Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundamentals and conventional methods used in smart electrical and mechanical systems. Detailing methods and procedures for the application of ML and AI, it is supported with illustrations of the systems, process diagrams visuals of the systems and/or their components, and supportive data and results leading to the benefits and challenges of the relevant applications. The multidisciplinary theme of the book will help researchers build a synergy between electrical and mechanical engineering systems.
The book guides readers on not only how to effectively solve problems but also provide high accuracy needed for successful implementation. Interdisciplinary in nature, the book caters to the needs of the electrical and mechanical engineering industry by offering details on the application of AI and ML in robotics, design and manufacturing, image processing, power system operation and forecasting with suitable examples.
1. Smart Electrical and Mechanical Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xi 7. Chapter One Machine learning and its applications - p. 1 8. Chapter Two A Multiple Object Recognition Approach via DenseNet-161 Model - p. 39 9. Chapter Three Deep learning-based image processing for analyzing combustion behavior of gel fuel droplets - p. 65 10. Chapter Four Deep learning-based methods for detecting surface defects in steel plates - p. 87 11. Chapter Five Extremum center interpolation-based EMD approach for fault detection of reciprocating compressor - p. 109 12. Chapter Six Renewable energy sources forecasting and integration using machine learning - p. 123 13. Chapter Seven Role of big data analytic and machine learning in power system contingency analysis - p. 151 14. Chapter Eight Ensemble classifier-based protection scheme for hybrid microgrid - p. 185 15. Chapter Nine A linear discriminant analysis based protection scheme for DC microgrid under stressed scenarios - p. 205 16. Chapter Ten Machine learning application to power system forecasting - p. 225 17. Chapter Eleven Machine learning application to industrial control systems - p. 237 18. Chapter Twelve Data-driven based optimal power system operation and control in a weak grid - p. 259 19. Chapter Thirteen Short-term load forecasting in the presence of grid uncertainties using new methods based on deep learning - p. 275 20. Index - p. 293
Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning is an international contributed work with the most up-to-date fundamentals and conventional methods used in smart electrical and mechanical systems. Detailing methods and procedures for the application of ML and AI, it is supported with illustrations of the systems, process diagrams visuals of the systems and/or their components, and supportive data and results leading to the benefits and challenges of the relevant applications. The multidisciplinary theme of the book will help researchers build a synergy between electrical and mechanical engineering systems.
The book guides readers on not only how to effectively solve problems but also provide high accuracy needed for successful implementation. Interdisciplinary in nature, the book caters to the needs of the electrical and mechanical engineering industry by offering details on the application of AI and ML in robotics, design and manufacturing, image processing, power system operation and forecasting with suitable examples.
1. Smart Electrical and Mechanical Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xi 7. Chapter One Machine learning and its applications - p. 1 8. Chapter Two A Multiple Object Recognition Approach via DenseNet-161 Model - p. 39 9. Chapter Three Deep learning-based image processing for analyzing combustion behavior of gel fuel droplets - p. 65 10. Chapter Four Deep learning-based methods for detecting surface defects in steel plates - p. 87 11. Chapter Five Extremum center interpolation-based EMD approach for fault detection of reciprocating compressor - p. 109 12. Chapter Six Renewable energy sources forecasting and integration using machine learning - p. 123 13. Chapter Seven Role of big data analytic and machine learning in power system contingency analysis - p. 151 14. Chapter Eight Ensemble classifier-based protection scheme for hybrid microgrid - p. 185 15. Chapter Nine A linear discriminant analysis based protection scheme for DC microgrid under stressed scenarios - p. 205 16. Chapter Ten Machine learning application to power system forecasting - p. 225 17. Chapter Eleven Machine learning application to industrial control systems - p. 237 18. Chapter Twelve Data-driven based optimal power system operation and control in a weak grid - p. 259 19. Chapter Thirteen Short-term load forecasting in the presence of grid uncertainties using new methods based on deep learning - p. 275 20. Index - p. 293
