Bøker av Sun

POWER SYSTEM SIMULATION USING SEMI-ANALYTICAL METHODSRobust coverage of semi-analytical and traditional numerical methods for power system simulationIn Power System Simulation Using Semi-Analytical Methods, distinguished researcher Dr. Kai Sun delivers a comprehensive treatment of semi-analytical simulation and current semi-analytical methods for power systems. The book presents semi-analytical solutions on power system dynamics via mathematical tools, and covers parallel contingency analysis and simulations. The book offers an overview of power system simulation and contingency analysis supported by data, tables, illustrations, and case studies on realistic power systems and experiments.Readers will find open-source code in MATLAB along with examples for key algorithms introduced in the book. You'll also find:* A thorough background on power system simulation, including models, numerical solution methods, and semi-analytical solution methods* Comprehensive explorations of semi-analytical power system simulation via a variety of mathematical methods such as the Adomian decomposition, differential transformation, homotopy analysis and holomorphic embedding methods* Practical discussions of semi-analytical simulations for realistic large-scale power grids* Fulsome treatments of parallel power system simulationPerfect for power engineers and applied mathematicians with an interest in high-performance simulation of power systems and other large-scale network systems, Power System Simulation Using Semi-Analytical Methods will also benefit researchers and postgraduate students studying power system engineering.

Explore the concepts, algorithms, and applications underlying federated learningIn Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers delivers a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy.In the book, readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues that apply to wireless communications. Readers will also find:* A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL* Comprehensive explorations of wireless communication network design and optimization for federated learning* Practical discussions of novel federated learning algorithms and frameworks for future wireless networks* Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distributionPerfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.