Kundu, Madhusree.
Chemometric Monitoring : Product Quality Assessment, Process Fault Detection, and Applications. - Milton : CRC Press, 2017. - 1 online resource (359 pages)
""3.1.1.1 The process description""
""Cover""; ""Half title""; ""Title page""; ""Copyright page""; ""Dedication page""; ""Contents""; ""Preface""; ""Acknowledgments""; ""About the Authors""; ""Introduction""; ""Chapter 1: Data generation, collection, analysis, and preprocessing""; ""1.1 Data: Different data types and presentation of data""; ""1.2 Data generation: Design of experiments""; ""1.2.1 Factorial design and illustration""; ""1.2.1.1 The effect of Zn loading""; ""1.2.1.2 Two-factor interaction effects""; ""1.2.1.3 Three-factor interaction effects""; ""1.3 Computer-based data acquisition""; ""1.3.1 Sensor/transducer"" ""1.3.2 Analog-to-digital (A/D) converter""""1.3.3 Digital-to-analog (D/A) converter""; ""1.4 Basic statistical measures and regression""; ""1.4.1 Mean, median, mode""; ""1.4.2 Variance and standard deviation""; ""1.4.3 Covariance and correlation coefficient""; ""1.4.4 Frequency""; ""1.4.5 Distribution""; ""1.4.6 Uncertainty""; ""1.4.7 Confidence interval""; ""1.4.8 Hypothesis Testing""; ""1.4.9 Correlation""; ""1.4.10 Regression""; ""1.4.11 Chi-squared test""; ""1.5 Stochastic and stationary processes""; ""1.6 Data preprocessing""; ""1.6.1 Outlier detection""; ""1.6.2 Data reconciliation"" ""1.6.3 Data smoothing and filtering""""1.6.3.1 Smoothing signal""; ""1.6.3.2 Filtering signal""; ""1.6.4 Transform and transformation""; ""References""; ""Chapter 2: Chemometric techniques: Theoretical postulations""; ""2.1 Chemometrics""; ""2.2 Principal component analysis (PCA)""; ""2.2.1 PCA decomposition of data""; ""2.2.2 Principle of nearest neighborhood""; ""2.2.3 Hotelling T2 and Q statistics""; ""2.3 Similarity""; ""2.3.1 PCA similarity""; ""2.3.2 Distance-based similarity""; ""2.3.3 Combined similarity factor""; ""2.3.4 Dissimilarity and Karhunen-Loeve (KL) expansion"" ""2.3.5 Moving window-based pattern matching using similarity/dissimilarity factors""""2.4 Clustering""; ""2.4.1 Hierarchical clustering""; ""2.4.2 Nonhierarchical clustering""; ""2.4.3 Modified K-means clustering using similarity factors""; ""2.5 Partial least squares (PLS)""; ""2.5.1 Linear PLS""; ""2.5.2 Dynamic PLS""; ""2.6 Cross-correlation coefficient""; ""2.7 Sammonâ#x80;#x99;s nonlinear mapping""; ""2.8 Moving window-based PCA""; ""2.8.1 Mathematical postulates of recursive PCA""; ""2.9 Discriminant function and hyperplane""; ""2.9.1 Linear discriminant analysis (LDA)"" ""2.9.2 Support vector machine (SVM)""""2.9.2.1 Determination of decision function in SVM""; ""2.9.2.2 Determination of optimal separating hyperplane in SVM""; ""2.10 Multiclass decision function""; ""2.10.1 One against the rest approach""; ""2.10.2 One against one approach""; ""2.10.3 Decision directed acyclic graph (DDAG)-based approach""; ""2.10.3.1 DDAG algorithm""; ""References""; ""Chapter 3: Classification among various process operating conditions""; ""3.1 Yeast fermentation bioreactor process""; ""3.1.1 Modeling and dynamic simulation of yeast fermentation bioreactor""
"Data collection, compression, storage, and interpretation have become mature technologies over the years. Extraction of meaningful information from the process historical database seems to be a natural and logical choice. In view of this, the proposed book aims to apply the data driven knowledge base in ensuring safe process operation through timely detection of process abnormal and normal operating conditions, assuring product quality and analyzing biomedical signal leading to diagnostic tools. The book poses an open invitation for an interface which is required henceforth, in practical implementation of the propositions and possibilities referred in the book. It poses a challenge to the researchers in academia towards the development of more sophisticated algorithms. The proposed book also incites applications in diversified areas. Key Features:Presents discussion of several modern and popular chemometric techniquesIntroduces specific illustrative industrial applications using the chemometric techniquesDemonstrates several applications to beverage quality monitoringProvides all the algorithms developed for the automated device design, data files, sources for biomedical signals and their pre-processing steps, and all the process models requited to simulate process normal/faulty dataIncludes casestudy-based approach to the topics with MATLAB and SIMULINK source codes "--Provided by publisher.
