Presentation Open Access

A deep learning approach to anomaly detection in nuclear reactors

Francesco Caliva


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <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>
</oai_dc:dc>
35
49
views
downloads
All versions This version
Views 3535
Downloads 4949
Data volume 598.0 MB598.0 MB
Unique views 3434
Unique downloads 4747

Share

Cite as