Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network
Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calcul...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-07-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2021.2.00299 |
| _version_ | 1797266185187753984 |
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| author | Lai Wenzhe Shao Liangshan |
| author_facet | Lai Wenzhe Shao Liangshan |
| author_sort | Lai Wenzhe |
| collection | DOAJ |
| description | Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identification of coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event to cause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calculation analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does not reach the designated position is easy to cause important hazard of gas explosion accident, the probability to cause an 0.567. Based on the constructed NTM deep learning network algorithm, the risk factors and their weights in the early warning identification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen that the highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazard sources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75. |
| first_indexed | 2024-03-07T16:20:17Z |
| format | Article |
| id | doaj.art-f2eba060309f460b8d2697c543656777 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-25T00:56:40Z |
| publishDate | 2023-07-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-f2eba060309f460b8d2697c5436567772024-03-11T10:05:45ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562023-07-018240741810.2478/amns.2021.2.00299Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning networkLai Wenzhe0Shao Liangshan11Business Administration, Liaoning Technical University, Huludao, Liaoning125105, China2System Engineering Institute, Liaoning Technical University, Huludao, Liaoning125105, ChinaAmong all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identification of coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event to cause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calculation analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does not reach the designated position is easy to cause important hazard of gas explosion accident, the probability to cause an 0.567. Based on the constructed NTM deep learning network algorithm, the risk factors and their weights in the early warning identification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen that the highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazard sources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75.https://doi.org/10.2478/amns.2021.2.00299ntm deep learning networkcoal mine accidentgas explosionhazard warning |
| spellingShingle | Lai Wenzhe Shao Liangshan Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network Applied Mathematics and Nonlinear Sciences ntm deep learning network coal mine accident gas explosion hazard warning |
| title | Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network |
| title_full | Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network |
| title_fullStr | Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network |
| title_full_unstemmed | Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network |
| title_short | Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network |
| title_sort | projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on ntm deep learning network |
| topic | ntm deep learning network coal mine accident gas explosion hazard warning |
| url | https://doi.org/10.2478/amns.2021.2.00299 |
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