LEADER 01078cam2 22003011 450 001 SOBE00026316 005 20120619102035.0 100 $a20120619d1976 |||||ita|0103 ba 101 $aita 102 $aIT 200 0 $a4: libri XXIX-XXXI$fAmmiano Marcellino 210 $aBologna$cZanichelli$d1976 215 $a276 p.$d20 cm 225 2 $aProsatori di Roma 300 $aTesto orig. a fronte 410 1$1001SOBE00025985$12001 $a*Prosatori di Roma 461 1$1001SOBE00026309$12001 $aIstorie / Ammiano Marcellino ; testo latino, traduzione e note di Anna Resta Barrile 700 0$aAmmianus Marcellinus$3A600200060853$4070$0256090 702 1$aResta Barrile, Anna$3SOBA00004154$4070 801 0$aIT$bUNISOB$c20120619$gRICA 850 $aUNISOB 852 $aUNISOB$j870|Coll|11|k$m49811 912 $aSOBE00026316 940 $aM 102 Monografia moderna SBN 941 $aM 957 $a870|Coll|11|k$b000016$i-4$gSI$d49811$racquisto$tN$1cutolo$2UNISOB$3UNISOB$420120619101945.0$520120619102022.0$6cutolo 996 $a4: libri XXIX-XXXI$91717488 997 $aUNISOB 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