Ocorrência de aminas aromáticas como subprodutos da degradação dos corantes sudan III e disperso amarelo 9

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Simulação em tempo real de sistemas de distribuição de energia elétrica utilizando-se estruturas com descrição de hardware em software

Pós-graduação em Engenharia Elétrica - FEIS

An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An efficient codification to solve distribution network reconfiguration for loss reduction problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Distribution network reconfiguration using an efficient evolutionary algorithm

Network reconfiguration is an important tool to optimize the operating conditions of a distribution system. This is accomplished modifying the network structure of distribution feeders by changing the open/close status of sectionalizing switches. This not only reduces the power losses, but also relieves the overloading of the network components. Network reconfiguration belongs to a complex family of problems because of their combinatorial nature and multiple constraints. This paper proposes a solution to this problem, using a specialized evolutionary algorithm, with a novel codification, and a brand new way of implement the genetic operators considering the problem characteristics. The algorithm is presented and tested in a real distribution system, showing excellent results and computational efficiency. © 2007 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Corn yield with reduction of insecticidal sprayings against fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae)

The ideal time is more important than amount of insecticidal spraying to adequate the control of Spodoptera frugiperda J.E. Smith (Lepidoptera: Noctuidae) in corn. This study aimed to evaluate lufenuron sequential sprayings effect and its rotation with other active ingredients on the population, damage caused by S. frugiperda and the impact on corn yield. The experiment was carried out in the field with six treatments: (1) one lufenuron spraying, (2) two lufenuron sprayings, (3) three lufenuron sprayings (4) four lufenuron sprayings, (5) sprayings with spinosad, lufenuron, thiamethoxam+lambdacyhalothrin and deltamethrin (in sequence, at ten days intervals) (6) control treatment. Sprayings started twenty days after the seedling had emerged and then every ten days for a maximum of four sprays. Both caterpillar population (20.9-21.7 larvae/plot) and index of damage (1.2-1.7) observed in corn plants were significantly lower in treated plots compared to control (untreated) (31.7 larvae/plot and index of damage 2.7), regardless of spraying amount. The results showed that multiple insecticide applications to control S. frugiperda do not guarantee higher yields in corn, ranging from 6375.2 to 7650.1 kg ha -1. Only one spraying of lufenuron was enough to prevent significant reduction in corn yield (6749.9 kg ha -1). © 2012 Asian Network for Scientific Information.

Análise de pathway e network em tecido adiposo in vivo e o papel da vitamina D na adipogênese in vitro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)