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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorHering, Amanda S.
dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorDairi, Abdelkader
dc.date.accessioned2021-03-01T06:41:41Z
dc.date.available2021-03-01T06:41:41Z
dc.date.issued2021
dc.identifier.citationHarrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Case studies. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 255–303. doi:10.1016/b978-0-12-819365-5.00014-0
dc.identifier.isbn9780128193655
dc.identifier.doi10.1016/b978-0-12-819365-5.00014-0
dc.identifier.urihttp://hdl.handle.net/10754/667740
dc.description.abstractAddressing anomaly detection and attribution is essential to promptly detect abnormalities, and it aids the decision-making of operators, allowing them to better optimize performance, take corrective actions, and maintain downstream processes. Recently, deep learning models have developed rapidly, especially in terms of their learning capabilities. In this chapter, we propose a novel hybrid deep-learning-based anomaly detection method. In particular, we focus on the benefits of deep learning models due to their greedy learning features and the sensitivity of clustering approaches to reveal anomalies in the monitoring process. In this chapter, we discuss and present applications of some deep-learning-based monitoring methods. We apply the developed approaches to monitor many processes, such as detection of obstacles in driving environments for autonomous robots and vehicles, monitoring of wastewater treatment plants, and detection of ozone pollution.
dc.publisherElsevier BV
dc.relation.ispartofDOI:10.1016/c2018-0-05141-5
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/B9780128193655000140
dc.rightsArchived with thanks to Elsevier
dc.titleCase studies
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionBaylor University, Dept of Statistical Science, Waco, TX, United States.
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
dc.contributor.institutionUniversity of Science and Technology of Oran-Mohamed Boudiaf, Computer Science Department, Signal, Image and Speech Laboratory, Oran, Algeria.
dc.identifier.pages255-303
kaust.personHarrou, Fouzi
kaust.personSun, Ying
display.relations<b>Is Part Of:</b><br/> <ul><li><i>[Book]</i> <br/> Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches. (2021). doi:10.1016/c2018-0-05141-5. DOI: <a href="https://doi.org/10.1016/c2018-0-05141-5" >10.1016/c2018-0-05141-5</a> Handle: <a href="http://hdl.handle.net/10754/667757" >10754/667757</a></a></li></ul>


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