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Two exact first-order k-space formulations for low-rank viscoacoustic wave propagation on staggered grids

Petroleum Science 2023年第3期20卷 1521-1531页

作者： Hong-Yu Zhou Yang Liu Jing Wang China Universityof Petroleum(Bejing) State Key Laboratory of Petroleum Resources and Prospecting and CNPC Key Laboratory of Geophysical ProspectingBeijing102249China China University of Petroleum(Bejing) Karamay CampusKaramayXinjiang834000China

Wave propagation in the viscoacoustic media is physically dispersive and dissipated.Completely excluding the numerical dispersion error from the physical dispersion in the viscoacoustic wave simu-lation is indispensable to understanding the intrinsic property of the wave propagation in attenuated media for the petroleum exploration geophysics.In recent years,a viscoacoustic wave equation char-acterized by fractional Laplacian gains wide attention in geophysical community.However,the first-order form of the viscoacoustic wave equation,often solved by the conventional staggered-grid pseu-dospectral method,suffers from the numerical dispersion error in time due to the low-order finite-difference approximation.It is challenging to completely eliminate the error because the viscoacoustic wave equation contains two temporal derivatives,which stem from the time stepping and the amplitude attenuation terms,respectively.To tackle the issue,we derive two exact first-order k-space viscoacoustic formulations that can fully exclude the numerical error from the physical dispersion.For the homoge-neous case,two formulations agree with the viscoacoustic analytical solution very well and have the same efficiency.For the heterogeneous case,our second k-space formulation is more efficient than the first one because the second formulation significantly reduces the number of the wavenumber-space mixed-domain operators,which are the expensive part of the viscoacoustic k-space simulation.Nu-merical cases validate that the two first-order k-space formulations are effective and efficient alternatives to the current staggered-grid pseudospectral formulation for the viscoacoustic wave simulation.

关键词： Viscoacoustic K-space Staggered-grid low-rank

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A Patch-Based low-rank Minimization Approach for Speckle Noise Reduction in Ultrasound Images

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Advances in Applied Mathematics and Mechanics 2022年第1期14卷 155-180页

作者： Xiao-Guang Lv Fang Li Jun Liu Sheng-Tai Lu School of Science Jiangsu Ocean UniversityLianyungangJiangsu 222005China Shanghai Key Laboratory of PMMP and School of Mathematical Sciences East China Normal UniversityShanghai 200241China School of Mathematics and Statistics and Key Laboratory of Applied Statistics of MOE Northeast Normal UniversityChangchunJilin 130024China

Ultrasound is a low-cost,non-invasive and real-time imaging modality that has proved popular for many medical applications.Unfortunately,the acquired ultrasound images are often corrupted by speckle noise from scatterers smaller than ultrasound beam wavelength.The signal-dependent speckle noise makes visual observation difficult.In this paper,we propose a patch-based low-rank approach for reducing the speckle noise in ultrasound images.After constructing the patch group of the ultrasound images by the block-matching scheme,we establish a variational model using the weighted nuclear norm as a regularizer for the patch group.The alternating direction method of multipliers(ADMM)is applied for solving the established nonconvex model.We return all the approximate patches to their original locations and get the final restored ultrasound images.Experimental results are given to demonstrate that the proposed method outperforms some existing state-of-the-art methods in terms of visual quality and quantitative measures.

关键词： Ultrasound images patch speckle noise low-rank weighted nuclear norm minimization

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Robust low-rank tensor factorization by cyclic weighted median

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Science China(Information Sciences) 2015年第5期58卷 145-155页

作者： MENG DeYu ZHANG Biao XU ZongBen ZHANG Lei GAO ChenQiang Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Department of Computing Hong Kong Polytechnic University Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.

