Machine learning for analysis of real nuclear plant data in the frequency domain
Journal article, 2022
Domain adaptation
Simulated data
Self-supervised learning
Unsupervised learning
Core diagnostics
Neutron noise
Clustering
Core monitoring
Actual plant data
Machine learning
Author
Stefanos Kollias
National Technical University of Athens (NTUA)
University of Lincoln
Miao Yu
University of Lincoln
J. Wingate
University of Lincoln
A. Durrant
University of Aberdeen
Georgios Leontidis
University of Aberdeen
Georgios Alexandridis
National Technical University of Athens (NTUA)
Andreas Stafylopatis
National Technical University of Athens (NTUA)
Antonios Mylonakis
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Paolo Vinai
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Christophe Demaziere
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Annals of Nuclear Energy
0306-4549 (ISSN) 1873-2100 (eISSN)
Vol. 177 109293Core monitoring techniques and experimental validation and demonstration (CORTEX)
European Commission (EC) (EC/H2020/754316), 2017-09-01 -- 2021-08-31.
Subject Categories
Other Computer and Information Science
Other Physics Topics
Areas of Advance
Energy
DOI
10.1016/j.anucene.2022.109293