27 resultados para Philosophy of set theory
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We use a toy model to illustrate how to build effective theories for singular potentials. We consider a central attractive 1/r(2) potential perturbed by a 1/r(4) correction. The power-counting rule, an important ingredient of effective theory, is established by seeking the minimum set of short-range counterterms that renormalize the scattering amplitude. We show that leading-order counterterms are needed in all partial waves where the potential overcomes the centrifugal barrier, and that the additional counterterms at next-to-leading order are the ones expected on the basis of dimensional analysis. (C) 2008 Elsevier B.V. All rights reserved.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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We discuss non-steady state electrical characteristics of a metal-insulator-metal structure. We consider an exponential distribution (in energy) of impurity states in addition to impurity states at a single energy level within the depletion region. We discuss thermal as well as isothermal characteristics and present an expression for the temperature of maximum current (Tm) and a method to calculate the density of exponentially distributed impurity states. We plot the theoretical curves for various sets of parameters and the variation of Tm, and Im (maximum current) with applied potential for various impurity distributions. The present model can explain the available experimental results. Finally we compare the non-steady state characteristics in three cases: (i) impurity states only at a single energy level, (ii) uniform energetic distribution of impurity states, and (iii) exponential energetic distribution of impurity states.
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Traditional mathematical tools, like Fourier Analysis, have proven to be efficient when analyzing steady-state distortions; however, the growing utilization of electronically controlled loads and the generation of a new dynamics in industrial environments signals have suggested the need of a powerful tool to perform the analysis of non-stationary distortions, overcoming limitations of frequency techniques. Wavelet Theory provides a new approach to harmonic analysis, focusing the decomposition of a signal into non-sinusoidal components, which are translated and scaled in time, generating a time-frequency basis. The correct choice of the waveshape to be used in decomposition is very important and discussed in this work. A brief theoretical introduction on Wavelet Transform is presented and some cases (practical and simulated) are discussed. Distortions commonly found in industrial environments, such as the current waveform of a Switched-Mode Power Supply and the input phase voltage waveform of motor fed by inverter are analyzed using Wavelet Theory. Applications such as extracting the fundamental frequency of a non-sinusoidal current signal, or using the ability of compact representation to detect non-repetitive disturbances are presented.
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This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.
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This paper presents a new methodology to evaluate in a predictive way the reliability of distribution systems, considering the impact of automatic recloser switches. The developed algorithm is based on state enumeration techniques with Markovian models and on the minimal cut set theory. Some computational aspects related with the implementation of the proposed algorithm in typical distribution networks are also discussed. The description of the proposed approach is carried out using a sample test system. The results obtained with a typical configuration of a Brazilian system (EDP Bandeirante Energia S.A.) are presented and discussed.
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Purpose: The purpose of this paper is to systematically describe the key practical contributions of the theory of constraints (TOC) to outbound (distribution) logistics. Design/methodology/approach: Based on theoretical research, this paper presents the main practical aspects of the approach suggested by TOC to outbound logistics and discusses the assumptions upon which it is based. Findings: This paper corroborates the thesis defended by TOC, according to which the current ways of managing outbound logistics, based mainly on sales forecasts lead to difficulties in handling trade-offs between logistics (stock and transportation) costs and stock-out levels. Research limitations/implications: The reported research is of a theoretical nature. Practical implications: TOC offers a proposal that is complementary in many aspects and very distinguishable in others about the way some key processes and elements of supply chain management (SCM) are managed, especially outbound logistics. Originality/value: Considering the dearth of papers dealing with the conceptual articulation and organization of this subject, the paper contributes to systematize the knowledge currently available about the contributions of the TOC to outbound logistics, highlighting the practical implications of applying TOC to outbound logistics. © Emerald Group Publishing Limited.
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The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.
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This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)