关键词： tensor factorization low-rank weighted median filter tensorface

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Co-pyrolysis characteristics and interaction route between low-rank coals and Shenhua coal direct liquefaction residue

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Chinese Journal of Chemical Engineering 2019年第11期27卷 2815-2824页

作者： Kai Li Xiaoxun Ma Ruiyu He Zhenni Li School of Chemical Engineering Northwest UniversityXi'an 710069China International Scientific and Technological Cooperation Base of the Ministry of Science and Technology(MOST)for Clean Utilization of Hydrocarbon Resources Xi'an 710069China Chemical Engineering Research Center of the Ministry of Education(MOE)for Advanced Use Technology of Shanbei Energy Xi'an 710069China Shaanxi Research Center of Engineering Technology for Clean Coal Conversion Xi'an 710069China Collaborative Innovation Center for Development of Energy and Chemical Industry in Northern Shaanxi Xi'an 710069China

To reasonably utilize the coal direct liquefaction residue(DLR), contrasting research on the co-pyrolysis between different low-rank coals and DLR was investigated using a TGA coupled with an FT-IR spectrophotometer and a fixed-bed reactor. GC–MS, FTIR, and XRD were used to explore the reaction mechanisms of the various co-pyrolysis processes. Based on the TGA results, it was confirmed that the tetrahydrofuran insoluble fraction of DLR helped to catalyze the conversion reaction of lignite. Also, the addition of DLR improved the yield of tar in the fixed-bed, with altering the composition of the tar. Moreover, a kinetic analysis during the co-pyrolysis was conducted using a distributed activation energy model. The co-pyrolysis reactions showed an approximate double-Gaussian distribution.

关键词： low-rank coal Coal direct liquefaction residue Co-pyrolysis Kinetics

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Unsupervised, Supervised and Semi-supervised Dimensionality Reduction by low-rank Regression Analysis

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Chinese Journal of Electronics 2021年第4期30卷 603-610页

作者： TANG Kewei ZHANG Jun ZHANG Changsheng WANG Lijun ZHAI Yun JIANG Wei School of Mathematics Liaoning Normal University College of Computer Science and Artificial Intelligence Wenzhou University Research Center for Information Science Theory and Methodology Institute of Scientific and Technical Information of China E-Government Research Center Chinese Academy of Governance

Techniques for dimensionality reduction have attracted much attention in computer vision and pattern recognition. However, for the supervised or unsupervised case, the methods combining regression analysis and spectral graph analysis do not consider the global structure of the subspace; For semi-supervised case,how to use the unlabeled samples more effectively is still an open problem. In this paper, we propose the methods by low-rank regression analysis(LRRA) to deal with these problems. For supervised or unsupervised dimensionality reduction, combining spectral graph analysis and LRRA can make a global constraint on the subspace. For semi-supervised dimensionality reduction, the proposed method incorporating LRRA can exploit the unlabeled samples more effectively. The experimental results show the effectiveness of our methods.

关键词： Dimensionality reduction low-rank Supervised Unsupervised Semi-supervised

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The mechanism and products for co-thermal extraction of biomass and low-rank coal with NMP

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International Journal of Minerals,Metallurgy and Materials 2019年第12期26卷 1512-1522页

作者： Jun Zhao Hai-bin Zuo Jing-song Wang Qing-guo Xue State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing

The high-value utilization of low-rank coal would allow for expanding energy sources,improving energy efficiencies,and alleviating environmental issues.In order to use low-rank coal effectively,the hypercoals(HPCs)were co-extracted from two types of low-rank coal and biomass via N-methyl-2-purrolidinone(NMP)under mild conditions.The structures of the HPCs and residues were characterized by proximate and ultimate analysis,Raman spectra,and Fourier transform infrared(FT-IR)spectra.The carbon structure changes within the raw coals and HPCs were discussed.The individual thermal dissolution of Xibu(XB)coal,Guandi(GD)coal,and the biomass demonstrated that the biomass provided the lowest thermal dissolution yield Y1 and the highest thermal soluble yield Y2 at 280℃,and the ash content of three HPCs decreased as the extraction temperature rose.Co-thermal extractions in NMP at various coal/biomass mass ratios were performed,demonstrating a positive synergic effect for Y2 in the whole coal/biomass mass ratios.The maximum value of Y2 was 52.25wt% for XB coal obtained with a XB coal/biomass of 50wt% biomass.The maximum value of Y2 was 50.77wt% for GD coal obtained with a GD coal/biomass of 1:4.The difference for the optimal coal/biomass mass ratios between XB and GD coals could be attributed to the different co-extraction mechanisms for this two type coals.

