The complexity of many failure mechanisms and variations in component manufacture often make standard probability distributions inadequate for reliability modeling. Finite mixture distributions provide the necessary flexibility for modeling such complex phenomena but add considerable difficulty to the inference. This difficulty is overcome by drawing an analogy to neural networks. With appropropriate modifications, a neural network can represent a finite mixture CDF or PDF exactly. Training with Bayesian Regularization gives an efficient empirical Bayesian inference of the failure time distribution. Training also yields an effective number of parameters from which the number of components in the mixture can be estimated. Credible sets for functions of the model parameters can be estimated using a simple closed-form expression. Complete, censored, and inpection samples can be considered by appropriate choice of the likelihood function. In this work, architectures for Exponential, Weibull, Normal, and Log-Normal mixture networks have been derived. The capabilities of mixture networks have been demonstrated for complete, censored, and inspection samples from Weibull and Log-Normal mixtures. Furthermore, mixture networks' ability to model arbitrary failure distributions has been demonstrated. A sensitivity analysis has been performed to determine how mixture network estimator errors are affected my mixture component spacing and sample size. It is shown that mixture network estimators are asymptotically unbiased and that errors decay with sample size at least as well as with MLE.

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Šiuo darbu pristatoma sukurta sistema, kuria galima modeliuoti ir valdyti rizikas, kylančias iš IT, susijusias su IS nepasiekiamumu ar lėtu veikimu. Sistema realizuota pasitelkus neuroninius tinklus ir yra apmokoma sukaupta statistine informacija iš informacinių sistemų. Jai nurodoma, kurios statistinės informacijos laiko eilutes norima modeliuoti – t.y. kurios iš jų yra rizikos išraiška (serverių apkrovimas, IS atsakymo laikas ir pan.). Sistema pati nustato koreliuojančias statistines laiko eilutes, sugrupuoja susijusias ir kiekvienai grupei sukuria po modelį – apibendrina iki tol nežinomą priklausomybę tarp laiko eilučių pasitelkusi neuroninį tinklą. Kiekvienam iš tų modelių pateikus įtakojančių parametrų reikšmes, sistema sumodeliuoja rizikos parametro reikšmę. Eksperimentai parodė, jog sistema gali būti sėkmingai naudojama mišriame IT ūkyje ir geba modeliuoti įvairius IT bei IS komponentų parametrus, kurie sąlygoja rizikas. / By this work we present an IT risk management system, which is capable to model and manage risks that arise from IT wich are related with IS downtimes and slow response times. The system is implemented by using a proposed neural network architecture as a heart of the modeling engine. It is trained with accumulated datasets from existing information systems. The user shows for the system which statistical data time series one needs to model – i.e. the one which represents the risk (like server load, IS response time, etc.). The system automatically determines correlated statistical time series, groups them and creates a separate model for each group – this model generalizes until then unknown relationship between time series by invoking neural network. The model then accepts values of the input parameters and the system models the value of the risk parameter. Experiments have shown that the proposed system can be successfully used in a mixed IT environment and can be rewarding for one who tracks IT risks coming from various IT and IS components.

An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of a material. This ANN model establishes a relationship between flow stress and dislocation structure content. The density of geometrically necessary dislocations (GNDs) was calculated based on analysis of local lattice curvature evolution. The model includes essential statistical measures extracted from the distributions of dislocation microstructures, including substructure cell size, wall thickness, and GND density as the input variables to the ANN model. The model was able to successfully predict the flow stress of aluminum alloy 6022 as a function of its dislocation structure content. Furthermore, a sensitivity analysis was performed to identify the significance of individual dislocation parameters on the flow stress. The results show that an ANN model can be used to calibrate and predict inelastic material properties that are often cumbersome to model with rigorous dislocation-based plasticity models.

