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• W4313367455 endingPage "104553" @default.
• W4313367455 startingPage "104553" @default.
• W4313367455 abstract "Installing safety components to prevent a severe accident from developing from an initiating event is essential. Although various approaches have been presented to detect and identify initiating events, their development relies on the data simulated by computer codes like the Modular Accident Analysis Program. This study focuses on developing approaches to detect and identify initiating events using the data generated by a nuclear power plant (NPP) on-site simulator. By relying on historical data to construct an empirical model that can predict normal operation sensing readings, an abnormal event that leads to excessive discrepancies between the acquired and predicted readings can be detected. Several single- and multiple-sensor feature extraction methods have been presented to find low-dimensional representations for initiating events to facilitate accurate event identification. The results from detailed experiments containing data of 13 event categories with 185 events generated by the Maanshan NPP plant simulator demonstrated the feasibility of developing on-site simulator-based schemes for detecting and identifying initiating events." @default.
• W4313367455 created "2023-01-06" @default.
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• W4313367455 date "2023-02-01" @default.
• W4313367455 modified "2023-10-18" @default.
• W4313367455 title "On-site simulator-based initiating event detection and identification in NPPs" @default.
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• W4313367455 doi "https://doi.org/10.1016/j.pnucene.2022.104553" @default.
• W4313367455 hasPublicationYear "2023" @default.
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