Ruitao Lu – författare

This book explores admissible consensus analysis and design problems concerning singular multi-agent systems, addressing various impact factors including time delays, external disturbances, switching topologies, protocol states, topology structures, and performance constraint. It also discusses the state-space decomposition method, a key technique that can decompose the motions of singular multi-agent systems into two parts: the relative motion and the whole motion. The relative motion is independent of the whole motion. Further, it describes the admissible consensus analysis and determination of the design criteria for different impact factors using the Lyapunov method, the linear matrix inequality tool, and the generalized Riccati equation method. This book is a valuable reference resource for graduate students of control theory and engineering and researchers in the field of multi-agent systems.

This book explores admissible consensus analysis and design problems concerning singular multi-agent systems, addressing various impact factors including time delays, external disturbances, switching topologies, protocol states, topology structures, and performance constraint. It also discusses the state-space decomposition method, a key technique that can decompose the motions of singular multi-agent systems into two parts: the relative motion and the whole motion. The relative motion is independent of the whole motion. Further, it describes the admissible consensus analysis and determination of the design criteria for different impact factors using the Lyapunov method, the linear matrix inequality tool, and the generalized Riccati equation method. This book is a valuable reference resource for graduate students of control theory and engineering and researchers in the field of multi-agent systems.

This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.