Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation
ABSTRACTAs the human’s role in the operation of maritime autonomous surface ships (MASSs) is concentrated on less manpower, several issues have been raised regarding the capacity of single manpower. This indicates the necessity of developing monitoring technology for abnormal navigational situations...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-10-01
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| Series: | Journal of International Maritime Safety, Environmental Affairs, and Shipping |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/25725084.2022.2154116 |
| _version_ | 1811196824793382912 |
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| author | Widiastuti Tyasayumranani Taewoong Hwang Taemin Hwang Ik-Hyun Youn |
| author_facet | Widiastuti Tyasayumranani Taewoong Hwang Taemin Hwang Ik-Hyun Youn |
| author_sort | Widiastuti Tyasayumranani |
| collection | DOAJ |
| description | ABSTRACTAs the human’s role in the operation of maritime autonomous surface ships (MASSs) is concentrated on less manpower, several issues have been raised regarding the capacity of single manpower. This indicates the necessity of developing monitoring technology for abnormal navigational situations to prevent maritime accidents. Since boating under the influence (BUI) of alcohol is one of the major causes of maritime accidents in Korea, this study focused on BUI of alcohol as abnormal navigation to be monitored. The model suggests a methodology for detecting BUI ships based on their trajectory and behavior. The trajectory and behavior-related features are extracted using AIS and geographic information system datasets and clustered to the anomaly and normal navigation patterns. The proposed model can aid the decision-making of humans monitoring the MASS in detecting abnormal ships in the vicinity of MASSs. |
| first_indexed | 2024-04-12T01:05:52Z |
| format | Article |
| id | doaj.art-bbcf775f568c4726a37400395a9aed41 |
| institution | Directory Open Access Journal |
| issn | 2572-5084 |
| language | English |
| last_indexed | 2024-04-12T01:05:52Z |
| publishDate | 2022-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of International Maritime Safety, Environmental Affairs, and Shipping |
| spelling | doaj.art-bbcf775f568c4726a37400395a9aed412022-12-22T03:54:16ZengTaylor & Francis GroupJournal of International Maritime Safety, Environmental Affairs, and Shipping2572-50842022-10-016422423510.1080/25725084.2022.2154116Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operationWidiastuti Tyasayumranani0Taewoong Hwang1Taemin Hwang2Ik-Hyun Youn3Department of Maritime Transportation Systems, Graduate School of Mokpo National Maritime University, Mokpo, Republic of KoreaDepartment of Maritime Transportation Systems, Graduate School of Mokpo National Maritime University, Mokpo, Republic of KoreaDepartment of Maritime Transportation Systems, Graduate School of Mokpo National Maritime University, Mokpo, Republic of KoreaDepartment of Navigation & Information Systems, Mokpo National Maritime University, Mokpo, Republic of KoreaABSTRACTAs the human’s role in the operation of maritime autonomous surface ships (MASSs) is concentrated on less manpower, several issues have been raised regarding the capacity of single manpower. This indicates the necessity of developing monitoring technology for abnormal navigational situations to prevent maritime accidents. Since boating under the influence (BUI) of alcohol is one of the major causes of maritime accidents in Korea, this study focused on BUI of alcohol as abnormal navigation to be monitored. The model suggests a methodology for detecting BUI ships based on their trajectory and behavior. The trajectory and behavior-related features are extracted using AIS and geographic information system datasets and clustered to the anomaly and normal navigation patterns. The proposed model can aid the decision-making of humans monitoring the MASS in detecting abnormal ships in the vicinity of MASSs.https://www.tandfonline.com/doi/10.1080/25725084.2022.2154116Anomaly detectionAIS datatrajectory miningfeature extractionnavigational pattern |
| spellingShingle | Widiastuti Tyasayumranani Taewoong Hwang Taemin Hwang Ik-Hyun Youn Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation Journal of International Maritime Safety, Environmental Affairs, and Shipping Anomaly detection AIS data trajectory mining feature extraction navigational pattern |
| title | Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation |
| title_full | Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation |
| title_fullStr | Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation |
| title_full_unstemmed | Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation |
| title_short | Anomaly detection model of small-scaled ship for maritime autonomous surface ships’ operation |
| title_sort | anomaly detection model of small scaled ship for maritime autonomous surface ships operation |
| topic | Anomaly detection AIS data trajectory mining feature extraction navigational pattern |
| url | https://www.tandfonline.com/doi/10.1080/25725084.2022.2154116 |
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