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An improved variational model for denoising magnetic resonance images

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2018年第9期76卷 2212-2222页

作者： Yuan, Jianjun Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper presents an improved variational model for denoising MR images degraded by Rician noise. An additional data-fidelity based on image gradient is introduced, which can retain more details and edges. In this model, an automatic estimation of standard deviation of Rician noise is designed to enhance the quality of denoised results. The alternating direction method of multipliers (admm) is adopted to implement numerical schedule, and gains the robust solution. Experimental results demonstrate that the proposed method is efficient, and has better denoising capability than the state-of-the-art models. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Variational model admm algorithm MRI denoising Gradient fidelity Rician noise

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On the Design of Target Beampatterns for Differential Microphone Arrays

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2019年第8期27卷 1295-1307页

作者： Pan, Chao Chen, Jingdong Beneslye, Jacob Shi, Guangming Xidian Univ Sch Artificial Intelligence Imaging & Brain Inspired Percept Lab Xian 710071 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Xian 710072 Shaanxi Peoples R China Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada Xidian Univ Sch Artificial Intelligence Optoelect Imaging & Brain Inspired Percept Lab Xian 710071 Shaanxi Peoples R China

Differential microphone arrays (DMAs) have many interesting properties and have been widely used in acoustic, audio, and speech applications. A critical part of a DMA is the differential beamformer, which is generally designed in two important steps: 1) specifying a target beampattern based on what differential sound pressure field the DMA is expected to respond to and 2) designing the differential beamforming filter so that the resulting beampattern matches the target one. Most efforts in the study of DMAs so far have focused on the second step while choosing one of the limited patterns available in the literature as the target beampattern. Since it governs how the array performs, how to design the target beampattern is an important problem, which this paper addresses. The major contributions of this paper consists of the following four aspects. First, a positive superposition theorem is presented, which shows that the linear combination of effective beampatterns with non-negative coefficients is always an effective beampattern. Second, we propose a general approach to the design of target DMA beampatterns based on the positive superposition theorem. Third, an overview of the classical target beampatterns is provided and discussion is made on how to form effective base patterns. Fourth, we show that the smallest first null of a DMA is pi/(2N) with N being the DMA order, which provides the rule of setting nulls in practice. Finally, with examples, we show that with the use of the alternating-direction-method-of-multipliers algorithm, the proposed approach is able to generate useful DMA target beampatterns.

关键词： Differential microphone arrays differential beamforming target beampattern admm algorithm

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A novel privacy-preserved voluntary outage management model on a transactive energy scheme

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第17期16卷 3462-3472页

作者： Riki, Mahshid Koochaki, Mohaddese Moeini-Aghtaie, Moein Abbaspour, Ali Mazaheri, Hesam Sharif Univ Technol Dept Elect Engn Tehran Iran Sharif Univ Technol Dept Energy Engn Tehran 6511155 Iran Texas A&M Univ Dept Elect & Computet Engn College Stn TX USA

Unpredictable occurrence of natural disasters in modern power systems has significantly increased the attentions to the outage management of multi-microgrid (MMG) systems. In this regard, a multi-objective two-stage outage management scheme is presented in this paper to protect undamaged microgrids (MGs) against load curtailment and minimize the curtailed loads simultaneously. Considering the importance of power system economic, this scheme is designed in a privacy-preserved transactive energy (TE) market and enables all MGs to participate in outage management voluntarily. In detail, a DC optimization is autonomously operated in the first stage in each MG to supply its demand loads. Then, the second stage is designed to exchange the surplus power of the last stage with the damaged MGs. These voluntary power transactions are pre-specified in contracts, adjusted between each MG and distributed system operator (DSO). Moreover, in this stage, the admm algorithm based on DC-OPF has prepared a privacy protection platform and proved the results' accuracy. The presented test grid is the IEEE 34-node test system and the numeric results demonstrate the effectiveness of the proposed multi-objective scheme.

