Operational modal analysis (OMA) has received tremendous interest from engineering fields in recent years. This paper develops a compact-bandwidth regularization approach for OMA within the signal processing framework...
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Operational modal analysis (OMA) has received tremendous interest from engineering fields in recent years. This paper develops a compact-bandwidth regularization approach for OMA within the signal processing framework. The key ingredient lies in the fact that a structural mode is always compact in the frequency domain and this results in the compact-bandwidth constraint for structural modes. To implicitly enforce the compact-bandwidth constraint, the compact-bandwidth regularization is introduced to conventional blind modal separation. Then, the alternating minimization algorithm is used to iteratively get the solution in the immediately-at-once or one-by-one manner. In proceeding so, the spectrum-peak-based rule is adopted for choice of initial modal parameters and the L-curve method in conjunction with a theoretically derived bound is applied to estimate a proper regularizationparameter. Numerical examples and an experimental test case are studied along with comparison to some usual OMA approaches to see the performance and advantages of the proposed approach in modal identification. (C) 2019 Elsevier Ltd. All rights reserved.
This work aims at studying a method to automatically estimate regularizationparameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem ...
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ISBN:
(纸本)9781479999880
This work aims at studying a method to automatically estimate regularizationparameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) is proposed to estimate regularizationparameters. It has good theoretical properties (uniqueness, robustness) from which a grid search based approach is proposed. It results in a fast approach to estimate the regularizationparameters.
A hyperspectral image is a 3D data cube in which every pixel provides local spectral information about a scene of interest across a large number of contiguous bands. The observed images may suffer from degradation due ...
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A hyperspectral image is a 3D data cube in which every pixel provides local spectral information about a scene of interest across a large number of contiguous bands. The observed images may suffer from degradation due to the measuring device, resulting in a convolution or blurring of the images. Hyperspectral image deconvolution (HID) consists in removing the blurring to improve the spatial resolution of images at best. A Tikhonov-like HID criterion with non-negativity constraint is consid- ered here. This method considers separable spatial and spectral regularization terms whose strength are controlled by two regularizationparameters. First part of this thesis proposes the maximum curvature criterion (MCC) and the minimum distance criterion (MDC) to automatically estimate these regularizationparameters by formulating the deconvolution problem as a multi-objective opti- mization problem. The second part of this thesis proposes the sliding block regularized (SBR-LMS) algorithm for the online deconvolution of hypserspectral images as provided by whiskbroom and pushbroom scanning systems. The proposed algorithm accounts for the convolution kernel non- causality and including non-quadratic regularization terms while maintaining a linear complexity compatible with real-time processing in industrial applications. The third part of this thesis pro- poses joint unmixing-deconvolution methods based on the Tikhonov criterion in both offline and online contexts. The non-negativity constraint is added to improve their performances.
This work aims at studying a method to automatically estimate regularizationparameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem ...
详细信息
ISBN:
(纸本)9781479999897
This work aims at studying a method to automatically estimate regularizationparameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) is proposed to estimate regularizationparameters. It has good theoretical properties (uniqueness, robustness) from which a grid search based approach is proposed. It results in a fast approach to estimate the regularizationparameters.
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