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Multi-layer additive tensor decomposition of infrared video for titanium alloy tensile testing

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期124卷

作者： Zhang, Tao Liu, Jian Ai, Yibo Zhang, Weidong Univ Sci & Technol Natl Ctr Mat Serv Safety Beijing 100083 Peoples R China Univ Arizona Dept Syst & Ind Engn Tucson AZ 85712 USA

Infrared video (in mathematics terms, tensor) has been widely used in the tensile testing of metallic materials, such as titanium alloy and steel. The infrared video of the tensile testing process can effectively and efficiently determine the properties of metallic materials, e.g., Young's modulus, Poisson's ratio, yield strength, etc. The infrared video with structural properties, such as smoothness and sparsity, can be used to characterize the tensile testing process. To extract the features in the infrared video with structural properties, we propose a multi-layer additive tensor decomposition (MLATD) method based on regularization tensor regression for tensile testing. It decomposes a tensor into three classes of components: the multi-smooth layers (including background and foreground), the sparse layers (including between-tensor and in-tensor), and the noise layer. The scree plot is proposed to determine the number of multi-smooth layers, which is a downward curve of the difference between the smooth layers and the sparse layer. The alternating direction method of multipliers (admm) algorithm is proposed to solve the proposed method. The decomposition results of the simulation data and real-world case study revealed that the proposed method outperforms the existing state-of-the-art methods.

关键词： Tensor decomposition admm algorithm Multi-layer decomposition Infrared video Tensile testing

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A novel elastic net-based NGBMC(1,n) model with multi-objective optimization for nonlinear time series forecasting

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2021年 96卷 105696-105696页

作者： Yu, Lang Ma, Xin Wu, Wenqing Wang, Yong Zeng, Bo Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Chongqing Univ Coll Math & Stat Chongqing 401331 Peoples R China Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Peoples R China Southwest Petr Univ Sch Sci Chengdu 610500 Peoples R China Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China

Nonlinear grey Bernoulli multivariate model NGBMC (1, n) is known as a novel forecasting model for nonlinear time series with small samples. However, ill-posed problem would make it less efficient and even cause large errors. In order to improve its generality, a hybrid method combining Elastic Net and multi-objective optimization is introduced in this work. This method effectively solves the essential defect of the ill-posed problem of the NGBMC (1, n) model, making the NGBMC (1, n) model more stable, more reliable, and more interpretable. The parameter identification of the new model uses the alternating direction method of multipliers, and the nonlinear parameter of the model and the regularization parameter of Elastic Net regression are optimized by the multi-objective grey wolf optimizer (MOGWO). Eight numerical cases all show that the use of the Elastic Net regularization method and multi-objective optimization technology can significantly improve the prediction accuracy of the NGBMC (1, n) model for future data. In addition, the hybrid method combing the regularization and optimization strategies proposed in this paper is a general framework for the grey prediction models, which has a high potential in improving the other grey models. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Grey system Multi-objective optimization MOGWO Regularization Elastic net regression admm algorithm

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Distributed Dispatch and Profit Allocation for Parks Using Co-Operative Game Theory and the Generalized Nash Bargaining Approach

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ENERGIES 2024年第23期17卷 6143页

作者： Wang, Hanwen Li, Xiang Hu, Haojun Zhou, Yizhou Suqian Wanda Elect Power Ind Co Ltd Suqian 223800 Peoples R China Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China

To improve the regulatory capacity of distributed resources within the park and enhance the flexibility of market transactions, this paper introduces a distributed dispatch and profit allocation method grounded in cooperative game theory and the generalized Nash bargaining framework. Initially, models for individual park equipment are established. Subsequently, a distributed dispatch model is constructed, followed by the development of a profit allocation strategy based on contribution levels, using the generalized Nash bargaining method. The model is solved using the alternating direction method of multipliers. The results show that the proposed approach achieves fast convergence, optimizes resource sharing and mutual support within the park, lowers operational costs, ensures a fairer distribution of profits, and promotes increased cooperation among park entities.

