A Perturbation Analysis of Low-Rank Matrix Recovery by Schatten p-Minimization

A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.

Fitting evolutionary process of matrix protein 2 family from influenza A virus using analytical solution of differential equation

The evolution of protein family is a process along the time course, thus any mathematical methods that can describe a process over time could be possible to describe an evolutionary process. In our previously concept-initiated study, we attempted to use the differential equation to describe the evolution of hemagglutinins from influenza A viruses, and to discuss various issues related to the building of differential equation. In this study, we attempted not only to use the differential equation to describe the evolution of matrix protein 2 family from influenza A virus, but also to use the analytical solution to fit its evolutionary process. The results showed that the fitting was possible and workable. The fitted model parameters provided a way to further determine the evolutionary dynamics and kinetics, a way to more precisely predict the time of occurrence of mutation, and a way to figure out the interaction between protein family and its environment.

Proximity point algorithm for low-rank matrix recovery from sparse noise corrupted data

The method of recovering a low-rank matrix with an unknown fraction whose entries are arbitrarily corrupted is known as the robust principal component analysis （RPCA）. This RPCA problem, under some conditions, can be exactly solved via convex optimization by minimizing a combination of the nuclear norm and the 11 norm. In this paper, an algorithm based on the Douglas-Rachford splitting method is proposed for solving the RPCA problem. First, the convex optimization problem is solved by canceling the constraint of the variables, and ~hen the proximity operators of the objective function are computed alternately. The new algorithm can exactly recover the low-rank and sparse components simultaneously, and it is proved to be convergent. Numerical simulations demonstrate the practical utility of the proposed algorithm.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Electrical Data Matrix Decomposition in Smart Grid

As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.

Low-Rank Positive Approximants of Symmetric Matrices

Given a symmetric matrix X, we consider the problem of finding a low-rank positive approximant of X. That is, a symmetric positive semidefinite matrix, S, whose rank is smaller than a given positive integer, , which is nearest to X in a certain matrix norm. The problem is first solved with regard to four common norms: The Frobenius norm, the Schatten p-norm, the trace norm, and the spectral norm. Then the solution is extended to any unitarily invariant matrix norm. The proof is based on a subtle combination of Ky Fan dominance theorem, a modified pinching principle, and Mirsky minimum-norm theorem.

一种非平滑相干干扰鲁棒的自适应波束形成器

针对文献报道的鲁棒自适应波束形成(robust adaptive beamforming,RAB)算法,分析了在存在相干干扰时其性能严重下降甚至失效的原因:通过将接收信号协方差矩阵分解为阵列流形矩阵左和共轭右乘一个矩阵P的描述形式,在存在相干干扰时,P为非对角阵,其非对角元素表征了信号与干扰间的互相关,该成分造成了RAB算法的失效;另表明:快拍数有限可视为特殊的相干干扰情况。为此,提出了一种构建P为对角阵的协方差矩阵拟合方法;并据拟合的协方差矩阵,给出了对非平滑相干干扰RAB方法。仿真验证了分析的有效性,所提方法在相干干扰时仍能实现非相干干扰时的性能,且收敛速度优于报道方法。

高阶S_(21)拟合策略在耦合矩阵提取方法中的运用

对测试或仿真得到的采样数据进行有理函数拟合是滤波器耦合矩阵提取方法的重要一步,针对拟合数据与采样数据在幅度值较小的传输零点附近偏差较大的问题,提出一种高阶传输系数(S_(21))拟合策略。该策略通过对采样的传输系数使用具有N阶(N为滤波器阶数)分子多项式的有理函数进行拟合以提高拟合准确度,从而准确定位传输零点。然后从N个拟合得到的传输零点中选取N_(z)个(N_(z)为实际滤波器的传输零点个数)有效传输零点重构传输系数的分子多项式,以保证提取的耦合矩阵的传输零点个数与实际滤波器相同。为验证效果,使用具有三个传输零点的九阶同轴滤波器对传统柯西方法、应用高阶传输系数拟合策略的柯西方法与基于模型的矢量拟合方法(MVF)进行试验,结果显示应用了该策略的柯西方法相较于传统柯西方法与MVF可以提高传输零点的拟合准确度。由于柯西方法对噪声的健壮性不高,最后结合柯西方法与MVF的步骤,提出一种通过矢量拟合定位S参数零点的耦合矩阵提取方法,该方法相较于MVF可以更加精确地拟合上S参数的零点,同时相较于柯西方法对噪声的抗性更好。

