Made available in DSpace on 2016-04-18T21:39:46Z (GMT). No. of bitstreams: 3 Fabiano Rodrigues Ferreira1.pdf: 1311600 bytes, checksum: 0965dfbc230f89f36675c671139868c2 (MD5) Fabiano Rodrigues Ferreira2.pdf: 3285153 bytes, checksum: a89026ec6080ef90e4d32d6b1d3ecf78 (MD5) Fabiano Rodrigues Ferreira3.pdf: 3363149 bytes, checksum: 1aeb3cbfdd346437db3d3527f88d4d81 (MD5) Previous issue date: 2008-02-29 / Fundo Mackenzie de Pesquisa / Brazilian educational scenario suffers from the lack of incentive to a musical apprenticeship that leads students to reflect about their own reality. Due to the actual hegemonic politics, educational processes, in general, are characterized by diminishing student s potential for reflection, in a society that priorizes a strict technicist teaching, as contemporary society is. As a result, students are often not able to stablish relationships between what was learned and their own lives. Thus, it is necessary to have some mechanisms that could help the adaptation to student s cultural context, leading to a meaningful ethnic learning. Learning objects concept can be understood as examples of technological resources that appear in a way to organize and structure digital educational data. Such concept, althought is a new paradigm into educational ambit, has been widely used on educational systems by constant & crescent deliver of learning objects by Internet. In this way, this work focuses an adaptive learning object architecture, applied to the learning process of Brazilian musical rhythms, as an example. Such objects are dynamically retrieved from repositories through techniques based on self-organizing maps. Objects are selected in order to create learning resources adequate to some desirable adaptivity factor, as previous knowledge, learning styles or cultural aspects. / O cenário educacional brasileiro sofre com a falta de incentivo a um aprendizado musical que realmente faça o educando refletir sobre sua realidade. Devido à política hegemônica atual, os processos educativos, em geral, estão imersos numa alienação descontextualizante e no assistencialismo. O poder de pensamento e reflexão do educando acaba diminuindo consideravelmente numa sociedade que preza mais pelo ensino puramente tecnicista do que pelo incentivo à reflexão, como é o caso da sociedade contemporânea. O resultado disso acaba sendo uma inorganicidade educacional que faz com que o aluno não faça relação daquilo que aprendeu com sua própria vida. Torna-se necessário, portanto, estabelecer mecanismos que auxiliem a adaptação ao contexto cultural do mesmo, levando a uma etnoaprendizagem significativa e contextualizada. Entendem-se os objetos de aprendizagem como exemplos de recursos tecnológicos que surgiram como forma de organizar e estruturar materiais educacionais digitais. Tal conceito, embora seja um paradigma novo no âmbito da educação tem sido amplamente utilizado nos sistemas educacionais atuais através da constante e crescente disponibilização dos mesmos pela Internet. Dessa forma, este trabalho enfoca uma arquitetura de objetos de aprendizagem digitais adaptativos com uma aplicação no processo de aprendizagem de ritmos musicais brasileiros, como exemplo de utilização. Tais objetos são dinamicamente recuperados a partir de repositórios, através de técnicas baseadas em mapas auto-organizáveis. Objetos são selecionados de maneira a criar recursos de aprendizagem que sejam adequados a algum fator de adaptabilidade desejável para o contexto, como conhecimentos prévios, estilos de aprendizagem ou aspectos culturais.

This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-01-04T13:19:42Z No. of bitstreams: 1 LeandroAntonioPasa_TESE.pdf: 2290404 bytes, checksum: 02409d9ddb38171fd363b99f26390d89 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-01-05T18:51:41Z (GMT) No. of bitstreams: 1 LeandroAntonioPasa_TESE.pdf: 2290404 bytes, checksum: 02409d9ddb38171fd363b99f26390d89 (MD5) / Made available in DSpace on 2017-01-05T18:51:41Z (GMT). No. of bitstreams: 1 LeandroAntonioPasa_TESE.pdf: 2290404 bytes, checksum: 02409d9ddb38171fd363b99f26390d89 (MD5) Previous issue date: 2016-02-19 / A quantidade de informa??es coletadas e armazenadas cresce a cada dia nas mais diversas ?reas do conhecimento e t?cnicas de minera??o de dados s?o aplicadas a estes conjuntos de dados com o objetivo de extrair conhecimento ?til. A utiliza??o de um ou outro algoritmo, ou o mesmo algoritmo com diferentes atributos pode levar a diferentes resultados, devido ? diversidade dos conjuntos de dados. Na busca por solu??es eficientes para este problema, foram desenvolvidos m?todos de comit?s de m?quinas. Um comit? de m?quinas ? um conjunto de redes neurais trabalhando independentemente cujos resultados s?o combinados em uma ?nica sa?da, alcan?ando uma melhor generaliza??o do que cada uma das redes trabalhando separadamente. A proposta deste trabalho ? desenvolver um novo m?todo para comit?s de mapas de Kohonen, em que a combina??o (fus?o) dos mapas seja ponderada por ?ndices de valida??o de agrupamentos, que seja v?lido para combina??o de mapas de tamanhos iguais e mapas de tamanhos diferentes. O algoritmo proposto foi testado em variados conjuntos de dados provenientes do reposit?rio UCI e do Conjunto de Problemas Fundamentais de Agrupamento. As simula??es computacionais demonstram que o m?todo proposto neste trabalho ? capaz de alcan?ar resultados promissores, conseguindo elevar a performance em compara??o com um ?nico mapa de Kohonen. / The amount of collected and stored information is growing every day in several areas of knowledge and data mining techniques are applied to these datasets in order to extract useful knowledge. One or another algorithm, or the same algorithm with different attributes, can lead to different results due to the dataset diversity. To solve this problem, machines committees methods were developed. A machine committee is a set of neural networks working independently and the results are combined into a single output, achieving a better generalization. The purpose of this work is to develop a new method for Kohonen maps ensemble, where the maps fusion is weighted by cluster validation indices and is suitable for equal size maps fusion and for different size maps fusion. The proposed algorithm has been tested in multiple data sets from the UCI Machine Learning Repository and Fundamental Clustering Problems Suite. Computer simulations show the proposed method is able to reach encouraging results, obtaining raising performance compared with a single Kohonen map.

