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Estimation of rare event probabilities in complex aerospace and other systems : a practical approach / / Jérôme Morio and Mathieu Balesdent
| Estimation of rare event probabilities in complex aerospace and other systems : a practical approach / / Jérôme Morio and Mathieu Balesdent |
| Autore | Morio Jérôme |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Waltham, MA : , : Elsevier, , [2016] |
| Descrizione fisica | 1 online resource (217 p.) |
| Collana | Woodhead Publishing in mechanical engineering |
| Soggetto topico |
Probabilities
Industrial engineering - Statistical methods Probabilitats Enginyeria industrial - Mètodes estadístics |
| ISBN |
0-08-100111-8
0-08-100091-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems: A Practical Approach; Copyright; Dedication; Contents; Preface; Foreword; Biography of the external contributors to this book; Abbreviations; Chapter 1: Introduction to rare event probability estimation; 1.1 The book purposes; 1.2 What are the events of interest considered in this book?; 1.3 The book organization; References; Part One: Essential Background in Mathematics and System Analysis; Chapter 2: Basics of probability and statistics; 2.1 Probability theory operators; 2.1.1 Elements of vocabulary
2.1.2 Notion of dependence of random events andconditional probabilities 2.1.3 Continuous random variables; 2.1.3.1 Definitions; 2.1.3.2 Parameters of continuous random variables; 2.1.4 Continuous multivariate random variables; 2.1.4.1 Definitions and theorems; 2.1.4.2 Dependence of multivariate random variables ; 2.1.5 Point estimation ; 2.2 Random variable modeling; 2.2.1 Overview of common probability distributions; 2.2.1.1 Univariate distributions; Uniform distribution; Exponential distribution; Gaussian distribution; Truncated Gaussian distribution; Log-normal distribution Cauchy distributionChi-squared distribution; Gamma and beta distributions; Laplace distribution; Some properties of univariate distributions; 2.2.1.2 Multivariate distributions; Multivariate normal distribution; 2.2.2 Kernel-based laws; 2.3 Convergence theorems and sampling algorithms; 2.3.1 Strong law of large numbers ; 2.3.2 Central limit theorem ; 2.3.3 Simulation of complex laws using the Metropolis-Hastings algorithm; 2.3.3.1 Markov chain; 2.3.3.2 Some properties of transition kernels; 2.3.3.3 The Metropolis-Hastings algorithm ; 2.3.3.4 Transformation of random variables; References Chapter 3: The formalism of rare event probability estimation in complex systems3.1 Input-output system; 3.1.1 Description; 3.1.2 Formalism; 3.2 Time-variant system; 3.2.1 Description; 3.2.2 Formalism; 3.3 Characterization of a probability estimation; References; Part Two: Practical Overview of the Main Rare Event EstimationTechniques; Chapter 4: Introduction; 4.1 Categories of estimation methods; 4.2 General notations; 4.3 Description of the toy cases; 4.3.1 Identity function; 4.3.2 Polynomial square root function; 4.3.3 Four-branch system; 4.3.4 Polynomial product function; References Chapter 5: Simulation techniques5.1 Crude Monte Carlo; 5.1.1 Principle; 5.1.2 Application on a toy case; Four-branch system; 5.1.3 Conclusion; 5.2 Simple variance reduction techniques; 5.2.1 Quasi-Monte Carlo; 5.2.2 Conditional Monte Carlo; 5.2.2.1 Principle; 5.2.2.2 Conclusion; 5.2.3 Control variates; 5.2.3.1 Principle; 5.2.3.2 Application on a toy case; Four-branch system; 5.2.3.3 Conclusion; 5.2.4 Antithetic variates; 5.2.4.1 Principle; 5.2.4.2 Application to a toy case; Identity function; 5.2.4.3 Conclusion; 5.3 Importance sampling; 5.3.1 Principle of importance sampling 5.3.2 Nonadaptive importance sampling |
| Record Nr. | UNINA-9910797756303321 |
Morio Jérôme
|
||
| Waltham, MA : , : Elsevier, , [2016] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Estimation of rare event probabilities in complex aerospace and other systems : a practical approach / / Jérôme Morio and Mathieu Balesdent
| Estimation of rare event probabilities in complex aerospace and other systems : a practical approach / / Jérôme Morio and Mathieu Balesdent |
