Submitted by (ana.araujo@ufrpe.br) on 2016-06-28T16:05:18Z No. of bitstreams: 1 David Augusto Silva.pdf: 1453777 bytes, checksum: 4516b869e7e749b770a803eb7e91a084 (MD5) / Made available in DSpace on 2016-06-28T16:05:18Z (GMT). No. of bitstreams: 1 David Augusto Silva.pdf: 1453777 bytes, checksum: 4516b869e7e749b770a803eb7e91a084 (MD5) Previous issue date: 2011-07-01 / The techniques for Time Series Analysis and Forecasting have great presence on the literature over the years. The computational resources combined with statistical techniques are improving the predictive results, and these results have been become increasingly accurate. Computational methods base on Artificial Neural Networks (ANN) and Evolutionary Computing (EC) are presenting a new approach to solve the Time Series Analysis and Forecasting problem. These computational methods are contained in the branch of Artificial Intelligence (AI), and they are biologically inspired, where the ANN models are based on the neural structure of intelligent organism, and the EC uses the concept of nature selection of Charles Darwin. Both methods acquire experience from prior knowledge and example of the given problem. In particular, for the Time Series Forecasting Problem, the objective is to find the predictive model with highest forecast perfomance, where the performance measure are statistical errors. However, there is no universal criterion to identify the best performance measure. Since the ANNs are the predictive models, the EC will constantly evaluate the forecast performance of the ANNs, using a fitness functions to guide the predictive model for an optimal solution. The Data Envelopment Analysis (DEA) was employed to predictive determine the best combination of variables based on the relative efficiency of the best models. Therefore, this work to study the optimization Fitness Function process with Data Envelopment Analysis applied the Intelligence Hybrid System for time series forecasting problem. The data analyzed are composed by financial data series, agribusiness and natural phenomena. The C language program was employed for implementation of the hybrid intelligent system and the R Environment version 2.12 for analysis of DEA models. In general, the perspective of using DEA procedure to evaluate the fitness functions were satisfactory and serves as an additional resource in the branch of time series forecasting. Researchers need to compute the results under different perspectives, whether in the matter of the computational cost of implementing a particular function or which function was more efficient in the aspect of assessing which combinations are unwanted saving time and resources. / As técnicas de análise e previsão de séries temporais alcançaram uma posição de distinção na literatura ao longo dos anos. A utilização de recursos computacionais, combinada com técnicas estatísticas, apresenta resultados mais precisos quando comparados com os recursos separadamente. Em particular, técnicas que usam Redes Neurais Artificiais (RNA) e Computação Evolutiva (CE), apresenta uma posição de destaque na resolução de problemas de previsão na análise de séries temporais. Estas técnicas de Inteligência Artificial (AI) são inspiradas biologicamente, no qual o modelo de RNA é baseado na estrutura neural de organismos inteligentes, que adquirem conhecimento através da experiência. Para o problema de previsão em séries temporais, um fator importante para o maior desempenho na previsão é encontrar um método preditivo com a melhor acurácia possível, tanto quanto possível, no qual o desempenho do método pode ser analisado através de erros de previsão. Entretanto, não existe um critério universal para identificar qual a melhor medida de desempenho a ser utilizada para a caracterização da previsão. Uma vez que as RNAs são os modelos de previsão, a CE constantemente avaliará o desempenho de previsão das RNAs, usando uma função de fitness para guiar o modelo preditivo para uma solução ótima. Desejando verificar quais critérios seriam mais eficientes no momento de escolher o melhor modelo preditivo, a Análise Envoltória de Dados (DEA) é aplicada para fornecer a melhor combinação de variáveis visando a otimização do modelo. Portanto, nesta dissertação, foi estudado o processo de otimização de Funções de Fitness através do uso da Análise Envoltória de Dados utilizando-se de técnicas hibridas de Inteligência Artificial aplicadas a área de previsão de séries temporais. O banco de dados utilizado foi obtido de séries históricas econômico- financeiras, fenômenos naturais e agronegócios obtidos em diferentes órgãos específicos de cada área. Quanto à parte operacional, utilizou-se a linguagem de programação C para implementação do sistema híbrido inteligente e o ambiente R versão 2.12 para a análise dos modelos DEA. Em geral, a perspectiva do uso da DEA para avaliar as Funções de Fitness foi satisfatório e serve como recurso adicional na área de previsão de séries temporais. Cabe ao pesquisador, avaliar os resultados sob diferentes óticas, quer seja sob a questão do custo computacional de implementar uma determinada Função que foi mais eficiente ou sob o aspecto de avaliar quais combinações não são desejadas poupando tempo e recursos.

