Da: Majestic Books, Hounslow, Regno Unito
EUR 137,25
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New. pp. 262.
Condizione: New. pp. 262.
Condizione: New.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 155,12
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New. pp. 262.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 167,91
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 164,71
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 183,89
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 185,47
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: moluna, Greven, Germania
EUR 171,35
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: 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.
Da: Revaluation Books, Exeter, Regno Unito
EUR 136,83
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 262 pages. 8.75x5.75x1.00 inches. In Stock. This item is printed on demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 145,16
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. 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.