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The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

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 conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom Gated Rec...

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Detalles Bibliográficos
Autores principales: Wielgosz, Maciej, Mertik, Matej, Skoczeń, Andrzej, De Matteis, Ernesto
Lenguaje:eng
Publicado: 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1016/j.engappai.2018.06.012
http://cds.cern.ch/record/2290735

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