16 resultados para iterative determinant maximization
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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)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.
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A transferência de energia térmica da superfície corporal para a água é provavelmente o aspecto mais importante do equilíbrio térmico em mamíferos marinhos, mas os respectivos cálculos dependem do conhecimento da temperatura da superfície, T S, cuja medição direta em animais em liberdade constitui um problema difícil de resolver. Um método iterativo é proposto para a predição de T S de cetáceos em liberdade, a partir da temperatura corporal profunda, da velocidade de deslocamento e da temperatura e propriedades termodinâmicas da água.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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We propose a method for accelerating iterative algorithms for solving symmetric linear complementarity problems. The method consists in performing a one-dimensional optimization in the direction generated by a splitting method even for non-descent directions. We give strong convergence proofs and present numerical experiments that justify using this acceleration.
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An iterative Neumann series method, employing a real auxiliary scattering integral equation, is used to calculate scattering lengths and phase shifts for the atomic Yukawa and exponential potentials. For these potentials the original Neumann series diverges. The present iterative method yields results that are far better, in convergence, stability and precision, than other momentum space methods. Accurate result is obtained in both cases with an estimated error of about 1 in 10(10) after some 8-10 iterations.
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The results observed in this work support the view that coronary perfusion pressure affects ventricular performance independently of metabolic effects; a mechanism operating in beat-to-beat regulation is proposed.
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A parameter-free variational iterative method is proposed for scattering problems. The present method yields results that are far better, in convergence, stability and precision, than any other momentum space method. Accurate result is obtained for the atomic exponential (Yukawa) potential with an estimated error of less than 1 in 1015 (1010) after some 13 (10) iterations.
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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)
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Introduction: Prognostic factors are used in the Intensive Care Unit (ICU) to predict morbidity and mortality , especially in patients on mechanical ventilation (MV ) . Training protocols are used in MV patients with the aim of promoting the success of the weaning process. Objective: To assess which variables determine the outcome of patients undergoing mechanical ventilation and compare the effects of two protocols for weaning. Method: Patients under MV for more than 48 hours had collected the following information: sex, age , ideal weight, height , Acute Physiology and Chronic Health Evaluation (APACHE II), risk of mortality, Glasgow Coma Scale (GCS) and index Quick and perfunctory (IRRS) breathing. Patients with unsuccessful weaning performed one of weaning protocols: Progressive T - tube or tube - T + Threshold ® IMT. Patients were compared for outcome (death or non- death in the ICU ) and the protocols through the t test or Mann-Whitney test was considered significant when P <0.05. Results: Of 128 patients evaluated 56.25% were men, the mean age was 60.05 ± 17.85 years and 40.62 % patients died, and they had higher APACHE II scores, mortality risk, time VM and IRRS GCS and the lower value (p<0.05). The age, initial and final maximal inspiratory pressure, time of weaning and duration of MV was similar between protocols. Conclusion: The study suggests that the GCS, APACHE II risk of mortality, length of MV and IRRS variables determined the evolution of MV patients in this sample. Not found differences in the variables studied when comparing the two methods of weaning.