LEADER 01784nam--2200433---450- 001 990003490380203316 005 20110204090814.0 010 $a978-88-87541-52-6 035 $a000349038 035 $aUSA01000349038 035 $a(ALEPH)000349038USA01 035 $a000349038 100 $a20110128d2009----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aAtti del Convegno di studi su Pietro A. Zveteremich$el'uomo, lo slavista, l'intellettuale$eMessina, 18 aprile 2008$fa cura di Aleksandra Parysievicz Lanzafame 210 $aMessina$c[Universita' degli studi di Messina]$d2009 215 $a180 p.$cill.$d25 cm 300 $aIn testa al front. : Facoltà di lettere e filosofia, Dipartimento di lingue, letterature e culture moderne, Centro interdipartimentale di studi umanistici 300 $aTit. della cop. e del dorso : Pietro Zveteremich: l'uomo, lo slavista, l'intellettuale 410 0$12001 454 1$12001 461 1$1001-------$12001 600 1 $aZveteremich, Pietro$xConvegni$yMessina$z2009 702 1$aZVETEREMICH,$bPietro 702 $aPARYSIEWICZ LANZAFAME,$bAlexandra 710 12$aConvegno di studi su Pietro A. Zveteremich: l'uomo, lo slavista, l'intellettuale$eMessina$f2008$0609271 712 $aUniversita degli studi di Messina$bFacolta di Lettere e Filosofia$cDipartimento di Lingue, Letterature e Culture Moderne 801 0$aIT$bsalbc$gISBD 912 $a990003490380203316 951 $aII.7.E.11$b9715 DSLL 959 $aBK 969 $aDSLL 979 $aDSLL$b90$c20110128$lUSA01$h1338 979 $aDSLL$b90$c20110131$lUSA01$h1039 979 $aDSLL$b90$c20110204$lUSA01$h0908 996 $aAtti del Convegno di studi su Pietro A. Zveteremich$91110887 997 $aUNISA LEADER 06239nam 22007215 450 001 9910741150903321 005 20200704112157.0 010 $a3-319-94051-1 024 7 $a10.1007/978-3-319-94051-9 035 $a(CKB)4100000005820401 035 $a(DE-He213)978-3-319-94051-9 035 $a(MiAaPQ)EBC5495833 035 $a(PPN)229918077 035 $a(EXLCZ)994100000005820401 100 $a20180820d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-Driven Prediction for Industrial Processes and Their Applications /$fby Jun Zhao, Wei Wang, Chunyang Sheng 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XVI, 443 p. 167 illus., 128 illus. in color.) 225 1 $aInformation Fusion and Data Science,$x2510-1528 311 $a3-319-94050-3 327 $aPreface -- 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. 330 $aThis book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities. 410 0$aInformation Fusion and Data Science,$x2510-1528 606 $aData mining 606 $aManufactures 606 $aArtificial intelligence 606 $aQuality control 606 $aReliability 606 $aIndustrial safety 606 $aOperations research 606 $aDecision making 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aManufacturing, Machines, Tools, Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/T22050 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aQuality Control, Reliability, Safety and Risk$3https://scigraph.springernature.com/ontologies/product-market-codes/T22032 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 615 0$aData mining. 615 0$aManufactures. 615 0$aArtificial intelligence. 615 0$aQuality control. 615 0$aReliability. 615 0$aIndustrial safety. 615 0$aOperations research. 615 0$aDecision making. 615 14$aData Mining and Knowledge Discovery. 615 24$aManufacturing, Machines, Tools, Processes. 615 24$aArtificial Intelligence. 615 24$aQuality Control, Reliability, Safety and Risk. 615 24$aOperations Research/Decision Theory. 676 $a006.312 700 $aZhao$b Jun$4aut$4http://id.loc.gov/vocabulary/relators/aut$0989096 702 $aWang$b Wei$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSheng$b Chunyang$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910741150903321 996 $aData-Driven Prediction for Industrial Processes and Their Applications$93554520 997 $aUNINA