The Experts below are selected from a list of 8220 Experts worldwide ranked by ideXlab platform
Shuxing Yang - One of the best experts on this subject based on the ideXlab platform.
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Weighted stochastic response surface method considering sample weights
Structural and Multidisciplinary Optimization, 2011Co-Authors: Fenfen Xiong, Ying Xiong, Wei Chen, Shuxing YangAbstract:Conventional stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treat every sample Point equally during the regression process and may produce inaccurate estimations of PCE coefficients. To address this issue, a new weighted stochastic response surface method (WSRSM) that considers the sample probabilistic weights in regression is studied in this work. Techniques for determining sample probabilistic weights for three sampling approaches Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are developed. The advantage of the proposed method is demonstrated through mathematical and engineering examples. It is shown that for various sampling techniques WSRSM consistently achieves higher accuracy of uncertainty propagation without introducing extra computational cost compared to the conventional SRSM. Insights into the relative accuracy and efficiency of various sampling techniques in implementation are provided as well.
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A New Weighted Stochastic Response Surface Method for Uncertainty Propagation
13th AIAA ISSMO Multidisciplinary Analysis Optimization Conference, 2010Co-Authors: Fenfen Xiong, Ying Xiong, Shuxing YangAbstract:Conventional stochastic response surface method (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treats every sample Points equally during the regression process and may produce inaccurate coefficient estimations in PCE. A new weighted stochastic response surface method (WSRSM) to overcome such limitation by considering the sample probabilistic weights in regression is studied in this work. Techniques that associate sample probabilistic weights to different sampling approaches such as Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR) and Latin Hypercube Design (LHD) are developed. The proposed method is demonstrated by several mathematical and engineering examples. Results show that for various sampling techniques, WSRSM can consistently improve the accuracy of uncertainty propagation compared to the conventional SRSM without adding extra computational cost. Insights into the relative accuracy and efficiency of using various sampling techniques in implementation are provided.
Fenfen Xiong - One of the best experts on this subject based on the ideXlab platform.
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Efficient Sampling Approaches for Stochastic Response Surface Method
Advanced Materials Research, 2012Co-Authors: Gao Rong Sun, Fenfen XiongAbstract:Stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) has been widely used for uncertainty propagation. It is necessary to select efficient sampling technique to estimate the PCE coefficients in SRSM. In this paper, the three advanced sampling approaches, namely, Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are introduced and investigated, whose performances are tested through several examples. It is shown that the results of UP for the three sampling approaches show great agreements to those of Monte Carlo simulation. Specifically, GQ yields the most accurate result of UP, followed by MCR and LHD, while MCR shows the best efficiency for lower PCE order.
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Weighted stochastic response surface method considering sample weights
Structural and Multidisciplinary Optimization, 2011Co-Authors: Fenfen Xiong, Ying Xiong, Wei Chen, Shuxing YangAbstract:Conventional stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treat every sample Point equally during the regression process and may produce inaccurate estimations of PCE coefficients. To address this issue, a new weighted stochastic response surface method (WSRSM) that considers the sample probabilistic weights in regression is studied in this work. Techniques for determining sample probabilistic weights for three sampling approaches Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are developed. The advantage of the proposed method is demonstrated through mathematical and engineering examples. It is shown that for various sampling techniques WSRSM consistently achieves higher accuracy of uncertainty propagation without introducing extra computational cost compared to the conventional SRSM. Insights into the relative accuracy and efficiency of various sampling techniques in implementation are provided as well.
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A New Weighted Stochastic Response Surface Method for Uncertainty Propagation
13th AIAA ISSMO Multidisciplinary Analysis Optimization Conference, 2010Co-Authors: Fenfen Xiong, Ying Xiong, Shuxing YangAbstract:Conventional stochastic response surface method (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treats every sample Points equally during the regression process and may produce inaccurate coefficient estimations in PCE. A new weighted stochastic response surface method (WSRSM) to overcome such limitation by considering the sample probabilistic weights in regression is studied in this work. Techniques that associate sample probabilistic weights to different sampling approaches such as Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR) and Latin Hypercube Design (LHD) are developed. The proposed method is demonstrated by several mathematical and engineering examples. Results show that for various sampling techniques, WSRSM can consistently improve the accuracy of uncertainty propagation compared to the conventional SRSM without adding extra computational cost. Insights into the relative accuracy and efficiency of using various sampling techniques in implementation are provided.
Nanguang Chen - One of the best experts on this subject based on the ideXlab platform.
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Design of an Advanced Time-Domain Diffuse
2010Co-Authors: Nanguang ChenAbstract:This paper describes the design of an improved mul- tichannel time-domain diffuse optical tomography (TD-DOT) sys- tem. For the spread spectrum TD-DOT, the image quality depends on the stability of the time-resolved signals, which can be affected by fluctuation in the source modulation depth and drift in the de- tector gain. A microcontroller-based bias controller is designed to lock the bias of a Mach-Zehnder intensity modulator at the pos- itive Quadrature Point. With additional temperature stabilization over the avalanche photodiodes, the temporal stability of the time- resolved signals has been significantly improved—the fluctuations of the temporal Point spread function have been limited to ±2% for a testing period of a few hours. The image quality and reproducibil- ity have been substantially improved with the stabilized signals. Index Terms—Optical control, optical modulation, optical time- domain reflectometry, optical tomography.
