Aprendizado não-supervisionado em redes neurais pulsadas de base radial: um estudo da capacidade de agrupamento para a classificação de pixels

Redes neurais pulsadas - redes que utilizam uma codificação temporal da informação - têm despontado como uma promissora abordagem dentro do paradigma conexionista, emergente da ciência cognitiva. Um desses novos modelos é a rede neural pulsada com função de base radial, que é capaz de armazenar informação nos tempos de atraso axonais dos neurônios. Um algoritmo de aprendizado foi aplicado com sucesso nesta rede pulsada, que se mostrou capaz de mapear uma seqüência de pulsos de entrada em uma seqüência de pulsos de saída. Mais recentemente, um método baseado no uso de campos receptivos gaussianos foi proposto para codificar dados constantes em uma seqüência de pulsos temporais. Este método tornou possível a essa rede lidar com dados computacionais. O processo de aprendizado desta nova rede não se encontra plenamente compreendido e investigações mais profundas são necessárias para situar este modelo dentro do contexto do aprendizado de máquinas e também para estabelecer as habilidades e limitações desta rede. Este trabalho apresenta uma investigação desse novo classificador e um estudo de sua capacidade de agrupar dados em três dimensões, particularmente procurando estabelecer seus domínios de aplicação e horizontes no campo da visão computacional.

A general technique to embed non-uniform discontinuities into standard solid finite elements

A general technique to embed non-uniform displacement discontinuities into standard solid finite elements is presented. The technique is based on the decomposition of the kinematic fields into a component related to the deformation of the solid portion of the element and one related to the rigid-body motion due to a displacement discontinuity. This decomposition simplifies the incorporation of discontinuity interfaces and provides a suitable framework to account for non-uniform discontinuity modes. The present publication addresses two families of finite element formulations: displacement-based and stress hybrid finite element. © 2005 Elsevier Ltd. All rights reserved.

Blade design and forming for fans using finite elements

The necessity of adapting the standardized fan models to conditions of higher temperature has emerged due to the growth of concerning referring to the consequences of the gas expelling after the Mont Blanc tunnel accident in Italy and France, where even though, with 100 fans in operation, 41 people died. However, since then, the defied solutions have pointed to aerodynamic disadvantages or have seemed nonappropriate in these conditions. The objective of this work is to present an alternative to the market standard fans considering a new technology in constructing blades. This new technology introduces the use of the stainless steel AISI 409 due to its good adaptation to temperatures higher than 400°C, particularly exposed to temperatures of gas exhaust from tunnels in fire situation. Furthermore, it presents a very good resistance to corrosion and posterior welding and pressing, due to its alloyed elements. The innovation is centered in the process of a deep drawing of metallic shells and posterior welding, in order to keep the ideal aerodynamic superficies for the fan ideal performance. On the other hand, the finite element method, through the elasto-plastic software COSMOS permitted the verification of the thickness and structural stability of the blade in relation to the aerodynamic efforts established in the project. In addition, it is not advisable the fabrication of blades with variable localized thickness not even, non-uniform ones, due to the verified concentration of tensions and the difficulties observed in the forming. In this way, this study recommends the construction of blades with uniform variations of thickness. © 2007 Springer.

Analysis of mechanical behavior in bending of polymer composites using the finite elements method

The use of composite materials has increased in the recent decades, mainly in the aeronautics and automotives industries. In the present study is elaborated a computational simulation program of the bending test using the finite elements method, in the commercial software ANSYS. This simulation has the objective of analyze the mechanical behavior in bending of two composites with polymeric matrix reinforced with carbon fibers. Also are realized bending tests of the 3 points to obtain the resistances of the materials. Data from simulation and tests are used to make a comparison between two failures criteria, Tsai-Wu and Hashin criterion. Copyright © 2009 SAE International.

Proposta de implementação de redes de base radial em tecnologias CMOS e BiCMOS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Three-dimensional analysis of reinforced concrete members via embedded discontinuity finite elements

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

Modeling of multiple cracks in reinforced concrete members using solid finite elements with high aspect ratio

This paper presents a new technique to model interfaces by means of degenerated solid finite elements, i.e., elements with a very high aspect ratio, with the smallest dimension corresponding to the thickness of the interfaces. It is shown that, as the aspect ratio increases, the element strains also increase, approaching the kinematics of the strong discontinuity. A tensile damage constitutive relation between strains and stresses is proposed to describe the nonlinear behavior of the interfaces associated with crack opening. To represent crack propagation, couples of triangular interface elements are introduced in between all regular (bulk) elements of the original mesh. With this technique the analyses can be performed integrally in the context of the continuum mechanics and complex crack patterns involving multiple cracks can be simulated without the need of tracking algorithms. Numerical tests are performed to show the applicability of the proposed technique, studding also aspects related to mesh objectivity.

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Modelos geoidais híbridos para o Estado de São Paulo: desenvolvimento e avaliação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Comparison of logistic and neural network models to fit to the egg production curve of White Leghorn hens

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened Steel turning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

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

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.