Power Engineering and Intelligent Systems (eBook, PDF)

Proceedings of PEIS 2024, Volume 2
Redaktion: Shrivastava, Vivek; Panigrahi, B. K.; Bansal, Jagdish Chand

• Format: PDF

Produktbeschreibung

This book presents a collection of the high-quality research articles in the field of power engineering, grid integration, energy management, soft computing, artificial intelligence, signal and image processing, data science techniques, and their real-world applications. The papers are presented at International Conference on Power Engineering and Intelligent Systems (PEIS 2024), held during March 16-17, 2024, at National Institute of Technology Srinagar, Uttarakhand, India.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 535
• Erscheinungstermin: 4. Dezember 2024
• Englisch
• ISBN-13: 9789819767144
• Artikelnr.: 72526457

Autorenporträt

Dr Vivek Shrivastava has approx. 20 years of diversified experience of scholarship of teaching and learning, accreditation, research, industrial, and academic leadership in India, China, and USA. Presently, he is holding the position of professor at National Institute of Technology Uttarakhand, India. Prior to his academic assignments, he has worked as System Reliability Engineer at SanDisk Semiconductors Shanghai China and USA. He has carried out research and consultancy and attracted significant funding projects from Ministry of Human Resources & Development, Government of India, and Board of Research in Nuclear Science (BRNS) subsidiary organization of Bhabha Atomic Research Organization. He has published over 80 journal articles, presented papers at conferences, and has published several chapters in books. He has supervised 05 Ph.D. and 16 Masters students and currently supervising several Ph.D. students. Dr. Jagdish Chand Bansal is Associate Professor (Senior Grade) at South Asian University New Delhi and Visiting Faculty at Maths and Computer Science, Liverpool Hope University UK. He also holds visiting professorship at NIT Goa, India. He obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi, he worked as Assistant Professor at ABV-Indian Institute of Information Technology and Management Gwalior and BITS Pilani. His primary areas of interest are Swarm Intelligence and Nature Inspired Optimization Techniques. Recently, he proposed a fission-fusion social structure-based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems in the engineering domain. He is also Associate Editor of Engineering Applications of Artificial Intelligence (EAAI) and ARRAY published by Elsevier. He is General Secretary of the Soft Computing Research Society (SCRS). He has also received Gold Medal at UG and PG levels. Prof. Bijaya Ketan Panigrahi is Professor in the Electrical Engineering Department, IIT Delhi, India. Prior to joining IIT Delhi in 2005, he has served as Faculty in Electrical Engineering Department, UCE Burla, Odisha, India, from 1992 to 2005. He is Fellow of INAE, India. His research interest includes application of soft computing and evolutionary computing techniques to power system planning, operation, and control. He has been also working in the field of bio-medical signal processing and image processing. He has served as Editorial Board Member, Associate Editor, and special issue Guest Editor of different international journals. He is also associated with various international conferences in various capacities. He has published more than 100 research papers in various international and national journals.

Inhaltsangabe

Chapter 1: Study of the Optimal Sizing of Battery Energy Storage Systems for Microgrid Applications.- Chapter 2: Integrated State of Charge and State of Health Method for Operating Range Prediction in Electric Vehicles.- Chapter 3: Crime Prediction Using Ensemble Machine Learning Approach.- Chapter 4: Gated Recurrent Unit with Attention mechanism for IC50 Prediction model using Amyotrophic Lateral Sclerosis Related Proteins.- Chapter 5: Exploring Echo State Network for Detection of Gait Freezing in Parkinson's Patients Optimized through Modified Metaheuristics.- Chapter 6: Computer Vision-Based Self-Inflicted Violence Detection in High-Rise Environments using Deep Learning.- Chapter 7: Two Sliding Mode Control Strategies for Maglev Systems with Help of Kalman Filter.- Chapter 8: A Deep Learning Framework on Embedded ADAS Platform for Lane and Road Detection.- Chapter 9: Innovative Convolutional Neural Network Approach to Enhance Real-Time Face Recognition Accuracy.- Chapter 10: Tomato Plant Leaf Disease Prediction and Suggestion Using Deep Learning. etc.