Prof. Jing Lv Profile

Prof. Jing Lv

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Proceedings Article | 20 February 2024 Paper

Bayesian network model construction of ship accidents under small sample conditions

Jing Lv, Lu Wang, Hanwen Fan, Haoqin Sun

Proceedings Volume 13064, 130643R (2024) https://doi.org/10.1117/12.3016043

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Ship accident risk research is often affected by insufficient data and cannot be analyzed accurately. In this paper, to make up for the defect of not being able to accurately identify the interrelationships among the influencing factors in the case of small samples, an information diffusion- based method is proposed to diffuse the information that can be collected at present, so as to obtain a sufficient sample size to realize the accurate mining of the influencing factors. Secondly, considering the defect that traditional Bayesian network assumes that the factors are indep intent of each other, a tree Bayesian network is introduced to further identify the interrelationships among the influencing factors. The dataset adopts the grounding, reefing, explosion, collision, and touchdown accidents of China Maritime Safety Administration in 2014- 2022, and 21 risk fact ors are identified from four perspectives, namely, ship, environment, management, and human factors, and the results of the study indicate that the failure of the shipping company to implement the safety training program, the insufficient professional knowledge of crew members, and the insufficient lookout of crew members are the main factors leading to the ship accidents. Finally, the validity of the data is verified by the mutual information value and belief variance among the indicators, and the five important indicators with the highest correlation are analyzed separately. In order to verify the effectiveness of the model, this paper randomly selects 30 test samples to bring in by means of scenario simulation, and the accuracy of the test results are all above 65%, which indicates that the model has a high degree of confidence, and it can provide reference suggestions for the shipping enterprises and the related maritime safety management departments.

Proceedings Article | 20 February 2024 Paper

Resilience evaluation of container shipping network based on cascade failure

Haoqin Sun, Qinran Zhang, Lu Wang

Proceedings Volume 13064, 130643Y (2024) https://doi.org/10.1117/12.3015809

KEYWORDS: Reflection, Transportation, Failure analysis, Network security, Engineering, Oceanography, Visualization, Terrorism

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In order to evaluate the dynamic resilience of container shipping network, this paper constructs a resilience evaluation system considering both operational capacity and recovery capacity. In order to consider the dynamic propagation of network failure node traffic, designs two node load distribution methods, namely port skipping over and port replacement, and four different load distribution ratios when port skipping over are selected. Based on this, constructs a cascade failure model of container shipping network. Taking RCEP regional container shipping network as an example, this paper explores the changes of network resilience under single point attack, deliberate attack and random attack, and the influence of different parameters on network resilience. The results show that once Singapore Port fails, it will have the greatest impact on network resilience. Comprehensive distribution strategy and local load distribution strategy can reduce the risk of cascading failure propagation after the failure of high node strength ports and small ports respectively, which is conducive to maintaining network resilience. In addition, appropriately increasing the capacity of the port and choosing port skipping over can also effectively slow down the decline rate of network resilience.

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