Advances of Machine Learning in Clean Energy and the Transportation Industry

$230.00

Pandian Vasant, PhD (Editor) – Research Associate, MERLIN Research Centre, TDTU, Vietnam
Valeriy Kharchenko, PhD (Editor) – Professor, Federal Scientific Agro-engineering Center VIM, Russia
Joshua Thomas, PhD (Editor) – Senior Lecturer, UOW Malaysia, KDU Penang University College
Gerhard-Wilhelm Weber, PhD (Editor) – Professor, Poznań University of Technology, Poland
Vladimir Panchenko, PhD (Editor) – Professor, Russian University of Transport, Russia

Series: Computer Science, Technology and Applications
BISAC: COM004000; COM094000
DOI: https://doi.org/10.52305/SJDR3905

This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe.

Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year – the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data.

The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers.

Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.

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Table of Contents

Preface

Chapter 1. RES-based Multipurpose Plant for Hydrogen Production
(Vytautas Adomavicius-Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania)

Chapter 2. Developing a Bayesian Network to Model Environmental, Organizational, and Human Risk Factors: A Case Study on Wind Turbines
(Maryam Ashrafi, PhD-Industrial Engineering and Management Systems Department, Amirkabir University of Technology, Teheran, Iran)

Chapter 3. Digital Technologies for the Implementation of Intelligent Diagnostics of the Insulation of Power Supply Systems with Insulated Neutral in Operating Mode
(Svetlana Ovchukova, Nadezhda Kondrateva and Andrey Shishov-Izhevsk State Agricultural Academy, Izhevsk, Russia, et al.)

Chapter 4. Irrigation System of Agricultural Fields with the Use of Solar Energy
(Leonid Yuferev and Alexander Parakhnich-Federal Scientific Agroengineering Center VIM, Moscow, Russian Federation)

Chapter 5. Strategies Hybrid Simulation for Regional Market Development of Renewable Energy
(P. N. Kuznetsov, PhD, D. Yu. Voronin, PhD, L. Yu Yuferev, DSc and V. P. Evstigneev, PhD-Sevastopol State University, Sevastopol, Russian Federation, et al.)

Chapter 6. RES-Based Power Plants Versus Polluting Power Plants: Pros and Cons
(Vytautas Adomavicius-Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania)

Chapter 7. A Comprehensive Study of System Building Blocks for Radio Frequency Energy Harvesting
(Bhuvnesh Khantwal, Reeta Verma and Paras-Department of E & CE College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India)

Chapter 8. The Management of Community Participation in Rural Infrastructure Development in the Mekong River Delta, Vietnam
(Nguyen Xuan Quyet, Pham Thi My Dung, Duc Anh Nguyen, Dinh Tuan Hai and Nguyen Viet Luan- Hochiminh City’s University of Food and Industry, Vietnam, et al.)

Chapter 9. Warning System for Cracked Pipes in Autonomous Vehicles
(Yair Wiseman-Computer Science Department, Bar-Ilan University, Ramat Gan, Israel)

Chapter 10. Contribution of Machine Learning to Rail Transport Safety
Habib Hadj-Mabrouk, PhD-Université Gustave Eiffel, Vice-présidence Recherche, Marne-la-Vallée, France)

Chapter 11. The Power of Variable Freeing and Variable Sum Bounds in Solving the Linear Knapsack Problem
Elias Munapo-Department of Statistics and Operations Research, School of Economics Sciences, North West University, Mafikeng, South Africa)

Index

Additional information

Binding

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