关键词： park electricity market generalized Nash bargaining cooperative game theory admm algorithm

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Efficient Aperture Fill Time Correction for Wideband Sparse Array Using Improved Variable Fractional Delay Filters

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SENSORS 2024年第13期24卷 4327页

作者： Gu, Jie Xu, Min Zhou, Wenjing Shen, Mingwei Natl Key Lab Electromagnet Space Secur Chengdu 610036 Peoples R China Hohai Univ Coll Informat Sci & Engn Nanjing 210098 Peoples R China

To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficient matrix result in high system complexity. To alleviate the computational burden of solving VFD filter coefficients, a novel multi-regultion minimax (MRMM) model utilizing the sparse representation technique has been presented. The error function is constrained by the introduction of L2-norm and L1-norm regularizations within the minimax criterion. The L2-norm effectively resolves the problems of overfitting and non-unique solutions that arise in the sparse optimization of traditional minimax (MM) models. Meanwhile, the use of multiple L1-norms enables the optimal design of the smallest sub-filter number and order of the VFD filter. To solve the established nonconvex model, an improved sequential-alternating direction method of multipliers (S-admm) algorithm for filter coefficients is proposed, which utilizes sequential alternation to iteratively update multiple soft-thresholding problems. The experimental results show that the optimized VFD filter reduces system complexity significantly and corrects AFT effectively in a wideband sparse array.

关键词： wideband sparse array variable fractional delay (VFD) minimax criterion admm algorithm

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Smart coordinated multi-energy intra-scheduling inter-sharing and cost-saving redistribution for multiple microgrids

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SUSTAINABLE CITIES AND SOCIETY 2023年 97卷

作者： Lin, Xin (Maxwell) Ye, Shengxuan Wen, Qianyun Zhou, Weihua Huangfu, Jiangtao Xu, Yangbing Zhejiang Univ Sch Management Hangzhou 310058 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Hangzhou Humpback Whale Technol Co Ltd Hangzhou 311215 Peoples R China Linkoping Univ Dept Management & Engn SE-58183 Linkoping Sweden

Sustainable and clean multi-energy systems have been hybrid coordinated in microgrid (MG) to fulfill regional multi-energy users economically, but shortages and surpluses still exist. The growth of win-win energy sharing between MGs attracts attention to smart information center (SIC) concept in MGs' community, which facilitates sustainable solutions across three key objectives: lower carbon emission, sufficient utilization, and long-term cooperation. This paper designs a two-stage decision framework for day-ahead operations of SIC, where the first stage minimizes the total cost (i.e. operational cost and carbon emission cost) by the proposed multi-energy intra-scheduling and inter-sharing model (MIIM) and the second stage implements the smart cost-saving redistribution model (SCRM) to stimulate MGs in long-term joining of energy sharing. The extended alternating direction multipliers method algorithm (E-admm) is developed to protect the privacy of MGs' data while approximating the optimal solution of MIIM. The case study proves that a) demand response and energy sharing in MIIM result in cost savings of 2.01% for carbon emissions and 7.25% for total operations;b) renewable energy has been fully utilized and the total multi-energy loss has been avoided nearly 92.22% in MIIM;c) the asymmetric Nash bargaining approach fits the SCRM with incentive cooperation, whereby those who contribute more gain a higher share;d) the optimal gap of E-admm is less than 0.2% by iterated insensitive decision result from each MG, which computes in polynomial time when the scale of MG increases;e) the two-stage framework performs better with more heterogeneity of MGs. Furthermore, we provide valuable insights for guidance and models with worldwide generalization potential.

关键词： Multi -energy scheduling Multiple microgrids Energy sharing Smart information center admm algorithm Asymmetric Nash bargaining Sustainable energy system

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Tensor-guided learning for image denoising using anisotropic PDEs

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MACHINE VISION AND APPLICATIONS 2024年第3期35卷 48-48页

作者： Limami, Fakhr-eddine Hadri, Aissam Afraites, Lekbir Laghrib, Amine Sultan Moulay Slimane Univ EMI FST Beni Mellal Beni Mellal Morocco Ibnou Zohr Univ SIV Lab Agadir Morocco

In this article, we introduce an advanced approach for enhanced image denoising using an improved space-variant anisotropic Partial Differential Equation (PDE) framework. Leveraging Weickert-type operators, this method relies on two critical parameters: lambda\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document} and theta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta $$\end{document}, defining local image geometry and smoothing strength. We propose an automatic parameter estimation technique rooted in PDE-constrained optimization, incorporating supplementary information from the original clean image. By combining these components, our approach achieves superior image denoising, pushing the boundaries of image enhancement methods. We employed a modified Alternating Direction Method of Multipliers (admm) procedure for numerical optimization, demonstrating its efficacy through thorough assessments and affirming its superior performance compared to alternative denoising methods.

