Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Esta tese tem por objetivo propor uma estratégia de obtenção automática de parâmetros hidrodinâmicos e de transporte através da solução de problemas inversos. A obtenção dos parâmetros de um modelo físico representa um dos principais problemas em sua calibração, e isso se deve em grande parte à dificuldade na medição em campo desses parâmetros. Em particular na modelagem de rios e estuários, a altura da rugosidade e o coeficiente de difusão turbulenta representam dois dos parâmetros com maior dificuldade de medição. Nesta tese é apresentada uma técnica automatizada de estimação desses parâmetros através deum problema inverso aplicado a um modelo do estuário do rio Macaé, localizado no norte do Rio de Janeiro. Para este estudo foi utilizada a plataforma MOHID, desenvolvida na Universidade Técnica de Lisboa, e que tem tido ampla aplicação na simulação de corpos hídricos. Foi realizada uma análise de sensibilidade das respostas do modelo com relação aos parâmetros de interesse. Verificou-se que a salinidade é uma variável sensível a ambos parâmetros. O problema inverso foi então resolvido utilizando vários métodos de otimização através do acoplamento da plataforma MOHID a códigos de otimização implementados em Fortran. O acoplamento foi realizado de forma a não alterar o código fonte do MOHID, possibilitando a utilização da ferramenta computacional aqui desenvolvida em qualquer versão dessa plataforma, bem como a sua alteração para o uso com outros simuladores. Os testes realizados confirmam a eficiência da técnica e apontam as melhores abordagens para uma rápida e precisa estimação dos parâmetros. / This thesis presents a strategy for automatically obtaining hydrodynamic and transport parameters by means of the solution of inverse problems. Obtaining the parameters of a physical model represents a major problem in its calibration, and this is largely due to the difficulty associated to the field measurements of these parameters. In particular by modeling rivers and estuaries, the roughness height and the turbulent diffusion coefficient represent two of the most difficult parameters to measure or estimate. Here, an automated technique for estimation of these parameters through an inverse problem is applied to a model of the Macaé estuary, located in northern Rio de Janeiro. For this investigation, hydrodynamic and transport models were built in the MOHID platform, developed in the Technical University of Lisbon, which has had wide application in simulation of water bodies. A sensitivity analysis was performed regarding the model responses with respect to the parameters of interest. It was verified that salinity is a sensitive variable for both parameters. The inverse problem was then solved using various optimization methods by coupling the MOHID platform to optimization codes implemented in Fortran. The coupling was carried in a way to not change the MOHID source code, allowing the use of the computational tool here developed in any version of this platform, as well as its modification for use with other simulators. The tests confirm the efficiency of the technique and suggest the best approaches for a fast and accurate estimation of the parameters.

Cette thèse propose une nouvelle méthode pour modéliser les fonctions non linéaires de modulations d’amplitude et de fréquence de signaux multicomposantes non stationnaires de durée longue. La méthode repose sur une décomposition du signal en segments courts pour une modélisation locale sur les segments. Pour initialiser la modélisation, nous avons conçu une première étape qui peut être considérée comme un estimateur indépendant et non paramétrique des fonctions de modulations. L’originalité de l’approche réside dans la définition d’une matrice de convergence totale intégrant simultanément les valeurs d’amplitude et de fréquence et utilisé pour l’association d’un pic à une composante selon un critère d’acceptation stochastique. Suite à cette initialisation, la méthode estime les fonctions de modulations par l'enchaînement des étapes de segmentation, modélisation et fusion. Les fonctions de modulations estimées localement par maximum de vraisemblance sont connectées dans l'étape de fusion, qui supprime les discontinuités, et produit l’estimation globale sur la durée totale du signal. Les étapes sont conçues afin de pouvoir modéliser des signaux multicomposantes avec des morts et naissances, ce qui en fait une de ses originalités par rapport aux techniques existantes. Les résultats sur des signaux réels et simulés ont illustré les bonnes performances et l’adaptabilité de la méthode proposée. / In this thesis, a novel method is proposed for modeling the non-linear amplitude and frequency modulations of non-stationary multi-component signals of long duration. The method relies on the decomposition of the signal into short time segments to carry out local modelings on these segments. In order to initialize the modeling, a first step is designed which can be considered as an independent estimator of the modulations over the entire duration of the signal. The originality of this approach lies in the definition of the total divergence matrix integrating simultaneously the amplitude and frequency values, which are employed for the association of a peak to a component according to a stochastic acceptation criteria. Following the initialization, the proposed method estimates the modulations by the step sequence of segmentation, modeling and fusion. The locally obtained modulation functions estimated by maximum likelihood are finally connected in the fusion step which suppresses their discontinuity and yields the global estimation over the entire signal duration. All these steps are defined in order to be able to model multicomponent signals with births and deaths, making one of its original features compared to existing techniques. The results on real and simulated signals have shown the good performance and adaptability of the proposed method.

