Presentation Open Access

A deep learning approach to anomaly detection in nuclear reactors

Francesco Caliva


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Presented at IJCNN 2018, this presentation contains&nbsp;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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "University of Lincoln", 
      "@type": "Person", 
      "name": "Francesco Caliva"
    }
  ], 
  "url": "https://zenodo.org/record/1410092", 
  "datePublished": "2018-07-13", 
  "keywords": [
    "deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1410092", 
  "@id": "https://doi.org/10.5281/zenodo.1410092", 
  "@type": "PresentationDigitalDocument", 
  "name": "A deep learning approach to anomaly detection in nuclear reactors"
}
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