基于模糊认知地图-贝叶斯网络的能源行业HSEE综合改进模型

A. Azadeh, P. Pourreza, Morteza Saberi, O. Hussain, Elizabeth Chang
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引用次数: 0

摘要

健康、安全、环境和人体工程学(HSEE)是任何组织的重要因素。组织总是必须评估其在这些因素中对所需基准的遵从性,并在需要时采取主动行动来改进它们。在本文中,我们提出了一个模糊认知地图-贝叶斯网络(BN)模型来帮助组织完成这一过程。采用模糊认知图(FCM)方法构建BN的图形模型,以确定输入与它们对量化HSEE的影响之间的关系。采用噪声或法和电磁法确定输入之间的条件概率,量化HSEE值。利用这一点,我们找出了对HSEE量化最具影响力的输入因素,然后可以对其进行管理,以提高组织对HSEE的合规性。利用贝叶斯网络在HSEE建模中的强大功能,并通过FCM对其进行增强,是本研究工作的主要贡献,它开启了这一研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An integrated fuzzy cognitive map-Bayesian network model for improving HSEE in energy sector
Health, Safety, Environment and Ergonomie (HSEE) are important factors in any organization. An organization always have to assess its compliance in these factors to the required benchmarks and take proactive actions to improve them if required. In this paper, we propose a Fuzzy Cognitive Map-Bayesian network (BN) model in order to assist organizations in doing this process. Fuzzy Cognitive Map (FCM) method is used for constructing graphical model of BN to ascertain the relationships between the inputs and the impact which they will have on the quantified HSEE. Noisy-OR method and EM are used to ascertain the conditional probability between the inputs and quantifying the HSEE value. Using this, we find out the most influential input factor on HSEE quantification which can then be managed for improving an organization's compliance to HSEE. Leveraging the power of Bayesian network in modeling HSEE and augmenting it with FCM is the main contribution of this research work which opens this line of research.
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