Self- and semi-supervised learning for evacuation time modeling within fire emergencies in nuclear power plants-Reference-Cited by-同舟云学术

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Self- and semi-supervised learning for evacuation time modeling within fire emergencies in nuclear power plants

Published:2024-08 Issue: Volume:188 Page:1256-1267
ISSN:0957-5820
Container-title:Process Safety and Environmental Protection
language:en
Short-container-title:Process Safety and Environmental Protection

Author:

Jang Geonhui,Singh Sumit Kumar,Lim Saerin,Bae Jinsoo,Heo Jongkook,Zhang Yu,Shin Weon Gyu,Kim Seoung Bum

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference36 articles.

1. Emulation of wildland fire spread simulation using deep learning;Allaire;Neural Netw.,2021

2. Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities;Ayodeji;Prog. Nucl. Energy,2022

3. Deep-Learning Emulators of Transient Compartment Fire Simulations for Inverse Problems and Room-Scale Calorimetry;Buffington;Fire Technol.,2021

4. Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network;Dao;Energy,2024

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1. Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization;Alexandria Engineering Journal;2024-11

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