Os motores de indução desempenham um importante papel na indústria, fato este que destaca a importância do correto diagnóstico e classificação de falhas ainda em fase inicial de sua evolução, possibilitando aumento na produtividade e, principalmente, eliminando graves danos aos processos e às máquinas. Assim, a proposta desta tese consiste em apresentar um multiclassificador inteligente para o diagnóstico de motor sem defeitos, falhas de curto-circuito nos enrolamentos do estator, falhas de rotor e falhas de rolamentos em motores de indução trifásicos acionados por diferentes modelos de inversores de frequência por meio da análise das amplitudes dos sinais de corrente de estator no domínio do tempo. Para avaliar a precisão de classificação frente aos diversos níveis de severidade das falhas, foram comparados os desempenhos de quatro técnicas distintas de aprendizado de máquina; a saber: (i) Rede Fuzzy Artmap, (ii) Rede Perceptron Multicamadas, (iii) Máquina de Vetores de Suporte e (iv) k-Vizinhos-Próximos. Resultados experimentais obtidos a partir de 13.574 ensaios experimentais são apresentados para validar o estudo considerando uma ampla faixa de frequências de operação, bem como regimes de conjugado de carga em 5 motores diferentes. / Induction motors play an important role in the industry, a fact that highlights the importance of correct diagnosis and classification of faults on these machines still in early stages of their evolution, allowing increase in productivity and mainly, eliminating major damage to the processes and machines. Thus, the purpose of this thesis is to present an intelligent multi-classifier for the diagnoses of healthy motor, short-circuit faults in the stator windings, rotor broken bars and bearing faults in induction motors operating with different models of frequency inverters by analyzing the amplitude of the stator current signal in the time domain. To assess the classification accuracy across the various levels of faults severity, the performances of four different learning machine techniques were compared; namely: (i) Fuzzy ARTMAP network, (ii) Multilayer Perceptron Network, (iii) Support Vector Machine and (iv) k-Nearest-Neighbor. Experimental results obtained from 13.574 experimental tests are presented to validate the study considering a wide range of operating frequencies and also load conditions using 5 different motors.

Este trabalho tem por objetivo desenvolver uma abordagem fuzzy focando na estimação de curto prazo da demanda de potência ativa de um alimentador de sistema de distribuição de energia elétrica. A motivação para este trabalho encontra-se na redução do erro de estimação para que o sistema de distribuição como um todo seja corretamente operado. O destaque da abordagem desenvolvida é a metodologia de seleção de entradas para o sistema de estimação, que o treina fornecendo-lhe informações não redundantes e não desnecessárias sobre o comportamento da série temporal. Os resultados, obtidos com treinamento e teste de um sistema de inferência fuzzy multicamadas, mostram que as estimações realizadas selecionando as entradas do sistema de forma criteriosa apresentam menor erro que quando não há critério de seleção. Conclui-se então que a metodologia foi funcional e eficiente para o caso estudado, o que faz com que este trabalho resulte em válidas contribuições nas áreas de sistemas inteligentes, de sistemas dinâmicos e inclusive na forma metodológica de especificação de modelos de estimação de séries temporais. / This work aims to develop a fuzzy approach focusing on the short-term active power demand forecast in a feeder of an electrical energy distribution system. This work motivation lies on the reduction of the forecast error so that the whole distribution system can be correctly operated. The highlight of the developed approach is the methodology to select the inputs for the estimation system, which trains it giving to it non-redundant and non-unnecessary information about the time series behavior. The results, obtained by training and testing a multilayer fuzzy inference system, show that the estimations made by following a criterion to select the inputs have smaller error than when there is no selection criterion at all. It is therefore concluded that the methodology was functional and efficient for the case under study, what makes this work result in valid contributions for the fields of intelligent systems, dynamic systems and in the methodological way to specify models to estimate time series.

