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Statistical Process Monitoring Using by Harrou Fouzi (13 results)
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US$ 75.82
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Condition: New. pp. 262.
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US$ 140.61
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US$ 151.80
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
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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
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Statistical Process Monitoring Using Advanced Data-driven and Deep Learning Approaches: Theory and Practical Applications
Published by Elsevier Science Ltd, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Language: English
US$ 159.89
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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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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Published by Elsevier Science & Technology, Elsevier, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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US$ 154.57
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Add to basketTaschenbuch. Condition: 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.
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
Published by Elsevier Science & Technology, Elsevier, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Language: English
US$ 169.79
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Add to basketTaschenbuch. 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.
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
Published by Elsevier Science & Technology|Elsevier, 2020
ISBN 10: 0128193654 ISBN 13: 9780128193655
Language: English
US$ 151.02
Convert currencyUS$ 55.69 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .
