Control of a negative-ion accelerator source using neural networks

Howell, Jo Ann; Barnes, Cris W.; Brown, Stanley K.; Flake, Gary William; Jones, R.D.; Lee, Y.C.; Qian, S.P.; Wright, R.

doi:10.1016/0168-9002(90)91492-t

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…Much early discussion during the late 1980s and early 1990s focused on applying rule-based systems to accelerator control and tuning [29][30][31][32][33]. In the early 1990s, scientists at Los Alamos National Lab had some experimental success with neural-network-based ion source control [34][35][36]. Other early studies at the University of New Mexico focused on orbit/trajectory control [37][38][39][40][41], fault detection and management [42,43], and root-cause analysis of errors (e.g.…”

Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

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Machine learning" (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators [1].

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Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In the early 1990s at Los Alamos, a NN-based PID tuner for a low level RF system was implemented [74]. Also at Los Alamos, several neural network schemes were used to control a negative ion source [75,76,77].…”

Section: ) Previous Efforts To Apply Neural Network To Particle Accmentioning

confidence: 99%

Neural Networks for Modeling and Control of Particle Accelerators

Edelen

Biedron

Chase

et al. 2016

IEEE Trans. Nucl. Sci.

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Abstract-Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results, due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

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“…In spite of these shortcomings, the algorithm was quite robust when applied to thousands of images. 10 6 The Inputs and Outputs of the Neural Network…”

Section: Tracking Face Regionsmentioning

confidence: 99%

Human emotion recognition from motion using a radial basis function network architecture

Rosenblum

Yacoob

Davis

Proceedings of 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects

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In this paper a radial basis function network architecture is developed that learns the correlation between facial feature motion patterns and human emotions. We describe a hierarchical approach which at the highest level identi es emotions, at the mid level determines motions of facial features, and at the low level recovers motion directions. Individual emotion networks were trained to recognize the \smile" and \surprise" emotions. Each network was trained by viewing a set of sequences of one emotion for many subjects. The trained neural network was then tested for retention, extrapolation and rejection ability. Success rates were about 88% for retention, 73% for extrapolation, and 79% for rejection. The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Topographic Engineering Center under Contract DACA76-92-C-0009 is gratefully acknowledged, as is the help of Sandy German in preparing this paper. Recent research in computer vision The problem of recognizing facial expressions has recently attracted attention in the computer vision community 15{17, 21, 23]. With the increase of interest in human-computer interaction and related applications, the need arises to analyze human messages that are communicated through body part gestures and facial expressions.

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Control of a negative-ion accelerator source using neural networks

Cited by 12 publications

References 4 publications

Opportunities in Machine Learning for Particle Accelerators

Opportunities in Machine Learning for Particle Accelerators

Neural Networks for Modeling and Control of Particle Accelerators

Human emotion recognition from motion using a radial basis function network architecture

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