| Autore | Morio Jérôme |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Waltham, MA : , : Elsevier, , [2016] |
| Descrizione fisica | 1 online resource (217 p.) |
| Collana | Woodhead Publishing in mechanical engineering |
| Soggetto topico |
Probabilities
Industrial engineering - Statistical methods Probabilitats Enginyeria industrial - Mètodes estadístics |
| ISBN |
0-08-100111-8
0-08-100091-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems: A Practical Approach; Copyright; Dedication; Contents; Preface; Foreword; Biography of the external contributors to this book; Abbreviations; Chapter 1: Introduction to rare event probability estimation; 1.1 The book purposes; 1.2 What are the events of interest considered in this book?; 1.3 The book organization; References; Part One: Essential Background in Mathematics and System Analysis; Chapter 2: Basics of probability and statistics; 2.1 Probability theory operators; 2.1.1 Elements of vocabulary
2.1.2 Notion of dependence of random events andconditional probabilities 2.1.3 Continuous random variables; 2.1.3.1 Definitions; 2.1.3.2 Parameters of continuous random variables; 2.1.4 Continuous multivariate random variables; 2.1.4.1 Definitions and theorems; 2.1.4.2 Dependence of multivariate random variables ; 2.1.5 Point estimation ; 2.2 Random variable modeling; 2.2.1 Overview of common probability distributions; 2.2.1.1 Univariate distributions; Uniform distribution; Exponential distribution; Gaussian distribution; Truncated Gaussian distribution; Log-normal distribution Cauchy distributionChi-squared distribution; Gamma and beta distributions; Laplace distribution; Some properties of univariate distributions; 2.2.1.2 Multivariate distributions; Multivariate normal distribution; 2.2.2 Kernel-based laws; 2.3 Convergence theorems and sampling algorithms; 2.3.1 Strong law of large numbers ; 2.3.2 Central limit theorem ; 2.3.3 Simulation of complex laws using the Metropolis-Hastings algorithm; 2.3.3.1 Markov chain; 2.3.3.2 Some properties of transition kernels; 2.3.3.3 The Metropolis-Hastings algorithm ; 2.3.3.4 Transformation of random variables; References Chapter 3: The formalism of rare event probability estimation in complex systems3.1 Input-output system; 3.1.1 Description; 3.1.2 Formalism; 3.2 Time-variant system; 3.2.1 Description; 3.2.2 Formalism; 3.3 Characterization of a probability estimation; References; Part Two: Practical Overview of the Main Rare Event EstimationTechniques; Chapter 4: Introduction; 4.1 Categories of estimation methods; 4.2 General notations; 4.3 Description of the toy cases; 4.3.1 Identity function; 4.3.2 Polynomial square root function; 4.3.3 Four-branch system; 4.3.4 Polynomial product function; References Chapter 5: Simulation techniques5.1 Crude Monte Carlo; 5.1.1 Principle; 5.1.2 Application on a toy case; Four-branch system; 5.1.3 Conclusion; 5.2 Simple variance reduction techniques; 5.2.1 Quasi-Monte Carlo; 5.2.2 Conditional Monte Carlo; 5.2.2.1 Principle; 5.2.2.2 Conclusion; 5.2.3 Control variates; 5.2.3.1 Principle; 5.2.3.2 Application on a toy case; Four-branch system; 5.2.3.3 Conclusion; 5.2.4 Antithetic variates; 5.2.4.1 Principle; 5.2.4.2 Application to a toy case; Identity function; 5.2.4.3 Conclusion; 5.3 Importance sampling; 5.3.1 Principle of importance sampling 5.3.2 Nonadaptive importance sampling |
| Record Nr. | UNINA-9910815465303321 |
|
Morio Jérôme
|
||
| Waltham, MA : , : Elsevier, , [2016] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial data analytics for diagnosis and prognosis : a random effects modelling approach / / Shiyu Zhou, Yong Chen
| Industrial data analytics for diagnosis and prognosis : a random effects modelling approach / / Shiyu Zhou, Yong Chen |
| Autore | Zhou Shiyu <1970-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (353 pages) |
| Disciplina | 658.00727 |
| Soggetto topico |
Random data (Statistics)
Industrial management - Mathematics Industrial engineering - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-5231-4353-3