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Design of an Advanced Time-Domain Diffuse Optical Tomography System
IEEE Journal of Selected Topics in Quantum Electronics, 2010Co-Authors: Nanguang ChenAbstract:This paper describes the design of an improved multichannel time-domain diffuse optical tomography (TD-DOT) system. For the spread spectrum TD-DOT, the image quality depends on the stability of the time-resolved signals, which can be affected by fluctuation in the source modulation depth and drift in the detector gain. A microcontroller-based bias controller is designed to lock the bias of a Mach-Zehnder intensity modulator at the positive Quadrature Point. With additional temperature stabilization over the avalanche photodiodes, the temporal stability of the time-resolved signals has been significantly improved-the fluctuations of the temporal Point spread function have been limited to ±2% for a testing period of a few hours. The image quality and reproducibility have been substantially improved with the stabilized signals.
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Source stabilization for high quality time-domain diffuse optical tomography
Design and Quality for Biomedical Technologies II, 2009Co-Authors: Nanguang ChenAbstract:We report a new close-loop feedback control method to keep a Mach-Zehnder electro-optic modulator (MZ-EOM) biased at the Quadrature Point and simultaneously correct the bias drift caused by the temperature changes as well as the inherent photorefractive effect. The modulator is a key part of our high speed time-domain diffuse optical tomography system. It modulates the dual-wavelength near-infrared light with the high speed pseudorandom bit sequence (PRBS) signal for the temporal Point spread function (TPSF) measurements. Our method applies a periodical low frequency square wave with 50% duty cycle as the pilot tone upon the MZ-EOM together with the PRBS and sweep the bias voltage of the MZ-EOM in a self-adaptive step. A constant fraction of the modulated output power is measured by a photodiode via a tap coupler. After demodulation, the modulation depth versus the bias voltage can be measured from which the peak value corresponding to the Quadrature Point can be located quickly by curve fitting. Our stabilization technique is simple, fast and cost effective and is effective to correct the bias drift caused by the photorefractive and the change of ambient conditions. The experiment results show the TPSFs measurements can be stabilized to within ±2% in an hour duration, which helps improved the image quality.
Ying Xiong - One of the best experts on this subject based on the ideXlab platform.
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Weighted stochastic response surface method considering sample weights
Structural and Multidisciplinary Optimization, 2011Co-Authors: Fenfen Xiong, Ying Xiong, Wei Chen, Shuxing YangAbstract:Conventional stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treat every sample Point equally during the regression process and may produce inaccurate estimations of PCE coefficients. To address this issue, a new weighted stochastic response surface method (WSRSM) that considers the sample probabilistic weights in regression is studied in this work. Techniques for determining sample probabilistic weights for three sampling approaches Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are developed. The advantage of the proposed method is demonstrated through mathematical and engineering examples. It is shown that for various sampling techniques WSRSM consistently achieves higher accuracy of uncertainty propagation without introducing extra computational cost compared to the conventional SRSM. Insights into the relative accuracy and efficiency of various sampling techniques in implementation are provided as well.
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A New Weighted Stochastic Response Surface Method for Uncertainty Propagation
13th AIAA ISSMO Multidisciplinary Analysis Optimization Conference, 2010Co-Authors: Fenfen Xiong, Ying Xiong, Shuxing YangAbstract:Conventional stochastic response surface method (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treats every sample Points equally during the regression process and may produce inaccurate coefficient estimations in PCE. A new weighted stochastic response surface method (WSRSM) to overcome such limitation by considering the sample probabilistic weights in regression is studied in this work. Techniques that associate sample probabilistic weights to different sampling approaches such as Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR) and Latin Hypercube Design (LHD) are developed. The proposed method is demonstrated by several mathematical and engineering examples. Results show that for various sampling techniques, WSRSM can consistently improve the accuracy of uncertainty propagation compared to the conventional SRSM without adding extra computational cost. Insights into the relative accuracy and efficiency of using various sampling techniques in implementation are provided.
Wei Chen - One of the best experts on this subject based on the ideXlab platform.
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Weighted stochastic response surface method considering sample weights
Structural and Multidisciplinary Optimization, 2011Co-Authors: Fenfen Xiong, Ying Xiong, Wei Chen, Shuxing YangAbstract:Conventional stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treat every sample Point equally during the regression process and may produce inaccurate estimations of PCE coefficients. To address this issue, a new weighted stochastic response surface method (WSRSM) that considers the sample probabilistic weights in regression is studied in this work. Techniques for determining sample probabilistic weights for three sampling approaches Gaussian Quadrature Point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are developed. The advantage of the proposed method is demonstrated through mathematical and engineering examples. It is shown that for various sampling techniques WSRSM consistently achieves higher accuracy of uncertainty propagation without introducing extra computational cost compared to the conventional SRSM. Insights into the relative accuracy and efficiency of various sampling techniques in implementation are provided as well.
