897 resultados para Sistema inteligente de controle automático
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The continuous gas lift method is the main artificial lifting method used in the oil industry for submarine wells, due to its robustness and the large range of flow rate that the well might operate. Nowadays, there is a huge amount of wells producing under this mechanism. This method of elevation has a slow dynamics due to the transients and a correlation between the injected gas rate and the of produced oil rate. Electronics controllers have been used to adjust many parameters of the oil wells and also to improve the efficiency of the gas lift injection system. This paper presents a intelligent control system applied to continuous gas injection in wells, based in production s rules, that has the target of keeping the wells producing during the maximum period of time, in its best operational condition, and doing automatically all necessary adjustments when occurs some disturbance in the system. The author also describes the application of the intelligent control system as a tool to control the flow pressure in the botton of the well (Pwf). In this case, the control system actuates in the surface control valve
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Este trabalho avalia o desempenho de um controlador fuzzy (tipo Takagi-Sugeno-Kang) quando, utilizando tecnologia sem fio para conectar as entradas e a saída do controlador aos sensores/atuadores, sofre perda das informações destes canais, resultado de perdas de pacotes. Tipicamente são utilizados controladores PID nas malhas de controle. Assim, o estudo realizado compara os resultados obtidos com os controladores fuzzy com os resultados dos controladores PID. Além disso, o trabalho visa estudar o comportamento deste controlador implementado em uma arquitetura microprocessada utilizando números inteiros nos cálculos, interpolação com segmentos de reta para as funções de pertinência da entrada e singletons nas funções de pertinência da saída. Para esse estudo foi utilizado, num ambiente Matlab/Simulink, um controlador fuzzy e o aplicativo True Time para simular o ambiente sem fio. Desenvolvido pelo Departamento de Controle Automático da Universidade de Lund, o True Time é baseado no Matlab/Simulink e fornece todas as ferramentas necessárias para a criação de um ambiente de rede (com e sem fio) virtual. Dado o paradigma de que quanto maior for a utilização do canal, maior a degradação do mesmo, é avaliado o comportamento do sistema de controle e uma proposta para diminuir o impacto da perda de pacotes no controle do sistema, bem como o impacto da variação das características internas da planta e da arquitetura utilizada na rede. Inicialmente são realizados ensaios utilizando-se o controlador fuzzy virtual (Simulink) e, posteriormente, o controlador implementado com dsPIC. Ao final, é apresentado um resumo desses ensaios e a comprovação dos bons resultados obtidos com um controlador fuzzy numa malha de controle utilizando uma rede na entrada e na saída do controlador.
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Este trabalho descreve um sistema de análise de dados com a finalidade de gerar um sistema de controle utilizando técnica inteligente para adição de fluoreto de alumínio (AlF3) em fornos de redução de alumínio. O projeto baseia-se nos conceitos de lógica fuzzy, nos quais o conhecimento acumulado pelo especialista do processo é traduzido de maneira qualitativa em um conjunto de regras linguísticas do tipo SE
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A presente dissertação trata da estipulação de limite de crédito para empresas clientes, de modo automático, com o uso de técnicas de Inteligência Computacional, especificamente redes neurais artificiais (RNA). Na análise de crédito as duas situações mais críticas são a liberação do crédito, de acordo com o perfil do cliente, e a manutenção deste limite ao longo do tempo de acordo com o histórico do cliente. O objeto desta dissertação visa a automação da estipulação do limite de crédito, implementando uma RNA que possa aprender com situações já ocorridas com outros clientes de perfil parecido e que seja capaz de tomar decisões baseando-se na política de crédito apreendida com um Analista de Crédito. O objetivo é tornar o sistema de crédito mais seguro para o credor, pois uma análise correta de crédito de um cliente reduz consideravelmente os índices de inadimplência e mantém as vendas num patamar ótimo. Para essa análise, utilizouse a linguagem de programação VB.Net para o sistema de cadastro e se utilizou do MatLab para treinamento das RNAs. A dissertação apresenta um estudo de caso, onde mostra a forma de aplicação deste software para a análise de crédito. Os resultados obtidos aplicando-se as técnicas de RNAs foram satisfatórias indicando um caminho eficiente para a determinação do limite de crédito.
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The petroleum production pipeline networks are inherently complex, usually decentralized systems. Strict operational constraints are applied in order to prevent serious problems like environmental disasters or production losses. This paper describes an intelligent system to support decisions in the operation of these networks, proposing a staggering for the pumps of transfer stations that compose them. The intelligent system is formed by blocks which interconnect to process the information and generate the suggestions to the operator. The main block of the system uses fuzzy logic to provide a control based on rules, which incorporate knowledge from experts. Tests performed in the simulation environment provided good results, indicating the applicability of the system in a real oil production environment. The use of the stagger proposed by the system allows a prioritization of the transfer in the network and a flow programming
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The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Engenharia Elétrica - FEIS
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The petroleum production pipeline networks are inherently complex, usually decentralized systems. Strict operational constraints are applied in order to prevent serious problems like environmental disasters or production losses. This paper describes an intelligent system to support decisions in the operation of these networks, proposing a staggering for the pumps of transfer stations that compose them. The intelligent system is formed by blocks which interconnect to process the information and generate the suggestions to the operator. The main block of the system uses fuzzy logic to provide a control based on rules, which incorporate knowledge from experts. Tests performed in the simulation environment provided good results, indicating the applicability of the system in a real oil production environment. The use of the stagger proposed by the system allows a prioritization of the transfer in the network and a flow programming
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El artículo está incluido en un número monográfico especial con los trabajos del I Simposio Pluridisciplinar sobre Diseño, Evaluación y Descripción de Contenidos Educativos Reutilizables (Guadalajara, Octubre 2004).Resumen basado en el de la publicación
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The artificial lifting of oil is needed when the pressure of the reservoir is not high enough so that the fluid contained in it can reach the surface spontaneously. Thus the increase in energy supplies artificial or additional fluid integral to the well to come to the surface. The rod pump is the artificial lift method most used in the world and the dynamometer card (surface and down-hole) is the best tool for the analysis of a well equipped with such method. A computational method using Artificial Neural Networks MLP was and developed using pre-established patterns, based on its geometry, the downhole card are used for training the network and then the network provides the knowledge for classification of new cards, allows the fails diagnose in the system and operation conditions of the lifting system. These routines could be integrated to a supervisory system that collects the cards to be analyzed
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This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)
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The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules