תוצאות חיפוש - "algorithm"

Tolerance graphs מאת Golumbic, Martin Charles

תוכן הענינים: “...Introduction -- Early work on tolerance graphs -- Trees, cotrees and bipartite graphs -- Interval probe graphs -- Bitolerance graphs and ordered sets -- Unit and 50% tolerance graphs -- Comparability and invariance results -- Bounded bitolerance recognition -- Algorithms on tolerance graphs -- The hierarchy of bitolerance orders -- Tolerance models on trees -- Phi-tolerance models -- Directed tolerance graphs -- Open questions and further directions....”
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Bioinformatics and molecular evolution מאת Higgs, Paul G.

תוכן הענינים: “...The revolution in biological information -- Nucleic acids, proteins, and amino acids -- Molecular evolution and population genetics -- Models of sequence evolution -- Information resources for genes and proteins -- Sequence alignment algorithms -- Searching sequence databases -- Phylogenetic methods -- Patterns in protein families -- Probabilistic methods and machine learning -- Further topics in molecular evolution and phylogenetics -- Genome evolution -- DNA microarrays and the 'omes....”
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Advanced dynamic-system simulation model replication and Monte Carlo studies / מאת Korn, Granino A. (Granino Arthur), 1922-

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Advanced dynamic-system simulation model replication and Monte Carlo studies / מאת Korn, Granino A. (Granino Arthur), 1922-

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Insect media an archaeology of animals and technology / מאת Parikka, Jussi, 1976-

תוכן הענינים: “...Introduction : insects in the age of technology -- Nineteenth-century insect technics : the uncanny affects of insects -- Genesis of form: insect architecture and swarms -- Technics of nature and temporality : Uexkøll's ethology -- Metamorphosis, intensity, and devouring space : elements for an insect game theory -- Intermezzo -- Animal ensembles, robotic affects : bees, milieus, and individuation -- Biomorphs and boids : swarming algorithms -- Sexual selection in the biodigital: teknolust and the weird life of SRAs -- Epilogue : insect media as an art of transmutation....”
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Insect media an archaeology of animals and technology / מאת Parikka, Jussi, 1976-

תוכן הענינים: “...Introduction : insects in the age of technology -- Nineteenth-century insect technics : the uncanny affects of insects -- Genesis of form: insect architecture and swarms -- Technics of nature and temporality : Uexkøll's ethology -- Metamorphosis, intensity, and devouring space : elements for an insect game theory -- Intermezzo -- Animal ensembles, robotic affects : bees, milieus, and individuation -- Biomorphs and boids : swarming algorithms -- Sexual selection in the biodigital: teknolust and the weird life of SRAs -- Epilogue : insect media as an art of transmutation....”
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The book of Qt 4 the art of building Qt applications / מאת Molkentin, Daniel

תוכן הענינים: “...Basics, tools, and first code -- The tools needed to create dialogs -- GUI design using the Qt designer -- Developing a GUI application based on a main window -- Laying out widgets -- Dialogs -- Events, drag and drop, and the clipboard -- Displaying data using "interview" -- The GtSql module -- The graphics library "Arthur" -- Input/output interfaces -- Threading with QThread -- Handling XML with QtXml -- Internationalization -- Debugging help -- Tulip: containers and algorithms....”
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The book of Qt 4 the art of building Qt applications / מאת Molkentin, Daniel

תוכן הענינים: “...Basics, tools, and first code -- The tools needed to create dialogs -- GUI design using the Qt designer -- Developing a GUI application based on a main window -- Laying out widgets -- Dialogs -- Events, drag and drop, and the clipboard -- Displaying data using "interview" -- The GtSql module -- The graphics library "Arthur" -- Input/output interfaces -- Threading with QThread -- Handling XML with QtXml -- Internationalization -- Debugging help -- Tulip: containers and algorithms....”
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Modem theory an introduction to telecommunications / מאת Blahut, Richard E.