Orientador: Secundino Soares Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-13T03:22:09Z (GMT). No. of bitstreams: 1 Monte_Bruno_M.pdf: 1808851 bytes, checksum: 7268c8ef4bb1363f9876841d3a0ce345 (MD5) Previous issue date: 2009 / Resumo: O planejamento energético de sistemas hidrotérmicos caracteriza-se pela otimização dos recursos hidráulicos através da maximização da operação hidrelétrica e da minimização da operação térmica. Seu objetivo é garantir um atendimento à carga de maneira econômica e confiável durante todo o horizonte de estudo. Este problema pode ser caracterizado como de natureza complexa, dado que suas características o definem como um problema de grande porte, dinâmico, estocástico e não-linear. Não obstante muitas técnicas já terem sido propostas para solução deste problema, não existe, ainda, uma metodologia unânime que aborde todas essas características com eficiência. A Programação Dinâmica, que é uma das técnicas mais populares utilizadas, tem sua aplicação limitada em sistemas reais, dado que exige um elevado esforço computacional. Neste trabalho, foi proposta uma metodologia alternativa para abordagem do planejamento da operação de médio prazo de sistemas hidrotérmicos. A metodologia proposta é baseada em um Sistema de Inferência Neural-Nebulosa Adaptativo atuando em paralelo com um modelo de otimização determinístico com perfeita previsão de vazão. A informação do otimizador determinístico é utilizada no treinamento da rede, que gera uma base de regra de inferência nebulosa que reproduzirá o comportamento ótimo da usina através da definição da vazão turbinada, em cada estágio, em função das varáveis de entrada estipuladas. A performance da metodologia Neural Nebulosa proposta foi comparada com outras modelagens, como a Programação Dinâmica Determinística, a Programação Dinâmica Estocástica e o Controle de Malha Aberta, através de simulações em cinco usinas hidrelétricas do parque gerador brasileiro considerando as vazões afluentes do histórico. Os resultados indicaram que a metodologia Neural Nebulosa proposta apresentou desempenho similar a abordagens mais tradicionais e que se configuram computacionalmente menos eficiente. / Abstract: The long term hydrothermal scheduling lies in the optimization of the water resource usage through the maximization of the hydroelectric production and the minimization of the thermal plants operation. Its goal is to assure an economic and reliable load supply throughout the study stages. This problem can be characterized by exhibiting a complex nature, since its characteristics define it as a large scale, dynamic, stochastic and nonlinear problem. Although many optimization approaches have already been proposed to answer the hydrothermal scheduling problem, until now, there is no unanimous approach that is able to cope efficiently with all the problem issues. Dynamic Programming, which is one of the most commonly used techniques to deal with this problem, is limited regarding its application on real systems since its computational requirements tend to be heavy. In this work we proposed an alternative approach to deal with the long term hydrothermal scheduling. The proposed technique is based on an Adaptive Neuro-Fuzzy Inference System working in parallel with a deterministic optimization model with perfect inflows forecasting. The optimal operation information is processed by the network that produces fuzzy rules describing the optimal decisions of the plant through the definition of the amount of discharge in each stage and depending on the chosen input variables. The performance of the proposed Neuro-Fuzzy approach was compared to other policies, including Deterministic Dynamic Programming, Stochastic Dynamic Programming and Open- Loop Feedback Control, by simulation using historical inflow records of five different Brazilian hydroelectric power plants. The results demonstrated that the Neuro-Fuzzy approach provided similar and competitive performance to less computationally efficient and commonly used policies. / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

