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.

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.

Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

Compaction correction is a key part of paleogeomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porositydepth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

Seismic Data Recovery with Curvelet Bivariate Shrinkage Function Based on Compressed Sensing

Recovery of under-sampled seismic data is a critical problem,in oil and gas exploration,therefore recovery algorithms with iterative shrinkage based on compressed sensing have been recently proposed. However most of these algorithms usually adopt a soft shrinkage function,which assumes that all of the sparse coefficients are independent of each other in curvelet or other domains,little attention has so far been devoted to the inter-dependencies of coefficients. In this paper,the dependencies of parent-child curvelet coefficients of seismic data are exploited by Bayesian estimation,moreover the new seismic data recovery algorithm via curvelet-based bivariate shrinkage function is proposed. First the respective parent-child curvelet coefficients joint distribution models of fully-sampled seismic data and noise signal caused by missing traces are established,then the bivariate shrinkage function according to the Bayesian maximum posterior probability estimation is obtained,finally the Landweber iterative shrinkage algorithm is used in the recovery process.When compared with existing recovery algorithms,it is proved that the proposed algorithm can obtain higher PSNR performance,and maintains the texture details better in events of seismic data

A new approach for high fidelity seismic data recovery by fractal interpolation

Recovering accurate data is important for both earthquake and exploration seismology studies when data are sparsely sampled or partially missing. We present a method that allows for precise and accurate recovery of seismic data using a localized fractal recovery method. This method requires that the data are self- similar on local and global spatial scales. We present examples that show that the intrinsic structure associated with seismic data can be easily and accurately recovered by using this approach. This result, in turn, indicates that seismic data are indeed self-similar on local and global scales. This method is applicable not only for seismic studies, but also for any field studies that require accurate recovery of data from sparsely sampled datasets with partially missing data. Our ability to recover the missing data with high fidelity and accuracy will qualitatively improve the images of seismic tomography.

RGBD Salient Object Detection by Structured Low-Rank Matrix Recovery and Laplacian Constraint

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.

Recovery of Corrupted Low-Rank Tensors

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.

The Impact of Business Expertise on Information System Data and Analytics Resilience (ISDAR) for Disaster Recovery and Business Continuity: An Exploratory Study

Disaster recovery (DR) and business continuity (BC) have been important areas of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business continuity, a firm must be able to recover from both man-made and natural disasters. This is especially true for maintaining and recovering the lifeline of the organization and its data. Although the literature has discussed the importance of disaster recovery and business continuity, there is not much known about how Information System Data Analytics Resilience (ISDAR) and the organization’s ability to recover from lost information. In this research, we take a step in this direction and analyze the relationship of IS personnel expertise on ISDAR and investigate Information System (IS) personnel understanding of the firm’s competitive priorities, IS Personnel understanding of business policies and objectives, IS personnel’s ability to solve business problems, IS personnel initiatives in changing business processes and their determination and attentiveness to focus on achieving confident leadership in data and analytics resilience. We collected data through a survey of IS and business managers from 302 participants. Our results show that there is evidence to support our hypothesis and that there may indeed be a relationship between these variables.

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images,this work proposes a reversible data hiding in encrypted image based on iterative recovery.The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR(XOR),which improves the security of encrypted images.And then,a pixel-typemark generation method based on block-compression is designed to reduce the extra burden of key management and transfer.At last,an iterative recovery strategy is proposed to optimize the marked decrypted image,which allows the original image to be obtained only using the encryption key.The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image.Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones,and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.

Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

基于低秩张量补全的非侵入式负荷监测缺失数据修复方法

非侵入式负荷监测技术(non-intrusive load monitoring,NILM)作为实现智能电网用户侧细粒度感知的重要手段,有助于实现需求响应、提高“源-网-荷”互动效率和优化用能,助力实现“30·60目标”。高质量的量测信息是数据驱动型NILM的基础,但由于数据采集装置故障、通道拥塞以及延时等都会导致数据缺失,尤其是严重的连续性缺失,由此造成非侵入式负荷监测与分解的精度下降,影响用户画像、需求响应等高级应用。因此,针对该问题,提出了一种基于CP分解的正则化低秩张量补全的量测数据缺失修复方法。算法突破传统单维数据处理局限,对NILM多维量测数据构建了三阶观测张量,从而利用数据内部时序关联性和参量维度间电气关联性进行正则化低秩张量补全。并针对每次核范数计算过程中奇异值分解计算量过大问题,采用基于CP因子矩阵分解的核范数计算降低计算量,减少计算时长,并证明了变换的等效性。最后基于NILM公开数据集iAWE进行了实验,实验结果表明所提出的方法可以提高数据修复精度,在高缺失率和连续缺失情况下仍能有较好地补全效果,并且通过非侵入式负荷分解实验证明其可有效提高分解精度,对智能电网提升细粒度感知能力具有良好的实际意义。

用于数据采集器测试的时间脉冲插值分析方法

地震数据采集器时间服务精度是评价设备性能的重要指标,一般使用标准时间源输出的时间脉冲信号采样数据进行分析计算,但受仪器采样率限制,时间偏差测试分辨率不高。设计了基于一阶差分和直方图分析的脉冲信号上升沿识别、高低电位分析和时间偏差分析算法,研究使用非线性插值分析方法对地震数据采集器采集的标准时间脉冲信号进行波形升采样恢复,并基于所提算法进行实验分析和讨论,在提高地震数据采集器时间偏差测试分辨率的同时,规范了测试数据处理方法和流程。

黑龙江省洪涝灾害应急管理研究与优化路径

在全球气候变暖背景下,黑龙江省所面临的极端自然灾害趋强趋重趋频,也呈现出风险隐患日益突出、应急管理基础薄弱、防控难度不断加大等特点。本文围绕黑龙江省“8·3”特大暴雨灾害的应急管理展开研究,运用具体案例的实证分析的方法,对此次特大暴雨的灾情概况进行了汇总,并从应急管理的全过程出发,对此次灾害进行了问题剖析。通过分析,明确指出了当前黑龙江省存在着城市韧性不足、灾害应急准备欠佳、城市应急响应效率低下和灾后恢复措施不足等问题,提出了在自然灾害情况下完善基础设施、提升预警能力、加大数字技术应用和灾后重建等方面的优化路径。

基于Oracle Data Guard构建医院信息系统的容灾备份方案

目的:为解决医院信息系统异地容灾备份问题,提出方案。方法:通过Oracle Data Guard,采用最大性能模式,构建容灾备份系统。结果:实践表明,创建的物理备用数据库能满足系统设计要求。结论:通过对比常用的容灾备份方案,表明采用Oracle Data Guard技术实现数据备份在资金、性能上的优越性。

应用于0.5~12.5Gb/s CMOS时钟数据恢复电路的相位插值器设计

文中采用28 nm CMOS工艺,设计了一款应用于半速率CDR电路中的相位插值器。该插值器采用锁相环提供的正交参考时钟,通过编码控制的DAC电流源调整电流权重控制输出相位,一个周期内可实现128次相位插值。为了提高接收器在多通道、多协议的性能,提出了输入时钟整形电路对斜率进行调节,提高了线性度。仿真结果表明,插值器在6.25 GHz工作频率下线性度良好,微分非线性(DNL)最大不超过1 LSB,积分非线性(INL)最大不超过2 LSB,实现了高线性度、宽频率范围的设计目标。

基于缺失数据的交通速度预测算法

交通速度预测是智能交通系统的基础,可以缓解交通拥堵,节约公共资源,提高人们的生活质量。在真实情况下,采集到的交通速度数据通常存在缺失,而现有研究成果大多数只考虑了数据相对完整的场景。文章主要针对缺失场景下的交通速度数据进行研究,捕捉其中的时空相关性,并对未来交通速度进行预测。为了充分利用到交通数据的时空特征,提出了一种新的基于深度学习的交通速度预测模型。首先,提出了“还原-预测”算法,先使用自监督学习方法让模型还原缺失数据,再对交通速度进行预测;其次,引入了对比学习的方法,使得速度时间序列的特征表示更鲁棒;最后,模拟了不同数据缺失率的场景,通过实验验证了所提方法在各种缺失率下的预测准确率都优于现有方法,并设计了实验对对比学习方法和不同的还原算法进行分析,证明了所提方法的有效性。

基于深度学习的浮选回收率预测建模研究

针对现有浮选回收率预测模型拟合度不高、预测误差大等问题,以某铜矿实际工况数据为基础,利用箱图和滤波算法对数据进行预处理,采用传统机器学习算法(DT、SVR和RF算法)和深度学习算法(DNN和CNN算法)构建相应浮选回收率预测模型。对5种回收率预测模型的拟合效果、预测效果进行了对比分析,并采用现场数据进行验证。结果表明:传统机器学习算法模型中RF预测精度最佳,±2%误差区域命中率为80.1%,±4%误差区域命中率为93.0%;深度学习模型预测效果均优于传统机器学习算法模型,DNN和CNN预测模型的R~2分别为0.854、0.907,±2%误差区域命中率分别为91.6%、90.6%,±4%误差区域命中率分别为96.6%、98.1%。CNN模型略优于DNN模型,但训练耗时较长,深度学习算法模型中首选DNN模型。研究结果可为浮选回收率实时预测及浮选过程协同优化提供技术支持。

基于航空重力梯度数据重建高分辨率局部重力场的径向基函数方法

基于径向基函数技术,研究了利用航空重力梯度数据重建局部重力场模型的方法,建立了泊松小波径向基函数和航空重力梯度张量之间的解析关系.以频率域变换和等效源方法为例讨论了不同梯度分量转换方法对重力场建模的影响,分析了基函数埋深深度对建模的影响.以澳大利亚珀斯Kauring试验场为例,采用实测航空重力梯度垂直分量构建了高分辨率重力场模型.结果表明,局部地形扰动对建模的影响较大,即使在平原或丘陵区域,也要考虑其影响.此外,不同梯度分量转换方法对于区域重力异常恢复的影响较小.再者,基函数埋深深度对建模影响较大;相比于采用重力数据建模而言,基于航空重力梯度数据求解时,基函数的适宜埋深深度更浅.联合航空重力梯度数据求解能显著提高重力场模型在高频和甚高频波段的分辨率和精度.与实测航空重力数据对比表明,利用航空重力梯度数据可构建分辨率约为0.15 km、精度优于1 mGal的重力场模型.