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Other literature type . 2021
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Presentation . 2021
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Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation

Authors: A. Durrant;

Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation

Abstract

Problem Case • We aim to unfold reactor transfer function to provide core diagnostics. • Derivation of core perturbation characteristics to classify and locate its origin. • Yet this is challenging due to the limited number of neutron detectors in western type reactors. • We ask, can we use machine learning to successfully approximate the reactor transfer function? • However, to effectively train ML algorithms large quantities of data are required. More details can be found in the presentation file

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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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