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06066nam a22006375i 4500 |
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978-3-319-94051-9 |
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DE-He213 |
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20210620010215.0 |
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180820s2018 gw | s |||| 0|eng d |
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|a 9783319940519
|9 978-3-319-94051-9
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|a 10.1007/978-3-319-94051-9
|2 doi
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|a QA76.9.D343
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|a 006.312
|2 23
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|a Zhao, Jun.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Data-Driven Prediction for Industrial Processes and Their Applications
|h [electronic resource] /
|c by Jun Zhao, Wei Wang, Chunyang Sheng.
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| 250 |
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|a 1st ed. 2018.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2018.
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| 300 |
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|a XVI, 443 p. 167 illus., 128 illus. in color.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Information Fusion and Data Science,
|x 2510-1528
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| 505 |
0 |
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|a Preface -- Introduction -- Why the prediction is required for industrial process -- Introduction to industrial process prediction -- Category of industrial process prediction -- Common-used techniques for industrial process prediction -- Brief summary -- Data preprocessing techniques -- Anomaly detection of data -- Correction of abnormal data -- Methods of packing missing data -- Data de-noising techniques -- Data fusion methods -- Discussion -- Industrial time series prediction -- Introduction -- Methods of phase space reconstruction -- Prediction modeling -- Benchmark prediction problems -- Cases of industrial applications -- Discussion -- Factor-based industrial process prediction -- Introduction -- Methods of determining factors -- Factor-based single-output model -- Factor-based multi-output model -- Cases of industrial applications -- Discussion -- Industrial Prediction intervals with data uncertainty -- Introduction -- Common-used techniques for prediction intervals -- Prediction intervals with noisy outputs -- Prediction intervals with noisy inputs and outputs -- Time series prediction intervals with missing input -- Industrial cases of prediction intervals -- Discussion -- Granular computing-based long term prediction intervals -- Introduction -- Basic theory of granular computing -- Techniques of granularity partition -- Long-term prediction model -- Granular-based prediction intervals -- Multi-dimension granular-based long term prediction intervals -- Discussion -- Parameters estimation and optimization -- Introduction -- Gradient-based methods -- Evolutionary algorithms -- Nonlinear Kalman-filter estimation -- Probabilistic methods -- Gamma-test based noise estimation -- Industrial applications -- Discussion -- Parallel computing considerations -- Introduction -- CUDA-based parallel acceleration -- Hadoop-based distributed computation -- Other techniques -- Industrial applications to parallel computing -- Discussion -- Prediction-based scheduling of industrial system -- Introduction -- Scheduling of blast furnace gas system -- Scheduling of coke oven gas system -- Scheduling of converter gas system -- Scheduling of oxygen system -- Predictive scheduling for plant-wide energy system -- Discussion.
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|a This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
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|a Data mining.
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|a Manufactures.
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|a Artificial intelligence.
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|a Quality control.
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|a Reliability.
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| 650 |
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|a Industrial safety.
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| 650 |
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|a Operations research.
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| 650 |
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|a Decision making.
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| 650 |
1 |
4 |
|a Data Mining and Knowledge Discovery.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030
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| 650 |
2 |
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|a Manufacturing, Machines, Tools, Processes.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T22050
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| 650 |
2 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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| 650 |
2 |
4 |
|a Quality Control, Reliability, Safety and Risk.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T22032
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| 650 |
2 |
4 |
|a Operations Research/Decision Theory.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/521000
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| 700 |
1 |
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|a Wang, Wei.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 700 |
1 |
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|a Sheng, Chunyang.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
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8 |
|i Printed edition:
|z 9783319940502
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783319940526
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783030067854
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| 830 |
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|a Information Fusion and Data Science,
|x 2510-1528
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-94051-9
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
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