Plataforma de previsão do consumo elétrico para edifícios inteligentes

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Dispositivo de interligação multi-rádio de longo alcance para redes de sensores com alimentação solar

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Análisis de la incidencia de las redes sociales en la construcción de un movimiento. La Ola Verde en las elecciones presidenciales de 2010

Esta monografía pretende revisar la incidencia de las redes sociales, tanto tradicionales como virtuales, en el proceso de construcción del movimiento "la Ola Verde" en las elecciones presidenciales de 2010. Este movimiento se constituyó en un fenómeno político y social efímero que en pocos meses logró competir contra maquinarias políticas ya consolidadas pero que no logró la victoria electoral anhelada. Este trabajo de grado está estructurado en tres secciones las cuales nacieron a partir de una caracterización de tres etapas que atravesó la Ola Verde, estos son: la génesis, el crecimiento y el declive del movimiento. De igual manera, la presente monografía analiza el rol de las emociones en las redes sociales tradicionales y en las redes sociales virtuales (Facebook y Twitter).

Técnicas avanzadas para la gestión de la producción : heurísticas y sistemas inteligentes.

El trabajo ha sido desarrollado en el Departamento de Organización y Gestión de Empresas en la Escuela Técnica Superior de Ingeniería Industrial de la Universidad de Valladolid. El objetivo fundamental del libro producto del proyecto, es el de facilitar al alumno una visión integral de las técnicas metaheurísticas orientadas a la optimización. Los temas tratados se desglosan en: 1) Introducción, 2) Redes neuronales, 3) Algoritmos genéticos, 4) Recocido simulado, 5) Búsqueda Tabú, 6) Otras técnicas. El trabajo permitirá instaurar metodologías que motiven más directamente a los alumnos en su aprendizaje.

Sistemas hipermedias adaptativos : Sistemas inteligentes en educación : una utopía realizada.

Analizar la evolución de los sistemas hipermedias adaptativos y su influencia en la aplicación a la educación. Literatura publicada sobre el tema. Revisión bibliográfica. Revisión bibliográfica sobre la temática. Análisis de contenido, análisis comparativo. La investigación analiza los sistemas adaptativos, acercando el estudio de los sistemas expertos a partir de las aportaciones de la Inteligencia artificial al mundo de la educación. En primer lugar se realiza una descripción de los antecedentes de las tecnologías de la información en la educación. Se segundo lugar se aborda la influencia de las teorías del aprendizaje en el diseño de sistemas instruccionales, analizando la incorporación de las tecnologías de la información y la comunicación de forma crítica, contextualizada y reflexiva. En tercer lugar se analizan las características del software educativo, como punto de partida para acercarse al estudio de los sistemas hipermedias adaptativos. Seguidamente se estudian los sistemas multimedia aplicados a la educación, indicando los requisitos a tener en cuenta para el diseño de multimedias para la enseñanza. Finalmente, se aborda el estudio de los sistemas multimedia adaptativos y su aplicación en el desarrollo de sistemas tutores inteligentes y redes bayesianas en educación. Una de las preocupaciones fundamentales en educación actualmente es la personalización de los procesos de enseñanza, se trata de ofrecer al alumnado contenidos y estrategias de aprendizaje acordes con sus estilo de aprendizaje y capacidades. La utilización del ordenador como recurso de apoyo para los procesos de enseñanza ofrece la ventaja de permitir una interacción personal entre el aprendiz y el sistema tutor de enseñanza; la flexibilidad y adaptación de los sistemas tutoriales está en función de la capacidad del sistema de reconocer las capacidades del aprendiz. Debido a los diferentes estilos de aprendizaje, existen dificultades para elaborar modelos exactos que se identifiquen claramente con un estilo en concreto. Mediante el reconocimiento de algunas de las características del alumnado, y su interacción con el sistema se pretenden obtener criterios que permitan diseñar sistemas inteligentes. El conjunto de actividades que se pueden realizar con estos sistemas es objeto de estudio tanto por investigadores del área de Inteligencia Artificial como por las áreas de la Pedagogía y la Didáctica. Es necesario profundizar en la investigación iniciada, para desarrollar de forma efectiva sistemas inteligentes en la educación on-line.

