Presentation Open Access
A deep learning approach to anomaly detection in nuclear reactors
Francesco Caliva
BibTeX Export
@misc{francesco_caliva_2018_1410092,
author = {Francesco Caliva},
title = {{A deep learning approach to anomaly detection in
nuclear reactors}},
month = jul,
year = 2018,
publisher = {Zenodo},
doi = {10.5281/zenodo.1410092},
url = {https://doi.org/10.5281/zenodo.1410092}
}
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|---|---|---|
| Views | 35 | 35 |
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| Data volume | 598.0 MB | 598.0 MB |
| Unique views | 34 | 34 |
| Unique downloads | 47 | 47 |
Indexed in
- Publication date:
- July 13, 2018
- DOI:
-
- Keyword(s):
- deep learning, convolutional neural networks, clustering trained representations, denoising autoencoders, signal processing, nuclear reactors, unfolding, anomaly detection
- Grants:
-
European Commission:
- CORTEX - Core monitoring techniques and experimental validation and demonstration (754316)
- License (for files):
- Creative Commons Attribution 4.0 International