关键词： DC optimization optimisation load curtailment privacy-preserved voluntary outage management model privacy protection platform privacy-preserved transactive energy two-stage outage management scheme admm algorithm undamaged microgrids voluntary power transactions modern power systems distributed system operator unpredictable occurrence IEEE 34-node test system distributed power generation power distribution reliability transactive energy scheme load flow power system economic demand side management multiobjective scheme natural disasters multimicrogrid systems power generation reliability

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A Penalized Regression-Based Biclustering Approach in Gene Expression Data

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JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 2024年 1-18页

作者： Wei, Mengxi Zheng, Zhi Zhang, Weiping Univ Sci & Technol China Dept Stat & Finance Hefei 230026 Peoples R China

Clustering serves as a pivotal instrument in the realm of gene expression data analysis. This paper proposes a Biclustering Coefficient Estimation (BCE) method to identify groups in the individuals and genes. An alternating direction method of multipliers (admm) algorithm with a double fusion penalty is developed to solve the problem. The authors rigorously establish the oracle properties for the proposed penalized estimator. Numerical studies, including simulations and analysis of a lung adenocarcinoma dataset, suggest that the proposed method is expected to simultaneously recover reasonable potential groups of samples and covariates and provide satisfactory estimates of group coefficients.

关键词： admm algorithm biclustering analysis lung adenocarcinoma penalized fusion

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Non-convex and non-smooth variational decomposition for image restoration

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APPLIED MATHEMATICAL MODELLING 2019年 69卷 355-377页

作者： Tang Liming Zhang Honglu He Chuanjiang Fang Zhuang Hubei Univ Nationalities Sch Sci Enshi 445000 Peoples R China Chongqing Univ Coll Math & Stat Chongqing 401331 Peoples R China

The variational image decomposition model decomposes an image into a structural and an oscillatory component by regularization technique and functional minimization. It is an important task in various image processing methods, such as image restoration, image segmentation, and object recognition. In this paper, we propose a non-convex and non-smooth variational decomposition model for image restoration that uses non-convex and non-smooth total variation (TV) to measure the structure component and the negative Sobolev space H-1 to model the oscillatory component. The new model combines the advantages of non-convex regularization and weaker-norm texture modeling, and it can well remove the noises while preserving the valuable edges and contours of the image. The iteratively reweighted l(1) (IRL1) algorithm is employed to solve the proposed non-convex minimization problem. For each subproblem, we use the alternating direction method of multipliers (admm) algorithm to solve it. Numerical results validate the effectiveness of the proposed model for both synthetic and real images in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). (C) 2018 Elsevier Inc. All rights reserved.

关键词： Variational decomposition Image restoration Non-convex Non-smooth IRL1 algorithm admm algorithm

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Rapid online plant leaf area change detection with high-throughput plant image data

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JOURNAL OF APPLIED STATISTICS 2023年第14期50卷 2984-2998页

作者： Zhan, Yinglun Zhang, Ruizhi Zhou, Yuzhen Stoerger, Vincent Hiller, Jeremy Awada, Tala Ge, Yufeng Univ Nebraska Dept Stat Lincoln NE 68583 USA Univ Nebraska Agr Res Div Lincoln NE 68583 USA Univ Nebraska Sch Nat Resources Lincoln NE 68583 USA Univ Nebraska Dept Biol Syst Engn Lincoln NE 68583 USA Univ Georgia Dept Stat Athens GA USA

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

关键词： Supervised learning admm algorithm online detection adaptive cusum plant leaf area high-throughput plant phenotyping (HTPP)

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On the application of Gaussian graphical models to paired data problems

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STATISTICS AND COMPUTING 2024年第6期34卷 1-19页

作者： Ranciati, Saverio Roverato, Alberto Univ Bologna Dept Stat Sci Bologna Italy Univ Padua Dept Stat Sci Padua Italy

