5575838
doi
10.5281/zenodo.5575838
oai:zenodo.org:5575838
user-cortex
user-eu
George Ioannou
Institute of Communication and Computer Systems National Technical University of Athens
Vasudha Verma
Paul Scherrer Institute
Georgios Alexandridis
Institute of Communication and Computer Systems National Technical University of Athens
Abdelhamid Dokhane
Paul Scherrer Institute
Andreas Stafylopatis
Institute of Communication and Computer Systems National Technical University of Athens
Deep Learning-based Anomaly Detection in Nuclear Reactor Cores
Thanos Tasakos
Institute of Communication and Computer Systems National Technical University of Athens
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>•The introduction of a deep learning methodology for the classification of different perturbation types and their position in the reactor core, using convolutional neural networks<br>
•The performance of a complementary robustness analysis to assess the system's performance on noisy or missing data<br>
•The assessment of the system's functionality on plant measurements obtained from the Gösgennuclear power plan in Switzerland</p>
Zenodo
2021-10-03
info:eu-repo/semantics/lecture
5575837
user-cortex
user-eu
award_title=Core monitoring techniques and experimental validation and demonstration; award_number=754316; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/754316; funder_id=00k4n6c32; funder_name=European Commission;
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https://zenodo.org/records/5575838/files/2021_Alexandridis_MC2021_presentation1_V1.pdf
public
10.5281/zenodo.5575837
isVersionOf
doi