No description available.

Smart homes are custom-fitted systems for users to manage their home environments. Smart homes consist of devices which has the possibility to communicate between each other. In a smart home system, the communication is used by a central control unit to manage the environment and the devices in it. Setting up a smart home today involves a lot of manual customizations to make it function as the user wishes. What smart homes lack is the possibility to learn from users behaviour and habits in order to provide a customized environment for the user autonomously. The purpose of this thesis is to examine whether environmental data can be collected and used in a small smart home system to learn about the users behaviour. To collect data and attempt this learning process, a system is set up. The system uses a central control unit for mediation between wireless electrical outlets and sensors. The sensors track motion, light, temperature as well as humidity. The devices and sensors along with user interactions in the environment make up the collected data. Through studying the collected data, the system is able to create rules. These rules are used for the system to make decisions within its environment to suit the users’ needs. The performance of the system varies depending on how the data collection is handled. Results find that collecting data in intervals as well as when an action is made from the user is important. / Smarta hem är system avsedda för att hjälpa användare styra sin hemmiljö. Ett smart hem är uppbyggt av enheter med möjlighet att kommunicera med varandra. För att kontrollera enheterna i ett smart hem, används en central styrenhet. Att få ett smart hem att vara anpassat till användare är ansträngande och tidskrävande. Smarta hemsystem saknar i stor utsträckning möjligheten att lära sig av användarens beteende. Vad ett sådant lärande skulle kunna möjliggöra är ett skräddarsytt system utan användarens involvering. Syftet med denna avhandling är att undersöka hur användardata från en hemmiljö kan användas i ett smart hemsystem för att lära sig av användarens beteende. Ett litet smart hemsystem har skapats för att studera ifall denna inlärningsmetod är applicerbar. Systemet består av sensorer, trådlösa eluttag och en central styrenhet. Den centrala styrenheten används för att kontrollera de olika enheterna i miljön. Sensordata som sparas av systemet består av rörelse, ljusstyrka, temperatur och luftfuktighet. Systemet sparar även användarens beteende i miljön. Systemet skapar regler utifrån sparad data med målet att kunna styra enheterna i miljön på ett sätt som passar användaren. Systemets agerande varierade beroende på hur data samlades in. Resultatet visar vikten av att samla in data både i intervaller och när användare tar ett beslut i miljön.

Zinchenko E.E. in her master's thesis looks as widely as possible at the virtual, as the problem appears the uncertainty of the term "virtual". The object of research is the virtual reality and the subject is related aspects of the ontology, psychology, sociology and virtual technologies. The aim is a detailed study of the different approaches to the "virtual" and develop their own unified concept of this problem based on a single modern ontology. The material for the study in addition to literature represents by specific popular computer games, as well as the reviews and opinions of real players. The first chapter is dedicated to the most elaborated concepts in the field of "virtualistics" of S.S. Khoruzhiy and N.A. Nosov, as well as developing own structure of "virtual". The second chapter is devoted to the practical implementation of appropriate technology. The theoretical framework is based on the works of K. Jung, Z. Freud, G. Gibson, I.V. Burlakov and on projects of young game designers developed their ideas to existing games. Particular attention in this chapter is on computer games as the most common means of immersion in the virtual environment. Zinchenko E.E. adheres to a neutral position on the upbeat virtual reality, considering, in particular, the positive aspect of the phenomenon of the virtual. / В своей магистерской диссертации Зинченко Е.Е. максимально широко смотрит на виртуальное, так как проблемой выступает неопределенность самого термина «виртуальное». Объектом исследования выступает виртуальная реальность, а предметом - взаимосвязанные аспекты онтологии, психологии, социологии и технологий виртуального. Целью работы является подробное изучение различных подходов к «виртуальному» и разработка собственной единой концепции данной проблематики на базе единой современной онтологии. В качестве материала для исследования помимо литературных источников используются популярные и специфические компьютерные игры, а также отзывы и мнения реальных игроков. Первая глава посвящена наиболее проработанным концепциям в области «виртуалистики» Хоружего С.С. и Носова Н.А., а также разработке собственной структуры «виртуального». Вторая глава посвящена практической реализации соответствующих технологий. Теоретическая база основана на работах К. Юнга, З. Фрейда, Дж. Гибсона, Бурлакова И.В. и на проектах молодых геймдизайнеров, реально воплощающих свои идеи в существующих играх. Особое внимание в данной главе уделяется компьютерным играм как самому распространенному средству погружения в виртуальную среду. Зинченко Е.Е. придерживается нейтрально-оптимистичной позиции относительно виртуальной реальности, рассматривая, в том числе, положительный аспект феномена виртуального.

