Langmuir-Blodgett films incorporating an ionic europium complex

Langmuir-Blodgett (LB) technique is a powerful tool to fabricate ultrathin films with highly ordered structures and controllable molecular array for efficient energy and electron transfer, allowing the construction of devices at molecular level. One method to obtain LB films consists in the mixture of classical film-forming molecules, for example Stearic Acid (SA) and functional metal complex. In this work NH(4)[Eu(bmdm)(4)], where the organic ligand bmdm is (butyl methoxy-dibenzoyl-methane) or (1-(4-methoxyphenyl)-3-(4-tert-butylphenyl)propane-1,3-dione) was used to build up Langmuir and LB films. Langmuir isotherms were obtained from (i) NH(4)[Eu(bmdm)(4)] complex and (ii) NH(4)[Eu(bmdm)(4)]/SA (1:1). Results indicated that (i) form multilayer structure; however the surface pressure was insufficient to obtain LB films, and (ii) can easily reproduce and build LB films. The dependence of number of layers in the UV absorption spectra suggest that the complex did not hydrolyze or show decomposition, UV spectral differences observed between the solution and the LB film indicate that the complex has a highly ordered arrangement in the film and the complex has an interaction with SA. Excitation spectra confirm a ligand-europium energy transfer mechanism. The transition lines of Eu(3+) ion were observed in emission spectra of all films, the photoluminescence spectra indicate a fluorescence enhanced effect with the number of LB layers. (C) 2009 Elsevier B.V. All rights reserved.

Rare Earth Doped SnO(2) Nanoscaled Powders and Coatings: Enhanced Photoluminescence in Water and Waveguiding Properties

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

Detection of catechol using mixed Langmuir-Blodgett films of a phospholipid and phthalocyanines as voltammetric sensors

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

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes

This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Estudio de PMN cerámico dopado con KNbO3 y LiNbO 3 sintetizado por Pechini

PMN belongs to a special class of materials named relaxor ferroelectrics. It has high volumetric efficiency due to its high dielectric constant, which makes it in a perfect material for application in multilayer capacitors. When prepared the columbite route its preparation has many advantages. In this work, the preparations of columbite and PMN were done by Pechini and Partial Oxalate methods, respectively. The effects of the KNbO3 and LiNbO3 dopants added in various concentrations. The idea is founded on the correlations that they have with BaTiO3 y PbTiO3, respectively. The whole process was supervised by TG/DTA, XRD, SEM and determination of the specific surface area of the powders. LiNbO3 carries out the pre-sinterization of the particles, observed by a reduction in the surface area. There are not particle grow, but occur its lengthening. However, for KNbO3 these particle growth, but the agglomerates are softer. The effect produced by the doping during the synthesis of the PMN powder is different from the one produced in the columbite precursor. Pure precursor shows an average particle size of 0,2μm, but the addition of 5,0mol% of dopants carries out the formation of agglomerates close to 4μm. LiNbO 3 addition carries out spherical particles and pre-sinterization, while KNbO3 addition does not change the particles shape.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

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.

Avaliação estatística de métodos de poda aplicados em neurônios intermediários da rede neural MLP

There are several papers on pruning methods in the artificial neural networks area. However, with rare exceptions, none of them presents an appropriate statistical evaluation of such methods. In this article, we proved statistically the ability of some methods to reduce the number of neurons of the hidden layer of a multilayer perceptron neural network (MLP), and to maintain the same landing of classification error of the initial net. They are evaluated seven pruning methods. The experimental investigation was accomplished on five groups of generated data and in two groups of real data. Three variables were accompanied in the study: apparent classification error rate in the test group (REA); number of hidden neurons, obtained after the application of the pruning method; and number of training/retraining epochs, to evaluate the computational effort. The non-parametric Friedman's test was used to do the statistical analysis.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

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.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

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.

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.