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A practical approach to improve alarm system performance: Application to power plant

Chinese Journal of Chemical Engineering 2019年第5期27卷 1094-1102页

作者： Jay Sompura Amit Joshi Babji srinivasan rajagopalan srinivasan Department of Electrical Engineering Indian Institute of Technology GandhinagarIndia Department of Chemical Engineering Indian Institute ofTechnology GandhinagarIndia Department of Chemical Engineering Indian Institute ofTechnology MadrasIndia

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.

关键词： Chattering alarm Correspondence analysis Density based clustering Duplicate alarms Combined-cycle power plant Association rule mining

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Template-Based Gait Authentication Through Bayesian Thresholding

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 209-219页

作者： Ebenezer R.H.P.Isaac Susan Elias srinivasan rajagopalan K.S.Easwarakumar IEEE the Department of Computer Science and Engineering Indian Institute of Technology Madras the School of Electronics Engineering VIT University the Department of Physiology and Biomedical Engineering Mayo Clinic the Department of Computer Science and Engineering Anna University

While gait recognition is the mapping of a gait sequence to an identity known to the system, gait authentication refers to the problem of identifying whether a given gait sequence belongs to the claimed identity. A typical gait authentication system starts with a feature representation such as a gait template, then proceeds to extract its features, and a transformation is ultimately applied to obtain a discriminant feature set. Almost every authentication approach in literature favours the use of Euclidean distance as a threshold to mark the boundary between a legitimate subject and an impostor. This article proposes a method that uses the posterior probability of a Bayes' classifier in place of the Euclidean distance. The proposed framework is applied to template-based gait feature representations and is evaluated using the standard CASIA-B gait database. Our study experimentally demonstrates that the Bayesian posterior probability performs significantly better than the de facto Euclidean distance approach and the cosine distance which is established in research to be the current state of the art.

关键词： Bayes’ rule biometrics gait recognition linear discriminant analysis verification

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