Abstract
Port State Control (PSC) inspection is the most important mechanism to ensure world marine safe. This paper presents a risk assessment system, which estimates the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. The target factors adopted in Paris MOU PSC inspection and Tokyo MOU PSC inspection are considered in this system as well as the new factors discovered in the PSC inspection database. A risk assessment system based on Support Vector Machine (SVM) is developed to classify candidate ships to high risk or low risk, respectively, based on the target factors. Experiment results show that the proposed system enhances the risk assessment accuracy effectively.
Original language | English |
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Title of host publication | Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 |
Pages | 818-823 |
Number of pages | 6 |
Volume | 2 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Event | 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, Hong Kong Duration: 19 Aug 2007 → 22 Aug 2007 |
Conference
Conference | 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 19/08/07 → 22/08/07 |
Keywords
- Inspection
- Port state control
- Risk assessment
- Target factors
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software
- Theoretical Computer Science