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.

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

Fault identification in distribution lines using intelligent systems and statistical methods

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

Weight vector estimation of circular arrays of antennas using Artificial Neural Networks

This paper presents a model for the control of the radiation pattern of a circular array of antennas, shaping it to address the radiation beam in the direction of the user, in order to reduce the transmitted power and to attenuate interference. The control of the array is based on Artificial Neural Networks (ANN) of the type RBF (Radial Basis Functions), trained from samples generated by the Wiener equation. The obtained results suggest that the objective was reached.

Harmonic content identification based on neural method for single phase power systems

An alternative method is presented in this paper to identify the harmonic components of non-linear loads in single phase power systems based on artificial neural networks. The components are identified by analyzing the single phase current waveform in time domain in half-cycle of the ac voltage source. The proposed method is compared to the fast Fourier transform. Simulation and experimental results are presented to validate the proposed approach.

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.

Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

Avaliação da formação de fendas marginais e da microinfiltração após envelhecimento artificial, empregando adesivos autocondicionantes fotopolimerizáveis ou duais associados à resinas compostas de diferentes viscosidades

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeito dos anti-inflamatórios esteróides na reação inflamatória e na fertilidade de éguas normais e susceptíveis à endometrite persistente após inseminação artificial

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Quantificação de citocinas no líquido amniótico de bezerros nelore oriundos de produção in vitro, tranferência de embrião e inseminação artificial colhido no momento do parto

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cultivo de camarão com aerador e substrato artificial, identificação de bactérias da wetland construída para o tratamento do efluente

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Sistema de supervisão e controle de irrigação utilizando técnicas de inteligência artificial

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conforto térmico e eficiência da inseminação artificial em tempo fixo em búfalas leiteiras mantidas em sistemas silvipastoris na Amazônia Oriental

O trabalho foi desenvolvido na U.P.A. “Senador Álvaro Adolpho”, Embrapa Amazônia Oriental, Belém, Pará (1º25¢ S 48º26¢ O), local de tipo climático Afi (quente e úmido). O objetivo foi avaliar o uso de sistemas silvipastoris (SSP) como ferramenta de manejo para proporcionar maior conforto térmico a búfalas leiteiras, e incrementar sua eficiência reprodutiva após a utilização da inseminação artificial em tempo fixo. Foram utilizados dois SSP´s, durante dois períodos do ano, onde: Período 1 (Abril a Junho), com maior precipitação pluviométrica e Período 2 (Setembro a Novembro), com menor precipitação pluviométrica. Foram mensuradas a freqüência cardíaca (FC), freqüência respiratória (FR), temperatura retal (TR) e movimentos ruminais (MR), sempre às 9h00min. O índice de conforto animal foi calculado conforme a fórmula: ICA = TR/38,33 + FR/23. Os animais de cada sistema foram tratados com dois diferentes protocolos para sincronização do estro e ovulação, formando os Grupos SSP 1/Ovsynch, SSP 2/Ovsynch (estro sincronizado com Ovsynch), SSP 1/Prog e SSP 2/Prog (estro sincronizado com Ovsynch + 1g de progesterona intravaginal). Os ovários de todas as búfalas foram monitorados por ultra-sonografia no D0, D7 e D9 e as búfalas foram inseminadas no D10 (D0=dia do início da sincronização). As médias de FC foram de 57,35±8,24 bat/min no Período 1 e 62,48 ±7,79 bat/min no Período 2 (P<0,01). A FR média foi de 25,66 ±10,53 mov/min no Período 1 e de 33,38 ±18,23 mov/min no Período 2 (P<0,01). Os animais mantidos no SSP 1 apresentaram TR superior aos do SSP 2 (39,02 ±0,53ºC versus 38,65 ±0,41ºC, P<0,01). As médias do ICA variaram entre 1,89 e 3,55. No Período 1 obteve-se variação de 1,89 a 2,42 e média de 2,12 ±0,46. No Período 2, a média do ICA foi de 2,46 ±0,79, com variação de 1,91 a 3,55. Houve diferença significativa das médias de ICA entre os períodos (P<0,01). O diâmetro do folículo dominante no D9 foi superior para os animais que receberam progesterona (10,40 ±1,22 mm versus 12,21 ±3,42 mm; P=0,05). A taxa de prenhez total foi de 48,21%, sendo que no Período 1 houve 56,66% de fêmeas gestantes, contra 38,46% no Período 2 (SSP1/Ovsynch: 40,0%; SSP2/Ovsynch: 38,46%; SSP1/Prog: 46,66% e SSP2/Prog: 69,23%; P>0,05). Com base nos resultados, ressalta-se a importância do manejo do ambiente físico para a criação de bubalinos na Amazônia Oriental, o que pode evitar gastos energéticos para a termorregulação animal e possibilitar melhores índices reprodutivos.