An efficient Meta-VSW method for ship behaviors recognition and application
Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consid...
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| Veröffentlicht in: | Ocean engineering Jg. 311; S. 118870 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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01.11.2024
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| ISSN: | 0029-8018 |
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| Abstract | Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption.
•An efficient and universal Meta-trajectory Variable Sliding Window (Meta-VSW) method for ship behavior recognition is proposed.•The computational speed and accuracy of the Meta-VSW method are analyzed in comparison with commonly used unsupervised algorithms.•The universality and effectiveness of the Meta-VSW method have been validated through the massive unknown data from two different ships.•The results of ship behavior recognition were applied to the analysis of operational efficiency and fuel consumption. |
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| AbstractList | Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption.
•An efficient and universal Meta-trajectory Variable Sliding Window (Meta-VSW) method for ship behavior recognition is proposed.•The computational speed and accuracy of the Meta-VSW method are analyzed in comparison with commonly used unsupervised algorithms.•The universality and effectiveness of the Meta-VSW method have been validated through the massive unknown data from two different ships.•The results of ship behavior recognition were applied to the analysis of operational efficiency and fuel consumption. |
| ArticleNumber | 118870 |
| Author | Cai, Zhiyuan Yu, Long Li, Lecheng Fan, Qidong Li, Congbo |
| Author_xml | – sequence: 1 givenname: Zhiyuan orcidid: 0000-0002-7096-6658 surname: Cai fullname: Cai, Zhiyuan organization: State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China – sequence: 2 givenname: Qidong orcidid: 0000-0002-2814-5784 surname: Fan fullname: Fan, Qidong organization: State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China – sequence: 3 givenname: Lecheng surname: Li fullname: Li, Lecheng organization: State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China – sequence: 4 givenname: Long orcidid: 0000-0002-9177-7741 surname: Yu fullname: Yu, Long email: yulone@sjtu.edu.cn organization: State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China – sequence: 5 givenname: Congbo surname: Li fullname: Li, Congbo organization: Marine Design and Research Institute of China, Shanghai, 200011, China |
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| Cites_doi | 10.1016/j.oceaneng.2018.02.060 10.3390/jmse10020139 10.1145/3362161 10.1126/science.1136800 10.1016/j.engappai.2018.01.004 10.1109/TIP.2015.2438540 10.1016/j.scitotenv.2018.09.045 10.3390/jmse11040763 10.1016/j.patcog.2017.12.024 10.3390/s19122706 10.1145/2499621 10.3390/rs15061477 10.1109/ACCESS.2019.2920969 10.1016/j.oceaneng.2023.116434 10.1016/j.jvcir.2015.09.013 10.1016/j.oceaneng.2020.106919 10.1371/journal.pone.0219910 10.1007/s11042-023-15384-z 10.1016/j.eswa.2012.05.060 10.3390/s17081792 10.1016/j.ress.2021.107674 |
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| Keywords | Fuel consumption Operational efficiency Meta-trajectory Unsupervised algorithm Massive unknown data Ship behavior recognition |
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| SubjectTerms | Fuel consumption Massive unknown data Meta-trajectory Operational efficiency Ship behavior recognition Unsupervised algorithm |
| Title | An efficient Meta-VSW method for ship behaviors recognition and application |
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