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.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

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.

Electrochemical studies based on local interfacial ph changes of gold nanoparticles immobilized on polystyrene brushes

The electrochemical behavior of polystyrene modified with gold nanoparticle (Au NPs) was investigated in terms of pH-responsive polymer brush. A pH-responsive of modified polymer brush from tethered polystyrene was prepared and used for selective gating transport of anions andcations across the thin-film. An ITO-coated glass electrode was used as substrate and applied to study the switchable permeability of the polymer brush triggered by changes in pH of the aqueous environment. The pH-sensitive behavior of the polymer brush interface has been demonstrated by means of cyclic voltammetry (CV) and Localized Surface Plasmon Resonance (LSPR). CV experiments showed at ph values of 4 and 8 induces swelling and shrinking of the grafted polymer brushes, respectively, and this behavior is fast and reversible. LSPR measurements showed a blue shift of 33 nm in the surface resonance band changes by local pH. The paper brings an easy methodology to fabrication a variety of nanosensors based on the polymer brushes-nanoparticle assemblies. © 2013 by ESG.

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.

Electrochemical behavior of ruthenium-hexacyanoferrate modified glassy carbon electrode and catalytic activity towards ethanol electrooxidation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Ion beam analysis of a-C:H films on alloy steel substrate

An a-C:H thin film deposited by plasma immersion ion implantation and deposition on alloy steel (16MnCr5) was analyzed using a self-consistent ion beam analysis technique.In the self-consistent analysis, the results of each individual technique are combined in a unique model, increasing confidence and reducing simulation errors.Self-consistent analysis, then, is able to improve the regular ion beam analysis since several analyses commonly used to process ion beam data still rely on handling each spectrum independently.The sample was analyzed by particle-induced x-ray emission (for trace elements), elastic backscattering spectrometry (for carbon), forward recoil spectrometry (for hydrogen) and Rutherford backscattering spectrometry (for film morphology).The self-consistent analysis provided reliable chemical information about the film, despite its heavy substrate.As a result, we could determine precisely the H/C ratio, contaminant concentration and some morphological characteristics of the film, such as roughness and discontinuities.© 2013 Elsevier B.V.All rights reserved.

Influence of ethanol and glycerol concentration over functional and structural properties of zein-oleic acid films

This study was performed in order to determine the effect of the addition of different concentrations of glycerol and ethanol over functional and structural properties of zein-oleic acid films. Films were prepared from zein and oleic acid formulations, containing: 0, 10, 20 and 30% (w/w) of glycerol as plasticizer and 75, 80, 85, 90 and 95% (v/v) of ethanol as zein solvent. Water vapor permeability (WVP) at 4 and 24 C, opacity, water solubility and structural behavior of the film were assessed. The film water barrier properties, WVP and water solubility, were increased when higher ethanol concentration and lower glycerol concentration were used. Furthermore, WVP at 4 C was lower than WVP at 24 C due to the crystalline solid state of oleic acid at lower temperatures. Likewise, opacity, homogeneity and structure of the composite film were improved as ethanol increased and glycerol lowered. © 2013 Elsevier B.V. All rights reserved.

Fenômenos não-lineares, incluindo-se os não-ideias, em captura de energia utilizando-se dispositivos piezoelétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Caracterização de leite utilizando técnicas de ultra-som e redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O uso de rede neural artificial MLP na predição de estruturas secundárias de proteínas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Obtenção e caracterização de filmes nanoestruturados de polianilina/látex de borracha natural

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

Poliuretana contendo corante azóico e espaçador tipo bis-carbonato como material para fabricação de filmes nanoestruturados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)