Siemens gas turbines are monitored and controlled by a large number of sensors and actuators. Process information is stored in a database and used for offline calculations and analyses. Before storing the sensor readings, a compression algorithm checks the signal and skips the values that explain no significant change. Compression of 90 % is not unusual. Since data from the database is used for analyses and decisions are made upon results from these analyses it is important to have a system for validating the data in the database. Decisions made on false information can result in large economic losses. When this project was initiated no sensor validation system was available. In this thesis the uncertainties in measurement chains are revealed. Methods for fault detection are investigated and finally the most promising methods are put to the test. Linear relationships between redundant sensors are derived and the residuals form an influence structure allowing the faulty sensor to be isolated. Where redundant sensors are not available, a gas turbine model is utilized to state the input-output relationships so that estimates of the sensor outputs can be formed. Linear parametric models and an ANN (Artificial Neural Network) are developed to produce the estimates. Two techniques for the linear parametric models are evaluated; prediction and simulation. The residuals are also evaluated in two ways; direct evaluation against a threshold and evaluation with the CUSUM (CUmulative SUM) algorithm. The results show that sensor validation using compressed data is feasible. Faults as small as 1% of the measuring range can be detected in many cases.

The creep behavior of unreinforced 2124 aluminum and 2124 aluminum reinforced with 15 w/o silicon carbide whiskers was studied at temperatures from 250 F to 500 F. Tensile tests were conducted to determine the basic mechanical properties, and microstructural and chemical anyalyses were performed to characterize the starting materials. The creep, tensile, and microstructural data for the 2124+SiC composite were compared with a similarly processed unreinforced 2124 aluminum alloy. Applying the basic theories for power law creep developed for common metals and alloys, the creep stress exponents and activation energies for creep were determined from the experimental data. The results were used to identify creep deformation mechanisms and compared to predicted values based on a parametric approach for creep analysis. The results demonstrate the applicability of traditional creep analysis on non-traditional materials. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / Background: Tobacco use is associated with an increased risk of developing Alzheimer's disease (AD). 14% of the incidence of AD is associated with various types of tobacco exposure. Additional real-world evidence is warranted to reveal the association between tobacco use and AD in age/gender-specific subpopulations. Method: In this thesis, the relationships between diagnoses related to tobacco use and diagnoses of AD in gender- and age-specific subgroups were investigated, using health information exchange data. The non-parametric Kaplan-Meier method was used to estimate the incidence of AD. Furthermore, the log-rank test was used to compare incidence between individuals with and without tobacco related diagnoses. In addition, we used semi-parametric Cox models to examine the association between tobacco related diagnoses and diagnoses of AD, while adjusting covariates. Results: Tobacco related diagnosis was associated with increased risk of developing AD comparing to no tobacco related diagnosis among individuals aged 60-74 years (female hazard ratio [HR] =1.26, 95% confidence interval [CI]: 1.07 – 1.48, p-value = 0.005; and male HR =1.33, 95% CI: 1.10 - 1.62, p-value =0.004). Tobacco related diagnosis was associated with decreased risk of developing AD comparing to no tobacco related diagnosis among individuals aged 75-100 years (female HR =0.79, 95% CI: 0.70 - 0.89, p-value =0.001; and male HR =0.90, 95% CI: 0.82 - 0.99, p-value =0.023). Conclusion: Individuals with tobacco related diagnoses were associated with an increased risk of developing AD in older adults aged 60-75 years. Among older adults aged 75-100 years, individuals with tobacco related diagnoses were associated with a decreased risk of developing AD.

El proyecto de investigación propuesto se enmarca dentro del área de diseño de producto con aplicaciones de modelado sólido CAD/CAM (Computer Aided Design/Computer Aided Manufacturing). Concretamente, se pretende hacer un estudio de las herramientas de anotación asociativas disponibles en las aplicaciones comerciales de modelado CAD con el fin de analizar su uso, viabilidad, eficiencia y efectos en la modificación y reutilización de modelos digitales 3D, así como en la gestión y comunicación del conocimiento técnico vinculado al diseño. La idea principal de esta investigación doctoral es establecer un método para representar y evaluar el conocimiento implícito de los ingenieros de diseño acerca de un modelo digital, así como la integración dinámica de dicho conocimiento en el propio modelo CAD, a través de anotaciones, con el objetivo de poder almacenar y comunicar eficientemente la mayor cantidad de información útil acerca del modelo, y reducir el tiempo y esfuerzo requeridos para su alteración y/o reutilización. / Dorribo Camba, J. (2014). ANNOTATION MECHANISMS TO MANAGE DESIGN KNOWLEDGE IN COMPLEX PARAMETRIC MODELS AND THEIR EFFECTS ON ALTERATION AND REUSABILITY [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/45997

