MARC details
| 000 -LEADER |
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| fixed length control field |
02088nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230208113727.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230208b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780128193655 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
629.895 |
| Item number |
HAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Harrou, Fouzi |
| 245 ## - TITLE STATEMENT |
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| Title |
Statistical process monitoring using advanced data-driven and deep learning approaches: |
| Remainder of title |
theory and practical applications |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Elsevier |
| Place of publication, distribution, etc. |
Cambridge |
| Date of publication, distribution, etc. |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 315 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
USD |
| Price amount |
150.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Table of content<br/><br/>1. Introduction<br/><br/>2. Linear Latent Variable Regression (LVR)-Based Process Monitoring<br/><br/>3. Fault Isolation<br/><br/>4. Nonlinear latent variable regression methods<br/><br/>5. Multiscale latent variable regression-based process monitoring methods<br/><br/>6. Unsupervised deep learning-based process monitoring methods<br/><br/>7. Unsupervised recurrent deep learning schemes for process monitoring<br/><br/>8. Case studies<br/><br/>9. Conclusions and future perspectives<br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Multivariate analysis--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Process control--Statistical methods |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |