SNS-MEBN Based Method for Situational Awareness of Ship Navigation

2018 3rd International Conference on Information Systems Engineering (ICISE) Pub Date : 2018-05-04 DOI:10.1109/ICISE.2018.00021

Wanqi Wei, Shu Gao, X. Chu, Abdoulaye Sidibé

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Abstract

Situational Awareness of Ship Navigation plays an important role in the safety and security of waterway transport. However, the current ship navigation safety assessment method cannot effectively express the uncertain knowledge. In this paper we propose a method for Ship Navigation Safety based on Multi-Entity Bayesian Networks (SNS-MEBN) in order to evaluate the ship navigation safety. This could reduce the factors of ship navigation safety based on Rough Set Condition Information Entropy (RSCIE). SNS-MEBN is constructed by combining Multi-Entity Bayesian Networks (MEBN) with the reduction result, thus the expression of uncertain knowledge in the process of ship navigation safety assessment is realized. Inference of ship navigation safety is made by combining SNS-MEBN and Bayesian Network Joint Tree Reasoning (BNJTR) algorithms. Finally, to validate the proposed method, experiments are conducted over the navigation data of ships navigating from Hejiangmen to Wangyemiao, collected by the Changjiang Waterway Bureau were validated. The experimental results showed that proposed method has the better accuracy.

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基于SNS-MEBN的船舶航行态势感知方法

船舶航行态势感知对水路运输的安全保障起着重要作用。然而，现有的船舶航行安全评估方法不能有效地表达不确定性知识。为了对船舶航行安全进行评估，提出了一种基于多实体贝叶斯网络(SNS-MEBN)的船舶航行安全方法。利用粗糙集条件信息熵(RSCIE)来降低船舶航行安全的影响因素。将多实体贝叶斯网络(Multi-Entity Bayesian Networks, MEBN)与约简结果相结合，构建了多实体贝叶斯网络-MEBN，实现了船舶航行安全评估过程中不确定性知识的表达。结合SNS-MEBN算法和贝叶斯网络联合树推理(BNJTR)算法对船舶航行安全进行推理。最后，利用长江水利局收集的河江门至王冶庙的船舶航行数据，对所提出的方法进行了验证。实验结果表明，该方法具有较好的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 3rd International Conference on Information Systems Engineering (ICISE)

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