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Intelligent Beam Control in Accelerators (Particle Acceleration and Detection) - Hardcover

 
9783031285967: Intelligent Beam Control in Accelerators (Particle Acceleration and Detection)
  • PublisherSpringer
  • Publication date2023
  • ISBN 10 3031285964
  • ISBN 13 9783031285967
  • BindingHardcover
  • LanguageEnglish
  • Number of pages169

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9783031285998: Intelligent Beam Control in Accelerators (Particle Acceleration and Detection)

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ISBN 10:  3031285999 ISBN 13:  9783031285998
Publisher: Springer, 2024
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Zheqiao Geng
ISBN 10: 3031285964 ISBN 13: 9783031285967
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Hardcover. Condition: new. Hardcover. This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems. This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031285967

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems. 172 pp. Englisch. Seller Inventory # 9783031285967

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes the core systems and algorithms to achieve stable and optimal beam parameters in an acceleratorIntroduces the modern methods such as the multi-objective optimization and machine learning Provides recent research on using machine l. Seller Inventory # 822898022

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems. Seller Inventory # 9783031285967

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Geng, Zheqiao; Simrock, Stefan
Published by Springer, 2023
ISBN 10: 3031285964 ISBN 13: 9783031285967
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