ISBN: 9780128193655 - statistical process monitoring using advanced data-driven and deep learning approaches: theory and practical applications (8 Ergebnisse)

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Taschenbuch. Zustand: Neu. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches | Theory and Practical Applications | Fouzi Harrou (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Elsevier | EAN 9780128193655 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, productsafety[at]elsevier[dot]com | Anbieter: preigu.…

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Zustand: New. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven bas.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Englisch.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.…