LEADER 01780oam 2200493I 450 001 9910693481203321 005 20190520103747.0 035 $a(CKB)4330000001927353 035 $a(OCoLC)295200868 035 $a(EXLCZ)994330000001927353 100 $a20090102j200812 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aParametric gasification of oak and pine feedstocks using the TCPDU and slipstream water-gas shift catalysis /$fJason Hrdlicka [and three others] 210 1$aGolden, Colorado :$cNational Renewable Energy Laboratory,$dDecember 2008. 215 $a1 online resource (vii, 36 pages) $cillustrations 225 1 $aNREL/TP ;$v510-44557 300 $a"December 2008." 517 3 $aParametric gasification of oak and pine feedstocks using the Thermochemical Process Development Unit and slipstream water gas shift catalysis 606 $aBiomass gasification$xResearch 606 $aHydrogen$xResearch 606 $aBiomass chemicals$xEconomic aspects 606 $aHydrogen$xResearch$2fast 615 0$aBiomass gasification$xResearch. 615 0$aHydrogen$xResearch. 615 0$aBiomass chemicals$xEconomic aspects. 615 7$aHydrogen$xResearch. 700 $aHrdlicka$b Jason A$g(Jason Alan),$01417478 712 02$aNational Renewable Energy Laboratory (U.S.), 801 0$bSOE 801 1$bSOE 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCF 801 2$bGPO 801 2$bMERUC 801 2$bGPO 906 $aBOOK 912 $a9910693481203321 996 $aParametric gasification of oak and pine feedstocks using the TCPDU and slipstream water-gas shift catalysis$93525981 997 $aUNINA LEADER 04382nam 22006855 450 001 9910437957903321 005 20200707012427.0 010 $a3-642-32451-7 024 7 $a10.1007/978-3-642-32451-2 035 $a(CKB)2670000000317350 035 $a(EBL)1082549 035 $a(OCoLC)823728319 035 $a(SSID)ssj0000870795 035 $a(PQKBManifestationID)11523251 035 $a(PQKBTitleCode)TC0000870795 035 $a(PQKBWorkID)10819367 035 $a(PQKB)10199497 035 $a(DE-He213)978-3-642-32451-2 035 $a(MiAaPQ)EBC1082549 035 $a(MiAaPQ)EBC6310653 035 $a(PPN)168321858 035 $a(EXLCZ)992670000000317350 100 $a20121212d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnsupervised Classification $eSimilarity Measures, Classical and Metaheuristic Approaches, and Applications /$fby Sanghamitra Bandyopadhyay, Sriparna Saha 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (270 p.) 300 $aDescription based upon print version of record. 311 $a3-642-42836-3 311 $a3-642-32450-9 320 $aIncludes bibliographical references and index. 327 $aChap. 1 Introduction -- Chap. 2 Some Single- and Multiobjective Optimization Techniques -- Chap. 3 SimilarityMeasures -- Chap. 4 Clustering Algorithms -- Chap. 5 Point Symmetry Based Distance Measures and their Applications to Clustering -- Chap. 6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation -- Chap. 7 Symmetry Based Automatic Clustering -- Chap. 8 Some Line Symmetry Distance Based Clustering Techniques -- Chap. 9 Use of Multiobjective Optimization for Data Clustering -- References -- Index. 330 $aClustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging. 606 $aArtificial intelligence 606 $aBioinformatics 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 615 0$aArtificial intelligence. 615 0$aBioinformatics. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aComputational Biology/Bioinformatics. 615 24$aInformation Systems and Communication Service. 676 $a001.534 700 $aBandyopadhyay$b Sanghamitra$4aut$4http://id.loc.gov/vocabulary/relators/aut$0471674 702 $aSaha$b Sriparna$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437957903321 996 $aUnsupervised Classification$92523283 997 $aUNINA