Seismic Control of Structures Using Tuned Mass Dampers Theory And Practice

This book is in honor of Yasuhiko Takahara, a first-class researcher who has been active for some 50 years at the global level in systems research. Researchers and practitioners from Japan and other countries who have been influenced by Takahara have come together from far and wide to contribute their major research masterpieces in the field of systems research in the broadest sense.

This book presents the latest research on switching control, adaptive switching control, and their applications in the transient stability control and analysis of large-scale complex power systems. In large-scale complex power systems, renewable power generators, flexible power electronics converters, and distributed controllers are widely employed. Due to the poor overcurrent tolerance capability of power electronics converters and lacking of coordination mechanism, stability control in events, such as natural disasters, cascaded faults, and severe disturbances, is viewed as the key challenge in the operation of these systems. High-performance self-coordinated controllers are needed for the control of important power sources and power electronics converters. Adaptive switching controllers are a group of controllers designed by the authors for the control of various renewable power generators, synchronous generators, and modular multilevel converters.

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.

A magisterial study of the history and theory of one of the most controversial political movements

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.