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A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT

Chinese Journal of Aeronautics 2021年第7期34卷 157-169页

作者： Junlin LI Huaqing WANG Liuyang SONG College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing 100029China

sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time.This can effectively reduce the cost of signal storage,improve the efficiency of data transmission,and ultimately save the cost of equipment fault diagnosis in the aviation field.At present,the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms,which limits the adaptability of sparse decomposition.In this paper,a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform(TQWT)method to construct a self-adaptive complete dictionary.Finally,the sparse signal is obtained by the orthogonal matching pursuit(OMP)algorithm.The atoms obtained by this method are more flexible,and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals.Therefore,the sparse signal can better extract the fault characteristics.The simulation and experimental results show that the selfadaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries,such as discrete cosine transform(DCT),discrete Hartley transform(DHT)and discrete wavelet transform(DWT).In addition,the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals,and can more accurately extract the bearing fault feature frequency.

关键词： Complete dictionary Data transmission Fault diagnosis sparse matrices sparse signal Wavelet transform

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维普期刊数据库

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HIGH PERFORMANCE sparse SOLVER FOR UNSYMMETRICAL LINEAR EQUATIONS WITH OUT-OF-CORE STRATEGIES AND ITS APPLICATION ON MESHLESS METHODS

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Applied Mathematics and Mechanics(English Edition) 2006年第10期27卷 1339-1348页

作者：苑维然陈璞刘凯欣 LTCS & Department of Mechanics and Aerospace Engineering Peking UniversityBeijing 100871P.R.China LTCS & Department of Mechanics and Aerospace Engineering Peking UniversityBeijing 100871P.R.China Engineering Research InstitutePeking UniversityBeijing 100871P.R.China

A new direct method for solving unsymmetrical sparse linear systems（USLS） arising from meshless methods was introduced. Computation of certain meshless methods such as meshless local Petrov-Galerkin （MLPG） method need to solve large USLS. The proposed solution method for unsymmetrical case performs factorization processes symmetrically on the upper and lower triangular portion of matrix, which differs from previous work based on general unsymmetrical process, and attains higher performance. It is shown that the solution algorithm for USLS can be simply derived from the existing approaches for the symmetrical case. The new matrix factorization algorithm in our method can be implemented easily by modifying a standard JKI symmetrical matrix factorization code. Multi-blocked out-of-core strategies were also developed to expand the solution scale. The approach convincingly increases the speed of the solution process, which is demonstrated with the numerical tests.

关键词： sparse matrices linear equations meshless methods high performance computation

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Distributed sparse Signal Estimation in Sensor Networks Using H_∞-Consensus Filtering

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IEEE/CAA Journal of Automatica Sinica 2014年第2期1卷 149-154页

作者： Haiyang Yu Yisha Liu Wei Wang Research Center of Information and Control Dalian University of Technology School of Information Science and Technology Dalian Maritime University

This paper is concerned with the sparse signal recovery problem in sensor networks, and the main purpose is to design a filter for each sensor node to estimate a sparse signal sequence using the measurements distributed over the whole network. A so-called 1-regularized H_∞filter is established at first by introducing a pseudo-measurement equation, and the necessary and sufficient condition for existence of this filter is derived by means of Krein space Kalman filtering.By embedding a high-pass consensus filter into 1-regularized H∞filter in information form, a distributed filtering algorithm is developed, which ensures that all node filters can reach a consensus on the estimates of sparse signals asymptotically and satisfy the prescribed H∞performance constraint. Finally, a numerical example is provided to demonstrate effectiveness and applicability of the proposed method.

关键词： Kalman filters Sensors Estimation Mathematical model sparse matrices Covariance matrices

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PRECONDITIONING BLOCK LANCZOS ALGORITHM FOR SOLVING SYMMETRIC EIGENVALUE PROBLEMS

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Journal of Computational Mathematics 2000年第4期18卷 365-374页

作者： Hua Dai Peter Lancaster Nanjing University of Aeronautics and Astronautics Naming210018China University of Calgary CalgaryCanada

A preconditioned iterative method for computing a few eigenpairs of large sparse symmetric matrices is presented in this paper. The proposed method which combines the preconditioning techniques with the efficiency of block Lanczos algorithm is suitable for determination of the extreme eigenvalues as well as their multiplicities. The global convergence and the asymptotically quadratic convergence of the new method are also demonstrated. [ABSTRACT FROM AUTHOR]

关键词： eigenvalue eigenvector sparse matrices Lanczos method preconditioning

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Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 248-256页

作者： Zhen Zhang Dongbin Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Department of Electric Engineering College of Automation EngineeringQingdao University

In this paper,we propose a clique-based sparse reinforcement learning(RL) algorithm for solving cooperative tasks.The aim is to accelerate the learning speed of the original sparse RL algorithm and to make it applicable for tasks decomposed in a more general manner.First,a transition function is estimated and used to update the Q-value function,which greatly reduces the learning time.Second,it is more reasonable to divide agents into cliques,each of which is only responsible for a specific subtask.In this way,the global Q-value function is decomposed into the sum of several simpler local Q-value functions.Such decomposition is expressed by a factor graph and exploited by the general maxplus algorithm to obtain the greedy joint action.Experimental results show that the proposed approach outperforms others with better performance.

关键词： Sensors Games Learning (artificial intelligence) Approximation algorithms Algorithm design and analysis Heuristic algorithms sparse matrices

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Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

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Computational Visual Media 2015年第1期1卷 59-68页

作者： Yongxia Zhang Yi Liu Xuemei Li Caiming Zhang School of Computer Science and Technology Shandong University Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

关键词： multimedia computing noise cancellation signal denoising sparse matrices video signal processing video surveillance

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