81 citations · 111 across the 4 of their papers we have counts for
4 papers
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń +1
This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduc…
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high r…
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used syste…
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
Matej Mertik, Maciej Wielgosz, Andrzej Skoczeń
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a pl…