Gaussian graphical models are nowadays commonly applied to the comparison of groups sharing the same variables, by jointly learning their independence structures. We consider the case where there are exactly two dependent groups and the association structure is represented by a family of coloured Gaussian graphical models suited to deal with paired data problems. To learn the two dependent graphs, together with their across-graph association structure, we implement a fused graphical lasso penalty. We carry out a comprehensive analysis of this approach, with special attention to the role played by some relevant submodel classes. In this way, we provide a broad set of tools for the application of Gaussian graphical models to paired data problems. These include results useful for the specification of penalty values in order to obtain a path of lasso solutions and an admm algorithm that solves the fused graphical lasso optimization problem. Finally, we carry out a simulation study to compare our method with the traditional graphical lasso, and present an application of our method to cancer genomics where it is of interest to compare cancer cells with a control sample from histologically normal tissues adjacent to the tumor. All the methods described in this article are implemented in the R package pdglasso available at https://***/savranciati/pdglasso.

关键词： admm algorithm Coloured Gaussian graphical model Conditional independence Fused lasso penalty Graphical lasso Symmetry

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Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2024年第1期42卷 26-35页

作者： Wang, Wu Zhu, Zhongyi Renmin Univ China Ctr Appl Stat Beijing Peoples R China Renmin Univ China Sch Stat Beijing Peoples R China Fudan Univ Sch Management Dept Stat Shanghai Peoples R China

In this article, we are interested in detecting latent group structures and significant covariates in a high-dimensional panel data model with both individual and time fixed effects. The slope coefficients of the model are assumed to be subject dependent, and there exist group structures where the slope coefficients are homogeneous within groups and heterogeneous between groups. We develop a penalized estimator for recovering the group structures and the sparsity patterns simultaneously. We propose a new algorithm to optimize the objective function. Furthermore, we propose a strategy to reduce the computational complexity by pruning the penalty terms in the objective function, which also improves the accuracy of group structure detection. The proposed estimator can recover the latent group structures and the sparsity patterns consistently in large samples. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated with a real dataset.

关键词： admm algorithm Group detection Heterogeneity Panel data Penalized regression

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Incorporating Graphical Structure of Predictors in Sparse Quantile Regression

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2021年第3期39卷 783-792页

作者： Wang, Zhanfeng Liu, Xianhui Tang, Wenlu Lin, Yuanyuan Univ Sci & Technol China Sch Management Int Inst Finance Hefei Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang Jiangxi Peoples R China Jiangxi Univ Finance & Econ Res Ctr Appl Stat Nanchang Jiangxi Peoples R China Chinese Univ Hong Kong Dept Stat Shatin Hong Kong Peoples R China

Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset.

关键词： admm algorithm Graphical structure Quantile regression Sparse regression

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Fused graphical lasso for brain networks with symmetries

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2021年第5期70卷 1299-1322页

作者： Ranciati, Saverio Roverato, Alberto Luati, Alessandra Univ Bologna Dept Stat Sci Bologna Italy Univ Padua Dept Stat Sci Padua Italy

Neuroimaging is the growing area of neuroscience devoted to produce data with the goal of capturing processes and dynamics of the human brain. We consider the problem of inferring the brain connectivity network from time-dependent functional magnetic resonance imaging (fMRI) scans. To this aim we propose the symmetric graphical lasso, a penalized likelihood method with a fused type penalty function that takes into explicit account the natural symmetrical structure of the brain. Symmetric graphical lasso allows one to learn simultaneously both the network structure and a set of symmetries across the two hemispheres. We implement an alternating directions method of multipliers algorithm to solve the corresponding convex optimization problem. Furthermore, we apply our methods to estimate the brain networks of two subjects, one healthy and one affected by mental disorder, and to compare them with respect to their symmetric structure. The method applies once the temporal dependence characterizing fMRI data have been accounted for and we compare the impact on the analysis of different detrending techniques on the estimated brain networks. Although we focus on brain networks, symmetric graphical lasso is a tool which can be more generally applied to learn multiple networks in a context of dependent samples.

关键词： admm algorithm fMRI data graphical model with symmetries time series undirected graphical models

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