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Amanda Hering (11 results)
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US$ 154.24
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Ships from United Kingdom to U.S.A.Quantity: 3 available
Add to basketCondition: New. pp. 262.
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 186.80
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 192.70
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Condition: As New. Unread book in perfect condition.
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 212.85
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US$ 203.08
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Add to basketCondition: 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.
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Statistical Process Monitoring Using Advanced Data-driven and Deep Learning Approaches: Theory and Practical Applications
Language: English
Published by Elsevier Science Ltd, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
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Add to basketPaperback. Condition: Brand New. 262 pages. 8.75x5.75x1.00 inches. In Stock. This item is printed on demand.
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Taschenbuch. Condition: 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.
