The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Xiu-kai Chen - One of the best experts on this subject based on the ideXlab platform.
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variable order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Driss Boutat, Xiu-kai ChenAbstract:In this paper, a numerical method is proposed to estimate the variable-order fractional derivatives of an unknown signal in noisy environment. Firstly, the wavelet denoising process is adopted to reduce the noise effect for the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of a considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are used as the estimations of the unknown ones, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method. An efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal.Wavelet denoising is adopted to reduce the noise effect in a signal.Polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval.The variable-order fractional derivatives of a noisy signal are numerically obtained based on fitting polynomials.Numerical examples of four cases are considered to demonstrate the efficiency and robustness of the proposed method.
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Variable-order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Da-yan Liu, Yan-qiao Wei, Driss Boutat, Xiu-kai ChenAbstract:In this paper, an efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal. Firstly, the process of wavelet denoising is adopted to reduce the noise effect in the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are considered as the estimations of the original signal, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method.
Yi-ming Chen - One of the best experts on this subject based on the ideXlab platform.
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variable order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Driss Boutat, Xiu-kai ChenAbstract:In this paper, a numerical method is proposed to estimate the variable-order fractional derivatives of an unknown signal in noisy environment. Firstly, the wavelet denoising process is adopted to reduce the noise effect for the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of a considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are used as the estimations of the unknown ones, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method. An efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal.Wavelet denoising is adopted to reduce the noise effect in a signal.Polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval.The variable-order fractional derivatives of a noisy signal are numerically obtained based on fitting polynomials.Numerical examples of four cases are considered to demonstrate the efficiency and robustness of the proposed method.
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Variable-order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Da-yan Liu, Yan-qiao Wei, Driss Boutat, Xiu-kai ChenAbstract:In this paper, an efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal. Firstly, the process of wavelet denoising is adopted to reduce the noise effect in the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are considered as the estimations of the original signal, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method.
Driss Boutat - One of the best experts on this subject based on the ideXlab platform.
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variable order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Driss Boutat, Xiu-kai ChenAbstract:In this paper, a numerical method is proposed to estimate the variable-order fractional derivatives of an unknown signal in noisy environment. Firstly, the wavelet denoising process is adopted to reduce the noise effect for the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of a considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are used as the estimations of the unknown ones, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method. An efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal.Wavelet denoising is adopted to reduce the noise effect in a signal.Polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval.The variable-order fractional derivatives of a noisy signal are numerically obtained based on fitting polynomials.Numerical examples of four cases are considered to demonstrate the efficiency and robustness of the proposed method.
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Variable-order fractional numerical differentiation for noisy signals by wavelet denoising
Journal of Computational Physics, 2016Co-Authors: Yi-ming Chen, Da-yan Liu, Yan-qiao Wei, Driss Boutat, Xiu-kai ChenAbstract:In this paper, an efficient method is proposed to numerically estimate the variable-order fractional derivatives of a noisy signal. Firstly, the process of wavelet denoising is adopted to reduce the noise effect in the signal. Secondly, polynomials are constructed to fit the denoised signal in a set of overlapped Subintervals of the considered interval. Thirdly, the variable-order fractional derivatives of these fitting polynomials are considered as the estimations of the original signal, where the values obtained near the boundaries of each Subinterval are ignored in the overlapped parts. Finally, numerical examples are presented to demonstrate the efficiency and robustness of the proposed method.
Zhiping Qiu - One of the best experts on this subject based on the ideXlab platform.
