國立虎尾科技大學 |

Anomaly Detection in X-Ray Physics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Anomaly Detection in X-Ray Physics./
作者:	Humble, Ryan Alexander.
面頁冊數:	1 online resource (148 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Particle accelerators. -
電子資源:	click for full text (PQDT)
ISBN:	9798381019469

Anomaly Detection in X-Ray Physics.
Humble, Ryan Alexander.

Anomaly Detection in X-Ray Physics. - 1 online resource (148 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. These systems produce too many signals for the system operators to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in system performance can require time-intensive consultations with experts to identify the underlying problem. For this reason, despite the wealth of data that these complex systems produce, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Since there are no labels, the training data will inevitably be contaminated by anomalies.In Chapter 2, we introduce a fully automated, unsupervised beam-based diagnostic method for RF station fault detection at SLAC's Linac Coherent Light Source (LCLS). Our key contribution is the combination of high fidelity beam position monitoring data with bandwidth limited (and noisy) radio-frequency (RF) station diagnostic data to achieve highly accurate, nearly real time fault identification. In Chapter 3, we introduce coincident learning for anomaly detection (CoAD) and an unsupervised metric \\hat{F}_\\beta, that assumes and exploits that anomalies are coincident in two different slices of the input data. We present theoretical properties of our metric and demonstrate its performance on four data sets. In Chapter 4, we focus on a broader task of detecting any anomalous beam behavior at LCLS and develop the unsupervised Resilient Variational Autoencoder (ResVAE) model. We illustrate its resilience to contaminated training data and apply it to identifying anomalies in accelerator status at LCLS .In Chapter 5, we switch gears and address a problem in neural network pruning. We introduce a novel channel pruning approach for convolutional neural networks, called Soft Masking for cost-constrained Channel Pruning (SMCP), that is particularly suitable when pruning a large fraction of the network's channels. The core of our approach relies on regularly rewiring the network sparsity, through soft masking of the network weights, to minimize the accuracy drop for large pruning fractions. Our method outperforms prior works on both the ImageNet classification and PASCAL VOC detection datasets.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381019469Subjects--Topical Terms:

579836
Particle accelerators.
Index Terms--Genre/Form:

554714
Electronic books.

Anomaly Detection in X-Ray Physics.
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504 $a Includes bibliographical references
520 $a Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. These systems produce too many signals for the system operators to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in system performance can require time-intensive consultations with experts to identify the underlying problem. For this reason, despite the wealth of data that these complex systems produce, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Since there are no labels, the training data will inevitably be contaminated by anomalies.In Chapter 2, we introduce a fully automated, unsupervised beam-based diagnostic method for RF station fault detection at SLAC's Linac Coherent Light Source (LCLS). Our key contribution is the combination of high fidelity beam position monitoring data with bandwidth limited (and noisy) radio-frequency (RF) station diagnostic data to achieve highly accurate, nearly real time fault identification. In Chapter 3, we introduce coincident learning for anomaly detection (CoAD) and an unsupervised metric \\hat{F}_\\beta, that assumes and exploits that anomalies are coincident in two different slices of the input data. We present theoretical properties of our metric and demonstrate its performance on four data sets. In Chapter 4, we focus on a broader task of detecting any anomalous beam behavior at LCLS and develop the unsupervised Resilient Variational Autoencoder (ResVAE) model. We illustrate its resilience to contaminated training data and apply it to identifying anomalies in accelerator status at LCLS .In Chapter 5, we switch gears and address a problem in neural network pruning. We introduce a novel channel pruning approach for convolutional neural networks, called Soft Masking for cost-constrained Channel Pruning (SMCP), that is particularly suitable when pruning a large fraction of the network's channels. The core of our approach relies on regularly rewiring the network sparsity, through soft masking of the network weights, to minimize the accuracy drop for large pruning fractions. Our method outperforms prior works on both the ImageNet classification and PASCAL VOC detection datasets.
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