文章基本信息

标题：A Predictive, Context-Dependent Stochastic Model for Engineering Applications
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作者：Márcio J. da Silva ; Gustavo Künzel ; Carlos E. Pereira 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：2
页码：402-407
DOI：10.1016/j.ifacol.2022.04.227
语种：English
出版社：Elsevier
摘要：AbstractThis work explores the architecture of a context-dependent probabilistic model. We identify opportunities for providing reminders to operators in their environment as a means to address information overload. Hence, there is a need to represent a state of knowledge and help them stay vigilant during their jobs. Along with the architectural improvements, which further specialize information flows and develop a data-driven approach, continual learning techniques covered events in a probabilistic graphical model called Context-Dependent Recommendation Systems (CD-RS). We demonstrated, as a result, the use of statistical thinking and Design of Experiments (DoE), which are most clear in conducting a suitable experiment. Moreover, the validation of the model and experiments of the novel architecture based on the collected data from a real case study demonstrates the value of the proposed methods.
关键词：KeywordsData MiningPredictive SituationContext TestingIndustrial Alarm SystemRecommendation Systems