Classification and Location of Neutron Noise Perturbations Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Classification and Location of Neutron Noise Perturbations Using Convolutional Neural Networks

Published:2024-07-16 Issue: Volume: Page:1-11
ISSN:0029-5639
Container-title:Nuclear Science and Engineering
language:en
Short-container-title:Nuclear Science and Engineering

Author:

Chillarón Mónica¹,Vidal-Ferràndiz Antoni²,Vidal Vicente¹,Verdú Gumersindo³^ORCID

Affiliation:

1. Universitat Politècnica de València, Departamento de Sistemas Informáticos y Computación, València 46022, Spain

2. Universitat Politècnica de València, I. U. de Matemática Multidisciplinar, València 46022, Spain

3. Universitat Politècnica de València, Instituto de Seguridad Industrial, Radiofísica y Medioambiental, València 46022, Spain

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00295639.2024.2357395

Reference19 articles.

1. C. DEMAZIÈRE et al., “Overview of the Cortex Project,” Proc. Int. Conf. on the Physics of Reactors-Reactor Physics—Paving the Way Towards More Efficient Systems (PHYSOR2018) (2018).

2. T. TASAKOS et al., “Deep Learning-Based Anomaly Detection in Nuclear Reactor Cores,” Proc. Int. Conf. on Mathematics & Computational Methods Applied to Nuclear Science & Engineering (M&C 2021), Online, p. 2026 (2021).

3. Machine learning for analysis of real nuclear plant data in the frequency domain

4. A Deep Learning Approach to Anomaly Detection in Nuclear Reactors

5. Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements

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