773 resultados para minimization


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This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss 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. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

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In this work, we investigate the correlations between structural and rheological properties of emulsified aqueous sol and the porous microstructure of monolithic zirconia foams, manufactured by the integrative combination of the sol-gel and emulsification processes. Macroporous zirconia ceramics prepared using different amounts of decahydronaphthalene, as oil phase, are compared in terms of the emulsion microstructure and ceramic porosity. A combination of electrical conductivity, oil droplet diameter, and rheological measurements was used to highlight the key effect of the dynamic structural properties of the emulsion on the porosity of the ceramic zirconia foam. The minimization of drying shrinkage by appropriate sol-gel mineralization of the oil droplet wall enabled versatile and easy tuning of the ceramic foam microstructure, by fine adjustment of the emulsion characteristics. The foam with the highest porosity (90%) and the lowest bulk density (0.40 g cm-3) was prepared from emulsion with 80 wt% of decahydronaphthalene, which also showed a bicontinuous structure and elevated flow consistency. © The Royal Society of Chemistry 2013.

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The tetrahydroquinoline derivatives can be easily synthesized through Povarov reaction and have several important biological activities. This work describes a comparative study for the unequivocal assignment of molecular structure of different tetrahydroquinoline derivatives, through a complete analysis of NMR 1D and 2D NMR spectra (1H, 13C, COSY, HSQC, and HMBC), and the correlation this data with theoretical calculations of energy-minimization and chemical shift (δ), employing the theory level of DFT/B3LYP with set of the cc-pVDZ basis. For these derivatives the experimental analyses and the theoretical model adopted were sufficient to obtain a good description of its structures, and these results can be used to assign the structure of various others tetrahydroquinoline derivatives. © 2013 Springer Science+Business Media New York.

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This paper proposes a method by simulated annealing for building roof contours identification from LiDAR-derived digital elevation model. Our method is based on the concept of first extracting aboveground objects and then identifying those objects that are building roof contours. First, to detect aboveground objects (buildings, trees, etc.), the digital elevation model is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing algorithm. Experiments carried out with laser scanning digital elevation model showed that the methodology works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.

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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.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Matemática - IBILCE

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Biologia Geral e Aplicada - IBB

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

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)