Presentation Open Access

Deep Learning-based Anomaly Detection in Nuclear Reactor Cores

Thanos Tasakos; George Ioannou; Vasudha Verma; Georgios Alexandridis; Abdelhamid Dokhane; Andreas Stafylopatis


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    <subfield code="a">&lt;p&gt;&amp;bull;The introduction of a deep learning methodology for the classification of different perturbation types and their position in the reactor core, using convolutional neural networks&lt;br&gt;
&amp;bull;The performance of a complementary robustness analysis to assess the system&amp;#39;s performance on noisy or missing data&lt;br&gt;
&amp;bull;The assessment of the system&amp;#39;s functionality on plant measurements obtained from the G&amp;ouml;sgennuclear power plan in Switzerland&lt;/p&gt;</subfield>
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