A Bayesian predictive assistance system for resource optimization — A case study in industrial cleaning process

G. Shrestha, O. Niggemann
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引用次数: 2

Abstract

Optimizing the resource consumption by the products (machines) and making them environment friendly is the aim of almost all producers today. May it be due to cost of resources, their limited availability, their affect on the environment or consumer awareness. Ample research is being carried out at national and international level for resource optimization. Adding intelligence and learning capability is being increasingly used as an approach for resource optimization. Different methods and models for machine learning are available in the literature. Bayesian network is one of the widely used learning model for resource optimization in wide range of applications [1], [2]. In this paper, we present the use of Bayesian network for resource optimization and decision support system in an industrial cleaning process. The proposed Bayesian predictive assistance system assists the cleaner in choosing the optimal parameters and would be a self-learning system that stores the successful cleaning results in a global database for future cleaning cycle.
资源优化的贝叶斯预测辅助系统——以工业清洗过程为例
优化产品(机器)的资源消耗并使其对环境友好是当今几乎所有生产商的目标。这可能是由于资源的成本,他们的有限可用性,他们对环境或消费者意识的影响。目前正在国家和国际一级进行充分的资源优化研究。增加智能和学习能力正越来越多地被用作资源优化的方法。文献中有不同的机器学习方法和模型。贝叶斯网络是应用广泛的资源优化学习模型之一[1],[2]。本文介绍了贝叶斯网络在工业清洗过程资源优化和决策支持系统中的应用。所提出的贝叶斯预测辅助系统可以帮助清洁人员选择最优参数,并将成功的清洁结果存储在全局数据库中,以供未来的清洁周期使用。
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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