Determination of resonant frequencies of triangular and rectangular microstrip antennas, using artificial neural networks

This paper presents models that can be used in the design of microstrip antennas for mobile communications. The antennas can be triangular or rectangular. The presented models are compared with deterministic and empirical models based on artificial neural networks (ANN) presented in the literature. The models are based on Perceptron Multilayer (PML) and Radial Basis Function (RBF) ANN. RBF based models presented the best results. Also, the models can be embedded in CAD systems, in order to design microstrip antennas for mobile communications.

Avaliação estatística de métodos de poda aplicados em neurônios intermediários da rede neural MLP

There are several papers on pruning methods in the artificial neural networks area. However, with rare exceptions, none of them presents an appropriate statistical evaluation of such methods. In this article, we proved statistically the ability of some methods to reduce the number of neurons of the hidden layer of a multilayer perceptron neural network (MLP), and to maintain the same landing of classification error of the initial net. They are evaluated seven pruning methods. The experimental investigation was accomplished on five groups of generated data and in two groups of real data. Three variables were accompanied in the study: apparent classification error rate in the test group (REA); number of hidden neurons, obtained after the application of the pruning method; and number of training/retraining epochs, to evaluate the computational effort. The non-parametric Friedman's test was used to do the statistical analysis.

A neural approach to evaluate the effect of lightning in power transformers

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers. © 2009 The Berkeley Electronic Press. All rights reserved.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Development and application of an electronic tongue for detection and monitoring of nitrate, nitrite and ammonium levels in waters

This work presents the first study and development of an electronic tongue analysis system for the monitoring of nitrogen stable species: nitrate, nitrite and ammonium in water. The electronic tongue was composed of an array of 15 potentiometric poly(vinyl chloride) membrane sensors sensitive to cations and anions plus an artificial neural network (ANN) response model. The building of the ANN model was performed in a medium containing sodium, potassium, and chloride as interfering ions, thus simulating real environmental samples. The correlation coefficient in the cross-validation of nitrate, nitrite and ammonium was satisfactory in the three cases with values higher than 0.92. Finally, the utility of the proposed system is shown in the monitoring of the photoelectrocatalytic treatment of nitrate. © 2013 Elsevier B.V.

Proposta de um dicionário eletrônico terminológico onomasiológico bilíngue inglês-português no domínio das redes neurais artificiais

Pós-graduação em Estudos Linguísticos - IBILCE

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

Pós-graduação em Ciências Cartográficas - FCT

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)

Identificação do teor de nitrogênio foliar em cana-de-açúcar no estágio inicial de crescimento utilizando infravermelho próximo e sistemas inteligentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimação do diâmetro e rugosidade em um processo de furação utilizando multi sensores e redes neurais artificiais

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)

Análise de superfícies de peças retificadas com o uso de redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Redes neurais: aplicação no monitoramento da vida de ferramentas de corte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)