The aim of this work is to lay down a formalism for parametric models that encapsulates both Classical and Quantum Information Geometry. This will be done introducing parametric models on spaces of normal positive linear functionals on W*-algebras and providing a way of defining a Riemannian structure on this models that comes from the Jordan product of the W*-algebra. This Riemannian structure will have some features that are appealing from the viewpoint of Information Geometry. After introducing this W*-algebraic framework, we will move to Estimation Theory. We will see how and to what extent it is possible to formulate in this framework two well-known statistical bounds: the Cramér-Rao bound and the Helstrom bound. Finally, we will explicitly construct some examples that show how it is possible to reduce this general framework to obtain well-known structures in Classical and Quantum Information Geometry.

The construction sector is embossed with uncertainty, where cash flow prediction, time delays, and complex feature interaction make it hard to predict which future projects will be profitable or not. This thesis explores the prediction of project yield and project success for a company in the construction industry using supervised learning models. Leveraging historical project data, parametric traditional regression and machine learning techniques are employed to develop predictive models for project yield and project success. The models were chosen based on previously related work and consultations with employees with domain knowledge in the industry. The study aims to identify the most effective modeling approach for yield prediction and success in construction projects through comprehensive analysis and comparison. The features influencing project yield are investigated using SHAP (SHapley Additive exPla-nations) and permutation feature importance (PFI) values. These explainability techniquesprovide insights into feature importance in the models, thereby enhancing the understandingof the underlying factors driving project yield and project success. The results of this research contribute to the advancement of predictive modeling in the construction industry, offering valuable insights for project planning and decision-making. Construction companies can optimize resource allocation, mitigate risks, and improve projectoutcomes by accurately predicting project yield and success and understanding the keyfactors influencing it. The results in this thesis reveal that the machine-learning models outperform the parametric models overall. The best-performing models, based primarily on accuracy and ROI, were the random forest models with both binary and continuous outputs, leading to a suggested data-driven guideline for the company to use in their project decision-making process.

nÃo hà / Esta dissertaÃÃo tem por objetivo calcular as taxas de sobrevivÃncia dos participantes de Fundos de PensÃo do setor elÃtrico nacional, bem como encontrar o modelo paramÃtrico de sobrevivÃncia que melhor represente os dados em estudo. Para desenvolvimento do trabalho utilizamos dados de 14 entidades com informaÃÃes de participantes ativos e aposentados, com exceÃÃo dos invÃlidos, referentes ao perÃodo de 2001 a 2009, totalizando um nÃmero total de 100.000 vidas analisadas. Para calcular as taxas brutas de sobrevivÃncia, utilizamos o mÃtodo indireto, descrito em Ferreira (1985). ApÃs o cÃlculo das taxas originais, efetuamos o processo de suavizaÃÃo por mÃdias mÃveis, visando corrigir as flutuaÃÃes indesejadas obtidas na curva bruta de sobrevivÃncia. Mesmo apÃs o processo de suavizaÃÃo, optamos por restringir o estudo Ãs idades dentro do intervalo de 25 a 85 anos, dado o baixo nÃmero de Ãbitos e expostos nas idades supramencionadas. A partir da curva suavizada, aplicamos os modelos paramÃtricos de sobrevivÃncia de Gompertz, Gompertz-Makeham, Thiele e Helingman-Pollard, para testar o melhor ajuste da equaÃÃo. Os resultados mostraram que nenhum dos modelos paramÃtricos analisados se mostrou com robustez estatÃstica suficiente para se proceder a uma anÃlise preditiva com confiabilidade aceitÃvel. / This paper aims to calculate the survival rates of the participants of the Pension Funds electricity sector as well as finding the parametric survival model that best represents the data in the study. For development work we used data from 14 organizations with information of participants and retirees, with the exception of the disabled, for the period 2001 to 2009, amounting to a total of 100,000 lives analyzed. To calculate the crude rates of survival using the indirect method described in Ferreira (1985). After calculation of the original rates, we make the process of smoothing by moving averages in order to correct the unwanted fluctuations in the curve obtained crude survival. Even after the smoothing process, we chose to restrict the study to age within the range of 25 to 85 years, given the low number of deaths at ages above and exposed. From the smooth curve we apply the parametric models of survival Gompertz, Gompertz-Makeham, Thiele and Helingman-Pollard, to test the best fit of the equation. The results showed that none of the models proved to be analyzed with parametric statistical robust enough to conduct a predictive analysis with acceptable reliability.

