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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Análise dos resultados de ensaios de proficiência via modelos de regressão com variável explicativa aleatória / Analysis of proficiency tests results via regression models with random explanatory variable

Aline Othon Montanari 21 June 2004 (has links)
Em um programa de ensaio de prociência (EP) conduzido pelo Grupo de Motores, um grupo de onze laboratórios da área de temperatura realizaram medições em cinco pontos da escala de um termopar. Neste trabalho, propomos um modelo de regressão com variável explicativa X (aleatória) representando o termopar padrão que denominaremos por artefato e a variável dependente Y representando as medições dos laboratórios. O procedimento para a realização da comparação é simples, ambos termopares são colocados no forno e as diferenças entre as medições são registradas. Para a análise dos dados, vamos trabalhar com a diferença entre a diferença das medições do equipamento do laboratório e o artefato, e o valor de referência (que é determinado por 2 laboratórios que pertencem a Rede Brasileira de Calibração (RBC)). O erro de medição tem variância determinada por calibração, isto é, conhecida. Assim, vamos encontrar aproximações para as estimativas de máxima verossimilhança para os parâmetros do modelo via algoritmo EM. Além disso, propomos uma estratégia para avaliar a consistência dos laboratórios participantes do programa de EP / In a program of proficiency assay, a group of eleven laboratories of the temperature area had carried through measurements in ¯ve points on the scale of the thermopair. In this work, we propose a regression model with a random explanatory variable representing the temperature measured by the standard thermopair, which will be called device. The procedure for the comparison accomplishment is as follows. The device and the laboratory\'s thermopair to be tested are placed in the oven and the difererences between the measurements are registered. For the analysis of the data, the response variable is the diference between those diference and the reference value, which is determined by two laboratories that belong to the Brazilian Net of Calibration (RBC). The measurement error has variance determined by calibration which is known. Therefore, we ¯and the maximum likelihood estimates for the parameters of the model via EM algorithm. We consider a strategy to establish the consistency of the participant laboratories of the program of proficiency assay
12

Approche bayésienne de l'évaluation de l'incertitude de mesure : application aux comparaisons interlaboratoires

Demeyer, Séverine 04 March 2011 (has links)
La modélisation par équations structurelles est très répandue dans des domaines très variés et nous l'appliquons pour la première fois en métrologie dans le traitement de données de comparaisons interlaboratoires. Les modèles à équations structurelles à variables latentes sont des modèles multivariés utilisés pour modéliser des relations de causalité entre des variables observées (les données). Le modèle s'applique dans le cas où les données peuvent être regroupées dans des blocs disjoints où chaque bloc définit un concept modélisé par une variable latente. La structure de corrélation des variables observées est ainsi résumée dans la structure de corrélation des variables latentes. Nous proposons une approche bayésienne des modèles à équations structurelles centrée sur l'analyse de la matrice de corrélation des variables latentes. Nous appliquons une expansion paramétrique à la matrice de corrélation des variables latentes afin de surmonter l'indétermination de l'échelle des variables latentes et d'améliorer la convergence de l'algorithme de Gibbs utilisé. La puissance de l'approche structurelle nous permet de proposer une modélisation riche et flexible des biais de mesure qui vient enrichir le calcul de la valeur de consensus et de son incertitude associée dans un cadre entièrement bayésien. Sous certaines hypothèses l'approche permet de manière innovante de calculer les contributions des variables de biais au biais des laboratoires. Plus généralement nous proposons un cadre bayésien pour l'amélioration de la qualité des mesures. Nous illustrons et montrons l'intérêt d'une modélisation structurelle des biais de mesure sur des comparaisons interlaboratoires en environnement. / Structural equation modelling is a widespread approach in a variety of domains and is first applied here to interlaboratory comparisons in metrology. Structural Equation Models with latent variables (SEM) are multivariate models used to model causality relationships in observed variables (the data). It is assumed that data can be grouped into separate blocks each describing a latent concept modelled by a latent variable. The correlation structure of the observed variables is transferred into the correlation structure of the latent variables. A Bayesian approach of SEM is proposed based on the analysis of the correlation matrix of latent variables using parameter expansion to overcome identifiability issues and improving the convergence of the Gibbs sampler. SEM is used as a powerful and flexible tool to model measurement bias with the aim of improving the reliability of the consensus value and its associated uncertainty in a fully Bayesian framework. The approach also allows to compute the contributions of the observed variables to the bias of the laboratories, under additional hypotheses. More generally a global Bayesian framework is proposed to improve the quality of measurements. The approach is illustrated on the structural equation modelling of measurement bias in interlaboratory comparisons in environment.
13

