Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity

Estudo in vitro da resistência adesiva na dentina de sistemas adesivos multi-mode variando a estratégia de condicionamento

Dissertação para obtenção do grau de Mestre no Instituto Superior de Ciências da Saúde Egas Moniz

Classificação e previsão de cardiotocogramas usando algoritmos baseados em redes neuronais

Mestrado em Computação e Instrumentação Médica

Classificação de sons urbanos usando motifs e MFCC

A classificação automática de sons urbanos é importante para o monitoramento ambiental. Este trabalho apresenta uma nova metodologia para classificar sons urbanos, que se baseia na descoberta de padrões frequentes (motifs) nos sinais sonoros e utiliza-los como atributos para a classificação. Para extrair os motifs é utilizado um método de descoberta multi-resolução baseada em SAX. Para a classificação são usadas árvores de decisão e SVMs. Esta nova metodologia é comparada com outra bastante utilizada baseada em MFCC. Para a realização de experiências foi utilizado o dataset UrbanSound disponível publicamente. Realizadas as experiências, foi possível concluir que os atributos motif são melhores que os MFCC a discriminar sons com timbres semelhantes e que os melhores resultados são conseguidos com ambos os tipos de atributos combinados. Neste trabalho foi também desenvolvida uma aplicação móvel para Android que permite utilizar os métodos de classificação desenvolvidos num contexto de vida real e expandir o dataset.

Uma Análise Comparativa entre Sistemas de Combinação de Classificadores com Distribuição Vertical dos Dados

In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this work, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components

Decomposição de Dantzig-Wolfe aplicada ao problema de planejamento de reativos em sistemas de potência multi-áreas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação dos custos agroindustriais

A ferramenta de custos se mostra relevante do ponto de vista gerencial, auxiliando no controle de gastos e na tomada de decisões nas empresas. Para melhor entender este conceito, o material apresenta a taxonomia dos sistemas de custos, além de suas classificações quanto à natureza, à identificação com o produto e à variação quantitativa. Como o estoque representa grande parte dos custos de produção, são apresentadas três formas contábeis para avaliação dos mesmos: PEPS (primeiro a entrar, primeiro a sair), UEPS (último a entrar, primeiro a sair) e Custo Médio.

Análise e desenvolvimento de controladores preditivos multivariáveis baseados em multi-modelos bilineares

This work addresses issues related to analysis and development of multivariable predictive controllers based on bilinear multi-models. Linear Generalized Predictive Control (GPC) monovariable and multivariable is shown, and highlighted its properties, key features and applications in industry. Bilinear GPC, the basis for the development of this thesis, is presented by the time-step quasilinearization approach. Some results are presented using this controller in order to show its best performance when compared to linear GPC, since the bilinear models represent better the dynamics of certain processes. Time-step quasilinearization, due to the fact that it is an approximation, causes a prediction error, which limits the performance of this controller when prediction horizon increases. Due to its prediction error, Bilinear GPC with iterative compensation is shown in order to minimize this error, seeking a better performance than the classic Bilinear GPC. Results of iterative compensation algorithm are shown. The use of multi-model is discussed in this thesis, in order to correct the deficiency of controllers based on single model, when they are applied in cases with large operation ranges. Methods of measuring the distance between models, also called metrics, are the main contribution of this thesis. Several application results in simulated distillation columns, which are close enough to actual behaviour of them, are made, and the results have shown satisfactory

Avaliação da resistência ao cisalhamento de sistemas para reparo em metalocerâmicas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multi-objective adaptive evolutionary strategy for tuning compilations

Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to markedly speed-up the convergence of the original strategy.

Classificação de técnicas e auxílio à navegação de busca com base em sua carga cognitiva, precisão e eficiência

Navigation, in both virtual and real environments, is the process of a deliberated movement to a specific place that is usually away from the origin point, and that cannot be perceived from it. Navigation aid techniques (TANs) have as their main objective help finding a path through a virtual environment to a desired location and, are widely used because they ease the navigation on these unknown environments. Tools like maps, GPS (Global Positioning System) or even oral instructions are real world examples of TAN usage. Most of the works which propose new TANs for virtual environments aim to analyze their impact in efficiency gain on navigation tasks from a known place to an unknown place. However, such papers tend to ignore the effect caused by a TAN usage over the route knowledge acquisition process, which is important on virtual to real training transfer, for example. Based on a user study, it was possible to confirm that TANs with different strategies affects the performance of search tasks differently and that the efficiency of the help provided by a TAN is not inversely related to the cognitive load of the technique’s aids. A technique classification formula was created. This formula utilizes three factors instead of only efficiency. The experiment’s data were applied to the formula and we obtained a better refinement of help level provided by TANs.

Aspectos silviculturais da castanha-do-brasil (Bertholletia excelsa) em sistemas agroflorestais na Amazônia Central.

Este estudo avaliou o desempenho da castanha-do-brasil (Bertholletia excelsa) em sistemas agroflorestais implantados em ecossistema de terra firme na Amazônia Central. Foram avaliados 3 sítios de sistemas agroflorestais multi-estratificados, implantados em 1992, em áreas de pastagens degradadas, situadas no km 54 da BR-174, no Campo Experimental da Embrapa Amazônia Ocidental, em Manaus (AM).

Interpretação de imagens multitemporais de sensoriamento remoto.

Estudos multitemporais de dados de sensoriamento remoto dedicam-se ao mapeamento temático de uso da terra em diferentes instâncias de tempo com o objetivo de identificar as mudanças ocorridas em uma região em determinado período. Em sua maioria, os trabalhos de classificação automática supervisionada de imagens de sensoriamento remoto não utilizam um modelo de transformação temporal no processo de classificação. Pesquisas realizadas na última década abriram um importante precedente ao comprovarem que a utilização de um modelo de conhecimento sobre a dinâmica da região (modelo de transformação temporal), baseado em Cadeias de Markov Fuzzy (CMF), possibilita resultados superiores aos produzidos pelos classificadores supervisionados monotemporais. Desta forma, o presente trabalho enfoca um dos aspectos desta abordagem pouco investigados: a combinação de CMF de intervalos de tempo curtos para classificar imagens de períodos longos. A área de estudo utilizada nos experimentos é um remanescente florestal situado no município de Londrina-PR e que abrange todo o limite do Parque Estadual Mata dos Godoy. Como dados de entrada, são utilizadas cinco imagens do satélite Landsat 5 TM com intervalo temporal de cinco anos. De uma forma geral, verificou-se, a partir dos resultados experimentais, que o uso das Cadeias de Markov Fuzzy contribuiu significativamente para a melhoria do desempenho do processo de classificação automática em imagens orbitais multitemporais, quando comparado com uma classificação monotemporal. Ainda, pôde-se observar que as classificações com base em matrizes estimadas para períodos curtos sempre apresentaram resultados superiores aos das classificações com base em matrizes estimadas para períodos longos. Também, que a superioridade da estimação direta frente à extrapolação se reduz com o aumento da distância temporal. Os resultados do presente trabalho poderão servir de motivação para a criação de sistemas automáticos de classificação de imagens multitemporais. O potencial de sua aplicação se justifica pela aceleração do processo de monitoramento do uso e cobertura da terra, considerando a melhoria obtida frente a classificações supervisionadas tradicionais.

A BDI-based approach for the assessment of driver's decision-making in commuter scenarios

The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.