LEADER 05126nam 22005173u 450 001 9910465896303321 005 20210108030412.0 010 $a1-119-97616-2 035 $a(CKB)3710000000754843 035 $a(EBL)697607 035 $a(OCoLC)747411905 035 $a(MiAaPQ)EBC697607 035 $a(EXLCZ)993710000000754843 100 $a20160829d2011|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 200 10$aOptimal Design of Experiments$b[electronic resource] $eA Case Study Approach 210 $aHoboken $cWiley$d2011 215 $a1 online resource (305 p.) 300 $aDescription based upon print version of record. 311 $a1-119-97617-0 327 $aOptimal Design of Experiments : A Case Study Approach; Contents; Preface; Acknowledgments; 1 A simple comparative experiment; 1.1 Key concepts; 1.2 The setup of a comparative experiment; 1.3 Summary; 2 An optimal screening experiment; 2.1 Key concepts; 2.2 Case: an extraction experiment; 2.2.1 Problem and design; 2.2.2 Data analysis; 2.3 Peek into the black box; 2.3.1 Main-effects models; 2.3.2 Models with two-factor interaction effects; 2.3.3 Factor scaling; 2.3.4 Ordinary least squares estimation; 2.3.5 Significance tests and statistical power calculations; 2.3.6 Variance inflation 327 $a2.3.7 Aliasing2.3.8 Optimal design; 2.3.9 Generating optimal experimental designs; 2.3.10 The extraction experiment revisited; 2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity; 2.4 Background reading; 2.4.1 Screening; 2.4.2 Algorithms for finding optimal designs; 2.5 Summary; 3 Adding runs to a screening experiment; 3.1 Key concepts; 3.2 Case: an augmented extraction experiment; 3.2.1 Problem and design; 3.2.2 Data analysis; 3.3 Peek into the black box; 3.3.1 Optimal selection of a follow-up design; 3.3.2 Design construction algorithm; 3.3.3 Foldover designs 327 $a3.4 Background reading3.5 Summary; 4 A response surface design with a categorical factor; 4.1 Key concepts; 4.2 Case: a robust and optimal process experiment; 4.2.1 Problem and design; 4.2.2 Data analysis; 4.3 Peek into the black box; 4.3.1 Quadratic effects; 4.3.2 Dummy variables for multilevel categorical factors; 4.3.3 Computing D-efficiencies; 4.3.4 Constructing Fraction of Design Space plots; 4.3.5 Calculating the average relative variance of prediction; 4.3.6 Computing I-efficiencies; 4.3.7 Ensuring the validity of inference based on ordinary least squares; 4.3.8 Design regions 327 $a4.4 Background reading4.5 Summary; 5 A response surface design in an irregularly shaped design region; 5.1 Key concepts; 5.2 Case: the yield maximization experiment; 5.2.1 Problem and design; 5.2.2 Data analysis; 5.3 Peek into the black box; 5.3.1 Cubic factor effects; 5.3.2 Lack-of-fit test; 5.3.3 Incorporating factor constraints in the design construction algorithm; 5.4 Background reading; 5.5 Summary; 6 A "mixture" experiment with process variables; 6.1 Key concepts; 6.2 Case: the rolling mill experiment; 6.2.1 Problem and design; 6.2.2 Data analysis; 6.3 Peek into the black box 327 $a6.3.1 The mixture constraint6.3.2 The effect of the mixture constraint on the model; 6.3.3 Commonly used models for data from mixture experiments; 6.3.4 Optimal designs for mixture experiments; 6.3.5 Design construction algorithms for mixture experiments; 6.4 Background reading; 6.5 Summary; 7 A response surface design in blocks; 7.1 Key concepts; 7.2 Case: the pastry dough experiment; 7.2.1 Problem and design; 7.2.2 Data analysis; 7.3 Peek into the black box; 7.3.1 Model; 7.3.2 Generalized least squares estimation; 7.3.3 Estimation of variance components; 7.3.4 Significance tests 327 $a7.3.5 Optimal design of blocked experiments 330 $a""This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."" - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University ""It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion 606 $aExperimental design -- Data processing 606 $aIndustrial engineering -- Case studies 606 $aIndustrial engineering -- Experiments -- Computer-aided design 608 $aElectronic books. 