Магистерская диссертация Оглуздина К.А. "Когнитивная модель агента в качестве интеллектуальной системы, взаимодействующей с внешней средой" посвящена проблеме моделирования когнитивных агентов. В XX веке разрабатывались особого типа машины, которые могли бы осуществлять мыслительную деятельность, сопоставимую с человеческой. Этот проект был назван искусственный интеллект. Исчерпывающие представления об области искусственного интеллекта можно получить из работ С. Рассела, П. Норвинга, Гаазе-Рапопорт М. Г., Поспелова Д. А., Редько В. Г. и других. Объектом исследования являются искусственные интеллектуальные системы, а предметом - искусственные интеллектуальные агенты. Целью исследования является выявление основных свойств искусственных интеллектуальных агентов. Работа состоит из двух частей, каждая из которых поделена на два раздела. В первой части работы, в первом блоке изучается понятие интеллекта и его особенностей относительно естественных и искусственных систем, а второй блок посвящен анализу понятия агент, рассмотрению классификаций искусственных агентов в целом, и когнитивных агентов в частности. Во второй части работы, в первом блоке мы рассматриваем архитектуры когнитивных агентов, а также проведён их сравнительный анализ. Второй блок посвящен проблеме взаимодействия агентов в многоагентной системе. / Master's thesis of Ogluzdin K.A. "The cognitive agent model as an intellectual system interacting with the external environment" is devoted to the modeling of cognitive agents. In XX century, there was developed a special type of machine that could perform mental activity comparable to that of the human. This project was called artificial intelligence. Comprehensive understanding of the field of artificial intelligence can be obtained from the works of S. Russell, P. Norvinga, M.G. Haase-Rapoport, D.A. Pospelov, V.G. Redko and others. The object of study is the artificial intelligence system, and the subject is artificial intelligent agents. The aim of the study is to identify the basic properties of artificial intelligent agents. The paper consists of two parts, each of which is divided into two blocks. In the first part, in the first block there is studied the concept of intelligence and its features with respect to natural and artificial systems, while the second block is devoted to the analysis of the concept of agent, considering the classification of artificial agents in general, and cognitive agents in particular. In the second part, there is studied in the first section the architecture of cognitive agents, and there is conducted comparative analysis. The second block is devoted to the interaction between agents in multi-agent system.