基于协方差矩阵拟合的小平台共形阵稳健波束形成技术

针对无人水下航行器等小尺度平台机动且阵列孔径有限条件下的稳健探测需求,依据应用于小平台U形共形平面阵设计多种波束形成算法。分析快拍数、输入信噪比及导向向量误差等对波束形成方法稳健性的影响。虽然最小方差无畸变波束形成方法具有高阵增益的优点,但其在存在基阵导向向量误差和协方差矩阵估计误差的条件下性能下降较为严重,基于此提出了协方差矩阵拟合波束形成方法和双约束稳健最小方差无畸变响应波束形成方法,并通过数值仿真分析与其他多种波束形成方法对比验证稳健波束形成方法应用于水下小平台共形阵的稳健性。最后在考虑阵元位置误差时通过消声水池实验对多种波束形成方法的实际性能进行进一步对比验证。

Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-FreeWi-Fi Sensing

Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.

基于雷视融合轨迹匹配的高速公路车辆轨迹跟踪方法

针对高速公路车辆跟踪过程中,在进行雷达与视频数据融合时两类传感器之间探测目标匹配的难点问题,提出一种基于目标轨迹相似度匹配的高速公路车辆跟踪方法。首先,采用投影变换将雷达数据转化到视频数据所在的维度;其次,通过提出的曲线拟合算法将离散的轨迹点插值成连续的轨迹曲线;最后,将雷达探测目标投影到图像上的轨迹曲线与视频检测目标轨迹曲线进行相似度计算得到相似度矩阵,并通过对相似度矩阵进行筛选得到雷达探测目标和视频检测目标的匹配关系。采用高速公路真实场景下采集的车辆数据开展对比实验,结果表明在高速公路场景下的平均目标匹配成功率为94.71%,相比其他同类方法的平均匹配成功率提高3.01%和3.69%。所提出的方法能有效过滤伪目标,更适合在高速公路场景下的车辆跟踪中使用。

LOW-RANK MATRIX COMPLETION WITH POISSON OBSERVATIONS VIA NUCLEAR NORM AND TOTAL VARIATION CONSTRAINTS

In this paper,we study the low-rank matrix completion problem with Poisson observations,where only partial entries are available and the observations are in the presence of Poisson noise.We propose a novel model composed of the Kullback-Leibler(KL)divergence by using the maximum likelihood estimation of Poisson noise,and total variation(TV)and nuclear norm constraints.Here the nuclear norm and TV constraints are utilized to explore the approximate low-rankness and piecewise smoothness of the underlying matrix,respectively.The advantage of these two constraints in the proposed model is that the low-rankness and piecewise smoothness of the underlying matrix can be exploited simultaneously,and they can be regularized for many real-world image data.An upper error bound of the estimator of the proposed model is established with high probability,which is not larger than that of only TV or nuclear norm constraint.To the best of our knowledge,this is the first work to utilize both low-rank and TV constraints with theoretical error bounds for matrix completion under Poisson observations.Extensive numerical examples on both synthetic data and real-world images are reported to corroborate the superiority of the proposed approach.

Low-rank matrix recovery with total generalized variation for defending adversarial examples

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image structure.Taking advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural networks.However,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its over-smoothing.In our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery model.In the proposed model,TGV is used to better reconstruct texture information without over-smoothing.The reweighted nuclear norm and Li-norm can enhance the global structure information.Thus,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the image.To solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of multipliers.Experimental results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

基于部件组合的连衣裙款式图快速生成研究

款式图作为真实服装的一种平面表达方式,在服装生产过程中起着重要的作用。为了简化服装款式设计过程,提高款式设计的效率与服装信息表达的准确率,并针对不同体型生成个性化的款式图,以连衣裙为对象,基于部件数字化对款式图快速生成进行了研究。首先,采用薄板样条插值算法以使服装款式图拟合不同人体体型,使衣身能够快速达到满足特定体型的尺寸;其次,通过基于关键点的矩阵变形实现部件之间的自适应组合;最后,选用参数驱动和交互式修正关键点2种方法实现对服装款式细节的调整。以连腰鱼尾裙为例,阐述并实现了连衣裙款式图快速生成过程,验证了基于部件组合的款式图快速生成的可行性,为个性化定制和智能化款式设计提供理论支持。

基于Hessian矩阵的改进EDlines输电线识别算法

随着电力网络的飞速发展,采用无人机搭载高清摄像头来进行输电线巡检已成常态。为提高巡检的实时性和准确性,本研究提出了一种基于Hessian矩阵的改进EDlines输电线识别算法。首先,通过伽马变换对输电线图像进行预处理,利用Hessian矩阵特征值和特征向量以求取像素点主方向和主曲率,并获得输电线主体轮廓,从而摒弃了传统方法中梯度计算锚点和像素方向的繁琐步骤。接着,在主体轮廓基础上连接锚点以得到潜在直线线段像素链,并运用随机抽样一致性(RANSAC)算法来进行线段拟合。最后,根据直线间的距离和角度,迭代拟合以得到最终的输电线。实验结果表明,该方法能应对多种复杂环境下的输电线识别任务,抗干扰能力强,误检率显著降低,为高空输电线巡检提供了可靠的技术支持,具有重要的工程应用价值。

