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010 a| 2020938028;

015 a| GBC0780462| bnb;

016 7 a| 0198245722| Uk;

020 a| 9780128193655q| paperback;

020 a| 0128193654q| paperback;

020 z| 9780128193662q| electronic publication;

035 a| (WaSeSS)ssj0002546951;

042 a| lccopycat;

049 a| EREEa| NEHH;

050 00 a| TS156.8b| .H3465 2021;

082 04 a| 629.8952| 23;

100 1 a| Harrou, Fouzi.?| UNAUTHORIZED;

245 10 a| Statistical process monitoring using advanced data-driven and deep learning approachesh| [electronic resource] :b| theory and practical applications /c| Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi.;

260 a| Amsterdam, Netherland ;a| Cambridge,MA :b| Elsevier,c| [2021];

300 a| xii, 315 pages ;c| 23 cm;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| 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. --c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Process controlx| Statistical methods.=| ^A157521;

650 0 a| Multivariate analysisx| Data processing.=| ^A1133641;

650 0 a| Machine learning.=| ^A145211;

650 7 a| Machine learning.2| fast0| (OCoLC)fst01004795;

650 7 a| Multivariate analysisx| Data processing.2| fast0| (OCoLC)fst01029107;

650 7 a| Process controlx| Statistical methods.2| fast0| (OCoLC)fst01078040;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Sun, Ying,d| 1989-?| UNAUTHORIZED;

700 1 a| Hering, Amanda S.?| UNAUTHORIZED;

700 1 a| Madakyaru, Muddu.?| UNAUTHORIZED;

700 1 a| Dairi, Abdelkader.?| UNAUTHORIZED;

776 08 i| ebook version :z| 9780128193662;

856 40 z| Full text available from eBook - Chemical Engineering 2020 [EBCCE20]u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780128193655;

947 a| (OCoLC)on1175050752;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 5793904;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5793904-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 2/14/2022x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5793904-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 2/14/2022x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5793904-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 2/14/2022x| EBOOKz| JERESOURCE;

Statistical process monitoring using advanced data-driven and deep learning approaches theory and practical applications / Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi.

Author/creator	Harrou, Fouzi
Format	Electronic
Publication Info	Amsterdam, Netherland ; Cambridge,MA : Elsevier, [2021]
Description	xii, 315 pages ; 23 cm
Supplemental Content	Full text available from eBook - Chemical Engineering 2020 [EBCCE20]
Subjects	Process control--Statistical methods. Multivariate analysis--Data processing. Machine learning.

Other author/creator	Sun, Ying, 1989-
Other author/creator	Hering, Amanda S.
Other author/creator	Madakyaru, Muddu.
Other author/creator	Dairi, Abdelkader.
Abstract	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. -- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Issued in other form	ebook version : 9780128193662
Genre/form	Electronic books.
LCCN	2020938028
ISBN	9780128193655 paperback
ISBN	0128193654 paperback
ISBN	electronic publication

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Statistical process monitoring using advanced data-driven and deep learning approaches theory and practical applications / Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi.

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