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.

Semiannual, with semiannual and annual indexes. References to all scientific and technical literature coming from DOE, its laboratories, energy centers, and contractors. Includes all works deriving from DOE, other related government-sponsored information, and foreign nonnuclear information. Arranged under 39 categories, e.g., Biomedical sciences, basic studies; Biomedical sciences, applied studies; Health and safety; and Fusion energy. Entry gives bibliographical information and abstract. Corporate, author, subject, report number indexes.

Artificial Intelligence (AI) and Machine learning (ML) promise significant enhancements for particle accelerator operations, including applications in diagnostics, controls, and modeling. Challenges still exist in experimentally verifying AI/ML methods before deployment at user facilities. The ability to quickly generalize and adapt these methods to new operating configurations at the same facility or between facilities also remains a challenge and requires combining model-independent adaptive feedback with traditional ML tools. These methods also apply to the detection, classification, and prevention of operational anomalies that can cause accelerator damage or excessive beam loss in the case of abnormal operations. Opportunity exists in broadening AI/ML methods for early detection of a broad range of accelerator component or subsystem failures.

Presents extensions of papers reported in the proceedings of the 1989 SPIE symposium. In the applications group articles cover the use of expert systems in computer aided education, hydrocarbon (oil) exploration, satellite image analysis for oceanography, particle beam accelerator tuning, design of internal combustion engines. Papers in the theory group cover theories, algorithms, architectures and software tools that can be used for modules within future systems.

Electrostatic Accelerators have been at the forefront of modern technology since the development by Sir John Cockroft and Ernest Walton in 1932 of the first accelerator, which was the first to achieve nuclear transmutation and earned them the Nobel Prize in Physics in 1951. The applications of Cockroft and Walton's development have been far reaching, even into our kitchens where it is employed to generate the high voltage needed for the magnetron in microwave ovens. Other electrostatic accelerator related Nobel prize winning developments that have had a major socio-economic impact are; the electron microscope where the beams of electrons are produced by an electrostatic accelerator, X-rays and computer tomography (CT) scanners where the X-rays are produced using an electron accelerator and microelectronic technology where ion implantation is used to dope the semiconductor chips which form the basis of our computers, mobile phones and entertainment systems. Although the Electrostatic Accelerator field is over 90 years old, and only a handful of accelerators are used for their original purpose in nuclear physics, the field and the number of accelerators is growing more rapidly than ever. The objective of this book is to collect together the basic science and technology that underlies the Electrostatic Accelerator field so it can serve as a handbook, reference guide and textbook for accelerator engineers as well as students and researchers who work with Electrostatic Accelerators.

The High Energy Accelerator Conference has always been the monitor of the state of the art and the new trends in planning, construction and operation of large particle accelerators. It is held every three years. The 1992 conference is devoted to High Energy Hadron Accelerators and Colliders, Linear Colliders, e⁺e⁻ Storage Rings and related Technologies for these machines. In addition to status reports and contributed papers, the program features twelve survey talks which include summaries of individual poster papers.

Knowing our World: An Artificial Intelligence Perspective considers the methodologies of science, computation, and artificial intelligence to explore how we humans come to understand and operate in our world. While humankind’s history of articulating ideas and building machines that can replicate the activity of the human brain is impressive, Professor Luger focuses on understanding the skills that enable these goals. Based on insights afforded by the challenges of AI design and program building, Knowing our World proposes a foundation for the science of epistemology. Taking an interdisciplinary perspective, the book demonstrates that AI technology offers many representational structures and reasoning strategies that support clarification of these epistemic foundations. This monograph is organized in three Parts; the first three chapters introduce the reader to the foundations of computing and the philosophical background that supports the AI tradition. These three chapters describe the origins of AI, programming as iterative refinement, and the representations and very high-level language tools that support AI application building. The book’s second Part introduces three of the four paradigms that represent research and development in AI over the past seventy years: the symbol-based, connectionist, and complex adaptive systems. Luger presents several introductory programs in each area and demonstrates their use. The final three chapters present the primary theme of the book: bringing together the rationalist, empiricist, and pragmatist philosophical traditions in the context of a Bayesian world view. Luger describes Bayes' theorem with a simple proof to demonstrate epistemic insights. He describes research in model building and refinement and several philosophical issues that constrain the future growth of AI. The book concludes with his proposal of the epistemic stance of an active, pragmatic, model-revising realism.

These proceedings aim to provide a comprehensive overview of research, technology and applications in the field of accelerators. Contributions from the entire field of accelerators are presented, including low and high energy machines, and medical and industrial accelerators.