Aplicação da rede neural MLP (Multilayer Perceptron) em indústria de pisos e revestimentos do Pólo Cerâmico de Santa Gertrudes - SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

Evaluation of Different Conditions of Contact for Caprolactam Migration from Multilayer Polyamide Films into Food Simulants

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multilayer adsorption of Cu(II) and Cd(II) over Brazilian Orchid Tree (Pata-de-vaca) and its adsorptive properties

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

Effect of gamma irradiation on caprolactam migration from multilayer polyamide 6 films into food simulants: development and validation of a gas chromatographic method

A GC method to determine caprolactam in water, 15 ethanol, and olive oil food simulants was developed and validated. Linear ranges varied from 0.96 to 642.82 g/mL for water, 0.64 to 800.32 g/mL for 15 ethanol, and 1.06 to 1062.34 g/g for olive oil, with correlation coefficients higher than 0.999. Method precision studies showed RSD values lower than 5.45, while method accuracy studies showed recovery from 72 to 111 for all simulants. The effect of gamma irradiation on caprolactam migration from multilayer polyamide 6 (PA-6) films intended for cheese into water, 15 ethanol, olive oil, and 3 acetic acid simulants was also studied. For migration assay, non-irradiated and irradiated (12 kGy) films were placed in contact with the simulant and exposed at 40C for 10 days. The validated method was used to quantify caprolactam migration from multilayer PA-6 films into the simulants, which ranged from 1.03 to 7.59 mg/kg for non-irradiated films, and from 4.82 to 11.32 mg/kg for irradiated films. Irradiation caused almost no changes in caprolactam levels, with the exception of olive oil, which showed an increase in the caprolactam level. All multilayer PA-6 films were in accordance with the requirements of the legislation for caprolactam migration.

Endovascular repair of a juxtarenal saccular aneurysm using the Multilayer Flow Modulator: report of the first case performed in a Public Hospital in Brazil

Endovascular aortic aneurysm repair (EVAR) involving renal and visceral arteries remains a great challenge. Several techniques have been developed over the time to treat juxtarenal, pararenal and thoracoabdominal aneurysms, highlighting the fenestrated and branched endografts, parallel prostheses as Chimney, Periscope and Sandwich Techniques and the use of flow modulation by multilayer stent. We report a case of saccular juxtarenal aortic aneurysm with high surgical risk for complex airway access due to a history of radical laryngectomy for laryngeal neoplasm. Due to chronic aorto-iliac obstructive disease, ostial stenosis of renal artery and limited diameter of the suprarenal aorta, we discarded options involving fenestrated/branched endografts and involving parallel prostheses techniques. We present this case as a therapeutic challenge and a successful treatment option in the short-term evaluation.

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)

Electrical properties of polymer/metal interface in polymer light-emitting devices: electron injection barrier suppression

The electrical characterization of a high efficient multilayer polymer light emitting diode using poly[(2-methoxy-5-hexyloxy)-p-phenylenevinylene] as the emissive layer and an anionic fluorinated surfactant as the electron transport layer was performed. For the sake of comparison, a conventional single layer device was fabricated. The density current vs. voltage measurements revealed that the conventional device has a higher threshold voltage and lower current compared to the surfactant modified device. The effective barrier height for electron injection was suppressed. The influence of the interfaces and bulk contributions to the dc and high frequencies conductivities of the devices was also discussed. (c) 2006 Springer Science + Business Media, Inc.

Taking Advantage of Electrostatic Interactions To Grow Langmuir-Blodgett Films Containing Multilayers of the Phospholipid Dipalmitoylphosphatidylglycerol

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

Natural Polysaccharides as Active Biomaterials in Nanostructured Films for Sensing

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

Effect of Gamma Irradiation on Caprolactam Migration from Multi layer Polyamide 6 Films into Food Simulants: Development and Validation of a Gas Chromatographic Method

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Sensitivity analysis by neural networks applied to power systems transient stability

This work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.

Analysis of Acid Alizarin Violet N Dye Removal Using Sugarcane Bagasse as Adsorbent

With the development of the textile industry, there has been a demand for dye removal from contaminated effluents. In recent years, attention has been directed toward various natural solid materials that are capable of removing pollutants from contaminated water at low cost. One such material is sugarcane bagasse. The aim of the present study was to evaluate adsorption of the dye Acid Violet Alizarin N with different concentrations of sugarcane bagasse and granulometry in agitated systems at different pH. The most promising data (achieved with pH 2.5) was analyzed with both Freundlich and Langmuir isotherms equations. The model that better fits dye adsorption interaction into sugarcane bagasse is Freundlich equation, and thus the multilayer model. Moreover, a smaller bagasse granulometry led to greater dye adsorption. The best treatment was achieved with a granulometry value lower than 0.21 mm at pH 2.50, in which the total removal was estimated at a concentration of 16.25 mg mL(-1). Hence, sugarcane bagasse proves to be very attractive for dye removal from textile effluents.