A risk assessment system for improving port state control inspection

Rui Feng Xu; Qin Lu; Wen Jie Li; K. X. Li; Hai Sha Zheng

doi:10.1109/ICMLC.2007.4370255

A risk assessment system for improving port state control inspection

Rui Feng Xu^*
, Qin Lu
, Wen Jie Li
, K. X. Li
, Hai Sha Zheng

^*Corresponding author for this work

Hong Kong Polytechnic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

37 Scopus citations

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
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	818-823
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2007.4370255
State	Published - 2007
Externally published	Yes
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	2

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Inspection
Port state control
Risk assessment
Target factors

Access to Document

10.1109/ICMLC.2007.4370255

Cite this

Xu, R. F., Lu, Q., Li, W. J., Li, K. X., & Zheng, H. S. (2007). A risk assessment system for improving port state control inspection. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 818-823). Article 4370255 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 2). https://doi.org/10.1109/ICMLC.2007.4370255

@inproceedings{6a7c9b8de3834311a8e1e616d22ce9f9,

title = "A risk assessment system for improving port state control inspection",

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.",

keywords = "Inspection, Port state control, Risk assessment, Target factors",

author = "Xu, \{Rui Feng\} and Qin Lu and Li, \{Wen Jie\} and Li, \{K. X.\} and Zheng, \{Hai Sha\}",

year = "2007",

doi = "10.1109/ICMLC.2007.4370255",

language = "英语",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "818--823",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

Xu, RF, Lu, Q, Li, WJ, Li, KX & Zheng, HS 2007, A risk assessment system for improving port state control inspection. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370255, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 2, pp. 818-823, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370255

A risk assessment system for improving port state control inspection. / Xu, Rui Feng; Lu, Qin; Li, Wen Jie et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 818-823 4370255 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A risk assessment system for improving port state control inspection

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AU - Lu, Qin

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AU - Li, K. X.

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PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Inspection

KW - Port state control

KW - Risk assessment

KW - Target factors

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M3 - 会议稿件

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SN - 142440973X

SN - 9781424409730

T3 - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

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BT - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

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Y2 - 19 August 2007 through 22 August 2007

ER -

A risk assessment system for improving port state control inspection

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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