Performance evaluation of 3D computer vision techniques

This work presents the implementation and comparison of three different techniques of three-dimensional computer vision as follows: • Stereo vision - correlation between two 2D images • Sensorial fusion - use of different sensors: camera 2D + ultrasound sensor (1D); • Structured light The computer vision techniques herein presented took into consideration the following characteristics: • Computational effort ( elapsed time for obtain the 3D information); • Influence of environmental conditions (noise due to a non uniform lighting, overlighting and shades); • The cost of the infrastructure for each technique; • Analysis of uncertainties, precision and accuracy. The option of using the Matlab software, version 5.1, for algorithm implementation of the three techniques was due to the simplicity of their commands, programming and debugging. Besides, this software is well known and used by the academic community, allowing the results of this work to be obtained and verified. Examples of three-dimensional vision applied to robotic assembling tasks ("pick-and-place") are presented.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.

Precificação de opções de dólar no mercado brasileiro utilizando redes neurais e algoritmos genéticos

Esse trabalho comparou, para condições macroeconômicas usuais, a eficiência do modelo de Redes Neurais Artificiais (RNAs) otimizadas por Algoritmos Genéticos (AGs) na precificação de opções de Dólar à Vista aos seguintes modelos de precificação convencionais: Black-Scholes, Garman-Kohlhagen, Árvores Trinomiais e Simulações de Monte Carlo. As informações utilizadas nesta análise, compreendidas entre janeiro de 1999 e novembro de 2006, foram disponibilizadas pela Bolsa de Mercadorias e Futuros (BM&F) e pelo Federal Reserve americano. As comparações e avaliações foram realizadas com o software MATLAB, versão 7.0, e suas respectivas caixas de ferramentas que ofereceram o ambiente e as ferramentas necessárias à implementação e customização dos modelos mencionados acima. As análises do custo do delta-hedging para cada modelo indicaram que, apesar de mais complexa, a utilização dos Algoritmos Genéticos exclusivamente para otimização direta (binária) dos pesos sinápticos das Redes Neurais não produziu resultados significativamente superiores aos modelos convencionais.

Probabilistic neural network to predict cracks in taphole mud used in blast furnaces

One of the major problems facing Blast Furnaces is the occurrence of cracks in taphole mud, as the underlying causes are not easily identifiable. The absence of this knowledge makes it difficult the use of conventional techniques for predictability and mitigation. This paper will address the application of Probabilistic Neural Network using the Matlab software as a means to detect and control such cracks. The most relevant BF operational variables were picked through the statistic tool "Principal Component Analysis - PCA." Based upon the selection of these variables a probabilistic neural network was built. A set of BF operational data, consisting of 30 controlling variables, was divided into 2 groups, one of which for network training, and the other one to validate the neural network. The neural network got 98% of the cases right. The results show the effectiveness of this tool for crack prediction in relation to clay intrinsic properties and as a result of the fluctuation in operational variables.

Aplicação de redes neurais artificiais no monitoramento da operação de dressagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uso de absorvedores de vibrações eletromecânicos lineares e não-lineares em sistemas não-lineares e não-ideais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem matemática e análise dinâmica da torre de um pulverizador de pomares

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilização de lâminas serrilhadas no corte basal da cana-de- açúcar

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Localização de falhas em linhas de transmissão utilizando decomposição harmônica e redes neurais artificiais

Este trabalho propõe a utilização de uma nova metodologia para a localização de falhas em linhas de transmissão (LT). Esta metodologia consiste na utilização da decomposição harmônica da corrente de fuga de uma linha e na aplicação de uma Rede Neural Artificial (RNA) capaz de distinguir padrões da condição normal de funcionamento e padrões de situações de falhas de uma LT. Foi utilizado um modelo Pi capaz de absorver dados reais de tensão e corrente de três fases e de alterar valores de R, L e C segundo modificações ambientais. Neste modelo foram geradas falhas em todas as torres com diferentes valores de capacitância. A saída fornecida pelo modelo é a decomposição da corrente de fuga do trecho considerado. Os dados de entrada e saída do modelo foram utilizados no treinamento da RNA desenvolvida. A aquisição de dados reais de tensão e corrente foi feita através de analisadores de parâmetros de qualidade de energia elétrica instalados nas extremidades de um trecho de LT, Guamá-Utinga, pertencente à Centrais Elétricas do Norte do Brasil ELETRONORTE. O cálculo dos parâmetros construtivos foi feito através do método matricial e melhorado através da utilização do Método de Elementos Finitos (MEF). A RNA foi desenvolvida com o auxílio do software Matlab. Para treinamento da RNA foi utilizado o algoritmo de Retropropagação Resiliente que apresentou um bom desempenho. A RNA foi treinada com dois conjuntos de dados de treinamento para analisar possíveis diferenças entre as saídas fornecidas pelos dois grupos. Nos dois casos apresentou resultados satisfatórios, possibilitando a localização de falhas no trecho considerado.