A subspace fitting algorithm of acoustic vector sensor array and corresponding matrix pre-filter design

In order to ease the pass-band response distortion of the matrix pre-filter,a simple approach for designing matrix spatial filter is proposed,which minimizes the sum of the k maximal distortion norm（k is the number of the constraint points）within the pass-band,while constraining the filter response within the stop-band.Considering the costly amount of calculation of the high-resolution methods,an algorithm with small amount of calculation based on matrix pre-filtering and subspace fitting using acoustic vector array（MF-VSSF）is proposed.Through joint processing of signal subspace of both pressure and particle velocity,the pre-filtering matrix and the signal subspace is decreased to M-dimensional（M is the number of array-element）,hence reduces the time-consumption of the matrix pre-filter design and DOA searching.Simulation results show that,the method offers the same performance as MUSIC with pre-filtering,but has much lesser amount of calculation.Moreover,the designed prefilter can efficiently suppress the interference in the stop-band and improve the estimation and resolution performance of successive DOA estimators.

云服务器虚拟机通信串口数据安全监控仿真

云服务器虚拟机通信串口数据数量巨大,包含的数据类型非常多,其中不乏风险数据。由于通信串口数据包含大量的冗余信息和噪声,有效提取与安全性直接相关的特征变得尤为困难,导致对其安全性监控精度降低。因此,提出基于遗传算法与径向基函数融合算法(Genetic Algorithm Radial Basis Function,GA-RBF)的云服务器虚拟机通信串口数据安全性监控方法。在对通信串口数据完成采集与预处理后,搭建自编码器,提取数据中包含的安全要素,利用灰色关联度分析法筛选出其中的安全性因子。引入GA-RBF算法,构建通信串口数据安全性监控训练模型,通过计算精度训练函数、复杂度函数和收敛速度函数后,得到适应度函数,选取最优适应度,完成对通信串口数据安全性的精准监控。实验结果表明,所提方法在通信串口数据安全性监控中表现出色,达到了最高监控精度,且结果与实际值相符,有效提升了监控效能。

采用非接触设备进行动态包络线测试研究

为了高速车辆运行稳定,确保乘客人身安全,提出一种基于采用非接触设备的动态包络线测试研究。通过计算车身横向偏斜系数,得出横截面与相近轴线距离,结合车辆的动态包络线与结构参数结果,修正车辆极限,求得各控制点的偏移量。利用单目视觉摄像机将被测空间点投影到像平面,变换轨道中心线坐标系与摄像机像素坐标,完成动态包络线测试。实验证明,所提方法在直线与曲线轨道都能够准确测试动态包络线,保证工作人员更好地控制车辆速度,加强行驶安全,减少车身偏移。

一种新的相干信源DOA估计算法:加权空间平滑协方差矩阵的Toeplitz矩阵拟合

文献 [1]提出的最优加权空间平滑技术可以使相干源存在时的信源协方差矩阵恢复为对角阵 .由于文献 [1]中导出的最优权矩阵是空间信源方位的函数矩阵 ,本文利用最优加权空间平滑后阵列协方差矩阵的Toeplitz性 ,构造了一个全新的优化拟合的代价函数 ,并基于此提出了一种相干源方位估计的新算法 .与文献 [1]不同 ,算法的实现不需要方位估计的先验知识和协方差矩阵的去噪预处理 .分辨性能的蒙特卡罗仿真实验表明 ,新算法对空间相干信源的分辨性能优于常规的空间平滑算法和最大似然算法 ,在小阵列和信源空间间隔较近时 ,算法的优越性尤为突出 .

动态调整惯性权重的粒子群优化算法

针对高维复杂优化问题,提出一种改进适应度函数和动态调整惯性权重的粒子群优化算法。首先考虑了搜索点的函数值及其变化率,并将该信息加入适应度函数。利用维惯性权重矩阵自适应动态调整惯性权重,较好地平衡了算法的全局探索和局部开发,并分析了惯性权重随种群多样性的变化关系。在算法后期计算每一维的收敛度,以一定的概率对收敛度最小的维进行变异,以加快算法的收敛速度。对高维测试函数的实验表明,算法提高了全局搜索能力。