The Resource Advanced Mathematical and Computational Tools in Metrology and Testing Ix, (electronic resource)

Advanced Mathematical and Computational Tools in Metrology and Testing Ix, (electronic resource)

Label
Advanced Mathematical and Computational Tools in Metrology and Testing Ix
Title
Advanced Mathematical and Computational Tools in Metrology and Testing Ix
Title variation
AMCTM 9
Creator
Contributor
Subject
Genre
Language
  • eng
  • eng
Summary
This volume contains original, refereed worldwide contributions. They were prompted by presentations made at the ninth AMCTM Conference held in Göteborg (Sweden) in June 2011 on the theme of advanced mathematical and computational tools in metrology and also, in the title of this book series, in testing. The themes in this volume reflect the importance of the mathematical, statistical and numerical tools and techniques in metrology and testing and, also in keeping the challenge promoted by the Metre Convention, to access a mutual recognition for the measurement standards
Member of
Cataloging source
AU-PeEL
http://library.link/vocab/creatorName
Pavese, Franco
Dewey number
530.8
Language note
English
LC call number
QA465 .A58 2012
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Bar, Markus
  • Filtz, Jean-Remy
  • AMCTM IX
Series statement
Series on Advances in Mathematics for Applied Sciences
http://library.link/vocab/subjectName
  • Measurement -- Congresses
  • Metrology -- Congresses
  • Physical measurements -- Congresses
  • Measurement
  • Physical measurements
  • Metrology
  • Mathematics
  • Physical Sciences & Mathematics
  • Geometry
Label
Advanced Mathematical and Computational Tools in Metrology and Testing Ix, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Content category
text
Content type code
  • txt
Contents
  • Contents; Foreword; Recommended Tools for Sensitivity Analysis Associated to the Evaluation of Measurement Uncertainty A. Allard and N. Fischer; 1. Introduction; 2. Basic Sensitivity Methods; 2.1. The One At a Time Method (GUM-S1); 2.2. Pearson and Spearman Correlation Coefficients; 2.3. Application Case : Mass Calibration Example; 3. Variance-Based Sensitivity Indices; 3.1. Concepts; 3.2. Sobol' Estimation; 3.3. Local Polynomial Smoothers; 3.4. Application Case : Mass Calibration Example; 4. Conclusion; References; A Simple Confidence Interval for the Common Mean B. Arendacka
  • 1. Introduction2. A Simple Confidence Interval; 3. Simulation Study; 4. Discussion; Acknowledgments; References; Estimation of Detailed Deviation Zone of Inspected Surfaces A. Barari; 1. Introduction; 2. Parametric Space; 3. Estimation of Geometric Deviations; 4. Experimental Validation; 5. Conclusions; Acknowledgments; References; Case Study of Likelihood and Bayes Approaches for Measurement Based on Nonlinear Regression A. Bariska and R. Burgin; 1. Introduction; 1.1. Measurements Based on Regression Problems; 1.2. Solving Regression Problems; 1.3. Document Outline
  • 2. Overview of Likelihood and Bayes Approaches2.1. Likelihood; 2.2. Bayes; 3. Computational Methods; 3.1. Point Estimators; 3.1.1. Likelihood Approach; 3.1.2. Bayes Approach; 3.1.3. Initial Values; 3.2. Interval Estimators; 3.2.1. Likelihood Approach; 3.2.2. Bayes Approach; 4. Monte-Carlo Results; 4.1. Nonlinear Model Function; 4.2. Monte-Carlo Procedure; 4.3. Monte-Carlo Results; 5. Conclusions; Acknowledgments; References; Mathematical and Computational Aspects of Treatment Ordinal Measurement Results E. Bashkansky and T. Gadrich; 1. Introduction
  • 2. The Description of an Ordinal Measurement Error, Repeatability and Uncertainty2.1. Error; 2.2. Repeatability (Precision); 2.3. Measurement Uncertainty; 3. Repeated Ordinal Measurements; 4. Comparison Between Two Ordinal Measuring Systems; 5. Comparison Between Several Ordinal Measuring Systems; 6. Traceability Problem; 7. Conclusions; References; Calibration of Ordinal Metrical Scale E. Benoit; 1. Introduction; 2. Calibration; 2.1. Representational Theory of Measurement; 2. 1. 1 Definitions; 2.2 Calibration of Weak Scales; 3. Ordinal Metrical Scales; 4. Application Example: Colour
  • 5. ConclusionReferences; Comparison of Different Choices for a Prior Under Partial Information in a Bayesian Analysis O. Bodnar, G. Wubbeler and C. Elster; 1. Introduction; 2. Propriety of Posteriors; 3. Long-run Behavior; 4. Conclusion; References; Self-Consistent Reference Value Estimation W. Bremser; 1. Introduction; 2. Principles of the Approach; 3. Example: CCQM-P69; 4. Results and Discussion; 5. Conclusions; References; Uncertainty Modeling in 3D SEM Stereophotogrammetry L. Carli, M. Galetto and G. Genta; 1. Introduction; 2. Basic Principles of 3D SEM Stereophotogrammetry
