A risk assessment system for improving port state control inspection

Rui Feng Xu, Qin Lu, Wenjie Li, K. X. Li, Hai Sha Zheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages818-823
Number of pages6
Volume2
DOIs
Publication statusPublished - 1 Dec 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, Hong Kong
Duration: 19 Aug 200722 Aug 2007

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
CountryHong Kong
CityHong Kong
Period19/08/0722/08/07

Keywords

  • Inspection
  • Port state control
  • Risk assessment
  • Target factors

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Theoretical Computer Science

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