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Modem theory an introduction to telecommunications / מאת Blahut, Richard E.

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Bridge health monitoring, maintenance and safety special topic volume with invited peer reviewed papers only /

תוכן הענינים: “...Modal test and analysis of a bridge under the varying temperature condition -- Bridge significant failure mode identification strategy under traffic-load -- Fatigue reliability analysis of the stay cables of cable-stayed bridge under combined loads of stochastic traffic and wind -- Updating the finite element model of a bridge model using a hybrid optimization method -- An improved Taguchi method and its application in finite element model updating of bridges -- Nonlinear seismic response analysis of half through CFST arch bridge under 3-D earthquake waves -- Simplified seismic response assessment method and parametric study of multi-girder skew bridges -- Seismic testing of a long-span concrete filled steel tubular arch bridge -- Study on finite element model of bridge multi-pile foundation -- Sensor optimal placement for bridge structure based on single parents genetic algorithm with different fitness functions....”
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Bridge health monitoring, maintenance and safety special topic volume with invited peer reviewed papers only /

תוכן הענינים: “...Modal test and analysis of a bridge under the varying temperature condition -- Bridge significant failure mode identification strategy under traffic-load -- Fatigue reliability analysis of the stay cables of cable-stayed bridge under combined loads of stochastic traffic and wind -- Updating the finite element model of a bridge model using a hybrid optimization method -- An improved Taguchi method and its application in finite element model updating of bridges -- Nonlinear seismic response analysis of half through CFST arch bridge under 3-D earthquake waves -- Simplified seismic response assessment method and parametric study of multi-girder skew bridges -- Seismic testing of a long-span concrete filled steel tubular arch bridge -- Study on finite element model of bridge multi-pile foundation -- Sensor optimal placement for bridge structure based on single parents genetic algorithm with different fitness functions....”
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The Datafied Society : Studying Culture through Data /

תוכן הענינים: “...Data, Culture and the Ambivalence of Algorithms / William Uricchio; 9. Unknowing Algorithms: On Transparency of Unopenable Black Boxes / Johannes Passmann & Asher Boersma ; 10. ...”
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The Datafied Society : Studying Culture through Data /

תוכן הענינים: “...Data, Culture and the Ambivalence of Algorithms / William Uricchio; 9. Unknowing Algorithms: On Transparency of Unopenable Black Boxes / Johannes Passmann & Asher Boersma ; 10. ...”
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Complexity a guided tour / מאת Mitchell, Melanie

תוכן הענינים: “...Self-reproducing computer programs ; Genetic algorithms -- Computation writ large. Cellular automata, life, and the universe ; Computing with particles ; Information processing in living systems ; How to make analogies (if you are a computer) ; Prospects of computer modeling -- Network thinking. ...”
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Complexity a guided tour / מאת Mitchell, Melanie

תוכן הענינים: “...Self-reproducing computer programs ; Genetic algorithms -- Computation writ large. Cellular automata, life, and the universe ; Computing with particles ; Information processing in living systems ; How to make analogies (if you are a computer) ; Prospects of computer modeling -- Network thinking. ...”
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Statistical monitoring of complex multivariate processes with applications in industrial process control / מאת Krüger, Uwe, Dr

תוכן הענינים: “...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 / מאת Krüger, Uwe, Dr

תוכן הענינים: “...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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Resources utilization and productivity enhancement case studies / מאת Mital, Anil, Pennathur, Arunkumar

תוכן הענינים: “...The case for simplifying the maintenance process through product design / Anoop Desai and Anil Mital -- 2. A disassembly algorithm to maximize resource utilization: a case study / Anoop Desai and Anil Mital -- 3. ...”
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Sameness and repetition in contemporary media culture / מאת Tosca, Susana

תוכן הענינים: “...If you like that, you will love this: On sameness based algorithmic recommendation systems -- Chapter 6. Good artists copy, great artists steal: Creativity and originality in a new media landscape -- Chapter 7. ...”
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