关键词： low-rank coal biomass co-thermal extraction NMP hypercoal

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low-rank Combined Adaptive Sparsifying Transform for Blind Compressed Sensing Image Recovery

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Chinese Journal of Electronics 2020年第4期29卷 678-685页

作者： HE Ning WANG Ruolin LYU Jiayi XUE Jian Beijing Union University Logistics Department of Beijing Military Region Capital Normal University University of Chinese Academic of Sciences

Compressed sensing(CS) exploits the sparsity of images or image patches in a transform domain or synthesis dictionary to reconstruct images from undegraded images.Because the synthesis dictionary learning methods involves NP-hard sparse coding and expensive learning steps, sparsifying transform based blind compressed sending(BCS) has been shown to be effective and efficient in applications, while also enjoying good convergence guarantees.By minimizing the rank of an overlapped patch group matrix to efficiently exploit the nonlocal self-similarity features of the image, while the sparsifying transform model imposes the local features of the image.We propose a combined low-rank and adaptive sparsifying transform(LRAST) BCS method to better represent natural images.We utilized the patch coordinate(PCD) descent algorithm to optimize the method, and this enforced the intrinsic local sparsity and nonlocal self-similarity of the images simultaneously in a unified framework.The experimental results indicated a promising performance, even in comparison to state-of-theart methods.

关键词： low-rank Sparsifying transform Blind compressed sensing(BCS) Patch coordinate descent(PCD) Image recovery

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RGBD Salient Object Detection by Structured low-rank Matrix Recovery and Laplacian Constraint

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Transactions of Tianjin University 2017年第2期23卷 176-183页

作者： Chang Tang Chunping Hou School of Electrical and Information Engineering Tianjin University Tianjin 300072 China

A structured low-rank matrix recovery model for RGBD salient object detection is proposed. Firstly, the problem is described by a low-rank matrix recovery, and the hierarchical structure of RGB image is added to the sparsity term. Secondly, the depth information is fused into the model by a Laplacian regularization term to ensure that the image regions which share similar depth value will be allocated to similar saliency value. Thirdly, a variation of alternating direction method is proposed to solve the proposed model. Finally, both quantitative and qualitative experimental results on NLPR1000 and NJU400 show the advantage of the proposed RGBD salient object detection model.

关键词： RGBD saliency detection low-rank matrix recovery Laplacian regularization Structured sparsity

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Directional H^(2) Compression Algorithm: Optimisations and Application to a Discontinuous Galerkin BEM for the Helmholtz Equation

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Communications in Computational Physics 2022年第5期31卷 1585-1635页

作者： Nadir-Alexandre Messaï Sebastien Pernet Abdesselam Bouguerra ONERA/DTIS Universitéfédérale de Toulouse F-31000 Toulouse France Laboratoire de Physique de la Matière et du Rayonnement(LPMR) Département de Sciences de la MatièreUniversitéde Souk-AhrasBP 155341000 Souk-AhrasAlgeria

This study aimed to specialise a directional H^(2)(DH^(2))compression to matrices arising from the discontinuous Galerkin(DG)discretisation of the hypersingular equation in acoustics.The significantfinding is an algorithm that takes a DG stiffness matrix andfinds a near-optimal DH^(2) approximation for low and high-frequency problems.We introduced the necessary special optimisations to make this algorithm more efficient in the case of a DG stiffness matrix.Moreover,an automatic parameter tuning strategy makes it easy to use and versatile.Numerical comparisons with a classical Boundary Element Method(BEM)show that a DG scheme combined with a DH^(2) gives better computational efficiency than a classical BEM in the case of high-order finite elements and hp heterogeneous meshes.The results indicate that DG is suitable for an auto-adaptive context in integral equations.

关键词： Integral equation boundary element method Helmholtz equation discontinuous Galerkin directional H^(2)-matrix low-rank approximation all frequency compression algorithm

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Recovery of Corrupted low-rank Tensors

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Applied Mathematics 2017年第2期8卷 229-244页

作者： Haiyan Fan Gangyao Kuang Department of Electronic Science and Engineering National University of Defense Technology Changsha China

This paper studies the problem of recovering low-rank tensors, and the tensors are corrupted by both impulse and Gaussian noise. The problem is well accomplished by integrating the tensor nuclear norm and the l1-norm in a unified convex relaxation framework. The nuclear norm is adopted to explore the low-rank components and the l1-norm is used to exploit the impulse noise. Then, this optimization problem is solved by some augmented-Lagrangian-based algorithms. Some preliminary numerical experiments verify that the proposed method can well recover the corrupted low-rank tensors.

关键词： low-rank Tensor Tensor Recovery Augmented Lagrangian Method Impulsive Noise Mixed Noise

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