One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
The judgment of tunnel personnel’s safety status mainly requires the collection of construction personnel’s physical signs and cave environment data, and the early warning of abnormal status usually requires professional staff to make rapid judgments in a short time, which is costly and inefficient...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/3/1734 |
| _version_ | 1797625100444368896 |
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| author | Guosheng Huang Jinchuan Chen Lei Liu |
| author_facet | Guosheng Huang Jinchuan Chen Lei Liu |
| author_sort | Guosheng Huang |
| collection | DOAJ |
| description | The judgment of tunnel personnel’s safety status mainly requires the collection of construction personnel’s physical signs and cave environment data, and the early warning of abnormal status usually requires professional staff to make rapid judgments in a short time, which is costly and inefficient in terms of operation and maintenance. A single-classification support vector machine-based personnel safety status detection and early warning model is proposed to address this phenomenon. First, by deploying sensor devices at the site, we obtain data on the safety state of an actual tunnel construction scene and construct an OCSVM model for abnormal state prediction. Then the model is retained for early warning state testing, collecting relevant environmental data as well as construction personnel’s physical signs data from engineering examples. Finally, we conduct horizontal different parameter model experiments and vertical different early warning state proportional data experiments to evaluate the performance of the model for personnel information security state judgment. The experimental results show that the accuracy rate of personnel security status early warning reaches more than 90%. In particular, it provides a more efficient detection means for the judgment of personnel security status. |
| first_indexed | 2024-03-11T09:52:03Z |
| format | Article |
| id | doaj.art-69b5d1a8b8744fcbb10ed8bdbfab1380 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:03Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-69b5d1a8b8744fcbb10ed8bdbfab13802023-11-16T16:09:39ZengMDPI AGApplied Sciences2076-34172023-01-01133173410.3390/app13031734One-Class SVM Model-Based Tunnel Personnel Safety Detection TechnologyGuosheng Huang0Jinchuan Chen1Lei Liu2College of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaCollege of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaCollege of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaThe judgment of tunnel personnel’s safety status mainly requires the collection of construction personnel’s physical signs and cave environment data, and the early warning of abnormal status usually requires professional staff to make rapid judgments in a short time, which is costly and inefficient in terms of operation and maintenance. A single-classification support vector machine-based personnel safety status detection and early warning model is proposed to address this phenomenon. First, by deploying sensor devices at the site, we obtain data on the safety state of an actual tunnel construction scene and construct an OCSVM model for abnormal state prediction. Then the model is retained for early warning state testing, collecting relevant environmental data as well as construction personnel’s physical signs data from engineering examples. Finally, we conduct horizontal different parameter model experiments and vertical different early warning state proportional data experiments to evaluate the performance of the model for personnel information security state judgment. The experimental results show that the accuracy rate of personnel security status early warning reaches more than 90%. In particular, it provides a more efficient detection means for the judgment of personnel security status.https://www.mdpi.com/2076-3417/13/3/1734single-classification support vector machinepersonnel safety status detectiontunnel constructionOCSVM model |
| spellingShingle | Guosheng Huang Jinchuan Chen Lei Liu One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology Applied Sciences single-classification support vector machine personnel safety status detection tunnel construction OCSVM model |
| title | One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology |
| title_full | One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology |
| title_fullStr | One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology |
| title_full_unstemmed | One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology |
| title_short | One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology |
| title_sort | one class svm model based tunnel personnel safety detection technology |
| topic | single-classification support vector machine personnel safety status detection tunnel construction OCSVM model |
| url | https://www.mdpi.com/2076-3417/13/3/1734 |
| work_keys_str_mv | AT guoshenghuang oneclasssvmmodelbasedtunnelpersonnelsafetydetectiontechnology AT jinchuanchen oneclasssvmmodelbasedtunnelpersonnelsafetydetectiontechnology AT leiliu oneclasssvmmodelbasedtunnelpersonnelsafetydetectiontechnology |