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Publication . Other literature type . Presentation . 2018

A deep learning approach to anomaly detection in nuclear reactors

Caliva, Francesco;
Open Access
English
Published: 13 Jul 2018
Publisher: Zenodo
Abstract

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.

Subjects by Vocabulary

arXiv: Computer Science::Neural and Evolutionary Computation Computer Science::Computer Vision and Pattern Recognition

Subjects

deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection

Funded by
EC| CORTEX
Project
CORTEX
Core monitoring techniques and experimental validation and demonstration
  • Funder: European Commission (EC)
  • Project Code: 754316
  • Funding stream: H2020 | RIA
,
EC| CORTEX
Project
CORTEX
Core monitoring techniques and experimental validation and demonstration
  • Funder: European Commission (EC)
  • Project Code: 754316
  • Funding stream: H2020 | RIA
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ZENODO
Other literature type . 2018
Providers: ZENODO
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