Made available in DSpace on 2014-12-17T14:56:21Z (GMT). No. of bitstreams: 1 DaniloLS.pdf: 2499617 bytes, checksum: 328b5ce6d56f5a92a61ad220565411c7 (MD5) Previous issue date: 2006-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents / Derramamentos de ?leo sobre o mar, mesmo que acidentais, geram enormes conseq??ncias negativas para a ?rea afetada. Os preju?zos s?o ambientais e econ?micos, principalmente com a proximidade dessas manchas de ?reas de preserva??o e/ou zonas costeiras. O desenvolvimento de t?cnicas autom?ticas para a identifica??o de manchas de ?leo sobre a superf?cie marinha, capturadas atrav?s de imagens de Radar, auxiliam num completo monitoramento dos oceanos e mares. Contudo, manchas de diferentes origens podem ser visualizadas nesse tipo de produ??o de imagem, tornando o monitoramento dif?cil. O sistema proposto neste trabalho, baseado em t?cnicas de processamento digital de imagens e redes neurais artificiais, tem o objetivo de identificar a mancha analisada e discernir entre ?leo e os demais fen?menos geradores de mancha. Testes nos blocos funcionais que comp?em o sistema proposto permitem a implementa??o de diferentes algoritmos, assim como sua an?lise detalhada e pontual. Os algoritmos que tratam do processamento digital de imagem (filtragem do ru?do speckle e gradiente), assim como o de classifica??o (Perceptron de M?ltiplas Camadas, rede de fun??o de Base Radial, M?quina de Vetor de Suporte e M?quina de comit?) s?o apresentados e comentados.O desempenho final do sistema, com diferentes tipos de classificadores, ? apresentado atrav?s da curva ROC. As taxas de acertos s?o consideradas condizentes com o que a literatura de detec??o de manchas de ?leo na superf?cie oce?nica atrav?s de imagens de SAR apresenta

Neste trabalho é apresentado um instrumento para auxílio à tomada de decisão em processos que envolvem avaliações da resistência do solo à penetração de raízes. Seu desenvolvimento fundamenta-se na concepção de uma nova ferramenta instrumental avançada, que viabiliza em tempo quase real informações para análise da variabilidade espacial da resistência do solo à penetração de raízes, tanto para área como para perfil, devido aos processos de compactação natural ou artificial do solo. Ensaios para a medida da resistência do solo à penetração de raízes podem ser realizados tanto em ambiente laboratorial como diretamente em campo agrícola. Para o desenvolvimento utilizou-se o enfoque da instrumentação inteligente, bem como uma microsonda (ângulo de cone de 30º, diâmetro de base de 1,6 mm e comprimento total de 30 mm) sensoriada por célula de carga. Resultados mostram que medidas de resistência do solo à penetração de raízes podem ser realizadas até um limite de (49,03 +/- 0,07) Kgf com resolução de 1,57 Kgf. Adicionalmente, a versatilidade do sistema é verificada para a coleta de dados e interpretação da resistência do solo à penetração de raízes, uma vez que podem ser apresentados na forma de tabelas, gráficos unidimensionais, mapas bidimensionais e mapas tridimensionais. Desta maneira, o sistema possibilita ao usuário uma rápida interpretação sobre o estado de agregação do solo em áreas de cultivo agrícolas. / This work is presented an instrument for decision-making in agricultural processes based on the measurements and mapping of soil resistance to the root penetration. Its development was based on a new and advance instrumentation tool, which enables in almost real-time to acquire the necessary information for spatial variability analysis of the resistance to root of plants penetration in soils, due to, either, natural or artificial compaction soil processes, i.e., not only for an area of soil but also to soil profile. The system allows soil resistance essays for both laboratory and agricultural field. Moreover, intelligent instrumentation concept was focused in the development, as well as a microprobe (30º for the spire angle, 1,6 mm for the base diameter, and 30 mm of total length), sensored by strain-gage transducers. Results have shown that measurements of soil resistance to root of plant penetration are allowed up to the limit of (49,03 +/- 0,07) Kgf, with 1,57 Kgf of resolution. Additionally, the suitability of the system is verified for soil resistance data collection and its interpretation to root plant penetration, since they can be presented in format of tables, one-dimensional graphics, two-dimensional maps and three-dimensional maps. Therefore, this system allows to the users a fast interpretation of soil aggregation state in agricultural areas.

