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<dc:title xml:lang="pl"><![CDATA[Using neural networks with data quantization for time series analysis in LHC superconducting magnets]]></dc:title>
<dc:creator><![CDATA[Wielgosz, Maciej]]></dc:creator>
<dc:creator><![CDATA[Skoczeń, Andrzej]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[Large Hadron Collider]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[LSTM architecture]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[signal modelling]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[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.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[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.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Kulczycki, Piotr - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kacprzyk, Janusz - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kóczy, László T. - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Mesiar, Radko - ed.]]></dc:contributor>
<dc:date><![CDATA[2019]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86002/AMCS_2019_29_3_7.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/100986/edition/86002/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86002]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 29, number 3 (2019)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=113]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:100986]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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