La fabrication du pare-brise automobile est réalisée par un procédé de thermoformage dans un four tunnel où des feuilles de verre subissent un chauffage différentiel par rayonnement par des centaines d'éléments chauffants électriques contrôlés individuellement. Ces travaux ont pour objectif final de répondre à une problématique industrielle formulée en tant que problème d'optimisation. Elle consiste à aider le conducteur du four à retrouver la cartographie de puissance qui permet d'obtenir le champ de température nécessaire à la surface du verre afin d'aboutir à une forme souhaitée. Pour y parvenir, un modèle du four basé sur la méthode de réseau de composants est utilisé afin de simuler le cycle de chauffage. Dans un premier temps, la précision de la température calculée est améliorée par identification paramétrique en se référant à des données de mesures effectuées in situ. Une étude de sensibilité locale et globale a été réalisée au préalable. Par la suite, dans le but d'accélérer ces calculs, une méthode d'optimisation originale est proposée. Elle consiste à combiner la méthode métaheuristique du Recuit Simulé et l'Algorithme de Re-revêtement pour identifier l'émissivité multi-bande des matériaux. Après avoir effectué une validation sur un modèle simplifié 3D de four radiatif de traitement de matériaux, la méthode originale est appliquée pour le modèle du four réel. Outre l'amélioration de la précision des résultats de la simulation, la nouvelle démarche réduit considérablement le temps de calcul. Dans la deuxième partie du travail, plusieurs méthodes métaheuristiques, telles que l'Algorithme Génétique, le Recuit Simulé, la Recherche Tabou ainsi que leur hybridation sont expérimentées pour un modèle simplifié d'une enceinte radiative. Les résultats montrent que la combinaison de l'Algorithme Génétique et du Recuit Simulé a permis d'accélérer la convergence pour atteindre les champs de températures souhaités sur la surface du produit. Cette méthode est par la suite appliquée avec succès pour inverser le modèle du four afin de retrouver les paramètres de commande du four. / The manufacturing of automobile windshield is produced by a thermoforming process in a tunnel furnace where glass undergoes differential heating radiation by hundreds of electrical heating elements individually controlled. The final purpose of this work is to answer a real industrial problem, which is formulated as an optimization problem. It aims at assisting the furnace driver to find the setting that allows obtaining the required temperature distribution on the glass design in order to achieve the desired shape. Based on the method of network components, a model of the furnace is used to simulate the heating cycle. As a first step of this work, the accuracy of the temperature calculated is improved by parametric identification by referring to the data of measurements taken in situ. A local and global sensitivity analysis was performed beforehand. Thereafter, in order to accelerate these calculations, an original and optimization method is proposed. It consists in combining the Simulated Annealing metaheuristic method and the Replating Algorithm to identify multi-band emissivity. First, the original method validation is performed on a simplified 3D model of radiative enclosure, and then applied to the real furnace model. The new approach significantly reduces the computation time while improving the accuracy of the simulation results. In the second part of this work, several metaheuristic methods, such as Genetic Algorithm, Simulated Annealing, Tabu Search, and their hybridization are tested on a simplified model of a radiative enclosure. Results show that the combination of Genetic Algorithm and Simulated Annealing has accelerated the convergence to achieve the desired temperature fields on the product surface. This new method is successfully applied to the real furnace model to find the optimal control parameters.

Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based on PSO is proposed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. A new automatic image generation tool tailored specifically for the verification and comparison of various unsupervised image classification algorithms is then developed. A dynamic clustering algorithm which automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference is then developed. Finally, PSO-based approaches are proposed to tackle the color image quantization and spectral unmixing problems. In all the proposed approaches, the influence of PSO parameters on the performance of the proposed algorithms is evaluated. / Thesis (PhD)--University of Pretoria, 2006. / Computer Science / unrestricted