1-119-66630-9 1-119-66627-9 1-119-66629-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Industrial Data Analytics for Diagnosis and Prognosis -- Contents -- Preface -- Acknowledgments -- Acronyms -- Table of Notation -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Scope and Organization of the Book -- 1.3 How to Use This Book -- Bibliographic Note -- Part 1 Statistical Methods and Foundation for Industrial Data Analytics -- 2 Introduction to Data Visualization and Characterization -- 2.1 Data Visualization -- 2.1.1 Distribution Plots for a Single Variable -- 2.1.2 Plots for Relationship Between Two Variables -- 2.1.3 Plots for More than Two Variables -- 2.2 Summary Statistics -- 2.2.1 Sample Mean, Variance, and Covariance -- 2.2.2 Sample Mean Vector and Sample Covariance Matrix -- 2.2.3 Linear Combination of Variables -- Bibliographic Notes -- Exercises -- 3 Random Vectors and the Multivariate Normal Distribution -- 3.1 Random Vectors -- 3.2 Density Function and Properties of Multivariate Normal Distribution -- 3.3 Maximum Likelihood Estimation for Multivariate Normal Distribution -- 3.4 Hypothesis Testing on Mean Vectors -- 3.5 Bayesian Inference for Normal Distribution -- Bibliographic Notes -- Exercises -- 4 Explaining Covariance Structure: Principal Components -- 4.1 Introduction to Principal Component Analysis -- 4.1.1 Principal Components for More Than Two Variables -- 4.1.2 PCA with Data Normalization -- 4.1.3 Visualization of Principal Components -- 4.1.4 Number of Principal Components to Retain -- 4.2 Mathematical Formulation of Principal Components -- 4.2.1 Proportion of Variance Explained -- 4.2.2 Principal Components Obtained from the Correlation Matrix -- 4.3 Geometric Interpretation of Principal Components -- 4.3.1 Interpretation Based on Rotation -- 4.3.2 Interpretation Based on Low-Dimensional Approximation -- Bibliographic Notes -- Exercises.
5 Linear Model for Numerical and Categorical Response Variables -- 5.1 Numerical Response - Linear Regression Models -- 5.1.1 General Formulation of Linear Regression Model -- 5.1.2 Significance and Interpretation of Regression Coefficients -- 5.1.3 Other Types of Predictors in Linear Models -- 5.2 Estimation and Inferences of Model Parameters for Linear Regression -- 5.2.1 Least Squares Estimation -- 5.2.2 Maximum Likelihood Estimation -- 5.2.3 Variable Selection in Linear Regression -- 5.2.4 Hypothesis Testing -- 5.3 Categorical Response - Logistic Regression Model -- 5.3.1 General Formulation of Logistic Regression Model -- 5.3.2 Significance and Interpretation of Model Coefficients -- 5.3.3 Maximum Likelihood Estimation for Logistic Regression -- Bibliographic Notes -- Exercises -- 6 Linear Mixed Effects Model -- 6.1 Model Structure -- 6.2 Parameter Estimation for LME Model -- 6.2.1 Maximum Likelihood Estimation Method -- 6.2.2 Distribution-Free Estimation Methods -- 6.3 Hypothesis Testing -- 6.3.1 Testing for Fixed Effects -- 6.3.2 Testing for Variance-Covariance Parameters -- Bibliographic Notes -- Exercises -- Part 2 Random Effects Approaches for Diagnosis and Prognosis -- 7 Diagnosis of Variation Source Using PCA -- 7.1 Linking Variation Sources to PCA -- 7.2 Diagnosis of Single Variation Source -- 7.3 Diagnosis of Multiple Variation Sources -- 7.4 Data Driven Method for Diagnosing Variation Sources -- Bibliographic Notes -- Exercises -- 8 Diagnosis of Variation Sources Through Random Effects Estimation -- 8.1 Estimation of Variance Components -- 8.2 Properties of Variation Source Estimators -- 8.3 Performance Comparison of Variance Component Estimators -- Bibliographic Notes -- Exercises -- 9 Analysis of System Diagnosability -- 9.1 Diagnosability of Linear Mixed Effects Model -- 9.2 Minimal Diagnosable Class. 