Providing support to operators for monitoring safety functions using reinforcement learning
Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Park, JaeKwan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts - Meyers, Carina Mello Guimaraes ELSEVIER, 2022, the international review journal covering all aspects of nuclear energy, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:118 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.pnucene.2019.103123 |
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ELV048770574 |
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| 520 | |a Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. | ||
| 520 | |a Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. | ||
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10.1016/j.pnucene.2019.103123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000838.pica (DE-627)ELV048770574 (ELSEVIER)S0149-1970(19)30232-X DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Providing support to operators for monitoring safety functions using reinforcement learning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. Deep neural network Elsevier Operator support Elsevier Reinforcement learning Elsevier Diagnosis Elsevier Safety function status check Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:118 year:2020 pages:0 https://doi.org/10.1016/j.pnucene.2019.103123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 118 2020 0 |
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10.1016/j.pnucene.2019.103123 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000838.pica (DE-627)ELV048770574 (ELSEVIER)S0149-1970(19)30232-X DE-627 ger DE-627 rakwb eng 610 VZ 44.83 bkl Park, JaeKwan verfasserin aut Providing support to operators for monitoring safety functions using reinforcement learning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. Deep neural network Elsevier Operator support Elsevier Reinforcement learning Elsevier Diagnosis Elsevier Safety function status check Elsevier Kim, TaekKyu oth Seong, SeungHwan oth Enthalten in Elsevier Science Meyers, Carina Mello Guimaraes ELSEVIER Histone deacetylase 5 is a phosphorylation substrate of protein kinase D in osteoclasts 2022 the international review journal covering all aspects of nuclear energy Amsterdam [u.a.] (DE-627)ELV007755775 volume:118 year:2020 pages:0 https://doi.org/10.1016/j.pnucene.2019.103123 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.83 Rheumatologie Orthopädie VZ AR 118 2020 0 |
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providing support to operators for monitoring safety functions using reinforcement learning |
| title_auth |
Providing support to operators for monitoring safety functions using reinforcement learning |
| abstract |
Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. |
| abstractGer |
Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. |
| abstract_unstemmed |
Continuous monitoring and diagnosis are important for safe operation of nuclear facilities. In an emergency shutdown, the diagnostic tasks can be challenging for human operators who may be under intense stress and/or lack training. In recent years, studies using artificial-intelligence technologies have been actively conducted to help in diagnostic tasks. The current study proposes a data-driven approach that leverages deep reinforcement-learning techniques to intelligently learn effective strategies for state diagnosis of safety functions. First, a learning framework and key elements of reinforcement learning are designed as basic components. Then, a deep neural-network structure and a deep reinforcement algorithm are presented for diagnosis learning. The experimental results demonstrate the feasibility of deep reinforcement learning on diagnosing the safety functions of a nuclear facility. |
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| title_short |
Providing support to operators for monitoring safety functions using reinforcement learning |
| url |
https://doi.org/10.1016/j.pnucene.2019.103123 |
| remote_bool |
true |
| author2 |
Kim, TaekKyu Seong, SeungHwan |
| author2Str |
Kim, TaekKyu Seong, SeungHwan |
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| doi_str |
10.1016/j.pnucene.2019.103123 |
| up_date |
2024-07-06T19:45:03.151Z |
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