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Deep Transparent Prediction through Latent Representation Analysis

Autor: Kollias, D., Bouas, N., Vlaxos, Y., Brillakis, V., Seferis, M., Kollia, I., Sukissian, L., Wingate, J., Kollias, S.

The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, unified way for prediction purposes. It is well known

Externí odkaz: http://arxiv.org/abs/2009.07044

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Akademický článek

Sensitivity analysis in core diagnostics

Autor: Herb, J., Périn, Y., Yum, S., Mylonakis, A., Demazière, C., Vinai, P., Yu, M., Wingate, J., Hursin, M.

Publikováno v: In Annals of Nuclear Energy 1 December 2022 178

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Akademický článek

NEUTRON NOISE-BASED ANOMALY CLASSIFICATION AND LOCALIZATION USING MACHINE LEARNING

Autor: Demazière C, Mylonakis A, Vinai P, Durrant A, De Sousa Ribeiro F, Wingate J, Leontidis G, Kollias S

Publikováno v: EPJ Web of Conferences, Vol 247, p 21004 (2021)

A methodology is proposed in this paper allowing the classification of anomalies and subsequently their possible localization in nuclear reactor cores during operation. The method relies on the monitoring of the neutron noise recorded by in-core neut

Externí odkaz: https://doaj.org/article/647c4ed3f8864768854d6bd9ac9cb4cb

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