Machine Learning and Deep Learning Methods used in Safety Management of Nuclear Power Plants: A Survey

Shi, Yong; Xue, Xiaodong; Qu, Yi; Xue, Jia‐Yu; Zhang, Linzi

doi:10.1109/icdmw53433.2021.00120

Cited by 4 publications

(2 citation statements)

References 55 publications

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“…Our analysis of Figure 6 reveals that authors Zhang Hao, Ma Lei, and Liu Yang, belonging to the dark blue cluster, concentrate on big data in data security and the use of deep learning in software countermeasures [47][48][49]. The purple cluster, including Lim, Ming, and Tseng Ming-Lang et al, focuses on the industrial Internet of Things and machine learning in Industry 4.0 [50,51]. The green cluster comprises Shariatfar, Moeid, Lee, Yong-Cheol, and others, who have studied intelligent noise identification and monitoring in factories and construction sites more intensively [52,53].…”

Section: Author Distributionmentioning

confidence: 96%

“…As shown in Figure 5, eight clusters were obtained based on the intensity of cooperation between research institutions. The Chinese Academy of Sciences and the University of the Chinese Academy of Sciences had the closest collaboration, with their research focused on machine learning in fault detection in nuclear power plants [50][51][52], which aligns with China's policy of investing heavily in the nuclear power field. The Hong Kong Polytechnic University and Huazhong University of Science and Technology were ranked second in the intensity of collaboration, with their research primarily focused on using machine learning for construction risk and worker fatigue detection [53][54][55].…”

Section: Institute Distributionmentioning

confidence: 99%

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Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Wei

Tao²,

Pan

et al. 2023

Sustainability

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Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.

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Section: Author Distributionmentioning

confidence: 96%

Section: Institute Distributionmentioning

confidence: 99%

Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Wei

Tao²,

Pan

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Validation of codes for modeling and simulation of nuclear power plants: A review

Moshkbar-Bakhshayesh,

Mohtashami

2024

Nuclear Engineering and Design

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Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry

Khadka,

Sthapit,

Epiphaniou

et al. 2024

ACM Comput. Surv.

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The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive industry like the Nuclear Industry (NI) where consequences can be fatal in terms of both human lives and assets. We analyse ML based research works that have investigated adversaries and defence strategies in the NI. We then present the progress in the adoption of ML techniques, identify use cases where adversaries can threaten the ML-enabled systems and finally identify the progress on building Resilient Machine Learning (rML) systems entirely focusing on the NI domain.

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Machine Learning and Deep Learning Methods used in Safety Management of Nuclear Power Plants: A Survey

Cited by 4 publications

References 55 publications

Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Validation of codes for modeling and simulation of nuclear power plants: A review

Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry

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