Wielgosz, Maciej ; Skoczeń, Andrzej
Contributor:Kulczycki, Piotr - ed. ; Kacprzyk, Janusz - ed. ; Kóczy, László T. - ed. ; Mesiar, Radko - ed.
Title:Using neural networks with data quantization for time series analysis in LHC superconducting magnets
Subtitle: Group publication title: Subject and Keywords:Large Hadron Collider ; LSTM architecture ; signal modelling
Abstract:The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider (LHC) superconducting magnets. High-resolution data available in the post mortem database were used to train a set of models and compare their performance for various hyper-parameters such as input data quantization and the number of cells. A novel approach to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring system and making the process scalable. ; The paper shows that an RNN such as the LSTM or a gated recurrent unit (GRU) can be used for modeling high-resolution signals with the accuracy of over 0.95 and a small number of parameters, ranging from 800 to 1200. This makes the solution suitable for hardware implementation, which is essential in the case of monitoring the performance critical and high-speed signal of LHC superconducting magnets.
Publisher:Zielona Góra: Uniwersytet Zielonogórski
Date: Resource Type: DOI: Pages: Source:AMCS, volume 29, number 3 (2019) ; click here to follow the link
Language: License CC BY 4.0: Rights: