Fault location in underground systems through optimum-path forest

In this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Inferência espacial de clorofila a por redes neurais artificiais aplicadas a imagens multiespectrais e medidas tomadas in situ

Pós-graduação em Ciências Cartográficas - FCT

Detecção e classificação de curto-circuitos em sistemas de distribuição usando rede neural artificial ARTMAP nebulosa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Ajuste de parâmetros de controladores suplementares (POD) através de redes neurais artificiais em dispositivos FACTS TCSC e SSSC

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de redes neurais artificiais na predição de diâmetro e rugosidade durante o processo de furação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Treinamento de uma rede neural de base radial usando computação evolutiva: implementação e aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O uso de rede neural artificial MLP na predição de estruturas secundárias de proteínas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identificação de defeitos em tubulações metálicas utilizando redes neurais artificiais e o método dos elementos finitos

Pós-graduação em Engenharia Mecânica - FEB

Previsão de séries temporais financeiras por meio de redes neurais dinâmicas e processos de transformação de dados: uma abordagem empírico-comparativa

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Aplicação de redes neurais artificiais no monitoramento da operação de dressagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

SIRCo: uma arquitetura para Sensoriamento Inteligente de Rádios Cognitivos

The increased demand for using the Industrial, Scientific and Medical (ISM) unlicensed frequency spectrum has caused interference problems and lack of resource availability for wireless networks. Cognitive radio (CR) have emerged as an alternative to reduce interference and intelligently use the spectrum. Several protocols were proposed aiming to mitigate these problems, but most have not been implemented in real devices. This work presents an architecture for Intelligent Sensing for Cognitive Radios (ISCRa), and a spectrum decision model (SDM) based on Artificial Neural Networks (ANN), which uses as input a database with local spectrum behavior and a database with primary users information. For comparison, a spectrum decision model based on AHP, which employs advanced techniques in its spectrum decision method was implemented. Another spectrum decision model that considers only a physical parameter for channel classification was also implemented. Spectrum decision models evaluated, as well as ISCRa's architecture were developed in GNU-Radio framework and implemented on real nodes. Evaluation of SDMs considered metrics of: delivery rate, latency (Round Trip Time - RTT) and handoff. Experiments on real nodes showed that ISCRa architecture with ANN based SDM increased packet delivery rate and presented fewer frequency variation (handoff) while maintaining latency. Considering higher bandwidth as application's Quality of Service requirement, ANN-SDM obtained the best results when compared to other SDM for cognitive radio networks (CRN).

Kappa-PSO-ARTMAP Fuzzy: uma metodologia para detecção de intrusos baseado em seleção de atributos e otimização de parâmetros numa rede neural ARTMAP Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Análise da estabilidade estática de tensão de sistemas elétricos de potência usando uma rede neural baseada na teoria da ressonância adaptativa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)