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hybrid uncertainty propagation of coupled structural acoustic system with large fuzzy and interval parameters
Applied Acoustics, 2016Co-Authors: Chong Wang, Zhiping QiuAbstract:Abstract Based on the finite element framework and uncertainty analysis theory, this paper proposes a first-order Subinterval perturbation finite element method (FSPFEM) and a modified Subinterval perturbation finite element method (MSPFEM) to solve the uncertain structural–acoustic problem with large fuzzy and interval parameters. Fuzzy variables are used to represent the subjective uncertainties associated with the expert opinions; whereas, interval variables are adopted to quantify the objective uncertainties with limited information. By using the level-cut strategy and Subinterval methodology, the original large fuzzy and interval parameters are decomposed into several Subintervals with small uncertainty level. In both FSPFEM and MSPFEM, the Subinterval matrix and vector are expanded by the Taylor series. The inversion of Subinterval matrix in FSPFEM is approximated by the first-order Neumann series, while the modified Neumann series with higher order terms is adopted in MSPFEM. The eventual fuzzy interval frequency responses are reconstructed by the interval union operation and fuzzy decomposition theorem. A numerical example evidences the remarkable accuracy and effectiveness of the proposed methods to solve engineering structural–acoustic problems with hybrid uncertain parameters.
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Hybrid uncertainty propagation of coupled structural–acoustic system with large fuzzy and interval parameters
Applied Acoustics, 2016Co-Authors: Chong Wang, Zhiping QiuAbstract:Abstract Based on the finite element framework and uncertainty analysis theory, this paper proposes a first-order Subinterval perturbation finite element method (FSPFEM) and a modified Subinterval perturbation finite element method (MSPFEM) to solve the uncertain structural–acoustic problem with large fuzzy and interval parameters. Fuzzy variables are used to represent the subjective uncertainties associated with the expert opinions; whereas, interval variables are adopted to quantify the objective uncertainties with limited information. By using the level-cut strategy and Subinterval methodology, the original large fuzzy and interval parameters are decomposed into several Subintervals with small uncertainty level. In both FSPFEM and MSPFEM, the Subinterval matrix and vector are expanded by the Taylor series. The inversion of Subinterval matrix in FSPFEM is approximated by the first-order Neumann series, while the modified Neumann series with higher order terms is adopted in MSPFEM. The eventual fuzzy interval frequency responses are reconstructed by the interval union operation and fuzzy decomposition theorem. A numerical example evidences the remarkable accuracy and effectiveness of the proposed methods to solve engineering structural–acoustic problems with hybrid uncertain parameters.
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Subinterval perturbation methods for uncertain temperature field prediction with large fuzzy parameters
International Journal of Thermal Sciences, 2016Co-Authors: Chong Wang, Zhiping QiuAbstract:Abstract Based on the perturbation technology and fuzzy theory, this paper proposes a first-order Subinterval perturbation method (FSPM) and a modified Subinterval perturbation method (MSPM) to solve the uncertain heat conduction problem with large fuzzy parameters. Using the level-cut strategy and Subinterval methodology, the original fuzzy parameters with subjective probability are firstly decomposed into several Subinterval variables, while the eventual fuzzy temperature responses are reconstructed by the interval union operation and decomposition theorem. In both perturbation methods, the Subinterval matrix and vector are expanded by the Taylor series. The inversion of Subinterval matrix in FSPM is approximated by the first-order Neumann series, whereas the modified Neumann series with higher order terms is adopted to calculate the Subinterval matrix inverse in MSPM. Comparing the results with traditional Monte Carlo simulations, two numerical examples evidence the remarkable accuracy and effectiveness of the proposed methods to predict uncertain temperature field in engineering.
Bayan Heshig - One of the best experts on this subject based on the ideXlab platform.
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continuous background correction using effective points selected in third order minima segments in low cost laser induced breakdown spectroscopy without intensified ccd
Optics Express, 2018Co-Authors: Jianli Liu, Rui Zhang, Jianjun Chen, Jianan Liu, Jun Qiu, Xun Gao, Jicheng Cui, Bayan HeshigAbstract:This work presents a method that can automatically estimate and remove varying continuous background emission for low-cost laser-induced breakdown spectroscopy (LIBS) without intensified CCD. The algorithm finds all third-order minima points in spectra and uses these points to partition the spectra into multiple Subintervals. The mean value is then used as a threshold to select the effective points for the second-order minima in each Subinterval. Finally, a linear interpolation method is used to realize extension of these effective points and complete fitting of the background using polynomials. Using simulated and real LIBS spectra with different complexities examine the validity of proposed algorithm. Additionally, five elements of five standard cast iron alloy samples are calibrated and improved very well after background removal. The results successfully prove the validity of the background correction algorithm.