Método de inversão matricial rápido GSGR para vectoring em sistemas xDSL, com implementação em chip DSP

Este trabalho apresenta um método rápido de inversão de matrizes densas, e uma possível aplicação com métodos de Vectoring, em pré-codificação e cancelamento de crosstalk de sistemas xDSL. A família de tecnologias xDSL utiliza os pares trançados de fios de cobre telefônicos como meio físico para transmitir dados digitais. O crosstalk é a principal causa de degradação de sinais na mais nova geração de sistemas xDSL, o G.fast, e para combatê-lo são utilizadas técnicas de pré-codificação e cancelamento, chamadas de Vectoring. O método proposto, chamado de GSGR, consiste em uma abordagem diferente para o método clássico de Squared Givens Rotations (SGR), adequado a implementações em plataformas embarcadas de processamento digital de sinais. Foram realizados testes comparativos do método GSGR com métodos diretos clássicos de inversão, utilizando uma plataforma digital multicore baseada no chip TI DSP TMS320C6670 e a plataforma de software Matlab. Os resultados dos testes de inversão de matrizes usando dados reais e dados simulados mostraram que o GSGR foi superior em velocidade de execução sem apresentar perdas significativas de acurácia para a aplicação em sistemas xDSL.

Lógica Fuzzy aplicada ao controle de um sistema híbrido de geração de energia elétrica: eólica, fotovoltaica e biogás

Pós-graduação em Engenharia Mecânica - FEG

Modelos lineares de sistemas elétricos de potência: um estudo comparativo

Pós-graduação em Engenharia Elétrica - FEIS

Análise comparativa dos sistemas de jogo e das ações técnicas realizadas pela seleção brasileira de futebol

Since its origin, soccer has been conquering followers all over the world. As a consequence of this cultural phenomenon, countless works are being done aiming to analyze the game systems and the fundamentals carried out by a team. The purpose of this paper is to analyze the Brazilian Soccer Team’s game systems and actions with ball possession in the 1958, 1962 and 2002 World Cup finals. Specifically, the paper analyzed the actions that occur during the match, such as control, dribble, pass, shot, foul and tackling. In order to achieve that, the Skout software (Barros et al., 2006) was utilized and, through it, all fundamentals carried out by the Brazilian team were identified and codified in a virtual field. The data from each match was transported into the Matlab® software, in which the zone of major action of each player was analyzed, represented by the main axis. The outcome showed that the game systems put into practice by the Brazilian team in the 1958 and 1962 World Cup finals didn’t present significant changes, nevertheless, regarding the 2002 final, there was a great difference. The Brazilian team got similar percentages in the technical actions carried out in the three World Cup finals analyzed, however, in absolute numbers, the passes, the shots and the dribbles decreased while the fouls increased.

Estudo e aplicação de diferentes métodos de resolução para problemas dinâmicos não-lineares em presença de atrito

The friction phenomena is present in mechanical systems with two surfaces that are in contact, which can cause serious damage to structures. Your understanding in many dynamic problems became the target of research due to its nonlinear behavior. It is necessary to know and thoroughly study each existing friction model found in the literature and nonlinear methods to define what will be the most appropriate to the problem in question. One of the most famous friction model is the Coulomb Friction, which is considered in the studied problems in the French research center Laboratoire de Mécanique des Structures et des Systèmes Couplés (LMSSC), where this search began. Regarding the resolution methods, the Harmonic Balance Method is generally used. To expand the knowledge about the friction models and the nonlinear methods, a study was carried out to identify and study potential methodologies that can be applied in the existing research lines in LMSSC and then obtain better final results. The identified friction models are divided into static and dynamic. Static models can be Classical Models, Karnopp Model and Armstrong Model. The dynamic models are Dahl Model, Bliman and Sorine Model and LuGre Model. Concerning about nonlinear methods, we study the Temporal Methods and Approximate Methods. The friction models analyzed with the help of Matlab software are verified from studies in the literature demonstrating the effectiveness of the developed programming