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Deep Learning-based Anomaly Detection in Nuclear Reactor Cores

Thanos Tasakos; George Ioannou; Vasudha Verma; Georgios Alexandridis; Abdelhamid Dokhane; Andreas Stafylopatis


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  <identifier identifierType="DOI">10.5281/zenodo.5575838</identifier>
  <creators>
    <creator>
      <creatorName>Thanos Tasakos</creatorName>
      <affiliation>Institute of Communication and Computer Systems National Technical University of Athens</affiliation>
    </creator>
    <creator>
      <creatorName>George Ioannou</creatorName>
      <affiliation>Institute of Communication and Computer Systems National Technical University of Athens</affiliation>
    </creator>
    <creator>
      <creatorName>Vasudha Verma</creatorName>
      <affiliation>Paul Scherrer Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Georgios Alexandridis</creatorName>
      <affiliation>Institute of Communication and Computer Systems National Technical University of Athens</affiliation>
    </creator>
    <creator>
      <creatorName>Abdelhamid Dokhane</creatorName>
      <affiliation>Paul Scherrer Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Andreas Stafylopatis</creatorName>
      <affiliation>Institute of Communication and Computer Systems National Technical University of Athens</affiliation>
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  <titles>
    <title>Deep Learning-based Anomaly Detection in Nuclear Reactor Cores</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-10-03</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5575837</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&amp;bull;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&lt;br&gt;
&amp;bull;The performance of a complementary robustness analysis to assess the system&amp;#39;s performance on noisy or missing data&lt;br&gt;
&amp;bull;The assessment of the system&amp;#39;s functionality on plant measurements obtained from the G&amp;ouml;sgennuclear power plan in Switzerland&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/754316/">754316</awardNumber>
      <awardTitle>Core monitoring techniques and experimental validation and demonstration</awardTitle>
    </fundingReference>
  </fundingReferences>
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