The Experts below are selected from a list of 35412 Experts worldwide ranked by ideXlab platform
Jason D Mcewen - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Quantification for radio interferometric imaging i proximal mcmc methods
Monthly Notices of the Royal Astronomical Society, 2018Co-Authors: Xiaohao Cai, Marcelo Pereyra, Jason D McewenAbstract:Uncertainty Quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Since radio interferometric imaging requires solving a high-dimensional, ill-posed inverse problem, Uncertainty Quantification is difficult but also critical to the accurate scientific interpretation of radio observations. Statistical sampling approaches to perform Bayesian inference, likeMarkov chainMonte Carlo (MCMC) sampling, can in principle recover the full posterior distribution of the image, from which uncertainties can then be quantified. However, traditional high-dimensional samplingmethods are generally limited to smooth (e.g. Gaussian) priors and cannot be used with sparsity-promoting priors. Sparse priors, motivated by the theory of compressive sensing, have been shown to be highly effective for radio interferometric imaging. In this article proximal MCMC methods are developed for radio interferometric imaging, leveraging proximal calculus to support non-differential priors, such as sparse priors, in a Bayesian framework. Furthermore, three strategies to quantify uncertainties using the recovered posterior distribution are developed: (i) local (pixel-wise) credible intervals to provide error bars for each individual pixel; (ii) highest posterior density credible regions; and (iii) hypothesis testing of image structure. These forms of Uncertainty Quantification provide rich information for analysing radio interferometric observations in a statistically robust manner.
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Uncertainty Quantification for radio interferometric imaging ii map estimation
Monthly Notices of the Royal Astronomical Society, 2018Co-Authors: Xiaohao Cai, Marcelo Pereyra, Jason D McewenAbstract:Uncertainty Quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Statistical sampling approaches to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling, can in principle recover the full posterior distribution of the image, from which uncertainties can then be quantified. However, for massive data sizes, like those anticipated from the Square Kilometre Array, it will be difficult if not impossible to apply any MCMC technique due to its inherent computational cost. We formulate Bayesian inference problems with sparsity-promoting priors (motivated by compressive sensing), for which we recover maximum a posteriori (MAP) point estimators of radio interferometric images by convex optimization. Exploiting recent developments in the theory of probability concentration, we quantify uncertainties by post-processing the recovered MAP estimate. Three strategies to quantify uncertainties are developed: (i) highest posterior density credible regions, (ii) local credible intervals (cf. error bars) for individual pixels and superpixels, and (iii) hypothesis testing of image structure. These forms of Uncertainty Quantification provide rich information for analysing radio interferometric observations in a statistically robust manner. OurMAP-based methods are approximately 105 times faster computationally than state-of-theart MCMC methods and, in addition, support highly distributed and parallelized algorithmic structures. For the first time, our MAP-based techniques provide a means of quantifying uncertainties for radio interferometric imaging for realistic data volumes and practical use, and scale to the emerging big data era of radio astronomy.
Dong Chen - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Quantification of hyperspectral image denoising frameworks based on sliding window low rank matrix approximation
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Jingwei Song, Shaobo Xia, Jun Wang, Mitesh Patel, Dong ChenAbstract:Sliding-window-based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the Uncertainty Quantification of the restored HSI has not been addressed to date. Accurate Uncertainty Quantification of the denoised HSI facilitates applications such as multisource or multiscale data fusion, data assimilation, and product Uncertainty Quantification since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise Uncertainty Quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of handling Uncertainty in HSI patch mixing caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the Uncertainty of the observed HSI and provides the Uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form Uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10%-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form Uncertainty Quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive.
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Uncertainty Quantification for hyperspectral image denoising frameworks based on low rank matrix approximation
arXiv: Image and Video Processing, 2020Co-Authors: Shaobo Xia, Jingwei Song, Dong Chen, Jun WangAbstract:Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the Uncertainty Quantification of the restored HSI has not been addressed to date. Accurate Uncertainty Quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product Uncertainty Quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise Uncertainty Quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the Uncertainty of the observed HSI and provides the Uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form Uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form Uncertainty Quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.
Xiaohao Cai - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Quantification for radio interferometric imaging i proximal mcmc methods
Monthly Notices of the Royal Astronomical Society, 2018Co-Authors: Xiaohao Cai, Marcelo Pereyra, Jason D McewenAbstract:Uncertainty Quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Since radio interferometric imaging requires solving a high-dimensional, ill-posed inverse problem, Uncertainty Quantification is difficult but also critical to the accurate scientific interpretation of radio observations. Statistical sampling approaches to perform Bayesian inference, likeMarkov chainMonte Carlo (MCMC) sampling, can in principle recover the full posterior distribution of the image, from which uncertainties can then be quantified. However, traditional high-dimensional samplingmethods are generally limited to smooth (e.g. Gaussian) priors and cannot be used with sparsity-promoting priors. Sparse priors, motivated by the theory of compressive sensing, have been shown to be highly effective for radio interferometric imaging. In this article proximal MCMC methods are developed for radio interferometric imaging, leveraging proximal calculus to support non-differential priors, such as sparse priors, in a Bayesian framework. Furthermore, three strategies to quantify uncertainties using the recovered posterior distribution are developed: (i) local (pixel-wise) credible intervals to provide error bars for each individual pixel; (ii) highest posterior density credible regions; and (iii) hypothesis testing of image structure. These forms of Uncertainty Quantification provide rich information for analysing radio interferometric observations in a statistically robust manner.