The topic of the present thesis is an analysis of a sample data archive containing measured values of real and reactive power. The measurement in question took place in late 2006 and early 2007 using MEg40 recording measurement devices disposed in a station for transforming high voltage to low voltage in the Pražská energetika distribution network. The procedure of processing measured values, the preparation thereof for a subsequent processing by a neural network, and a final statistical evaluation of determined individual clusters -- typical daily take-off diagrams -- will be described. The results of the present thesis may be applied in the making of predictions of electrical energy consumption at a particular transformer station.

This master thesis present a method for building topological maps for mobile robot navigation using neural network and neural fuzzy network. The master thesis concentrates on classification method. Neural fuzzy network is compared with two neural networks. It was also designed control algorithm exploration environment for autonomous mobile robot. This will rereduce the time to build the map. I developed simulation program in Matlab, which simulate move mobile robot in unknown environment.

Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

This Master’s thesis deals with adaptation of fuzzy system parameters with main aim on artificial neural network. Current knowledge of methods connecting fuzzy systems and artificial neural networks is discussed in the search part of this work. The search in Student’s works is discussed either. Chapter focused on methods application deals with classifying ability verification of the chosen fuzzy-neural network with Kohonen learning algorithm. Later the model of fuzzy system with parameters adaptation based on fuzzyneural network with Kohonen learning algorithm is shown.

In this master thesis I recapitulated several methods for clustering input data. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.

Submitted by Samira Prince (prince@ufpa.br) on 2012-06-04T13:43:11Z No. of bitstreams: 2 Dissertacao_MineracaoDadosBaseada.pdf: 5299530 bytes, checksum: ff357bf983c4b641d8b7fd5ed78b78e7 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Approved for entry into archive by Samira Prince(prince@ufpa.br) on 2012-06-04T13:43:48Z (GMT) No. of bitstreams: 2 Dissertacao_MineracaoDadosBaseada.pdf: 5299530 bytes, checksum: ff357bf983c4b641d8b7fd5ed78b78e7 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Made available in DSpace on 2012-06-04T13:43:48Z (GMT). No. of bitstreams: 2 Dissertacao_MineracaoDadosBaseada.pdf: 5299530 bytes, checksum: ff357bf983c4b641d8b7fd5ed78b78e7 (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Previous issue date: 2011 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / As concessionárias de energia, para garantir que sua rede seja confiável, necessitam realizar um procedimento para estudo e análise baseado em funções de entrega de energia nos pontos de consumo. Este estudo, geralmente chamado de planejamento de sistemas de distribuição de energia elétrica, é essencial para garantir que variações na demanda de energia não afetem o desempenho do sistema, que deverá se manter operando de maneira técnica e economicamente viável. Nestes estudos, geralmente são analisados, demanda, tipologia de curva de carga, fator de carga e outros aspectos das cargas existentes. Considerando então a importância da determinação das tipologias de curvas de cargas para as concessionárias de energia em seu processo de planejamento, a Companhia de Eletricidade do Amapá (CEA) realizou uma campanha de medidas de curvas de carga de transformadores de distribuição para obtenção das tipologias de curvas de carga que caracterizam seus consumidores. Neste trabalho apresentam-se os resultados satisfatórios obtidos a partir da utilização de Mineração de Dados baseada em Inteligência Computacional (Mapas Auto-Organizáveis de Kohonen) para seleção das curvas típicas e determinação das tipologias de curvas de carga de consumidores residenciais e industriais da cidade de Macapá, localizada no estado do Amapá. O mapa auto-organizável de Kohonen é um tipo de Rede Neural Artificial que combina operações de projeção e agrupamento, permitindo a realização de análise exploratória de dados, com o objetivo de produzir descrições sumarizadas de grandes conjuntos de dados. / The energy utilities, for ensure that your network be reliable, need to perform a procedure for study and analysis based in your functions of delivery of energy in the points of the consumption. This study, generally called of systems planning of electric power distribution, is essential for ensure that variations in the energy demand doesn’t affect the system performance, that should whether keep operating of technique manner and viable economically. In these studies are generally analyzed, demand, typology of load curves, load factor and other aspects of the existing loads. Considering then the importance of the determining of the typologies of load curves for utilities in their planning process, the Electricity Company of Amapá (CEA) conducted a campaign of measures of load curves of the distribution transformers that were utilized for obtainment of the typologies of load curves that characterize your consumers. In this paper presents the satisfactory results obtained as from the utilization of Data Mining based in Computational Intelligence (Self-Organizing Maps of Kohonen) for selection of the typical curves and determination of the typologies of load curves of residential and industrial consumers for the city of Macapá, located in the state of Amapá. The self-organizing map of Kohonen is a type of artificial neural network that combines operations of projection and clustering, allowing the realization of exploratory data analysis, with the goal of producing summarized descriptions of large data sets.