustering of Animal Dataset; 4.4.2.2 Clustering of Instrument Dataset; 4.4.2.3 Clustering of Singer Dataset; 4.5 Analysis and Conclusions; References; 5 Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges; 5.1 Introduction; 5.2 Outline of the Survey; 5.3 EEG-Based Brain-Computer Interfacing Through Feature Classification; 5.3.1 Signal Acquisition; 5.3.1.1 Classification of EEG Signal
• Publisher Details: Cham, Switzerland : Springer Nature
• Publication Date: 2018
• Extent: 1 online resource, illustrations (some color)
• Subjects: 006.3; Engineering; Natural computation; Machine learning; Self-organizing systems; Database management; Telecommunication; Optical pattern recognition; Artificial intelligence; Data mining; COMPUTERS / General; Technology & Engineering -- Electronics -- General; Computers -- Computer Vision & Pattern Recognition; Computers -- Intelligence (AI) & Semantics; Computers -- Database Management -- Data Mining; Imaging systems & technology; Pattern recognition; Artificial intelligence; Data mining; Technology & Engineering -- Telecommunications; Communications engineering / telecommunications; Electronic books
• Languages: English
• ISBNs: 9783319985664; 3319985663
• Related ISBNs: 9783319985657
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (EBSCO, viewed November 7, 2018).; Note: Vendor-supplied metadata.
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