815 resultados para Discretization Algorithm


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Traditional Monte Carlo simulations of QCD in the presence of a baryon chemical potential are plagued by the complex phase problem and new numerical approaches are necessary for studying the phase diagram of the theory. In this work we consider a ℤ3 Polyakov loop model for the deconfining phase transition in QCD and discuss how a flux representation of the model in terms of dimer and monomer variable solves the complex action problem. We present results of numerical simulations using a worm algorithm for the specific heat and two-point correlation function of Polyakov loops. Evidences of a first order deconfinement phase transition are discussed. © 2013 American Institute of Physics.

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The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches. © 2013 IEEE.

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Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

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Multisensor data fusion is a technique that combines the readings of multiple sensors to detect some phenomenon. Data fusion applications are numerous and they can be used in smart buildings, environment monitoring, industry and defense applications. The main goal of multisensor data fusion is to minimize false alarms and maximize the probability of detection based on the detection of multiple sensors. In this paper a local data fusion algorithm based on luminosity, temperature and flame for fire detection is presented. The data fusion approach was embedded in a low cost mobile robot. The prototype test validation has indicated that our approach can detect fire occurrence. Moreover, the low cost project allow the development of robots that could be discarded in their fire detection missions. © 2013 IEEE.

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Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques. © 2013 IEEE.

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Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

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It is presented a software developed with Delphi programming language to compute the reservoir's annual regulated active storage, based on the sequent-peak algorithm. Mathematical models used for that purpose generally require extended hydrological series. Usually, the analysis of those series is performed with spreadsheets or graphical representations. Based on that, it was developed a software for calculation of reservoir active capacity. An example calculation is shown by 30-years (from 1977 to 2009) monthly mean flow historical data, from Corrente River, located at São Francisco River Basin, Brazil. As an additional tool, an interface was developed to manage water resources, helping to manipulate data and to point out information that it would be of interest to the user. Moreover, with that interface irrigation districts where water consumption is higher can be analyzed as a function of specific seasonal water demands situations. From a practical application, it is possible to conclude that the program provides the calculation originally proposed. It was designed to keep information organized and retrievable at any time, and to show simulation on seasonal water demands throughout the year, contributing with the elements of study concerning reservoir projects. This program, with its functionality, is an important tool for decision making in the water resources management.

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Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEG

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ABSTRACT: This paper presents a performance comparison between known propagation Models through least squares tuning algorithm for 5.8 GHz frequency band. The studied environment is based on the 12 cities located in Amazon Region. After adjustments and simulations, SUI Model showed the smaller RMS error and standard deviation when compared with COST231-Hata and ECC-33 models.

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O Feixe Gaussiano (FG) é uma solução assintótica da equação da elastodinâmica na vizinhança paraxial de um raio central, a qual se aproxima melhor do campo de ondas do que a aproximação de ordem zero da Teoria do Raio. A regularidade do FG na descrição do campo de ondas, assim como a sua elevada precisão em algumas regiões singulares do meio de propagação, proporciona uma forte alternativa no imageamento sísmicos. Nesta dissertação, apresenta-se um novo procedimento de migração sísmica pré-empilhamento em profundidade com amplitudes verdadeiras, que combina a flexibilidade da migração tipo Kirchhoff e a robustez da migração baseada na utilização de Feixes Gaussianos para a representação do campo de ondas. O algoritmo de migração proposto é constituído por dois processos de empilhamento: o primeiro é o empilhamento de feixes (“beam stack”) aplicado a subconjuntos de dados sísmicos multiplicados por uma função peso definida de modo que o operador de empilhamento tenha a mesma forma da integral de superposição de Feixes Gaussianos; o segundo empilhamento corresponde à migração Kirchhoff tendo como entrada os dados resultantes do primeiro empilhamento. Pelo exposto justifica-se a denominação migração Kirchhoff-Gaussian-Beam (KGB).Afim de comparar os métodos Kirchhoff e KGB com respeito à sensibilidade em relação ao comprimento da discretização, aplicamos no conjunto de dados conhecido como Marmousi 2-D quatro grids de velocidade, ou seja, 60m, 80m 100m e 150m. Como resultado, temos que ambos os métodos apresentam uma imagem muito melhor para o menor intervalo de discretização da malha de velocidade. O espectro de amplitude das seções migradas nos fornece o conteúdo de frequência espacial das seções das imagens obtidas.

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Neste trabalho avaliamos uma classe de operadores de continuação de campos de onda, baseados em equações unidirecionais e com aplicação direta à migração sísmica. O método de representação de equações de onda unidirecionais, desenvolvido neste trabalho, é válido para abertura angular arbitrária, baseia-se no conceito de rigidez de um semiespaço, na transformação Dirichlet-Neumann e em sua discretização por elementos finitos. O método de construção dos operadores de continuação requer a introdução de variáveis auxiliares cujo número cresce em função da maior abertura angular desejada para o operador. Efetuamos a implementação no domínio do espaço e da frequência o que permite sua imediata paralelização. Baseados em experimentos numéricos, que avaliam a relação de dispersão e a resposta ao impulso do operador, propomos prescrições que permitem especificar o número de variáveis auxiliares e o passo de continuação para o operador de migração. A aplicação do algoritmo nos dados do modelo de domo salino da SEG-EAGE demonstra a capacidade do algoritmo em migrar refletores com forte mergulho em meios com forte variação lateral de velocidade.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)