Comparison of One-Dimensional and Two-Dimensional Reference Signal Representation for Insulation Aging State Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43966044" target="_blank" >RIV/49777513:23220/22:43966044 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9905173" target="_blank" >https://ieeexplore.ieee.org/document/9905173</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/Diagnostika55131.2022.9905173" target="_blank" >10.1109/Diagnostika55131.2022.9905173</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of One-Dimensional and Two-Dimensional Reference Signal Representation for Insulation Aging State Recognition

Popis výsledku v původním jazyce

This paper compares the performance of one-dimensional and two-dimensional convolutional neural networks in the task of analyzing a reference signal while determining the degradation level of single-core polymer-insulated cable. In this work was designed the set of reference signals and several forms of representing of these signals in the form of one-dimensional and two-dimensional tensors. Then, an experimental determination of the most effective version of the reference signal is carried out in terms of classification accuracy and the most effective form of representation of this signal was found, as well as most efficient type of neural network.

Název v anglickém jazyce

Comparison of One-Dimensional and Two-Dimensional Reference Signal Representation for Insulation Aging State Recognition

Popis výsledku anglicky

This paper compares the performance of one-dimensional and two-dimensional convolutional neural networks in the task of analyzing a reference signal while determining the degradation level of single-core polymer-insulated cable. In this work was designed the set of reference signals and several forms of representing of these signals in the form of one-dimensional and two-dimensional tensors. Then, an experimental determination of the most effective version of the reference signal is carried out in terms of classification accuracy and the most effective form of representation of this signal was found, as well as most efficient type of neural network.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2022 International Conference on Diagnostics in Electrical Engineering (Diagnostika) : CDEE 2022

ISBN

978-1-66548-082-6

ISSN

—

e-ISSN

2464-708X

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

University of West Bohemia in Pilsen

Místo vydání

Pilsen

Místo konání akce

Pilsen, Czech Republic

Datum konání akce

6. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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