Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220940003> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4220940003 endingPage "2870" @default.
- W4220940003 startingPage "2859" @default.
- W4220940003 abstract "Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN." @default.
- W4220940003 created "2022-04-03" @default.
- W4220940003 creator A5054100231 @default.
- W4220940003 creator A5055346529 @default.
- W4220940003 creator A5076619123 @default.
- W4220940003 creator A5079943865 @default.
- W4220940003 date "2022-08-01" @default.
- W4220940003 modified "2023-10-02" @default.
- W4220940003 title "Graph neural network based multiple accident diagnosis in nuclear power plants: Data optimization to represent the system configuration" @default.
- W4220940003 cites W1968904260 @default.
- W4220940003 cites W2000585920 @default.
- W4220940003 cites W2029146575 @default.
- W4220940003 cites W2048420721 @default.
- W4220940003 cites W2093300917 @default.
- W4220940003 cites W2116341502 @default.
- W4220940003 cites W2290847742 @default.
- W4220940003 cites W2896143442 @default.
- W4220940003 cites W2982534622 @default.
- W4220940003 cites W3015130551 @default.
- W4220940003 cites W3104598318 @default.
- W4220940003 cites W3152893301 @default.
- W4220940003 doi "https://doi.org/10.1016/j.net.2022.02.024" @default.
- W4220940003 hasPublicationYear "2022" @default.
- W4220940003 type Work @default.
- W4220940003 citedByCount "3" @default.
- W4220940003 countsByYear W42209400032023 @default.
- W4220940003 crossrefType "journal-article" @default.
- W4220940003 hasAuthorship W4220940003A5054100231 @default.
- W4220940003 hasAuthorship W4220940003A5055346529 @default.
- W4220940003 hasAuthorship W4220940003A5076619123 @default.
- W4220940003 hasAuthorship W4220940003A5079943865 @default.
- W4220940003 hasBestOaLocation W42209400031 @default.
- W4220940003 hasConcept C11413529 @default.
- W4220940003 hasConcept C119857082 @default.
- W4220940003 hasConcept C121332964 @default.
- W4220940003 hasConcept C124101348 @default.
- W4220940003 hasConcept C132525143 @default.
- W4220940003 hasConcept C138268822 @default.
- W4220940003 hasConcept C153180895 @default.
- W4220940003 hasConcept C154945302 @default.
- W4220940003 hasConcept C185544564 @default.
- W4220940003 hasConcept C2779979336 @default.
- W4220940003 hasConcept C41008148 @default.
- W4220940003 hasConcept C50644808 @default.
- W4220940003 hasConcept C80444323 @default.
- W4220940003 hasConcept C81363708 @default.
- W4220940003 hasConceptScore W4220940003C11413529 @default.
- W4220940003 hasConceptScore W4220940003C119857082 @default.
- W4220940003 hasConceptScore W4220940003C121332964 @default.
- W4220940003 hasConceptScore W4220940003C124101348 @default.
- W4220940003 hasConceptScore W4220940003C132525143 @default.
- W4220940003 hasConceptScore W4220940003C138268822 @default.
- W4220940003 hasConceptScore W4220940003C153180895 @default.
- W4220940003 hasConceptScore W4220940003C154945302 @default.
- W4220940003 hasConceptScore W4220940003C185544564 @default.
- W4220940003 hasConceptScore W4220940003C2779979336 @default.
- W4220940003 hasConceptScore W4220940003C41008148 @default.
- W4220940003 hasConceptScore W4220940003C50644808 @default.
- W4220940003 hasConceptScore W4220940003C80444323 @default.
- W4220940003 hasConceptScore W4220940003C81363708 @default.
- W4220940003 hasFunder F4320322030 @default.
- W4220940003 hasFunder F4320322120 @default.
- W4220940003 hasIssue "8" @default.
- W4220940003 hasLocation W42209400031 @default.
- W4220940003 hasOpenAccess W4220940003 @default.
- W4220940003 hasPrimaryLocation W42209400031 @default.
- W4220940003 hasRelatedWork W2748454020 @default.
- W4220940003 hasRelatedWork W2767651786 @default.
- W4220940003 hasRelatedWork W2912288872 @default.
- W4220940003 hasRelatedWork W2961085424 @default.
- W4220940003 hasRelatedWork W3021430260 @default.
- W4220940003 hasRelatedWork W3027997911 @default.
- W4220940003 hasRelatedWork W3181746755 @default.
- W4220940003 hasRelatedWork W4287776258 @default.
- W4220940003 hasRelatedWork W4306674287 @default.
- W4220940003 hasRelatedWork W564581980 @default.
- W4220940003 hasVolume "54" @default.
- W4220940003 isParatext "false" @default.
- W4220940003 isRetracted "false" @default.
- W4220940003 workType "article" @default.