LEADER 04578nam 2200733 450 001 9910785549003321 005 20230125232906.0 010 $a1-283-89601-X 010 $a1-60650-344-8 024 7 $a10.5643/9781606503447 035 $a(CKB)2670000000234570 035 $a(EBL)954641 035 $a(OCoLC)830170654 035 $a(SSID)ssj0000738957 035 $a(PQKBManifestationID)12332618 035 $a(PQKBTitleCode)TC0000738957 035 $a(PQKBWorkID)10671549 035 $a(PQKB)10345448 035 $a(OCoLC)809681761 035 $a(CaBNvSL)swl00401235 035 $a(MiAaPQ)EBC954641 035 $a(Au-PeEL)EBL954641 035 $a(CaPaEBR)ebr10588195 035 $a(CaONFJC)MIL420851 035 $a(EXLCZ)992670000000234570 100 $a20190118d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aQuality recognition and prediction $esmarter pattern technology with the Mahalanobis-Taguchi system /$fShoichi Teshima, Yoshiko Hasegawa, Kazuo Tatebayashi 205 $a1st ed. 210 1$aNew York :$cMomentum Press, LLC,$d[2012] 210 4$dİ2012 215 $a1 online resource (242 p.) 300 $aDescription based upon print version of record. 311 $a1-60650-342-1 320 $aIncludes bibliographical references and index. 327 $aForeword -- Preface -- Acknowledgments -- 327 $a1. Pattern recognition and the MT system -- 1.1 Overview of pattern recognition and the fields of application -- 1.2 Standard execution procedure for pattern recognition -- 1.3 Fields with substantial experience in the use of MT system applications -- 327 $a2. Merits of the MT system and its computation methods -- 2.1 Characteristics shared by all MT system components -- 2.2 Features of the MT method -- 2.3 Features of the T method -- 2.4 The MT system computation formulas -- 327 $a3. Data handled by the MT system and feature extraction -- 3.1 Use of measured values in an unmodified form -- 3.2 Performing feature extraction -- 3.3 Feature extraction technique from character pattern -- 3.4 Feature extraction technique from waveform pattern -- 3.5 Differences between other waveform features and variation values/abundance values -- 327 $a4. MT method application procedure and important points to heed -- 4.1 Example of character recognition -- 4.2 Example of weather prediction -- 327 $a5. T method application procedures and key points -- 5.1 Yield prediction for manufacturing-production using T method-1 -- 5.2 Character pattern recognition using the RT method -- 327 $a6. Examples of actual applications -- 6.1 Blade wear monitoring via cutting vibration waveform (MT method) -- 6.2 Appearance inspection of a clutch disk -- 6.3 Monitoring of machine conditions (MT method) -- 6.4 Application to medical diagnosis (MT method) -- 6.5 Strength estimation based on raw material mixing (T method-1) -- 6.6. Real estate price prediction by T method-1 -- 327 $aAppendices -- A. Differences between the MT system and artificial intelligence -- B. Difference between the MT system and traditional statistical theory -- C. Supplementary considerations concerning mathematical formulas -- D. Strategy to use when data incorporates unmeasured values -- E. Fusion with artificial intelligence and other resources -- F. Mahalanobis distance computation using Microsoft Excel -- G. Paley's construct for generation of Hadamard matrice -- 327 $aBibliography and reference sources -- Bibliography (in English) -- Bibliography (in Japanese) -- References -- Glossary: definition of terms -- Index -- About the authors. 330 3 $aThe MT system is a diagnostic and predictive method for analyzing patterns in multivariate data that has provided benefits in many diverse applications over the past decade or so. It has proven itself superior in many cases to more traditional artificial intelligence applications such as neural nets. 606 $aPattern recognition systems 606 $aTaguchi methods (Quality control) 615 0$aPattern recognition systems. 615 0$aTaguchi methods (Quality control) 676 $a621.3819598 700 $aTeshima$b Shoichi$01507051 702 $aHasegawa$b Yoshiko 702 $aTatebayashi$b Kazuo 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910785549003321 996 $aQuality recognition and prediction$93737513 997 $aUNINA