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Uncertainty Quantification for radio interferometric imaging ii map estimation
Monthly Notices of the Royal Astronomical Society, 2018Co-Authors: Xiaohao Cai, Marcelo Pereyra, Jason D McewenAbstract:Uncertainty Quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Statistical sampling approaches to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling, can in principle recover the full posterior distribution of the image, from which uncertainties can then be quantified. However, for massive data sizes, like those anticipated from the Square Kilometre Array, it will be difficult if not impossible to apply any MCMC technique due to its inherent computational cost. We formulate Bayesian inference problems with sparsity-promoting priors (motivated by compressive sensing), for which we recover maximum a posteriori (MAP) point estimators of radio interferometric images by convex optimization. Exploiting recent developments in the theory of probability concentration, we quantify uncertainties by post-processing the recovered MAP estimate. Three strategies to quantify uncertainties are developed: (i) highest posterior density credible regions, (ii) local credible intervals (cf. error bars) for individual pixels and superpixels, and (iii) hypothesis testing of image structure. These forms of Uncertainty Quantification provide rich information for analysing radio interferometric observations in a statistically robust manner. OurMAP-based methods are approximately 105 times faster computationally than state-of-theart MCMC methods and, in addition, support highly distributed and parallelized algorithmic structures. For the first time, our MAP-based techniques provide a means of quantifying uncertainties for radio interferometric imaging for realistic data volumes and practical use, and scale to the emerging big data era of radio astronomy.
Dushan Boroyevich - One of the best experts on this subject based on the ideXlab platform.
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on the modeling and design of modular multilevel converters with parametric and model form Uncertainty Quantification
IEEE Transactions on Power Electronics, 2020Co-Authors: Niloofar Rashidi, Rolando Burgos, Christopher J Roy, Dushan BoroyevichAbstract:Modeling and design with parametric and model-form Uncertainty Quantification is an alternative to conventional model-based design approaches as it improves the existing modeling practice and validates the model used in the design of power converters. However, in the case of modular multilevel converters (MMCs), Uncertainty Quantification, as the main step in this design methodology, becomes challenging due to the inherent complexity and sheer size of such units. In this article, these limitations are discussed and a systematic study for developing a simplified testbed for Uncertainty Quantification of an MMC is presented. To this end, first sensitivity analysis is conducted to identify the key parameters whose tolerances contribute the most to the parametric Uncertainty of the selected design variables. Second, the effect of increasing the number of power cells in each arm on the estimated total Uncertainty, and thus the predictive capability of the MMC simulation models for medium- and high-voltage applications is studied. A simplified testbed for model validation of the power cell in the design of an MMC is developed accordingly. The development of this simplified testbed allows validating the models used and estimating uncertainties in the design with less computational cost and hardware prototyping.
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modeling and design of the modular multilevel converter with parametric and model form Uncertainty Quantification
European Conference on Cognitive Ergonomics, 2017Co-Authors: Niloofar Rashidi Mehrabadi, Rolando Burgos, Dushan Boroyevich, Christopher J RoyAbstract:This paper presents a design methodology with Uncertainty Quantification to estimate the required margin for two main design variables in a modular multilevel converter (MMC). In this methodology, the minimum required design margins are calculated by quantifying all sources of Uncertainty in the modeling and simulation of MMCs. To this end, an enhanced modeling framework is presented to take into account the parametric Uncertainty (PU) that results from manufacturing variability, and model-form Uncertainty (MFU) that results from inherent inaccuracies of the models used in the design process. In this paper, sensitivity analysis (SA) is used to guide the modeling effort and minimize the number of uncertain parameters required for inclusion in Uncertainty Quantification. Besides, a simplified testbed for model validation of the MMC is developed. This testbed is used for conducting low-power validation experiments and Monte-Carlo simulations to estimate PU and MFU, respectively.
Jingwei Song - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty Quantification of hyperspectral image denoising frameworks based on sliding window low rank matrix approximation
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Jingwei Song, Shaobo Xia, Jun Wang, Mitesh Patel, Dong ChenAbstract:Sliding-window-based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the Uncertainty Quantification of the restored HSI has not been addressed to date. Accurate Uncertainty Quantification of the denoised HSI facilitates applications such as multisource or multiscale data fusion, data assimilation, and product Uncertainty Quantification since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise Uncertainty Quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of handling Uncertainty in HSI patch mixing caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the Uncertainty of the observed HSI and provides the Uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form Uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10%-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form Uncertainty Quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive.
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Uncertainty Quantification for hyperspectral image denoising frameworks based on low rank matrix approximation
arXiv: Image and Video Processing, 2020Co-Authors: Shaobo Xia, Jingwei Song, Dong Chen, Jun WangAbstract:Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the Uncertainty Quantification of the restored HSI has not been addressed to date. Accurate Uncertainty Quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product Uncertainty Quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise Uncertainty Quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the Uncertainty of the observed HSI and provides the Uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form Uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form Uncertainty Quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.