La caractérisation expérimentale de sources acoustiques est l'une des étapes essentielles pour la réduction des nuisances sonores produites par les machines industrielles. L'objectif de la thèse est de mettre au point une procédure complète visant à localiser et à quantifier des sources acoustiques stationnaires ou non sur un maillage surfacique par la rétro-propagation d'un champ de pression mesuré par un réseau de microphones. Ce problème inverse est délicat à résoudre puisqu'il est généralement mal-conditionné et sujet à de nombreuses sources d'erreurs. Dans ce contexte, il est capital de s'appuyer sur une description réaliste du modèle de propagation acoustique direct. Dans le domaine fréquentiel, la méthode des sources équivalentes a été adaptée au problème de l'imagerie acoustique dans le but d'estimer les fonctions de transfert entre les sources et l'antenne, en prenant en compte le phénomène de diffraction des ondes autour de l'objet d'intérêt. Dans le domaine temporel, la propagation est modélisée comme un produit de convolution entre la source et une réponse impulsionnelle décrite dans le domaine temps-nombre d'onde. Le caractère sous-déterminé du problème acoustique inverse implique d'utiliser toutes les connaissances a priori disponibles sur le champ sources. Il a donc semblé pertinent d'employer une approche bayésienne pour résoudre ce problème. Des informations a priori disponibles sur les sources acoustiques ont été mises en équation et il a été montré que la prise en compte de leur parcimonie spatiale ou de leur rayonnement omnidirectionnel pouvait améliorer significativement les résultats. Dans les hypothèses formulées, la solution du problème inverse s'écrit sous la forme régularisée de Tikhonov. Le paramètre de régularisation a été estimé par une approche bayésienne empirique. Sa supériorité par rapport aux méthodes communément utilisées dans la littérature a été démontrée au travers d'études numériques et expérimentales. En présence de fortes variabilités du rapport signal à bruit au cours du temps, il a été montré qu'il est nécessaire de mettre à jour sa valeur afin d'obtenir une solution satisfaisante. Finalement, l'introduction d'une variable manquante au problème reflétant la méconnaissance partielle du modèle de propagation a permis, sous certaines conditions, d'améliorer l'estimation de l'amplitude complexe des sources en présence d'erreurs de modèle. Les développements proposés ont permis de caractériser, in situ, la puissance acoustique rayonnée par composant d'un groupe motopropulseur automobile par la méthode de la focalisation bayésienne dans le cadre du projet Ecobex. Le champ acoustique cyclo-stationnaire généré par un ventilateur automobile a finalement été analysé par la méthode d'holographie acoustique de champ proche temps réel. / Experimental characterization of acoustic sources is one of the essential steps for reducing noise produced by industrial machinery. The aim of the thesis is to develop a complete procedure to localize and quantify both stationary and non-stationary sound sources radiating on a surface mesh by the back-propagation of a pressure field measured by a microphone array. The inverse problem is difficult to solve because it is generally ill-conditioned and subject to many sources of error. In this context, it is crucial to rely on a realistic description of the direct sound propagation model. In the frequency domain, the equivalent source method has been adapted to the acoustic imaging problem in order to estimate the transfer functions between the source and the antenna, taking into account the wave scattering. In the time domain, the propagation is modeled as a convolution product between the source and an impulse response described in the time-wavenumber domain. It seemed appropriate to use a Bayesian approach to use all the available knowledge about sources to solve this problem. A priori information available about the acoustic sources have been equated and it has been shown that taking into account their spatial sparsity or their omnidirectional radiation could significantly improve the results. In the assumptions made, the inverse problem solution is written in the regularized Tikhonov form. The regularization parameter has been estimated by an empirical Bayesian approach. Its superiority over methods commonly used in the literature has been demonstrated through numerical and experimental studies. In the presence of high variability of the signal to noise ratio over time, it has been shown that it is necessary to update its value to obtain a satisfactory solution. Finally, the introduction of a missing variable to the problem reflecting the partial ignorance of the propagation model could improve, under certain conditions, the estimation of the complex amplitude of the sources in the presence of model errors. The proposed developments have been applied to the estimation of the sound power emitted by an automotive power train using the Bayesian focusing method in the framework of the Ecobex project. The cyclo-stationary acoustic field generated by a fan motor was finally analyzed by the real-time near-field acoustic holography method.

[pt] Esta dissertação de mestrado analisa os efeitos de previsão de séries temporais com redes neurais em conjunto com a técnica de poda, denominada de Regularização Bayesiana. Utilizam-se diversas séries simuladas cujo processo gerador é de fato linear para comparar as previsões feitas por meio de modelos auto-regressivos lineares e redes neurais. Apresenta-se,ao final, uma comparação entre os modelos citados acima, segundo à eficiência preditiva de cada um. / [en] This paper studies the performance of neural networks estimated with Bayesian regularization to model and forecast time series where the data generations process is in fact linear. A simulation experiment is carried out to compare the forecast made by linear autoregressive models and neural networks.