  • 3. Propagation of Uncertainties
Dimensions
unknown
Extent
1 online resource (468 p.)
Form of item
electronic
Isbn
9789814397957
Media category
computer
Media type code
  • c
Specific material designation
remote
System control number
  • (CKB)2550000000101626
  • (EBL)919144
  • (OCoLC)794328429
  • (SSID)ssj0000654200
  • (PQKBManifestationID)12293055
  • (PQKBTitleCode)TC0000654200
  • (PQKBWorkID)10661104
  • (PQKB)10556692
  • (WSP)00002681
  • (EXLCZ)992550000000101626
Label
Advanced Mathematical and Computational Tools in Metrology and Testing Ix, (electronic resource)
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Content category
text
Content type code
  • txt
Contents
  • Contents; Foreword; Recommended Tools for Sensitivity Analysis Associated to the Evaluation of Measurement Uncertainty A. Allard and N. Fischer; 1. Introduction; 2. Basic Sensitivity Methods; 2.1. The One At a Time Method (GUM-S1); 2.2. Pearson and Spearman Correlation Coefficients; 2.3. Application Case : Mass Calibration Example; 3. Variance-Based Sensitivity Indices; 3.1. Concepts; 3.2. Sobol' Estimation; 3.3. Local Polynomial Smoothers; 3.4. Application Case : Mass Calibration Example; 4. Conclusion; References; A Simple Confidence Interval for the Common Mean B. Arendacka
  • 1. Introduction2. A Simple Confidence Interval; 3. Simulation Study; 4. Discussion; Acknowledgments; References; Estimation of Detailed Deviation Zone of Inspected Surfaces A. Barari; 1. Introduction; 2. Parametric Space; 3. Estimation of Geometric Deviations; 4. Experimental Validation; 5. Conclusions; Acknowledgments; References; Case Study of Likelihood and Bayes Approaches for Measurement Based on Nonlinear Regression A. Bariska and R. Burgin; 1. Introduction; 1.1. Measurements Based on Regression Problems; 1.2. Solving Regression Problems; 1.3. Document Outline
  • 2. Overview of Likelihood and Bayes Approaches2.1. Likelihood; 2.2. Bayes; 3. Computational Methods; 3.1. Point Estimators; 3.1.1. Likelihood Approach; 3.1.2. Bayes Approach; 3.1.3. Initial Values; 3.2. Interval Estimators; 3.2.1. Likelihood Approach; 3.2.2. Bayes Approach; 4. Monte-Carlo Results; 4.1. Nonlinear Model Function; 4.2. Monte-Carlo Procedure; 4.3. Monte-Carlo Results; 5. Conclusions; Acknowledgments; References; Mathematical and Computational Aspects of Treatment Ordinal Measurement Results E. Bashkansky and T. Gadrich; 1. Introduction
  • 2. The Description of an Ordinal Measurement Error, Repeatability and Uncertainty2.1. Error; 2.2. Repeatability (Precision); 2.3. Measurement Uncertainty; 3. Repeated Ordinal Measurements; 4. Comparison Between Two Ordinal Measuring Systems; 5. Comparison Between Several Ordinal Measuring Systems; 6. Traceability Problem; 7. Conclusions; References; Calibration of Ordinal Metrical Scale E. Benoit; 1. Introduction; 2. Calibration; 2.1. Representational Theory of Measurement; 2. 1. 1 Definitions; 2.2 Calibration of Weak Scales; 3. Ordinal Metrical Scales; 4. Application Example: Colour
  • 5. ConclusionReferences; Comparison of Different Choices for a Prior Under Partial Information in a Bayesian Analysis O. Bodnar, G. Wubbeler and C. Elster; 1. Introduction; 2. Propriety of Posteriors; 3. Long-run Behavior; 4. Conclusion; References; Self-Consistent Reference Value Estimation W. Bremser; 1. Introduction; 2. Principles of the Approach; 3. Example: CCQM-P69; 4. Results and Discussion; 5. Conclusions; References; Uncertainty Modeling in 3D SEM Stereophotogrammetry L. Carli, M. Galetto and G. Genta; 1. Introduction; 2. Basic Principles of 3D SEM Stereophotogrammetry
  • 3. Propagation of Uncertainties
Dimensions
unknown
Extent
1 online resource (468 p.)
Form of item
electronic
Isbn
9789814397957
Media category
computer
Media type code
  • c
Specific material designation
remote
System control number
  • (CKB)2550000000101626
  • (EBL)919144
  • (OCoLC)794328429
  • (SSID)ssj0000654200
  • (PQKBManifestationID)12293055
  • (PQKBTitleCode)TC0000654200
  • (PQKBWorkID)10661104
  • (PQKB)10556692
  • (WSP)00002681
  • (EXLCZ)992550000000101626

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