Orientadores: Fernando Antonio Campos Gomide, Rosangela Ballini / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-12T14:32:10Z (GMT). No. of bitstreams: 1 Lima_EltonMariode_M.pdf: 1259231 bytes, checksum: 7a910e84bfb43d6c13b2deb8b6f511c2 (MD5) Previous issue date: 2008 / Resumo: Este trabalho propõe um modelo fuzzy funcional evolutivo que utiliza uma aplicação do aprendizado participativo para a construção de uma base de regras. O aprendizado participativo é um modelo de aprendizado baseado na noção de compatibilidade para a atualização do conhecimento do sistema. O aprendizado participativo pode ser traduzido em um algoritmo de agrupamento não supervisionado conhecido como agrupamento participativo. O algoritmo intitulado Aprendizado Participativo Evolutivo é proposto para construir um modelo fuzzy funcional evolutivo no qual as regras são obtidas a partir de um algoritmo de agrupamento não supervisionado. O algoritmo utiliza uma versão do agrupamento participativo para a determinação de uma base de regras correspondente ao modelo funcional do tipo Takagi-Sugeno evolutivo. A partir de uma noção generalizada, o modelo proposto é aplicado em problemas de previsão de séries temporais e os resultados são obtidos para a conhecida série Box-Jenkis, além da previsão de uma série de carga horária de energia elétrica. Os resultados são comparados com o modelo Takagi-Sugeno evolutivo que utiliza a noção de função potencial para agrupar os dados dinâmicamente e com duas abordagens baseadas em redes neurais. Os resultados mostram que o modelo proposto é eficiente e parcimonioso, abrindo potencial para aplicações e estudos futuros. / Abstract: This work introduces an approach to develop evolving fuzzy rule-based models using participatory learning. Participatory learning assumes that learning and beliefs about a system depend on what the learning mechanism knows about the system itself. Participatory learning naturally augments clustering and yields an e_ective unsupervised fuzzy clustering algorithms for on-line, real time domains and applications. Clustering is an essential step to construct evolving fuzzy models and plays a key role in modeling performance and model quality. A least squares recursive approach to estimate the consequent parameters of the fuzzy rules for on-line modeling is emphasized. Experiments with the classic Box-Jenkins benchmark are conducted to compare the performance of the evolving participatory learning with the evolving fuzzy system modeling approach and alternative fuzzy modeling and neural methods. The experiments show the e_ciency of evolving participatory learning to handle the benchmark problem. The evolving participatory learning method is also used to forecast the average hourly load of an electric generation plant and compared against the evolving fuzzy system modeling using actual data. The results confirm the potential of the evolving fuzzy participatory method to solve real world modeling problems. / Mestrado / Automação Industrial / Mestre em Engenharia Elétrica

Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

This thesis deals with the study of home automation systems. The main goal of this thesis is to prove their effectiveness. The sample project shows how to calculate the efficiency and profitability of investment in the home automation systems. The result of this work is a methodology for determining the efficiency of investment projects into automation systems. The work is divided into two parts. The theoretical part is to provide the reader with the basic understanding of automation systems. their functionality and their advantages and disadvantages. A technical model is provided describing an automation system iNELS. The practical part describes the procedures and the outcomes of the effectiveness of the system.

Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

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Master's thesis of Pliner A.A. "Cognitive Science: an integrated approach" is devoted to the study of cognitive science as a set of scientific research in various fields of philosophy, psychology, artificial intelligence, linguistics, united by a common research interest in the issue of knowledge. The emergence of interest in cognitive science could attributed to the middle of the XX century and related to the work of such thinkers as J. Miller, N. Chomsky, W. McCarthy and others. At the present stage of development, it is time to study the cognitive sciences as systems containing components operating in other systems and having different nature, and they connected with the original system in the complex functional dependence. The aim of investigation is the implementation of an integrated approach to the analysis of the phenomenon of cognitive sciences. The object of study - a cognitive science as a set. Subject of research – a specific complex relationship cognitive sciences and their implementation in a variety of applications. The work consists of four chapters. The first chapter is devoted to the history of the formation of the concept of cognitive science, the second chapter describes the basic methods of the cognitive sciences, the third chapter highlights the major features of complexing cognitive sciences, the fourth chapter builds complex cognitive science as an element of other polysystem. / Магистерская диссертация Плинер А.А. "Cognitive Science: комплексный подход" посвящена изучению когнитивных наук как комплекса научных исследований в различных областях философии, психологии, искусственного интеллекта, лингвистики, объединенных единым исследовательским интересом к вопросу познания. Возникновение интереса к когнитивным наукам можно отнести к середине XX в. и связать с работами таких мыслителей, как Дж. Миллер, Н. Хомский, У. Маккарти и других. На современном этапе развития пришло время изучения когнитивных наук как системы, содержащей элементы, функционирующие и в других системах, имеющих иную природу, и находящихся с первоначальной системой в сложной функциональной зависимости. Целью работы является осуществление комплексного подхода к анализу феномена когнитивных наук. Объект исследования – это когнитивные науки как комплекс. Предмет исследования – специфические комплексные взаимосвязи когнитивных наук и их реализация в различных сферах применения. Работа состоит из четырех глав. Первая глава посвящена истории формирования понятия когнитивных наук, вторая глава описывает основные методы когнитивных наук, третья глава выделяет основные комплексообразующие черты когнитивных наук, четвертая глава строит комплекс когнитивных наук как элемент других полисистем.

Магистерская диссертация Макуриной А.Ю. "Воображение как когнитивный процесс" посвящена воображению как когнитивному процессу и к проблеме его развития в процессе когнитивной деятельности. В известных психологических концепциях (Леонтьев В.Г., Майер Г., Маслоу А., Рибо Т. и другие). нет единого подхода к классификации воображения, нет определенного понимания когнитивной деятельности, нет единого взгляда на виды воображения. Целью исследования является изучение воображения как когнитивного процесса и его развития в процессе когнитивной деятельности. Объект исследования - воображение как когнитивный процесс и психическая деятельность. Предмет исследования – факторы развития воображения в процессе когнитивной деятельности. Работа состоит из двух глав. В первой главе рассмотрена дефиниция понятия «воображение», принятая в психологии, исследовано воображение как творческий процесс и форма психической деятельности, проанализирован онтогенез воображения и проведено разграничение понятия «воображение» от понятий «мышление», «представление», «память». Во второй главе дано подробное определение когнитивной деятельности, когнитивной мотивации и рассмотрено их влияние на развитие воображения, изучен феномен понимания и его связь с воображением. / Master's thesis of Makurina A.Y. "The imagination as a cognitive process" devoted to the imagination as a cognitive process and the issue of its development in the process of cognitive activity. In the known psychological concepts (V.G. Leont'ev, G. Meyer, A. Maslow, T. Ribot and others), there is no unit approach to the classification of the imagination, there is no certain understanding of cognitive activity, there is no common view on the types of imagination. The aim of the study is the investigation of imagination as a cognitive process and its development in the process of cognitive activity. The object of study - the imagination as a cognitive process and mental activity. Subject of research - the factors of development of imagination in the process of cognitive activity. The work consists of two chapters. The first chapter examined the definition of the concept "imagination", adopted in psychology, studied the imagination as a creative process and a form of mental activity, to analyze the ontogeny of the imagination and held distinction between "imagination" of the concepts of "thinking", "view", "memory". The second chapter provides a detailed definition of cognitive activity, cognitive motivation, and examines their impact on the development of imagination, also examines the phenomenon of understanding and its relationship with the imagination.