Search Results - "algorithms"

Perspectives on European Earthquake Engineering and Seismology Volume 2 /

Table of Contents: “…Performance-based Seismic Design and Assessment of Bridges.- 8. An Algorithm to Justify the Design of Single Story Precast Structures.- 9. …”
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Leadership in turbulent times : cultivating diversity and inclusion in the higher education workplace /

Table of Contents: “…Chapter 1. The algorithm arm race: How justice became a business in post-covid higher education / Jessica Jennrich -- Chapter 2. …”
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Pastplay : Teaching and Learning History with Technology /

Table of Contents: “…: learning Canadian history with the Virtual Historian / Stephane Levesque -- Interactive worlds as educational tools for understanding Arctic life / Richard Levy and Peter Dawson -- Tecumseh lies here : goals and challenges for a pervasive history game in progress / Timothy Compeau and Robert MacDougall -- The hermeneutics of screwing around; or what you do with a million books / Stephen Ramsay -- Abort, retry, pass, fail : games as teaching tools / Sean Gouglas, Mihaela Ilovan, Shannon Lucky, and Silvia Russell -- Ludic algorithms / Bethany Nowviskie -- Making and playing with models : using rapid prototyping to explore the history and technology of stage magic / William J. …”
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Perspectives on European Earthquake Engineering and Seismology Volume 2 /

Table of Contents: “…Performance-based Seismic Design and Assessment of Bridges.- 8. An Algorithm to Justify the Design of Single Story Precast Structures.- 9. …”
Get full text

Leadership in turbulent times : cultivating diversity and inclusion in the higher education workplace /

Table of Contents: “…Chapter 1. The algorithm arm race: How justice became a business in post-covid higher education / Jessica Jennrich -- Chapter 2. …”
Get full text

Pastplay : Teaching and Learning History with Technology /

Table of Contents: “…: learning Canadian history with the Virtual Historian / Stephane Levesque -- Interactive worlds as educational tools for understanding Arctic life / Richard Levy and Peter Dawson -- Tecumseh lies here : goals and challenges for a pervasive history game in progress / Timothy Compeau and Robert MacDougall -- The hermeneutics of screwing around; or what you do with a million books / Stephen Ramsay -- Abort, retry, pass, fail : games as teaching tools / Sean Gouglas, Mihaela Ilovan, Shannon Lucky, and Silvia Russell -- Ludic algorithms / Bethany Nowviskie -- Making and playing with models : using rapid prototyping to explore the history and technology of stage magic / William J. …”
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Recent developments in mathematical finance International Conference on Mathematical Finance, Shanghai, China, 10-13 May 2001 /

Table of Contents: “…Yang -- Using Stochastic Approximation Algorithms in Stock Liquidation 238 -- G. Yin, Q. Zhang, and R. …”
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Recent developments in mathematical finance International Conference on Mathematical Finance, Shanghai, China, 10-13 May 2001 /

Table of Contents: “…Yang -- Using Stochastic Approximation Algorithms in Stock Liquidation 238 -- G. Yin, Q. Zhang, and R. …”
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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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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Handbook of exchange rates

Table of Contents: “…High frequency finance: Using scaling laws to build trading models 21. Algorithmic Execution in Foreign Exchange 22. Foreign Exchange Strategy Based Products 23. …”
An electronic book accessible through the World Wide Web; click to view

The dynamic brain : an exploration of neuronal variability and its functional significance /

Table of Contents: “…A mixed-filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures / Todd P. …”
An electronic book accessible through the World Wide Web; click to view

Handbook of exchange rates

Table of Contents: “…High frequency finance: Using scaling laws to build trading models 21. Algorithmic Execution in Foreign Exchange 22. Foreign Exchange Strategy Based Products 23. …”
An electronic book accessible through the World Wide Web; click to view

The dynamic brain : an exploration of neuronal variability and its functional significance /

Table of Contents: “…A mixed-filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures / Todd P. …”
An electronic book accessible through the World Wide Web; click to view

Computation and the Humanities Towards an Oral History of Digital Humanities / by Nyhan, Julianne, Flinn, Andrew