9.3 Measurement System Evaluation Based on System Diagnosability -- Bibliographic Notes -- Exercises -- Appendix -- 10 Prognosis Through Mixed Effects Models for Longitudinal Data -- 10.1 Mixed Effects Model for Longitudinal Data -- 10.2 Random Effects Estimation and Prediction for an Individual Unit -- 10.3 Estimation of Time-to-Failure Distribution -- 10.4 Mixed Effects Model with Mixture Prior Distribution -- 10.4.1 Mixture Distribution -- 10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data -- 10.5 Recursive Estimation of Random Effects Using Kalman Filter -- 10.5.1 Introduction to the Kalman Filter -- 10.5.2 Random Effects Estimation Using the Kalman Filter -- Biographical Notes -- Exercises -- Appendix -- 11 Prognosis Using Gaussian Process Model -- 11.1 Introduction to Gaussian Process Model -- 11.2 GP Parameter Estimation and GP Based Prediction -- 11.3 Pairwise Gaussian Process Model -- 11.3.1 Introduction to Multi-output Gaussian Process -- 11.3.2 Pairwise GP Modeling Through Convolution Process -- 11.4 Multiple Output Gaussian Process for Multiple Signals -- 11.4.1 Model Structure -- 11.4.2 Model Parameter Estimation and Prediction -- 11.4.3 Time-to-Failure Distribution Based on GP Predictions -- Bibliographical Notes -- Exercises -- 12 Prognosis Through Mixed Effects Models for Time-to-Event Data -- 12.1 Models for Time-to-Event Data Without Covariates -- 12.1.1 Parametric Models for Time-to-Event Data -- 12.1.2 Non-parametric Models for Time-to-Event Data -- 12.2 Survival Regression Models -- 12.2.1 Cox PH Model with Fixed Covariates -- 12.2.2 Cox PH Model with Time Varying Covariates -- 12.2.3 Assessing Goodness of Fit -- 12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data -- 12.3.1 Structure of Joint Model and Parameter Estimation -- 12.3.2 Online Event Prediction for a New Unit. 12.4 Cox PH Model with Frailty Term for Recurrent Events -- Bibliographical Notes -- Exercises -- Appendix -- Appendix: Basics of Vectors, Matrices, and Linear Vector Space -- References -- Index. |
| Record Nr. | UNINA-9910555198203321 |
|
Zhou Shiyu <1970->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial data analytics for diagnosis and prognosis : a random effects modelling approach / / Shiyu Zhou, Yong Chen
| Industrial data analytics for diagnosis and prognosis : a random effects modelling approach / / Shiyu Zhou, Yong Chen |
| Autore | Zhou Shiyu <1970-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (353 pages) |
| Disciplina | 658.00727 |
| Soggetto topico |
Random data (Statistics)
Industrial management - Mathematics Industrial engineering - Statistical methods |
| ISBN |
1-5231-4353-3
1-119-66630-9 1-119-66627-9 1-119-66629-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Industrial Data Analytics for Diagnosis and Prognosis -- Contents -- Preface -- Acknowledgments -- Acronyms -- Table of Notation -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Scope and Organization of the Book -- 1.3 How to Use This Book -- Bibliographic Note -- Part 1 Statistical Methods and Foundation for Industrial Data Analytics -- 2 Introduction to Data Visualization and Characterization -- 2.1 Data Visualization -- 2.1.1 Distribution Plots for a Single Variable -- 2.1.2 Plots for Relationship Between Two Variables -- 2.1.3 Plots for More than Two Variables -- 2.2 Summary Statistics -- 2.2.1 Sample Mean, Variance, and Covariance -- 2.2.2 Sample Mean Vector and Sample Covariance Matrix -- 2.2.3 Linear Combination of Variables -- Bibliographic Notes -- Exercises -- 3 Random Vectors and the Multivariate Normal Distribution -- 3.1 Random Vectors -- 3.2 Density Function and Properties of Multivariate Normal Distribution -- 3.3 Maximum Likelihood Estimation for Multivariate Normal Distribution -- 3.4 Hypothesis Testing on Mean Vectors -- 3.5 Bayesian Inference for Normal Distribution -- Bibliographic Notes -- Exercises -- 4 Explaining Covariance Structure: Principal Components -- 4.1 Introduction to Principal Component Analysis -- 4.1.1 Principal Components for More Than Two Variables -- 4.1.2 PCA with Data Normalization -- 4.1.3 Visualization of Principal Components -- 4.1.4 Number of Principal Components to Retain -- 4.2 Mathematical Formulation of Principal Components -- 4.2.1 Proportion of Variance Explained -- 4.2.2 Principal Components Obtained from the Correlation Matrix -- 4.3 Geometric Interpretation of Principal Components -- 4.3.1 Interpretation Based on Rotation -- 4.3.2 Interpretation Based on Low-Dimensional Approximation -- Bibliographic Notes -- Exercises.
