Identification of steel bars immersed in reinforced concrete based on experimental results of eddy current testing and artificial neural network analysis

This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover. © 2013 Copyright Taylor and Francis Group, LLC.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.

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

Pós-graduação em Estudos Linguísticos - IBILCE

Análise da incerteza na representação de classes temáticas resultantes da aplicação de uma rede neural artificial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Um método para detecção e classificação de curtos-circuitos em redes de distribuição de energia elétrica baseado na transformada de Fourier e em redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de carga multinodal utilizando redes neurais de regressão generalizada

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento de um sistema dinâmico para predição de cargas elétricas por redes neurais através do paradigma de programação orientada a objeto sob a linguagem JAVA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de cargas elétricas através de um modelo híbrido de regressão com redes neurais

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)

Teoria da ressonância adaptativa através da linguagem Java para detecção e classificação de e-mails indesejados

Pós-graduação em Engenharia Elétrica - FEIS

Modelagem de um motor de indução trifásico operando com tensões desequilibradas por meio de redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de níveis de desgaste de dressadores de ponta única utilizando sinais de emissão acústica e redes neurais artificiais

Pós-graduação em Engenharia Elétrica - FEB

Reconhecimento de padrões lexicais por meio de redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

As contribuições da ciência cognitiva à composição musical

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