9781351651189 1351651188 9781315155135 1315155133 9781498780087 1498780083 9781351641678 1351641670
QD75.4.C45 / K863 2017
Chemometric Monitoring : Product Quality Assessment, Process Fault Detection, and Applications. - Milton : CRC Press, 2017. - 1 online resource (359 pages)
""3.1.1.1 The process description""
""Cover""; ""Half title""; ""Title page""; ""Copyright page""; ""Dedication page""; ""Contents""; ""Preface""; ""Acknowledgments""; ""About the Authors""; ""Introduction""; ""Chapter 1: Data generation, collection, analysis, and preprocessing""; ""1.1 Data: Different data types and presentation of data""; ""1.2 Data generation: Design of experiments""; ""1.2.1 Factorial design and illustration""; ""1.2.1.1 The effect of Zn loading""; ""1.2.1.2 Two-factor interaction effects""; ""1.2.1.3 Three-factor interaction effects""; ""1.3 Computer-based data acquisition""; ""1.3.1 Sensor/transducer"" ""1.3.2 Analog-to-digital (A/D) converter""""1.3.3 Digital-to-analog (D/A) converter""; ""1.4 Basic statistical measures and regression""; ""1.4.1 Mean, median, mode""; ""1.4.2 Variance and standard deviation""; ""1.4.3 Covariance and correlation coefficient""; ""1.4.4 Frequency""; ""1.4.5 Distribution""; ""1.4.6 Uncertainty""; ""1.4.7 Confidence interval""; ""1.4.8 Hypothesis Testing""; ""1.4.9 Correlation""; ""1.4.10 Regression""; ""1.4.11 Chi-squared test""; ""1.5 Stochastic and stationary processes""; ""1.6 Data preprocessing""; ""1.6.1 Outlier detection""; ""1.6.2 Data reconciliation"" ""1.6.3 Data smoothing and filtering""""1.6.3.1 Smoothing signal""; ""1.6.3.2 Filtering signal""; ""1.6.4 Transform and transformation""; ""References""; ""Chapter 2: Chemometric techniques: Theoretical postulations""; ""2.1 Chemometrics""; ""2.2 Principal component analysis (PCA)""; ""2.2.1 PCA decomposition of data""; ""2.2.2 Principle of nearest neighborhood""; ""2.2.3 Hotelling T2 and Q statistics""; ""2.3 Similarity""; ""2.3.1 PCA similarity""; ""2.3.2 Distance-based similarity""; ""2.3.3 Combined similarity factor""; ""2.3.4 Dissimilarity and Karhunen-Loeve (KL) expansion"" ""2.3.5 Moving window-based pattern matching using similarity/dissimilarity factors""""2.4 Clustering""; ""2.4.1 Hierarchical clustering""; ""2.4.2 Nonhierarchical clustering""; ""2.4.3 Modified K-means clustering using similarity factors""; ""2.5 Partial least squares (PLS)""; ""2.5.1 Linear PLS""; ""2.5.2 Dynamic PLS""; ""2.6 Cross-correlation coefficient""; ""2.7 Sammonâ#x80;#x99;s nonlinear mapping""; ""2.8 Moving window-based PCA""; ""2.8.1 Mathematical postulates of recursive PCA""; ""2.9 Discriminant function and hyperplane""; ""2.9.1 Linear discriminant analysis (LDA)"" ""2.9.2 Support vector machine (SVM)""""2.9.2.1 Determination of decision function in SVM""; ""2.9.2.2 Determination of optimal separating hyperplane in SVM""; ""2.10 Multiclass decision function""; ""2.10.1 One against the rest approach""; ""2.10.2 One against one approach""; ""2.10.3 Decision directed acyclic graph (DDAG)-based approach""; ""2.10.3.1 DDAG algorithm""; ""References""; ""Chapter 3: Classification among various process operating conditions""; ""3.1 Yeast fermentation bioreactor process""; ""3.1.1 Modeling and dynamic simulation of yeast fermentation bioreactor""
"Data collection, compression, storage, and interpretation have become mature technologies over the years. Extraction of meaningful information from the process historical database seems to be a natural and logical choice. In view of this, the proposed book aims to apply the data driven knowledge base in ensuring safe process operation through timely detection of process abnormal and normal operating conditions, assuring product quality and analyzing biomedical signal leading to diagnostic tools. The book poses an open invitation for an interface which is required henceforth, in practical implementation of the propositions and possibilities referred in the book. It poses a challenge to the researchers in academia towards the development of more sophisticated algorithms. The proposed book also incites applications in diversified areas. Key Features:Presents discussion of several modern and popular chemometric techniquesIntroduces specific illustrative industrial applications using the chemometric techniquesDemonstrates several applications to beverage quality monitoringProvides all the algorithms developed for the automated device design, data files, sources for biomedical signals and their pre-processing steps, and all the process models requited to simulate process normal/faulty dataIncludes casestudy-based approach to the topics with MATLAB and SIMULINK source codes "--Provided by publisher.
9781351651189 1351651188 9781315155135 1315155133 9781498780087 1498780083 9781351641678 1351641670
QD75.4.C45 / K863 2017