5 Linear Model for Numerical and Categorical Response Variables -- 5.1 Numerical Response - Linear Regression Models -- 5.1.1 General Formulation of Linear Regression Model -- 5.1.2 Significance and Interpretation of Regression Coefficients -- 5.1.3 Other Types of Predictors in Linear Models -- 5.2 Estimation and Inferences of Model Parameters for Linear Regression -- 5.2.1 Least Squares Estimation -- 5.2.2 Maximum Likelihood Estimation -- 5.2.3 Variable Selection in Linear Regression -- 5.2.4 Hypothesis Testing -- 5.3 Categorical Response - Logistic Regression Model -- 5.3.1 General Formulation of Logistic Regression Model -- 5.3.2 Significance and Interpretation of Model Coefficients -- 5.3.3 Maximum Likelihood Estimation for Logistic Regression -- Bibliographic Notes -- Exercises -- 6 Linear Mixed Effects Model -- 6.1 Model Structure -- 6.2 Parameter Estimation for LME Model -- 6.2.1 Maximum Likelihood Estimation Method -- 6.2.2 Distribution-Free Estimation Methods -- 6.3 Hypothesis Testing -- 6.3.1 Testing for Fixed Effects -- 6.3.2 Testing for Variance-Covariance Parameters -- Bibliographic Notes -- Exercises -- Part 2 Random Effects Approaches for Diagnosis and Prognosis -- 7 Diagnosis of Variation Source Using PCA -- 7.1 Linking Variation Sources to PCA -- 7.2 Diagnosis of Single Variation Source -- 7.3 Diagnosis of Multiple Variation Sources -- 7.4 Data Driven Method for Diagnosing Variation Sources -- Bibliographic Notes -- Exercises -- 8 Diagnosis of Variation Sources Through Random Effects Estimation -- 8.1 Estimation of Variance Components -- 8.2 Properties of Variation Source Estimators -- 8.3 Performance Comparison of Variance Component Estimators -- Bibliographic Notes -- Exercises -- 9 Analysis of System Diagnosability -- 9.1 Diagnosability of Linear Mixed Effects Model -- 9.2 Minimal Diagnosable Class. 9.3 Measurement System Evaluation Based on System Diagnosability -- Bibliographic Notes -- Exercises -- Appendix -- 10 Prognosis Through Mixed Effects Models for Longitudinal Data -- 10.1 Mixed Effects Model for Longitudinal Data -- 10.2 Random Effects Estimation and Prediction for an Individual Unit -- 10.3 Estimation of Time-to-Failure Distribution -- 10.4 Mixed Effects Model with Mixture Prior Distribution -- 10.4.1 Mixture Distribution -- 10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data -- 10.5 Recursive Estimation of Random Effects Using Kalman Filter -- 10.5.1 Introduction to the Kalman Filter -- 10.5.2 Random Effects Estimation Using the Kalman Filter -- Biographical Notes -- Exercises -- Appendix -- 11 Prognosis Using Gaussian Process Model -- 11.1 Introduction to Gaussian Process Model -- 11.2 GP Parameter Estimation and GP Based Prediction -- 11.3 Pairwise Gaussian Process Model -- 11.3.1 Introduction to Multi-output Gaussian Process -- 11.3.2 Pairwise GP Modeling Through Convolution Process -- 11.4 Multiple Output Gaussian Process for Multiple Signals -- 11.4.1 Model Structure -- 11.4.2 Model Parameter Estimation and Prediction -- 11.4.3 Time-to-Failure Distribution Based on GP Predictions -- Bibliographical Notes -- Exercises -- 12 Prognosis Through Mixed Effects Models for Time-to-Event Data -- 12.1 Models for Time-to-Event Data Without Covariates -- 12.1.1 Parametric Models for Time-to-Event Data -- 12.1.2 Non-parametric Models for Time-to-Event Data -- 12.2 Survival Regression Models -- 12.2.1 Cox PH Model with Fixed Covariates -- 12.2.2 Cox PH Model with Time Varying Covariates -- 12.2.3 Assessing Goodness of Fit -- 12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data -- 12.3.1 Structure of Joint Model and Parameter Estimation -- 12.3.2 Online Event Prediction for a New Unit. 12.4 Cox PH Model with Frailty Term for Recurrent Events -- Bibliographical Notes -- Exercises -- Appendix -- Appendix: Basics of Vectors, Matrices, and Linear Vector Space -- References -- Index. |
| Record Nr. | UNINA-9910830968103321 |
|
Zhou Shiyu <1970->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial methods for the effective development and testing of defense systems [[electronic resource] /] / National Research Council of the National Academies
| Industrial methods for the effective development and testing of defense systems [[electronic resource] /] / National Research Council of the National Academies |
| Pubbl/distr/stampa | Washington, D.C., : National Academies Press, c2012 |
| Descrizione fisica | 1 online resource (103 p.) |
| Disciplina | 620 |
| Soggetto topico |
Industrial engineering - Statistical methods