[pt] Este trabalho apresenta um modelo de Regressão Linear Nebulosa por Partes(RLNP). Trata-se de uma estrutura que envolve modelos de regressão linear por partes ponderadas por pertinências advindas da lógica nebulosa. Este modelo é comparado com o modelo de regressão linear. Os resultados mostram que o RLNP consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui bom poder de ajuste. / [en] In this dissertation a Fuzzy Piece-Wise Linear Regressive model FPLieR is developed. The model´s structure combines linear regressive models with fuzzy logic´s grade of membership in a piece-wise fashion. A comparision is made between this model and the linear regression one. The results show that FPLieR is able to find the linear substructure of simulated data and that in most cases it presents a good fit.

Cette thèse est consacrée à l'étude de certains modèles de déformation semi-paramétriques. Notre objectif est de proposer des méthodes récursives, issues d'algorithmes stochastiques, pour estimer les paramètres de ces modèles. Dans la première partie, on présente les outils théoriques existants qui nous seront utiles dans la deuxième partie. Dans un premier temps, on présente un panorama général sur les méthodes d'approximation stochastique, en se focalisant en particulier sur les algorithmes de Robbins-Monro et de Kiefer-Wolfowitz. Dans un second temps, on présente les méthodes à noyaux pour l'estimation de fonction de densité ou de régression. On s'intéresse plus particulièrement aux deux estimateurs à noyaux les plus courants qui sont l'estimateur de Parzen-Rosenblatt et l'estimateur de Nadaraya-Watson, en présentant les versions récursives de ces deux estimateurs.Dans la seconde partie, on présente tout d'abord une procédure d'estimation récursive semi-paramétrique du paramètre de translation et de la fonction de régression pour le modèle de translation dans la situation où la fonction de lien est périodique. On généralise ensuite ces techniques au modèle vectoriel de déformation à forme commune en estimant les paramètres de moyenne, de translation et d'échelle, ainsi que la fonction de régression. On s'intéresse finalement au modèle de déformation paramétrique de variables aléatoires dans le cadre où la déformation est connue à un paramètre réel près. Pour ces trois modèles, on établit la convergence presque sûre ainsi que la normalité asymptotique des estimateurs paramétriques et non paramétriques proposés. Enfin, on illustre numériquement le comportement de nos estimateurs sur des données simulées et des données réelles. / This thesis is devoted to the study of some semi-parametric deformation models.Our aim is to provide recursive methods, related to stochastic algorithms, in order to estimate the different parameters of the models. In the first part, we present the theoretical tools which we will use in the next part. On the one hand, we focus on stochastic approximation methods, in particular the Robbins-Monro algorithm and the Kiefer-Wolfowitz algorithm. On the other hand, we introduce kernel estimators in order to estimate a probability density function and a regression function. More particularly, we present the two most famous kernel estimators which are the one of Parzen-Rosenblatt and the one of Nadaraya-Watson. We also present their recursive version.In the second part, we present the results we obtained in this thesis.Firstly, we provide a recursive estimation method of the shift parameter and the regression function for the translation model in which the regression function is periodic. Secondly, we extend this estimation procedure to the shape invariant model, providing estimation of the height parameter, the translation parameter and the scale parameter, as well as the common shape function.Thirdly, we are interested in the parametric deformation model of random variables where the deformation is known and depending on an unknown parameter.For these three models, we establish the almost sure convergence and the asymptotic normality of each estimator. Finally, we numerically illustrate the asymptotic behaviour of our estimators on simulated data and on real data.

Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommodate extremes and other domain constraints such as dynamical relations among variables, spatial and temporal constraints, need to provide uncertainty estimates and feature correlations, among others. We adopted a hierarchical Bayesian version of the sparse regression framework and exploited its inherent flexibility to accommodate the constraints. We applied sparse regression for the feature selection problem of statistical downscaling of the climate variables with particular focus on their extremes. This is important for many impact studies where the climate change information is required at a spatial scale much finer than that provided by the global or regional climate models. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and inform extremes downscaling. We propose a general-purpose sparse Bayesian framework for covariate discovery that accommodates the non-Gaussian distribution of extremes within a hierarchical Bayesian sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates and provide uncertainty estimates, using a variational Bayes approximation. The method is applied for selecting informative atmospheric covariates at multiple spatial scales as well as indices of large scale circulation and global warming related to frequency of precipitation extremes over continental United States. Our results confirm the dependence relations that may be expected from known precipitation physics and generates novel insights which can inform physical understanding. We plan to extend our model to discover covariates for extreme intensity in future. We further extend our framework to handle the dynamic relationship among the climate variables using a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP). The extended model can achieve simultaneous clustering and discovery of covariates within each cluster. Moreover, the a priori knowledge about association between pairs of data-points is incorporated in the model through must-link constraints on a Markov Random Field (MRF) prior. A scalable and efficient variational Bayes approach is developed to infer posteriors on regression coefficients and cluster variables. / Computer and Information Science