O site de redes sociais Facebook como espaço da comunicação organizacional

Dissertação apresentada ao Programa de Mestrado em Comunicação da Universidade Municipal de São Caetano do Sul - USCS

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

Uma arquitetura baseada em agentes para configuração dinâmica de aplicações inteligentes em ambiente foundation fieldbus

The industrial automation is directly linked to the development of information tecnology. Better hardware solutions, as well as improvements in software development methodologies make possible the rapid growth of the productive process control. In this thesis, we propose an architecture that will allow the joining of two technologies in hardware (industrial network) and software field (multiagent systems). The objective of this proposal is to join those technologies in a multiagent architecture to allow control strategies implementations in to field devices. With this, we intend develop an agents architecture to detect and solve problems which may occur in the industrial network environment. Our work ally machine learning with industrial context, become proposed multiagent architecture adaptable to unfamiliar or unexpected production environment. We used neural networks and presented an allocation strategies of these networks in industrial network field devices. With this we intend to improve decision support at plant level and allow operations human intervention independent

Algoritmos inteligentes para auto-correção, auto-verificação e auto-validação de medidas em sensores

This work presents a set of intelligent algorithms with the purpose of correcting calibration errors in sensors and reducting the periodicity of their calibrations. Such algorithms were designed using Artificial Neural Networks due to its great capacity of learning, adaptation and function approximation. Two approaches willbe shown, the firstone uses Multilayer Perceptron Networks to approximate the many shapes of the calibration curve of a sensor which discalibrates in different time points. This approach requires the knowledge of the sensor s functioning time, but this information is not always available. To overcome this need, another approach using Recurrent Neural Networks was proposed. The Recurrent Neural Networks have a great capacity of learning the dynamics of a system to which it was trained, so they can learn the dynamics of a sensor s discalibration. Knowingthe sensor s functioning time or its discalibration dynamics, it is possible to determine how much a sensor is discalibrated and correct its measured value, providing then, a more exact measurement. The algorithms proposed in this work can be implemented in a Foundation Fieldbus industrial network environment, which has a good capacity of device programming through its function blocks, making it possible to have them applied to the measurement process

Análise comparativa do desempenho de um Controlador Fuzzy acoplado a um PID Neural sintonizado por um Algoritmo Genético com Controladores Inteligentes Convencionais

On this paper, it is made a comparative analysis among a controller fuzzy coupled to a PID neural adjusted by an AGwith several traditional control techniques, all of them applied in a system of tanks (I model of 2nd order non lineal). With the objective of making possible the techniques involved in the comparative analysis and to validate the control to be compared, simulations were accomplished of some control techniques (conventional PID adjusted by GA, Neural PID (PIDN) adjusted by GA, Fuzzy PI, two Fuzzy attached to a PID Neural adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA) to have some comparative effects with the considered controller. After doing, all the tests, some control structures were elected from all the tested techniques on the simulating stage (conventional PID adjusted by GA, Fuzzy PI, two Fuzzy attached to a PIDN adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA), to be implemented at the real system of tanks. These two kinds of operation, both the simulated and the real, were very important to achieve a solid basement in order to establish the comparisons and the possible validations show by the results

Identificação de nematóides através de técnicas de processamento inteligentes de imagens

abstract

Técnicas inteligentes hídridas para o controle de sistemas não lineares

A neuro-fuzzy system consists of two or more control techniques in only one structure. The main characteristic of this structure is joining one or more good aspects from each technique to make a hybrid controller. This controller can be based in Fuzzy systems, artificial Neural Networks, Genetics Algorithms or rein forced learning techniques. Neuro-fuzzy systems have been shown as a promising technique in industrial applications. Two models of neuro-fuzzy systems were developed, an ANFIS model and a NEFCON model. Both models were applied to control a ball and beam system and they had their results and needed changes commented. Choose of inputs to controllers and the algorithms used to learning, among other information about the hybrid systems, were commented. The results show the changes in structure after learning and the conditions to use each one controller based on theirs characteristics

MATIUDE - monitoramento e acesso a transdutores inteligentes (Padrão IEEE 1451) utilizando dispositivos embarcados (celulares e PDA´s)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)