Интеллектуальные системы применяются человеком во всех сферах жизнедеятельности, при этом существенно изменяя жизненные условия людей. В диссертации рассматриваются теоретические и технологические аспекты интеллектуальных систем. Автор понимает интеллектуальную систему как информационно-вычислительную систему с имеющейся базой знаний, алгоритмом действий и решающую задачи без помощи оператора. Для проектирования интеллектуальных систем важен системный подход. В диссертации искусственный интеллект берется в антропологическом измерении и рассматриваются разные подходы к исследованиям искусственного интеллекта. Интеллектуальные системы в диссертации иследованы в технологическом аспекте, в том числе описаны структурные и функциональные компоненты интеллектуальных систем. В ходе проведенного исследования были рассмотрены возможные варианты взаимодействия в системе «человек – техника» и заявлена актуальность проблемы поиска оптимальных взаимоотношений в системе «человек – интеллектуальная система». / Intelligent systems are used in all spheres of life, thus they significantly are changing the conditions of human life. The dissertation is devoted to theoretical and technological aspects of intelligent systems. The author understands the intelligent system as an information processing system with the existing knowledge base, algorithm of actions and solving problems without operator assistance. The systemic approach is important for the design of intelligent systems. The dissertation takes an artificial intelligence in the anthropological dimension and discusses different approaches to investigations of artificial intelligence. Intelligent systems in the dissertation are taken in the technological aspect, including description of the structural and functional components of intelligent systems. In the represent study the possible options for interaction in the system «man - technique» are examined and there is declared the urgency of the problem of finding the optimal relations in the system «man - intelligent system».

В работе производиться анализ многоагентных интеллектуальных систем, их различия, способы и направления применения. Описываются программы и методы создания аудио управляемого синтеза лица. Также обсуждаются различные цифровые голосовые помощники, виртуальные агенты. Рассматривается гипотеза и перспективы создания визуального виртуального цифрового помощника для средств массовой информации. / The paper analyzes multi-agent intelligent systems, their differences, ways and directions of application. Programs and methods for creating audio-controlled face synthesis are described. Various digital voice assistants and virtual agents are also discussed. The hypothesis and prospects of creating a visual virtual digital assistant for mass media are considered.

Este trabalho apresenta um sistema computacional que utiliza técnicas de Aprendizado de Máquina (AM) para auxiliar o diagnóstico oftalmológico. Trata-se de um sistema de medidas objetivas e automáticas dos principais vícios de refração ocular, astigmatismo, hipermetropia e miopia. O sistema funcional desenvolvido aplica técnicas convencionais de processamento a imagens do olho humano fornecidas por uma técnica de aquisição chamada Hartmann-Shack (HS), ou Shack-Hartmann (SH), com o objetivo de extrair e enquadrar a região de interesse e remover ruídos. Em seguida, vetores de características são extraídos dessas imagens pela técnica de transformada wavelet de Gabor e, posteriormente, analisados por técnicas de AM para diagnosticar os possíveis vícios refrativos presentes no globo ocular representado. Os resultados obtidos indicam a potencialidade dessa abordagem para a interpretação de imagens de HS de forma que, futuramente, outros problemas oculares possam ser detectados e medidos a partir dessas imagens. Além da implementação de uma nova abordagem para a medição dos vícios refrativos e da introdução de técnicas de AM na análise de imagens oftalmológicas, o trabalho contribui para a investigação da utilização de Máquinas de Vetores Suporte e Redes Neurais Artificiais em sistemas de Entendimento/Interpretação de Imagens (Image Understanding). O desenvolvimento deste sistema permite verificar criticamente a adequação e limitações dessas técnicas para a execução de tarefas no campo do Entendimento/Interpretação de Imagens em problemas reais. / This work presents a computational system that uses Machine Learning (ML) techniques to assist in ophthalmological diagnosis. The system developed produces objective and automatic measures of ocular refraction errors, namely astigmatism, hypermetropia and myopia from functional images of the human eye acquired with a technique known as Hartmann-Shack (HS), or Shack-Hartmann (SH). Image processing techniques are applied to these images in order to remove noise and extract the regions of interest. The Gabor wavelet transform technique is applied to extract feature vectors from the images, which are then input to ML techniques that output a diagnosis of the refractive errors in the imaged eye globe. Results indicate that the proposed approach creates interesting possibilities for the interpretation of HS images, so that in the future other types of ocular diseases may be detected and measured from the same images. In addition to implementing a novel approach for measuring ocular refraction errors and introducing ML techniques for analyzing ophthalmological images, this work investigates the use of Artificial Neural Networks and Support Vector Machines (SVMs) for tasks in Image Understanding. The description of the process adopted for developing this system can help in critically verifying the suitability and limitations of such techniques for solving Image Understanding tasks in \"real world\" problems.