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Enabling a Powerful Marine and Offshore Decision-Support Solution Through Bayesian Network Technique

机译:通过贝叶斯网络技术实现功能强大的海洋和近海决策支持解决方案

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摘要

A powerful practical solution is by far the most desired output when making decisions under the realm of uncertainty on any safety-critical marine or offshore units and their systems. With data and information typically being obtained incrementally, adopting Bayesian network (BN) is shown to realistically deal with the random uncertainties while at the same time making risk assessments easier to build and to check. A well-matched methodology is proposed to formalize the reasoning in which the focal mechanism of inference processing relies on the sound Bayes's rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). BN and ID feasibility is shown in a marine evacuation scenario and that of authorized vessels to floating, production, storage, and offloading collision, developed via a commercial computer tool. Sensitivity analysis and validation of the produced results are also presented.
机译:在不确定性范围内,对任何对安全至关重要的船舶或海上装置及其系统进行决策时,最有力的实用解决方案是最理想的输出。在通常以增量方式获取数据和信息的情况下,显示出采用贝叶斯网络(BN)可以切实处理随机不确定性,同时使风险评估更易于构建和检查。提出了一种匹配良好的方法,以对推理进行形式化,其中推理处理的焦点机制依赖于允许逻辑的声音贝叶斯规则/定理。用决策节点和效用节点扩展一个或多个有影响力的节点参数也会产生一个影响图(ID)。 BN和ID的可行性显示在海上疏散场景中,以及通过商用计算机工具开发的经授权的船只在漂浮,生产,储存和卸载碰撞中的可行性。还介绍了灵敏度分析和验证所产生的结果。

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