979 resultados para Alternative construction method


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Synthesis of polycationic compounds by the spray-drying technique is an interesting alternative in the domain of aqueous precursor synthesis methods. Spray drying yields high quality samples with good reproducibility. The possibility of scaling up for production of large quantities with fast processing time is well established by the commercial availability of powders of various compositions. In this paper, we have discussed the advantages and limitations of this method and demonstrated its interest by synthesizing a few polycationic compounds selected for their attractive properties of thermoelectricity [Bi1.68Ca2Co1.69O 8, La0.95A0.05CoO3 (A=Ca, Sr, Ba)] or magnetoresistance [La0.70A0.30MnO3 (A=Sr, Ba)]. We have confirmed the quality of these samples by reporting their structure, magnetic and transport properties. © 2010 Elsevier Ltd All rights reserved.

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This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

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This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

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Purpose: The aim of this study was to compare the accuracy of fit of three types of implant-supported frameworks cast in Ni-Cr alloy: specifically, a framework cast as one piece compared to frameworks cast separately in sections to the transverse or the diagonal axis, and later laser welded. Materials and Methods: Three sets of similar implant-supported frameworks were constructed. The first group of six 3-unit implant-supported frameworks were cast as one piece, the second group of six were sectioned in the transverse axis of the pontic region prior to casting, and the last group of six were sectioned in the diagonal axis of the pontic region prior to casting. The sectioned frameworks were positioned in the matrix (10 N(.)cm torque) and laser welded. To evaluate passive fit, readings were made with an optical microscope with both screws tightened and with only one-screw tightened. Data were submitted to ANOVA and Tukey-Kramer`s test (p < 0.05). Results: When both screws were tightened, no differences were found between the three groups (p > 0.05). In the single-screw-tightened test, with readings made opposite to the tightened side, the group cast as one piece (57.02 +/- 33.48 mu m) was significantly different (p < 0.05) from the group sectioned diagonally (18.92 +/- 4.75 mu m) but no different (p > 0.05) from the group transversally sectioned (31.42 +/- 20.68 mu m). On the tightened side, no significant differences were found between the groups (p > 0.05). Conclusions: Results of this study showed that casting diagonally sectioned frameworks lowers misfit levels of prosthetic implant-supported frameworks and also improves the levels of passivity to the same frameworks when compared to structures cast as one piece.

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In this work we introduce a new construction method of Atanassov's intuitionistic fuzzy sets (A-IFSs) from fuzzy sets. We use A-IFSs in image processing. We propose a new image magnification algorithm using A-IFSs. This algorithm is characterized by its simplicity and its efficiency.

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In this work we present a new construction method of IVFSs from Fuzzy Sets. We use these IVFSs for image processing. Concretely, in this contribution we introduce a new image magnification algorithm using IVFSs. This algorithm is based on block expansion and it is characterized by its simplicity.

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This work reports the synthesis of the MgNb(2)O(6) and the ferroelectric lead magnesium niobate Pb(Mg(1/3)Nb(2/3))O(3) (PMN) using fine precursor powders obtained from a chemical Oxide Precursor Method (OPM). To obtain pyrochlore-free PMN ceramics, the synthesis of the precursor MgNb(2)O(6) powders was studied for 2-25 mol% excess of MgO and 10 mol% excess of PbO. Structural and microstructural properties of the sintered ceramics obtained by the cited method and by the classical columbite method were studied and compared. Results lead to good quality, pyrochlore-free PMN ceramic prepared by OPM, presenting greater grain size if compared with ceramic prepared by columbite method.

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

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

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The soybean cyst nematode (Heterodera glycines) has become an increasingly severe problem in soybean production areas in Brazil. The development and use of resistant cultivars is the most efficient method of minimizing losses due to this pathogen. Our objective was to test the efficiency of an alternative method for screening soybean genotypes for resistance to H. glycines in field plots. The alternative method was compared to the standard method of sowing the test genotypes in fields found to be infested during the previous crop season. In the alternative method, the test genotypes are sown in the furrow following the uprooting of 45-day-old infected plants. The alternative method resulted in twice the cyst population and fewer escapes, and more consistent results than the standard method. The major advantage of the alternative method is that it permits screening in a more homogeneous distribution of H. glycines in the soil.

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This paper presents an alternative methodology for loading margin improvement and total real power losses reduction by using a continuation method. In order to attain this goal, a parameterizing equation based on the total real power losses and the equations of the reactive power at the slack and generation buses are added to the conventional power flow equations. The voltages at these buses are considered as control variables and a new parameter is chosen to reduce the real power losses in the transmission lines. The results show that this procedure leads to maximum loading point increase and consequently, in static voltage stability margin improvement. Besides, this procedure also takes to a reduction in the operational costs and, simultaneously, to voltage profile improvement. Another important result of this methodology is that the resulting operating points are close to that provided by an optimal power flow program. © 2004 IEEE.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)