Desenvolvimento de um protocolo de calibração utilizando espectrometria e simulação matemática, em feixes padrões de raios x / Development of a calibration protocol using spectrometry and mathematical simulation, in x ray standard beams

SANTOS, LUCAS R. dos 21 November 2017 (has links)
Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2017-11-21T11:20:13Z No. of bitstreams: 0 / Made available in DSpace on 2017-11-21T11:20:13Z (GMT). No. of bitstreams: 0 / A calibração, por definição, é o processo pelo qual se estabelece uma relação entre valores de medição de um padrão, com as suas respectivas incertezas, e as indicações com as incertezas associadas do instrumento de medição a ser calibrado. Um protocolo de calibração descreve a metodologia a ser aplicada em um processo de calibração. O método escolhido para a obtenção deste protocolo foi o da espectrometria de feixe de raios X associada à simulação pelo método de Monte Carlo, fundamentado no fato de que ambos são considerados métodos absolutos na determinação de parâmetros de feixes de radiação. Neste trabalho foi utilizado o método de Monte Carlo utilizado para obter a função resposta do detector utilizada para a correção dos espectros obtidos do feixe primário de radiação X; deste modo foram calculadas as taxas de kerma destes feixes e comparadas aos valores obtidos com as câmaras de ionização padrão secundário do Laboratório de Calibração de Instrumentos do IPEN (LCI/IPEN). Foram obtidos os coeficientes de calibração para o sistema padrão com diferenças em relação ao fornecido pelo laboratório primário entre 1,3% e 15,3%. Os resultados obtidos indicaram a viabilidade do estabelecimento deste protocolo de calibração utilizando a espectrometria como padrão de referência, com incertezas relativas de 0,62% para k=1. As incertezas associadas ao método proposto foram satisfatórias, para um laboratório padrão secundário e comparáveis a um laboratório primário. / Tese (Doutorado em Tecnologia Nuclear) / IPEN/T / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP
14

Approche bayésienne de l'évaluation de l'incertitude de mesure : application aux comparaisons interlaboratoires / Bayesian approach for the evaluation of measurement uncertainty applied to interlaboratory comparisons

Demeyer, Séverine 04 March 2011 (has links)
La modélisation par équations structurelles est très répandue dans des domaines très variés et nous l'appliquons pour la première fois en métrologie dans le traitement de données de comparaisons interlaboratoires. Les modèles à équations structurelles à variables latentes sont des modèles multivariés utilisés pour modéliser des relations de causalité entre des variables observées (les données). Le modèle s'applique dans le cas où les données peuvent être regroupées dans des blocs disjoints où chaque bloc définit un concept modélisé par une variable latente. La structure de corrélation des variables observées est ainsi résumée dans la structure de corrélation des variables latentes. Nous proposons une approche bayésienne des modèles à équations structurelles centrée sur l'analyse de la matrice de corrélation des variables latentes. Nous appliquons une expansion paramétrique à la matrice de corrélation des variables latentes afin de surmonter l'indétermination de l'échelle des variables latentes et d'améliorer la convergence de l'algorithme de Gibbs utilisé. La puissance de l'approche structurelle nous permet de proposer une modélisation riche et flexible des biais de mesure qui vient enrichir le calcul de la valeur de consensus et de son incertitude associée dans un cadre entièrement bayésien. Sous certaines hypothèses l'approche permet de manière innovante de calculer les contributions des variables de biais au biais des laboratoires. Plus généralement nous proposons un cadre bayésien pour l'amélioration de la qualité des mesures. Nous illustrons et montrons l'intérêt d'une modélisation structurelle des biais de mesure sur des comparaisons interlaboratoires en environnement. / Structural equation modelling is a widespread approach in a variety of domains and is first applied here to interlaboratory comparisons in metrology. Structural Equation Models with latent variables (SEM) are multivariate models used to model causality relationships in observed variables (the data). It is assumed that data can be grouped into separate blocks each describing a latent concept modelled by a latent variable. The correlation structure of the observed variables is transferred into the correlation structure of the latent variables. A Bayesian approach of SEM is proposed based on the analysis of the correlation matrix of latent variables using parameter expansion to overcome identifiability issues and improving the convergence of the Gibbs sampler. SEM is used as a powerful and flexible tool to model measurement bias with the aim of improving the reliability of the consensus value and its associated uncertainty in a fully Bayesian framework. The approach also allows to compute the contributions of the observed variables to the bias of the laboratories, under additional hypotheses. More generally a global Bayesian framework is proposed to improve the quality of measurements. The approach is illustrated on the structural equation modelling of measurement bias in interlaboratory comparisons in environment.

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