Le radar géologique est une méthode d'investigation géophysique basée sur la propagation d'ondes électromagnétiques dans le sous-sol. Avec des fréquences allant de 5 MHz à quelques GHz et une forte sensibilité aux propriétés électriques, le géoradar fournit des images de réflectivité dans des contextes et à des échelles très variés : génie civil, géologie, hydrogéologie, glaciologie, archéologie. Cependant, dans certains cas, la compréhension fine des processus étudiés dans la subsurface nécessite une quantification des paramètres physiques du sous-sol. Dans ce but, l'inversion des formes d'ondes complètes, méthode initialement développée pour l'exploration sismique qui exploite l'ensemble des signaux enregistrés, pourrait s'avérer efficace. Dans cette thèse, je propose ainsi des développements méthodologiques par une approche d'inversion multiparamètres (permittivité diélectrique et conductivité), pour des configurations en transmission, en deux dimensions.Ces développements sont ensuite appliqués à un jeu de données réelles acquises entre forages.Dans une première partie, je présente tout d'abord la méthode numérique utilisée pour modéliser la propagation des ondes électromagnétiques dans un milieu 2D hétérogène, élément indispensable pour mener à bien le processus d'imagerie. Ensuite, j’introduis puis étudie le potentiel des méthodes d’optimisation locale standards (gradient conjugué non linéaire, l-BFGS, Newton tronqué dans ses versions Gauss-Newton et Exact-Newton) pour découpler la permittivité diélectrique et la conductivité électrique. Je montre notamment qu’un découplage effectif n’est possible qu’avec un modèle initial suffisamment précis et la méthode la plus sophistiquée (Newton tronqué). Comme dans le cas général, ce modèle initial n’est pas disponible, il s’avère nécessaire d'introduire un facteur d'échelle qui répartit le poids relatif de chaque classe de paramètres dans l'inversion. Dans un milieu réaliste avec une acquisition entre puits, je montre que les différentes méthodes d'optimisation donnent des résultats similaires en matière de découplage de paramètres. C'est finalement la méthode l-BFGS qui est retenue pour l'application aux données réelles, en raison de coûts de calcul plus faibles.Dans une deuxième partie, j'applique cette méthodologie à des données réelles acquises entre deux forages localisés dans des formations carbonatées, à Rustrel (France, 84). Cette inversion est réalisée en parallèle d'une approche synthétique à l'aide d'un modèle représentatif du site étudié et des configurations d'acquisition similaires. Ceci permet de pouvoir comprendre, contrôler et valider les observations et conclusions obtenues sur les données réelles. Cette démarche montre que la reconstruction de la permittivité est très robuste. A contrario, l'estimation de la conductivité souffre de deux couplages majeurs, avec la permittivité diélectrique, d'une part, et avec l'amplitude de la source estimée, d'autre part. Les résultats obtenus sont confrontés avec succès à des données indépendantes (géophysique depuis la surface, analyse sur échantillons de roche), et permet de bénéficier d'une image haute-résolution des formations géologiques. Enfin, une analyse 3D confirme que les structures 3D à fort contraste de propriétés, telles que la galerie enfouie sur notre site, nécessiteraient une approche de modélisation 3D, notamment pour mieux expliquer les amplitudes observées. / Ground Penetrating Radar (GPR) is a geophysical investigation method based on electromagnetic waves propagation in the underground. With frequencies ranging from 5 MHz to a few GHz and a high sensitivity to electrical properties, GPR provides reflectivity images in a wide variety of contexts and scales: civil engineering, geology, hydrogeology, glaciology, archeology. However, in some cases, a better understanding of some subsurface processes requires a quantification of the physical parameters of the subsoil. For this purpose, inversion of full waveforms, a method initially developed for seismic exploration that exploits all the recorded signals, could prove effective. In this thesis, I propose methodological developments using a multiparameter inversion approach (dielectric permittivity and conductivity), for two-dimensional transmission configurations. These developments are then applied to a real data set acquired between boreholes.In a first part, I present the numerical method used to model the propagation of electromagnetic waves in a heterogeneous 2D environment, a much-needed element to carry out the process of imaging. Then, I introduce and study the potential of standard local optimization methods (nonlinear conjugate gradient, l-BFGS, Newton truncated in its Gauss-Newton and Exact-Newton versions) to fight the trade-off effects related to the dielectric permittivity and to the electrical conductivity. In particular, I show that effective decoupling is possible only with a sufficiently accurate initial model and the most sophisticated method (truncated Newton). As in the general case, this initial model is not available, it is necessary to introduce a scaling factor which distributes the relative weight of each parameter class in the inversion. In a realistic medium and for a cross-hole acquisition configuration, I show that the different optimization methods give similar results in terms of parameters decoupling. It is eventually the l-BFGS method that is used for the application to the real data, because of lower computation costs.In a second part, I applied the developed Full waveform inversion methodology to a set of real data acquired between two boreholes located in carbonate formations, in Rustrel (France, 84). This inversion is carried out together with a synthetic approach using a model representative of the studied site and with a similar acquisition configuration. This approach enables us to monitor and validate the observations and conclusions derived from data inversion. It shows that reconstruction of dielectrical permittivity is very robust. Conversely, conductivity estimation suffers from two major couplings: the permittivity and the amplitude of the estimated source. The derived results are successfully compared with independent data (surface geophysics and rock analysis on plugs) and provides a high resolution image of the geological formation. On the other hand, a 3D analysis confirms that 3D structures presenting high properties contrasts, such as the buried gallery present in our site, would require a 3D approach, notably to better explain the observed amplitudes.

This thesis deals with the basic principles of the functioning of the technology of generative design, using the method of topology optimization and finding their potential with the capabilities of the application in the field of furniture design. The main outcome is a functional prototype of a low relaxing chair, which specific construction is designed using the method of topology optimization.

