Proceedings of the IEEE, IEEE Transactions, IEEE Journals, IEEE Spectrum.

Issues for 1973- cover the entire IEEE technical literature.

The comprehensive and authoritative guide to power electronics in renewable energy systems Power electronics plays a significant role in modern industrial automation and high- efficiency energy systems. With contributions from an international group of noted experts, Power Electronics in Renewable Energy Systems and Smart Grid: Technology and Applications offers a comprehensive review of the technology and applications of power electronics in renewable energy systems and smart grids. The authors cover information on a variety of energy systems including wind, solar, ocean, and geothermal energy systems as well as fuel cell systems and bulk energy storage systems. They also examine smart grid elements, modeling, simulation, control, and AI applications. The book's twelve chapters offer an application-oriented and tutorial viewpoint and also contain technology status review. In addition, the book contains illustrative examples of applications and discussions of future perspectives. This important resource: Includes descriptions of power semiconductor devices, two level and multilevel converters, HVDC systems, FACTS, and more Offers discussions on various energy systems such as wind, solar, ocean, and geothermal energy systems, and also fuel cell systems and bulk energy storage systems Explores smart grid elements, modeling, simulation, control, and AI applications Contains state-of-the-art technologies and future perspectives Provides the expertise of international authorities in the field Written for graduate students, professors in power electronics, and industry engineers, Power Electronics in Renewable Energy Systems and Smart Grid: Technology and Applications offers an up-to-date guide to technology and applications of a wide-range of power electronics in energy systems and smart grids.

Power electronics is an area of extremely important and rapidly changing technology. Technological advancements in the area contribute to performance improvement and cost reduction, with applications proliferating in industrial, commercial, residential, military and aerospace environments. This book is meant to help engineers operating in all these areas to stay up-to-date on the most recent advances in the field, as well as to be a vehicle for clarifying increasingly complex theories and mathematics. This book will be a cost-effective and convenient way for engineers to get up-to-speed on the latest trends in power electronics. The reader will obtain the same level of informative instruction as they would if attending an IEEE course or a training session, but without ever leaving the office or living room! The author is in an excellent position to offer this instruction as he teaches many such courses. - Self-learning advanced tutorial, falling between a traditional textbook and a professional reference. - Almost every page features either a detailed figure or a bulleted chart, accompanied by clear descriptive explanatory text.

The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in this relatively young field. In doing so the authors develop a framework for learning-based control theory that shows how to learn directly suboptimal controllers from input-output data. There are three main challenges on the development of learning-based control. First, there is a need to generalize existing recursive methods. Second, as a fundamental difference between learning-based control and Reinforcement Learning, stability and robustness are important issues that must be addressed for the safety-critical engineering systems such as self-driving cars. Third, data efficiency of Reinforcement Learning algorithms need be addressed for safety-critical engineering systems. This monograph provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.

This book presents a thorough examination of index modulation, an emerging 5G modulation technique. It includes representative transmitter and receiver design, optimization, and performance analysis of index modulation in various domains. First, the basic spatial modulation system for the spatial domain is introduced. Then, the development of a generalized pre-coding aided quadrature spatial modulation system as well as a virtual spatial modulation system are presented. For the space-time domain, a range of differential spatial modulation systems are examined, along with the pre-coding design. Both basic and enhanced index modulated OFDM systems for the frequency domain are discussed, focusing on the verification of their strong capabilities in inter-carrier interference mitigation. Finally, key open problems are highlighted and future research directions are considered. Designed for researchers and professionals, this book is essential for anyone working in communications networking, 5G, and system design. Advanced-level students of engineering and computer science interested in efficiency techniques will also find the content valuable.