based on the current situation and existing problems of supply chain's storage optimization management, a new genetic type method with integrated gradient based algorithm method for storage optimization of supply ...
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ISBN:
(纸本)9781509000760
based on the current situation and existing problems of supply chain's storage optimization management, a new genetic type method with integrated gradient based algorithm method for storage optimization of supply chain is proposed. This method combines the fast convergence and strong ability of local search of gradient method and global search ability of genetic algorithm. The problem of storage optimization of supply chain is applied. The research results show that the proposed method is effective and suitable.
We develop the speed gradient-basedalgorithm for controlled transfer of energy in a two-level quantum system towards a predefined value of energy using as control spectral density of incoherent photons. The algorithm...
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Temperatures predicted by the Thermal Mathematical Models (TMMs) used in the thermal control design of spacecraft, usually present differences with the values measured during the thermal test campaign. Therefore, the ...
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Temperatures predicted by the Thermal Mathematical Models (TMMs) used in the thermal control design of spacecraft, usually present differences with the values measured during the thermal test campaign. Therefore, the TMMs must be correlated with the thermal tests to reduce these differences to admissible values. This task can be addressed in an automatized way considering the correlation as an optimization problem, where the differences between predicted and measured temperatures are minimized. This is achieved modifying the values assigned to some parameters used in the TMMs. The main drawback of this approximation is the risk of loosing the physical sense of some model parameters. The reason is that the thermal inverse problem, that is, calculate the thermal parameters that produce a specific temperature distribution, often has not a unique solution. A methodology of automatized correlation to calculate the correct values of the model parameters, in the sense that they maintain its physical interpretation, is presented in this article. The key point relies in setting up an overdetermined system of equations. The expression to calculate the minimum number of load cases required, is developed, and several case studies are presented to validate the proposed methodology. A gradientbased public available set of subroutines (TOLMIN) has been used as optimization algorithm.
Compressive sensing is a very important field of research in signal processing as it is based on the idea that a signal, sparse in a certain transform domain, can be completely recovered based on a small set of availa...
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ISBN:
(纸本)9781728169491
Compressive sensing is a very important field of research in signal processing as it is based on the idea that a signal, sparse in a certain transform domain, can be completely recovered based on a small set of available measurements. Many algorithms, dealing with sparse signal recovery have been proposed through the years. This paper focuses on convex optimization algorithms and explores their performance in two different transform domains - discrete Fourier and discrete cosine transforms. The observed algorithms are the adaptive gradient based algorithm, primal-dual interior point method and the log barrier algorithm, all which are used to solve different formulations of the l1-minimization problem.
In this paper a modified gradient based algorithm for solving Sylvester equations is presented. Different from the gradientbased method introduced by Ding and Chen [7] and the relaxed gradient based algorithm propose...
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In this paper a modified gradient based algorithm for solving Sylvester equations is presented. Different from the gradientbased method introduced by Ding and Chen [7] and the relaxed gradient based algorithm proposed by Niu et al. [18], the information generated in the first half-iterative step is fully exploited and used to construct the approximate solution. Theoretical analysis shows that the new method converges under certain assumptions. Numerical results are given to verify the efficiency of the new method. (C) 2011 Elsevier Inc. All rights reserved.
We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection t...
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In this paper, the Oppositional Whale Optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of A...
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In this paper, the Oppositional Whale Optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.
The paper observes the Hermite and the Fourier Transform domains in terms of Frequency Hopping Spread Spectrum signals sparsification. Sparse signals can be recovered from a reduced set of samples by using the Compres...
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ISBN:
(纸本)9781538656839
The paper observes the Hermite and the Fourier Transform domains in terms of Frequency Hopping Spread Spectrum signals sparsification. Sparse signals can be recovered from a reduced set of samples by using the Compressive Sensing approach. The under-sampling and the reconstruction of those signals are also analyzed in this paper. The number of measurements (available signal samples) is varied and reconstruction performance is tested in all considered cases and for both observed domains. The signal recovery is done using an adaptive gradient based algorithm. The theory is verified with the experimental results.
Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using...
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Sample maximum likelihood(SML) method is frequently used to identify errors-invariables(EIV) system. It generates the estimate through minimizing relevant cost function built on the mean input-output data and sample n...
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