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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications - Brossura
- EditoreElsevier Science Ltd
- Data di pubblicazione2020
- ISBN 10 0128193654
- ISBN 13 9780128193655
- RilegaturaCopertina flessibile
- Numero edizione1
- Numero di pagine315
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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
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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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Descrizione libro Condizione: New. New! This book is in the same immaculate condition as when it was published 1.16. Codice articolo 353-0128193654-new
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Statistical Process Monitoring Using Advanced Data-driven and Deep Learning Approaches: Theory and Practical Applications
Editore:
Elsevier Science Ltd
(2020)
ISBN 10: 0128193654
ISBN 13: 9780128193655
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Descrizione libro Paperback. Condizione: Brand New. 262 pages. 8.75x5.75x1.00 inches. In Stock. Codice articolo __0128193654
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
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Elsevier Elsevier Science & Technology
(2020)
ISBN 10: 0128193654
ISBN 13: 9780128193655
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Descrizione libro Taschenbuch. Condizione: 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. Codice articolo 9780128193655
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
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Descrizione libro Condizione: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Codice articolo ria9780128193655_lsuk
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications
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Elsevier Science & Technology|Elsevier
(2020)
ISBN 10: 0128193654
ISBN 13: 9780128193655
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Descrizione libro Condizione: 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 . Codice articolo 352964532
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
Editore:
Elsevier Science & Technology, Elsevier
(2020)
ISBN 10: 0128193654
ISBN 13: 9780128193655
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Taschenbuch
Quantità: 2
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Descrizione libro Taschenbuch. Condizione: 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. Codice articolo 9780128193655
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