Poster Open Access

3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Durrant A.; Leontidis G.; Kollias S.


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  <dc:creator>Durrant A.</dc:creator>
  <dc:creator>Leontidis G.</dc:creator>
  <dc:creator>Kollias S.</dc:creator>
  <dc:date>2019-06-07</dc:date>
  <dc:description>Poster on 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection at 9th European Commission Conferences on EURATOM Research and Training in Safety of Reactor Systems and Radioactive Waste Management (FISA 2019 - Euradwaste’ 19)</dc:description>
  <dc:identifier>https://zenodo.org/record/3243887</dc:identifier>
  <dc:identifier>10.5281/zenodo.3243887</dc:identifier>
  <dc:identifier>oai:zenodo.org:3243887</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/754316/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3243886</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Reactor safety, modeling, CORTEX</dc:subject>
  <dc:title>3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
</oai_dc:dc>
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