615 4$aExperimental design -- Data processing. 615 4$aIndustrial engineering -- Case studies. 615 4$aIndustrial engineering -- Experiments -- Computer-aided design. 676 $a670.285 700 $aGoos$b Peter$0876097 701 $aJones$b Bradley$042705 701 $aJones$b Bradley$042705 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910465896303321 996 $aOptimal Design of Experiments$92001403 997 $aUNINA LEADER 03669nam 2200661I 450 001 9910788050903321 005 20230801231809.0 010 $a0-429-10944-X 010 $a1-138-07165-X 010 $a1-4398-6993-6 010 $a1-4398-6994-4 035 $a(CKB)2670000000591344 035 $a(EBL)1447124 035 $a(SSID)ssj0001459009 035 $a(PQKBManifestationID)12627007 035 $a(PQKBTitleCode)TC0001459009 035 $a(PQKBWorkID)11456252 035 $a(PQKB)11553316 035 $a(MiAaPQ)EBC4742985 035 $a(MiAaPQ)EBC1447124 035 $a(Au-PeEL)EBL1447124 035 $a(CaPaEBR)ebr11166222 035 $a(OCoLC)901274733 035 $a(OCoLC)982122268 035 $a(EXLCZ)992670000000591344 100 $a20180611d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aHuman eye imaging and modeling /$fedited by E. Y. K. Ng, Jen Hong Tan, U. Rajendra Acharya and Jasjit S. Suri 205 $aFirst edition. 210 1$aBoca Raton, FL :$cCRC Press, an imprint of Taylor and Francis,$d2012. 215 $a1 online resource (420 p.) 300 $aDescription based upon print version of record. 311 $a1-322-61644-2 311 $a1-4665-6620-5 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Contents; Preface; Contributors; Section I; Chapter 1: Automated Identification of Diabetes Retinopathy Using Artificial Intelligence Techniques; Chapter 2: VAMPIRE: Vessel Assessment and Measurement Platform for Images of the Retina; Chapter 3: Formal Design and Development of a Glaucoma Classification System; Chapter 4: Computer-Aided Assessment of Optic Nerve; Chapter 5: A Survey of Instruments for Eye Diagnostics with Special Emphasis on Glaucoma Detection; Chapter 6: Imaging Modalities and Medical Applications in the Ocular Surface 327 $aChapter 7: Current Research on Ocular Surface TemperatureChapter 8: Computer Methods in the Estimation of Tear Evaporation by Thermography; Chapter 9: Tear Film Thermal Image Characteristics Analysis in Temporal and Spatial Aspects; Section II; Chapter 10: Biomechanical Modeling of the Human Eye with a Focus on the Cornea; Chapter 11: Modeling Retinal Laser Surgery in Human Eye; Chapter 12: A Geometric Model of the 3D Human Eye and Its Optical Simulation; Chapter 13: Human Eye Heat Distribution Using 3D Web-Splines Solution; Chapter 14: Modeling of Human Eye Exposed to Laser Radiation 327 $aChapter 15: Computational Bioheat Modeling in Human Eye with Local Blood Perfusion EffectChapter 16: Modeling and Simulation of Bioheat Transfer in the Human Eye with Edge-Based Smoothed Finite Element Method (ES-FEM); Chapter 17: A Numerical Approach to Bioheat and Mass Transfer in the Human Eye 330 3 $aAdvanced image processing and mathematical modeling techniques are increasingly being used for the early diagnosis of eye diseases. A comprehensive review of the field, Human Eye Imaging and Modeling details the latest advances and analytical techniques in ocular imaging and modeling. 606 $aEye$xDiseases$xDiagnosis 606 $aDiagnostic imaging 615 0$aEye$xDiseases$xDiagnosis. 615 0$aDiagnostic imaging. 676 $a617.7/15 702 $aAcharya$b U. Rajendra 702 $aNg$b E. Y. K. (Eddie Y. K.) 702 $aSuri$b Jasjit S. 702 $aHong Tan$b Jen 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910788050903321 996 $aHuman eye imaging and modeling$9265784 997 $aUNINA