Presentation Open Access

Anomaly detection in nuclear reactors using deep learning

Francesco Caliva

BibTeX Export

@misc{francesco_caliva_2020_4314941,
  author       = {Francesco Caliva},
  title        = {{Anomaly detection in nuclear reactors using deep 
                   learning}},
  month        = dec,
  year         = 2020,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4314941},
  url          = {https://doi.org/10.5281/zenodo.4314941}
}
15
8
views
downloads
All versions This version
Views 1515
Downloads 88
Data volume 138.6 MB138.6 MB
Unique views 1212
Unique downloads 88
More info on how stats are collected.
Indexed in
Publication date:
December 8, 2020
DOI:
10.5281/zenodo.4314941

Zenodo DOI Badge

DOI 

10.5281/zenodo.4314941

Markdown 

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4314941.svg)](https://doi.org/10.5281/zenodo.4314941)

reStructedText 

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4314941.svg
   :target: https://doi.org/10.5281/zenodo.4314941

HTML 

<a href="https://doi.org/10.5281/zenodo.4314941"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4314941.svg" alt="DOI"></a>

Image URL 

https://zenodo.org/badge/DOI/10.5281/zenodo.4314941.svg

Target URL 

https://doi.org/10.5281/zenodo.4314941

Grants:
European Commission:
  • CORTEX - Core monitoring techniques and experimental validation and demonstration (754316)
Meeting:
IAEA Consultancy Meeting on Machine Learning for Nuclear Data, Remote meeting, December 8-11, 2020
License (for files):
Creative Commons Attribution 4.0 International

Versions

Version 1 10.5281/zenodo.4314941 Dec 8, 2020
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.4314940. This DOI represents all versions, and will always resolve to the latest one. Read more.

Share

Cite as

Export