关键词： Inverse problems Parameters estimation admm algorithm Machine learning Weickert tensor

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COFadmm: A Computational Features Selection with Alternating Direction Method of Multipliers

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Procedia Computer Science 2014年 29卷 821-830页

作者： Mohammed El Anbari Sidra Alam Halima Bensmail Qatar Computing Research Center Carnegie Mellon University @ Qatar

Due to the explosion in size and complexity of Big Data, it is increasingly important to be able to solve problems with very large number of features. Classical feature selection procedures involves combinatorial optimization, with computational time increasing exponentially with the number of features. During the last decade, penalized regression has emerged as an attractive alternative for regularization and high dimensional feature selection problems. Alternating Direction Method of Multipliers (admm) optimization is suited for distributed convex optimization and distributed computing for big data. The purpose of this paper is to propose a broader algorithm COFadmm which combines the strength of convex penalized techniques in feature selection for big data and the power of the admm for optimization. We show that combining the admm algorithm with COFadmm can provide a path of solutions efficiently and quickly. COFadmm is easy to use, is available in C, Matlab upon request from the corresponding author.

关键词： features selection lasso least angle regression algorithm coordinate descent algorithm admm algorithm

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Reconstruction of time-varying small-world networks incorporating structural priors

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IFAC-PapersOnLine 2019年第3期52卷 49-54页

作者： Wenxin Zhang Guanxue Yang Lin Wang Department of Automation Shanghai Jiao Tong University Shanghai 200240 P.R. China School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China

When inferring the undergoing network structure, which describes the dynamic mutual influence among large scale variables, it is a challenge to take full advantage of structural prior information when it is available. In this paper, we focus on reconstruction of piecewise-constant time-varying small-world networks. Specifically, we propose an identification method incorporating structural properties as prior information, including the average degree of the network. On the one hand, we adjust the network sparsity by re-weighting l 1 norm according to the deviation of the estimated average degree, based on the assumption that the average degree is almost constant over time. On the other hand, for each node in the network, we encourage the existence of potential associated edges while discouraging non-existing edges based on predictions from the previous iteration. Finally, an adaptive LASSO algorithm is utilized to uncover the time-varying structures which performs better on small-world networks when comparing with the method without prior information.

关键词： Network reconstruction Time-varying network Small-world network Re-weighted LASSO admm algorithm

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Robust k-subspace discriminant clustering

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APPLIED SOFT COMPUTING 2019年 85卷 105858-000页

作者： Li, Chun-Na Shao, Yuan-Hai Guo, Yan-Ru Wang, Zhen Yang, Zhi-Min Hainan Univ Management Sch Haikou 570228 Hainan Peoples R China Zhejiang Univ Technol Zhijiang Coll Hangzhou 310024 Zhejiang Peoples R China Shanghai Univ Dept Math Shanghai 200444 Peoples R China Inner Monggolia Univ Sch Math Sci Hohhot 010021 Peoples R China

k-means, k-plane clustering (kPC) and q-flat are partition-based clustering methods that cluster samples around k centers, hyperplanes and affine subspaces, respectively. However, no discriminant or local information is considered. This paper proposes a robust k-subspace discriminant clustering (kSDC) method by introducing the discriminant and local information. kSDC constructs k subspaces with centers to proximate the samples in each subspace, and at the same time, maximizes the difference of the samples in between-cluster. By introducing the L1-norm discriminant and local information to each subspace, kSDC realizes robust dimensionality reduction and clustering, and its optimization problems can be solved through an effective alternating direction method of multipliers. Experimental results on benchmark data and image segmentation data show the effectiveness of our kSDC. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Clustering k-subspace clustering Robust clustering admm algorithm

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Subgroup analysis for the functional linear model

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2024年 231卷

作者： Sun, Yifan Liu, Ziyi Wang, Wu Renmin Univ China Ctr Appl Stat Sch Stat Beijing Peoples R China

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions.

关键词： admm algorithm B-spline basis Functional linear regression Minimax concave penalty Subgroup analysis

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