July 13, 2018 Presentation Open Access

Francesco Caliva

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  <dc:creator>Francesco Caliva</dc:creator>
  <dc:date>2018-07-13</dc:date>
  <dc:description>Presented at IJCNN 2018, this presentation contains the description of a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and k-means clustering of representations.</dc:description>
  <dc:identifier>https://zenodo.org/record/1410092</dc:identifier>
  <dc:identifier>10.5281/zenodo.1410092</dc:identifier>
  <dc:identifier>oai:zenodo.org:1410092</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/754316/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1410091</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>deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection</dc:subject>
  <dc:title>A deep learning approach to anomaly detection in nuclear reactors</dc:title>
  <dc:type>info:eu-repo/semantics/lecture</dc:type>
  <dc:type>presentation</dc:type>
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