In introduction of work, I presented the state of the art of measuring insertion loss of the EMI filter and the description of individual methods of measuring is also included. After that I dealt with global optimization methods and some of them I theoretically described. Mainly PSO method was studied carefully, and the principle of optimization in details are presented in the thesis, because this method was used for optimization of the filters parameters in the thesis. Next part of work deals with theoretical and mathematical expression of insertion loss of the EMI filters. Due to the mathematical expression of the insertion loss, the values of parasitic elements of given filter have been optimized in different measuring systems. This optimization was applied on synthetic data. By this step the functionality of optimization was proved and also the fitness function has been tuned and optimised. The last part of thesis works with optimization on measured data of given filters. I found out inaccuracy in mathematical expression of given model of EMI filter, therefore it was necessary to come up with the new filter model. The accuracy increases by using the new filter model. In next step the new model was enlarged by new spurious components.

The aim of this work is an optimalization of vector contact force sensor properties which prototype is at the moment designed. The principle of sensor is based on transformation active part of sensor using three tensiometers and following value indentification of loading force vector using neuron network. Sensor has a bad sensitivity when axis force load or near to axis of sensor. To solve this problem ''MKP'' model of sensor was used on which suitable optimalization method was applicated. From the result of optimalization modificated sensor will be proposed and will be made a utility verification using neuron network.

[pt] Atualmente nos veículos terrestres a melhora de seu desempenho é permanente. Esta melhora, em grande parte está relacionada com seus sistemas de controle. Para o desenho de sistema de controle, tais como, sistema eletrônico de estabilidade ou controle anti-capotagem é preciso prever o comportamento do veículo sob certas condições dinâmicas. Esta predição do comportamento dinâmico do veículo é feita por meio de modelos matemáticos que dependem diretamente de seus parâmetros, tais como a massa, a posição do centro da gravidade, os momentos de inércia entre outros. Tendo em consideração o contexto apresentado, os parâmetros dinâmicos do veículo tem um papel fundamental na melhora do desempenho do veículo, assim como a otimização de seus subsistemas. Esta dissertação apresenta o desenvolvimento de um procedimento de estimação dos parâmetros dinâmicos do veículo e a calibração do modelo matemático desenvolvido. Para o processo de estimação se desenvolvem dois modelos do veículo, um feito em MATLAB com 4 graus de liberdade (GDL) e ou outro é trabalhado como uma caixa preta (black-box), que é obtida por meio da biblioteca de ligações dinâmicas (DLL) de um programa de simulação veicular. Métodos de otimização, tais como Evolução Diferencial, Generalized Pattern Serach, Mesh Adaptative Pattern Search e Nelder Mead, são utilizados para estimar os parâmetros dinâmicos do veículo, isto por meio da minimização de um objetivo definido como a diferença entre a resposta real do veículo (obtida do programa de simulação veicular) e a calculada por cada método de otimização. Os parâmetros dinâmicos estimados com o melhor método de otimização, são inseridos no modelo matemático feito em MATLAB para calibrar o modelo. Finalmente, o modelo já calibrado é simulado com diferentes manobras para comprovar a robustez do método e a coerência dos parâmetros estimados. / [en] Nowadays, performance improvement on ground vehicles is prefominant. This improvement is highly correlated with its control systems. To design its control system, with parts as electronic system for stabilization or anti-rollover control, it is necessary to provide the behavior of the vehicle in certain dynamics conditions. This prediction, of the dynamics behavior of the vehicle, is done by mathematical models that depend directly on its dynamics parameters such as gravity center position, inertia moments, among others. Taking into account the actual background, dynamics parameters of the vehicle have an importante role in the improvement of vehicle s performance, as well as in the optimization of its subsystems. This thesis presentes the vehicle and calibration of the mathematical model developed. For the estimation step, two vehicle models were developed, the first one was done in MATLAB with 4 DOF and the other was treated as a black box, which is obtained by Dynamic-Link Libraries (DLLs) from the free vehicular simulation software. Optimization methods, such as Differential Evolution, Generalized Pattern Search, Mesh Adaptive Pattern Search and Nelder Mead, were used to estimate vehicle s dynamics parameters. The fitness function is defined as a difference between the real response of the vehicle (given by vehicular simulation software) and one calculated with each optimization method. The dynamics parameters estimated with the best optimization method are input for the mathematical model developed in MATLAB to calibrate the model. Finally, the calibrated model is simulated with different maneuvers, this is done to test the robustness of the method and the coherence of estimated parameters.