Zobrazeno 1 - 10
of 319
pro vyhledávání: '"Ramuhalli, P."'
Autor:
Chowdhury, Agniva, Ramuhalli, Pradeep
In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a
Externí odkaz:
http://arxiv.org/abs/2402.16326
Autor:
Rajput, Kishansingh, Schram, Malachi, Blokland, Willem, Alanazi, Yasir, Ramuhalli, Pradeep, Zhukov, Alexander, Peters, Charles, Vilalta, Ricardo
Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and abort operations for numerous reasons. These faults impact the availa
Externí odkaz:
http://arxiv.org/abs/2312.10040
Autor:
Alanazi, Yasir, Schram, Malachi, Rajput, Kishansingh, Goldenberg, Steven, Vidyaratne, Lasitha, Pappas, Chris, Radaideh, Majdi I., Lu, Dan, Ramuhalli, Pradeep, Cousineau, Sarah
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:
http://arxiv.org/abs/2304.10639
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advan
Externí odkaz:
http://arxiv.org/abs/2209.15570
Autor:
Blokland, Willem, Ramuhalli, Pradeep, Peters, Charles, Yucesan, Yigit, Zhukov, Alexander, Schram, Malachi, Rajput, Kishansingh, Jeske, Torri
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analy
Externí odkaz:
http://arxiv.org/abs/2110.12006
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
Akademický článek
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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
Publikováno v:
International Journal of Prognostics and Health Management, Vol 14, Iss 1 (2023)
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advan
Externí odkaz:
https://doaj.org/article/492e86b0f5a14ac2b779dd7f5f3a91af
Akademický článek
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