Heng Zhang – författare

Cyber Security for Industrial Control Systems: From the Viewpoint of Close-Loop provides a comprehensive technical guide on up-to-date new secure defending theories and technologies, novel design, and systematic understanding of secure architecture with practical applications. The book consists of 10 chapters, which are divided into three parts.The first three chapters extensively introduce secure state estimation technologies, providing a systematic presentation on the latest progress in security issues regarding state estimation. The next five chapters focus on the design of secure feedback control technologies in industrial control systems, displaying an extraordinary difference from that of traditional secure defending approaches from the viewpoint of network and communication. The last two chapters elaborate on the systematic secure control architecture and algorithms for various concrete application scenarios.The authors provide detailed descriptions on attack model and strategy analysis, intrusion detection, secure state estimation and control, game theory in closed-loop systems, and various cyber security applications. The book is useful to anyone interested in secure theories and technologies for industrial control systems.

Cyber Security for Industrial Control Systems: From the Viewpoint of Close-Loop provides a comprehensive technical guide on up-to-date new secure defending theories and technologies, novel design, and systematic understanding of secure architecture with practical applications. The book consists of 10 chapters, which are divided into three parts.The

This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.

This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.