Military research - United States Industrial engineering - Development - Testing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-12344-3
9786613527301 0-309-22271-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Workshop Summary -- Requirements Setting -- Design and Development -- Testing Methods -- Communication, Resources, and Infrastructure -- Organizational Structures and Related Issues -- References -- Appendix A: Workshop Agenda -- Appendix B: Overview of the Defense Milestone System -- Appendix C: Biographical Sketches of Panel Members and Staff -- Committee on National Statistics. |
| Record Nr. | UNINA-9910461662103321 |
| Washington, D.C., : National Academies Press, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial methods for the effective development and testing of defense systems [[electronic resource] /] / National Research Council of the National Academies
| Industrial methods for the effective development and testing of defense systems [[electronic resource] /] / National Research Council of the National Academies |
| Pubbl/distr/stampa | Washington, D.C., : National Academies Press, c2012 |
| Descrizione fisica | 1 online resource (103 p.) |
| Disciplina | 620 |
| Soggetto topico |
Industrial engineering - Statistical methods
Military research - United States Industrial engineering - Development - Testing |
| ISBN |
0-309-22273-7
1-280-12344-3 9786613527301 0-309-22271-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Workshop Summary -- Requirements Setting -- Design and Development -- Testing Methods -- Communication, Resources, and Infrastructure -- Organizational Structures and Related Issues -- References -- Appendix A: Workshop Agenda -- Appendix B: Overview of the Defense Milestone System -- Appendix C: Biographical Sketches of Panel Members and Staff -- Committee on National Statistics. |
| Record Nr. | UNINA-9910789915703321 |
| Washington, D.C., : National Academies Press, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Industrial methods for the effective development and testing of defense systems / / National Research Council of the National Academies
| Industrial methods for the effective development and testing of defense systems / / National Research Council of the National Academies |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Washington, D.C., : National Academies Press, c2012 |
| Descrizione fisica | 1 online resource (103 p.) |
| Disciplina | 620 |
| Soggetto topico |
Industrial engineering - Statistical methods
Military research - United States Industrial engineering - Development - Testing |
| ISBN |
0-309-22273-7
1-280-12344-3 9786613527301 0-309-22271-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Workshop Summary -- Requirements Setting -- Design and Development -- Testing Methods -- Communication, Resources, and Infrastructure -- Organizational Structures and Related Issues -- References -- Appendix A: Workshop Agenda -- Appendix B: Overview of the Defense Milestone System -- Appendix C: Biographical Sketches of Panel Members and Staff -- Committee on National Statistics. |
| Record Nr. | UNINA-9910827984903321 |
| Washington, D.C., : National Academies Press, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Selected topics in manufacturing : AITeM Young Researcher Award 2021 / / Luigi Carrino and Tullio Tolio
| Selected topics in manufacturing : AITeM Young Researcher Award 2021 / / Luigi Carrino and Tullio Tolio |
| Autore | Carrino Luigi |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (193 pages) |
| Disciplina | 519.5 |
| Collana | Lecture Notes in Mechanical Engineering |
| Soggetto topico |
Industrial engineering - Statistical methods
Production engineering - Automation |
| ISBN | 3-030-82627-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910523001103321 |
|
Carrino Luigi
|
||
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics in industry / edited by R. Khattree, C. R. Rao
| Statistics in industry / edited by R. Khattree, C. R. Rao |
| Pubbl/distr/stampa | Amsterdam : Elsevier, 2003 |
| Descrizione fisica | xxi, 1187 p. : ill. ; 25 cm |
| Disciplina | 670.15195 |
| Altri autori (Persone) |
Khattree, Ravindra
Rao, Calyampudi Radhakrishna |
| Collana | Handbook of statistics, 0169-7161 ; 22 |
| Soggetto topico |
Industrial engineering - Statistical methods
Research, Industrial - Methodology |
| ISBN | 0444506144 |
| Classificazione |
AMS 62-06
AMS 62P30 LC T57.35.S73 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991001592709707536 |
| Amsterdam : Elsevier, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