Table of Contents: “…--  ‘Individuation is There in all the Different Strata:’ an Oral History Conversation between John Burrows, Hugh Craig and Willard McCarty -- ‘It was a Time When the University was Still Taking Account of the Meaning of universitas scientiarum’: an Oral History Conversation between Wilhelm Ott and Julianne Nyhan -- ‘hic Rhodus, hic salta’: An Oral History Interview Between Tito Orlandi and Julianne Nyhan -- ‘They Took a Chance’: An Oral History Conversation between Susan Hockey and Julianne Nyhan --  ‘And Here We go Back Again to the Influence of Algorithmic Thinking’: An Oral History conversation between Judy Malloy and Julianne Nyhan -- ‘I Would Think of Myself as Sitting Inside the Computer, Moving Things Around in Order to Accomplish the Goal of my Programming’: An Oral History Conversation Between Mary Dee Harris and Julianne Nyhan -- ‘I Was Absolutely Convinced That There Had to be a Better Way’: An Oral History Conversation Between John Nitti and Julianne Nyhan -- ‘It’s a Little Mind-Boggling Actually’: An Oral History Conversation between Helen Agüera and Julianne Nyhan --  ‘I Heard About the Arrival of the Computer’: An Oral History Conversation Between Hans Rutimann and Julianne Nyhan --  ‘Langezeit habe ich der Universitaet nachgetrauert’: An Oral History Conversation between Michael Sperberg-McQueen and Julianne Nyhan.…”
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Computation and the Humanities Towards an Oral History of Digital Humanities / by Nyhan, Julianne, Flinn, Andrew

Table of Contents: “…--  ‘Individuation is There in all the Different Strata:’ an Oral History Conversation between John Burrows, Hugh Craig and Willard McCarty -- ‘It was a Time When the University was Still Taking Account of the Meaning of universitas scientiarum’: an Oral History Conversation between Wilhelm Ott and Julianne Nyhan -- ‘hic Rhodus, hic salta’: An Oral History Interview Between Tito Orlandi and Julianne Nyhan -- ‘They Took a Chance’: An Oral History Conversation between Susan Hockey and Julianne Nyhan --  ‘And Here We go Back Again to the Influence of Algorithmic Thinking’: An Oral History conversation between Judy Malloy and Julianne Nyhan -- ‘I Would Think of Myself as Sitting Inside the Computer, Moving Things Around in Order to Accomplish the Goal of my Programming’: An Oral History Conversation Between Mary Dee Harris and Julianne Nyhan -- ‘I Was Absolutely Convinced That There Had to be a Better Way’: An Oral History Conversation Between John Nitti and Julianne Nyhan -- ‘It’s a Little Mind-Boggling Actually’: An Oral History Conversation between Helen Agüera and Julianne Nyhan --  ‘I Heard About the Arrival of the Computer’: An Oral History Conversation Between Hans Rutimann and Julianne Nyhan --  ‘Langezeit habe ich der Universitaet nachgetrauert’: An Oral History Conversation between Michael Sperberg-McQueen and Julianne Nyhan.…”
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Heat transfer virtual lab for students and engineers : theory and guide for setting up / by Fridman, Ella, Mahajan, Harshad S.

Table of Contents: “…Design of LabVIEW VI program -- 4.1 Software: algorithm of the program -- 4.2 Introduction of LabVIEW controls used in the project -- 4.3 Design of front panel -- 4.4 Design of block diagram -- 4.5 How were the PID parameters' values derived for temperature control? …”
An electronic book accessible through the World Wide Web; click to view

Heat transfer virtual lab for students and engineers : theory and guide for setting up / by Fridman, Ella, Mahajan, Harshad S.

Table of Contents: “…Design of LabVIEW VI program -- 4.1 Software: algorithm of the program -- 4.2 Introduction of LabVIEW controls used in the project -- 4.3 Design of front panel -- 4.4 Design of block diagram -- 4.5 How were the PID parameters' values derived for temperature control? …”
An electronic book accessible through the World Wide Web; click to view

Understanding ultrasonic level measurement by Milligan, Stephen

Table of Contents: “…. -- Ultrasonic instrumentation -- The transducer -- Transducer environments -- Transducer accuracy -- Transducer resolution and accuracy -- Impedance matching -- Axis of transmission -- Beam width -- Beam spreading -- Ringdown -- The controllers -- Digital filtering -- Averaging echoes -- Echo extraction algorithms -- Summary -- Notes --…”
An electronic book accessible through the World Wide Web; click to view

Understanding ultrasonic level measurement by Milligan, Stephen

Table of Contents: “…. -- Ultrasonic instrumentation -- The transducer -- Transducer environments -- Transducer accuracy -- Transducer resolution and accuracy -- Impedance matching -- Axis of transmission -- Beam width -- Beam spreading -- Ringdown -- The controllers -- Digital filtering -- Averaging echoes -- Echo extraction algorithms -- Summary -- Notes --…”
An electronic book accessible through the World Wide Web; click to view