Výsledky vyhledávání - "Kishansingh Rajput"

Akademický článek

Multi-module-based CVAE to predict HVCM faults in the SNS accelerator

Autor: Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau

Publikováno v: Machine Learning with Applications, Vol 13, Iss , Pp 100484- (2023)

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type t

Externí odkaz: https://doaj.org/article/249bc77939d846c5abae8ea3e340255d

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Akademický článek

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Autor: Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I Radaideh, Sarah Cousineau, Sudarshan Harave

Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 4, p 045009 (2024)

Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task;

Externí odkaz: https://doaj.org/article/1612fda88dd3469a9ae4adcf8f62f9f5

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Akademický článek

Robust errant beam prognostics with conditional modeling for particle accelerators

Autor: Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta

Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 1, p 015044 (2024)

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoi

Externí odkaz: https://doaj.org/article/d3ad18216a774ffa9d295e312bb2694b

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Akademický článek

Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex

Autor: Malachi Schram, Kishansingh Rajput, Karthik Somayaji NS, Peng Li, Jason St. John, Himanshu Sharma

Publikováno v: Physical Review Accelerators and Beams, Vol 26, Iss 4, p 044602 (2023)

Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks, and Quantile Regression Models provide estimates of prediction uncertainties for data-driven deep learning models. However, they can be limited in their applications d

Externí odkaz: https://doaj.org/article/d39c2b4493924709919930edadbe75f9

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Akademický článek

Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator

Autor: Willem Blokland, Kishansingh Rajput, Malachi Schram, Torri Jeske, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov

Publikováno v: Physical Review Accelerators and Beams, Vol 25, Iss 12, p 122802 (2022)

High-power particle accelerators are complex machines with thousands of pieces of equipment that are frequently running at the cutting edge of technology. In order to improve the day-to-day operations and maximize the delivery of the science, new ana

Externí odkaz: https://doaj.org/article/cdaabc593e4d422197d9b4bc329fe872

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