Search Results - "simulation"

Education in the Democratic Republic of Congo priorities and options for regeneration.

Table of Contents: “…Education in the Democratic Republic of Congo: background and context -- Overview of the education system--growth and efficiency -- Education finance in the Democratic Republic of Congo -- Quality in primary and secondary education--learning outcomes and learning conditions -- Higher education -- Financial simulations.…”
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The Wigner Monte-Carlo method for nanoelectronic devices a particle description of quantum transport and decoherence / by Querlioz, Damien

Table of Contents: “…Theoretical framework of quantum transport in semiconductors and devices -- Particle-based Wigner Monte Carlo approach to device simulation -- Application of the Wigner Monte Carlo technique to RTD, MOSFET, and CNTFET -- Transition from quantum to semi-classical transport through decoherence theory.…”
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The Wigner Monte-Carlo method for nanoelectronic devices a particle description of quantum transport and decoherence / by Querlioz, Damien

Table of Contents: “…Theoretical framework of quantum transport in semiconductors and devices -- Particle-based Wigner Monte Carlo approach to device simulation -- Application of the Wigner Monte Carlo technique to RTD, MOSFET, and CNTFET -- Transition from quantum to semi-classical transport through decoherence theory.…”
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Social security programs and retirement around the world fiscal implications of reform /

Table of Contents: “…Microsimulation of social security reforms in Belgium / Raphael Desmet, Alain Jousten, Sergio Perelman, and Pierre Pestieau -- Simulating the response to reform of Canada's income security programs / Michael Baker, Jonathan Gruber, and Kevin Milligan -- Fiscal implications of reforms in retirement systems in Denmark / Paul Bingley, Nabanita Datta Gupta, and Peder J. …”
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Social security programs and retirement around the world fiscal implications of reform /

Table of Contents: “…Microsimulation of social security reforms in Belgium / Raphael Desmet, Alain Jousten, Sergio Perelman, and Pierre Pestieau -- Simulating the response to reform of Canada's income security programs / Michael Baker, Jonathan Gruber, and Kevin Milligan -- Fiscal implications of reforms in retirement systems in Denmark / Paul Bingley, Nabanita Datta Gupta, and Peder J. …”
An electronic book accessible through the World Wide Web; click to view

Fuel cells science and engineering materials, processes, systems and technology /

Table of Contents: “…Quality assurance -- pt. 5. Modeling and simulation -- pt. 6. Balance of plant design and components -- pt. 7. …”
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Fuel cells science and engineering materials, processes, systems and technology /

Table of Contents: “…Quality assurance -- pt. 5. Modeling and simulation -- pt. 6. Balance of plant design and components -- pt. 7. …”
An electronic book accessible through the World Wide Web; click to view

Statistical monitoring of complex multivariate processes with applications in industrial process control / by Krüger, Uwe, Dr

Table of Contents: “…Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index.…”
An electronic book accessible through the World Wide Web; click to view

Statistical monitoring of complex multivariate processes with applications in industrial process control / by Krüger, Uwe, Dr

Table of Contents: “…Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index.…”
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Advances in traffic psychology

Table of Contents: “…Driver behaviour and driving simulation -- pt. 6. Technology in vehicles and user acceptance.…”
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Advanced mobility and transport engineering

Table of Contents: “…Agent-oriented road traffic simulation -- An agent-based information system for searching and composing mobility aiding services -- Inter-vehicle services and communication -- Modeling and control of traffic flow -- Criteria and methods of evaluation of interactive systems : application to a regulation post in the transport domain.…”
An electronic book accessible through the World Wide Web; click to view

Advances in traffic psychology

Table of Contents: “…Driver behaviour and driving simulation -- pt. 6. Technology in vehicles and user acceptance.…”
An electronic book accessible through the World Wide Web; click to view

Advanced mobility and transport engineering

Table of Contents: “…Agent-oriented road traffic simulation -- An agent-based information system for searching and composing mobility aiding services -- Inter-vehicle services and communication -- Modeling and control of traffic flow -- Criteria and methods of evaluation of interactive systems : application to a regulation post in the transport domain.…”
An electronic book accessible through the World Wide Web; click to view

Indiscretions : Avant-Garde Film, Video, and Feminism / by Mellencamp, Patricia

Table of Contents: “…Historically speaking -- Visionary film and sexual difference -- Video politics -- Surveillance and simulation -- Theoretical objects -- Postmodern TV -- Uncanny feminism -- Last scene in the streets of modernism -- Taking a cue from Ariadne -- Images of language and indiscreet dialogues -- The avant-garde, the everyday, and the underground.…”
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Indiscretions : Avant-Garde Film, Video, and Feminism / by Mellencamp, Patricia

Table of Contents: “…Historically speaking -- Visionary film and sexual difference -- Video politics -- Surveillance and simulation -- Theoretical objects -- Postmodern TV -- Uncanny feminism -- Last scene in the streets of modernism -- Taking a cue from Ariadne -- Images of language and indiscreet dialogues -- The avant-garde, the everyday, and the underground.…”
Full text available:

Worship space acoustics by Kleiner, Mendel, 1946-

Table of Contents: “…Fundamentals : nature of sound -- Hearing -- Room acoustics fundamentals -- Sound-absorption and sound-absorbers -- Metrics for room acoustics -- Simulation and prediction -- Planning for good room acoustics -- Quiet -- Sound isolation -- Synagogues, churches, and mosques. …”
An electronic book accessible through the World Wide Web; click to view

Worship space acoustics by Kleiner, Mendel, 1946-

Table of Contents: “…Fundamentals : nature of sound -- Hearing -- Room acoustics fundamentals -- Sound-absorption and sound-absorbers -- Metrics for room acoustics -- Simulation and prediction -- Planning for good room acoustics -- Quiet -- Sound isolation -- Synagogues, churches, and mosques. …”
An electronic book accessible through the World Wide Web; click to view

Computational fluid dynamics for engineers

An electronic book accessible through the World Wide Web; click to view

Computational fluid dynamics for engineers

An electronic book accessible through the World Wide Web; click to view

Courts as policymakers school finance reform litigation / by Lukemeyer, Anna

Table of Contents: “…School finance reform and the courts -- Policy issues as defined by education finance scholars -- Research questions and methods -- Judicial definitions of equity and case outcomes : findings -- Judicial definitions of equity and treatment of key issues : findings -- Exploring the impact of different judicial choices using simulations -- Judicial treatment of school finance equity issues : summary and conclusions.…”
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