Tato práce pojednává o technikách behaviorálního modelování pro speciální úlohy v elektromagnetismu, které je možno formulovat jako problém aproximace, klasifikace, odhadu hustoty pravděpodobnosti nebo kombinatorické optimalizace. Zkoumané methody se dotýkají dvou základních problémů ze strojového učení a combinatorické optimalizace: ”bias vs. variance dilema” a NP výpočetní komplexity. Boltzmanův stroj je v práci navržen ke zjednodušování komplexních impedančních sítí. Bayesovský přístup ke strojovému učení je upraven pro regularizaci Parzenova okna se snahou o vytvoření obecného kritéria pro regularizaci pravděpodobnostní a regresní neuronové sítě.

<p>A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions.</p> <p>The nowcast model was developed based on historical tributary flows and water quality measurements (physical, chemical and biological). The model uses experimentally available information such as total dissolved solids, total suspended solids, pH, electrical conductivity, and water temperature to estimate whether the E. Coli counts would exceed the acceptable standard. For setting up this model, field data collection was carried out during 2019 beachgoer’s season.</p> <p>IDEM recommends posting an advisory at the beach indicating swimming and wading are not recommended when E. coli counts exceed advisory standards. Based on the advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony forming units per 100 milliliters (cfu/100ml). Advisories are removed when bacterial levels fall within the acceptable standard. However, the E. coli results were available after a time lag leading to beach closures from previous day results. Nowcast models allow beach managers to make real-time beach advisory decisions instead of waiting a day or more for laboratory results to become available.</p> <p>Using the historical data, an extensive experiment was carried out, to obtain the suitable input variables and optimal neural network architecture. The best feed-forward neural network model was developed using Bayesian Regularization Neural Network (BRNN) training algorithm. Developed ANN model showed an average prediction accuracy of around 87% in predicting the E. coli classes. </p>

<p dir="ltr">For turbocharged engine simulation analysis, manufacturer-provided data are typically used to predict the mass flow and efficiency of the turbine. To create a turbine map, physical tests are performed in labs at various turbine speeds and expansion ratios. These tests can be very expensive and time-consuming. Current testing methods can have limitations that result in errors in the turbine map. As such, only a modest set of data can be generated, all of which have to be interpolated and extrapolated to create a smooth surface that can then be used for simulation analysis.</p><p><br></p><p dir="ltr">The current method used by the manufacturer is a physics-informed polynomial regression model that depends on the Blade Speed Ratio (BSR ) in the polynomial function to model the efficiency and MFP. This method is memory-consuming and provides a lower-than-desired accuracy. This model is decades old and must be updated with new state-of-the-art Machine Learning models to be more competitive. Currently, CTT is facing up to +/-2% error in most turbine maps for efficiency and MFP and the aim is to decrease the error to 0.5% while interpolating the data points in the available region. The current model also extrapolates data to regions where experimental data cannot be measured. Physical tests cannot validate this extrapolation and can only be evaluated using CFD analysis.</p><p><br></p><p dir="ltr">The thesis focuses on investigating different AI techniques to increase the accuracy of the model for interpolation and evaluating the models for extrapolation. The data was made available by CTT. The available data consisted of various turbine parameters including ER, turbine speeds, efficiency, and MFP which were considered significant in turbine modeling. The AI models developed contained the above 4 parameters where ER and turbine speeds are predictors and, efficiency and MFP are the response. Multiple supervised ML models such as SVM, GPR, LMANN, BRANN, and GBPNN were developed and evaluated. From the above 5 ML models, BRANN performed the best achieving an error of 0.5% across multiple turbines for efficiency and MFP. The same model was used to demonstrate extrapolation, where the model gave unreliable predictions. Additional data points were inputted in the training data set at the far end of the testing regions which greatly increased the overall look of the map.</p><p><br></p><p dir="ltr">An additional contribution presented here is to completely predict an expansion ratio line and evaluate with CTT test data points where the model performed with an accuracy of over 95%. Since physical testing in a lab is expensive and time-consuming, another goal of the project was to reduce the number of data points provided for ANN model training. Furthermore, strategically reducing the data points is of utmost importance as some data points play a major role in the training of ANN and can greatly affect the model's overall accuracy. Up to 50% of the data points were removed for training inputs and it was found that BRANN was able to predict a satisfactory turbine map while reducing 20% of the overall data points at various regions.</p>