Interação genótipo ambiente para perímetro escrotal em bovinos da raça Nelore utilizando normas de reação

Pós-graduação em Zootecnia - FCAV

Estimação de parâmetros genéticos para características de crescimento em bovinos da raça Canchim com modelos de dimensão finita e infinita

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

Projeto otimizado de redes neurais artificiais para predição da rugosidade em processos de usinagem com a utilização da metodologia de projeto de experimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Imageamento da porosidade através de perfis geofísicos de poço

O imageamento da porosidade é uma representação gráfica da distribuição lateral da porosidade da rocha, estimada a partir de dados de perfis geofísicos de poço. Apresenta-se aqui uma metodologia para produzir esta imagem geológica, totalmente independente da intervenção do intérprete, através de um algoritmo, dito, interpretativo baseado em dois tipos de redes neurais artificiais. A primeira parte do algoritmo baseia-se em uma rede neural com camada competitiva e é construído para realizar uma interpretação automática do clássico gráfico o P_b - Φ_N, produzindo um zoneamento do perfil e a estimativa da porosidade. A segunda parte baseia-se em uma rede neural com função de base radial, projetado para realizar uma integração espacial dos dados, a qual pode ser dividida em duas etapas. A primeira etapa refere-se à correlação de perfis de poço e a segunda à produção de uma estimativa da distribuição lateral da porosidade. Esta metodologia ajudará o intérprete na definição do modelo geológico do reservatório e, talvez o mais importante, o ajudará a desenvolver de um modo mais eficiente as estratégias para o desenvolvimento dos campos de óleo e gás. Os resultados ou as imagens da porosidade são bastante similares às seções geológicas convencionais, especialmente em um ambiente deposicional simples dominado por clásticos, onde um mapa de cores, escalonado em unidades de porosidade aparente para as argilas e efetiva para os arenitos, mostra a variação da porosidade e a disposição geométrica das camadas geológicas ao longo da seção. Esta metodologia é aplicada em dados reais da Formação Lagunillas, na Bacia do Lago Maracaibo, Venezuela.

Aplicação de redes NeuroFuzzy ao processamento de peças automotivas por meio de injeção de polímeros

The injection molding of automotive parts is a complex process due to the many non-linear and multivariable phenomena that occur simultaneously. Commercial software applications exist for modeling the parameters of polymer injection but can be prohibitively expensive. It is possible to identify these parameters analytically, but applying classical theories of transport phenomena requires accurate information about the injection machine, product geometry, and process parameters. However, neurofuzzy networks, which achieve a synergy by combining the learning capabilities of an artificial neural network with a fuzzy set's inference mechanism, have shown success in this field. The purpose of this paper was to use a multilayer perceptron artificial neural network and a radial basis function artificial neural network combined with fuzzy sets to produce an inference mechanism that could predict injection mold cycle times. The results confirmed neurofuzzy networks as an effective alternative to solving such problems.

Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia

Pós-graduação em Engenharia Elétrica - FEIS

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis.

Segurança em redes sem fio: estudo sobre o desenvolvimento de conjuntos de dados para comparação de IDS

Pós-graduação em Engenharia Elétrica - FEIS

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%.

Non-parametric identification of a non-linear buckled beam using discrete-time Volterra Series

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

Monte Carlo simulation of water-tetrahydrofuran mixtures

Monte Carlo simulations of water-tetrahydrofuran (THF) mixtures were performed in the isothermal-isobaric ensemble (NPT) at T = 298 K and p = 1 atm. The interaction energy was calculated using the TIP4P model for water and a five-site united atom representation for the THF molecule. The potential energy surfaces for water-THF interactions were obtained by using combining rules and the original potential functions used for pure liquids. Theoretical values obtained for the average interaction energy as a function of concentration are in good agreement with available experimental data. Results from the partitioning of the total interaction energy into water-water, water-THF and THF-THF contributions are presented. These results are useful to distinguish between the quantitative contributions of these molecular interactions to the energetic behavior of the water-THF mixing process. The radial distribution functions for HW-OTHF and OW-OTHF site-site interactions show the salient features of hydrogen-bonded liquids. Comparison of the average number of water-water complexes interacting through hydrogen bonding in water-THF and water-methanol mixtures shows an enhancement of the water-water coordination number in a THF rich environment. © 1995.

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.

Electronic Properties of Water in Liquid Environment. A Sequential QM/MM Study Using the Free Energy Gradient Method

There is a continuous search for theoretical methods that are able to describe the effects of the liquid environment on molecular systems. Different methods emphasize different aspects, and the treatment of both the local and bulk properties is still a great challenge. In this work, the electronic properties of a water molecule in liquid environment is studied by performing a relaxation of the geometry and electronic distribution using the free energy gradient method. This is made using a series of steps in each of which we run a purely molecular mechanical (MM) Monte Carlo Metropolis simulation of liquid water and subsequently perform a quantum mechanical/molecular mechanical (QM/MM) calculation of the ensemble averages of the charge distribution, atomic forces, and second derivatives. The MP2/aug-cc-pV5Z level is used to describe the electronic properties of the QM water. B3LYP with specially designed basis functions are used for the magnetic properties. Very good agreement is found for the local properties of water, such as geometry, vibrational frequencies, dipole moment, dipole polarizability, chemical shift, and spin-spin coupling constants. The very good performance of the free energy method combined with a QM/MM approach along with the possible limitations are briefly discussed.