An efficient tensor completion method via truncated nuclear norm

Tensor completion aims to recover missing entries from partial observations for multi-dimensional data. Traditional tensor completion algorithms process the dimensional data by unfolding the tensor into matrices, which breaks the inherent correlation and dependencies in multiple channels and lead to critical information loss. In this paper, we propose a novel tensor completion model for visual multi-dimensional data completion under the tensor singular value decomposition (t-SVD) framework. In the proposed method, tensor is treated as a whole and a truncated nuclear norm regularization is employed to exploit the structural properties in a tensor and hidden information existing among the adjacent channels of a tensor. Besides, we introduce a weighted tensor to adjust the residual error of each frontal slices in consideration of their different recovery statistics. It does enhance the sparsity of all unfoldings of the tensor and accelerates the convergence of the proposed method. Experimental results on various visual datasets demonstrate the promising performance of the proposed method in comparison with the state-of-the-art tensor completion methods.     

一种基于动态角色标识和张量分解的推荐模型

社交网络推荐中,通常未依据用户兴趣变化进行用户角色动态标注,会造成推荐预测误差,并且用户评分数据稀疏造成评分预测不准确.根据以上问题,本文提出一种基于动态角色标识和张量分解的推荐模型.首先,针对用户角色无差别标识问题,引入信息熵指标度量用户兴趣多样性,对目标用户进行角色定量标识.其次,考虑到用户兴趣漂移现象,提出基于时间窗的动态角色标识方式,解决静态角色标识产生的个体评分数据无偏好差异问题,实现用户评分数据层次化处理.最后,为提高评分预测准确率,通过引入张量分解在数据维度转换和数据压缩的特性,构建基于"用户-项目-角色"张量分解的评分预测模型.同时,在张量分解的过程中,通过对数据缺失值处理,提高评分预测精度,生成目标用户推荐列表.实验表明,该方法缓解了用户无角色差异形成的预测误差问题,并能够有效改善评分数据稀疏情况下传统方法推荐精度不足的问题,提高推荐效率.     

一种应用于高阶数据修复的非负稀疏Tucker分解算法

针对传统的张量填充算法对于不满足低秩条件的张量填充效果难以保证,本文采用张量分 解的方法实现缺失张 量的修复,即对传统Tucker分解算法进行改造,在其目标函数中增加对核心张量和系数矩 阵的非负性以及 核心张量的稀疏性约束,再利用凸优化理论中的交替近端梯度算法(APGM)对目标函数进 行迭代寻优, 在分解的同时实现缺失数据点的填充。医学图像、彩色图像和视频图像的修复结果表明,本 文 算法能够对高阶非负张量的缺失实现较好地修复,修复的视觉效果和技术指标都优于当前主 流算法。     

基于车联网大数据分析的实时路况检测系统

为解决城市交通拥堵问题,给人们提供优质的出行体验,文中提出了基于车联网大数据分析的实时路况检测系统。使用GPS技术对行驶的车辆进行数据采集,通过数据清洗和数据修复得到样本集合,利用改进模糊C均值聚类算法对样本数据进行分析,得出各路段的平均车速,进而得到相应路段的交通状态。测试结果表明,该系统能够准确得获取道路上行驶车辆的交通数据,识别出当前路段的交通状态,从而证明了该系统设计的合理性和正确性。     

The nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data

The conventional data interpolation methods based on sparse representation usually assume that the signal is sparse under the overcomplete dictionary. Specially, they must confirm the dimensions of dictionary and the signal sparse level in advance. However, it is hard to know them if the signal is complicated or dynamically changing. In this paper, we proposed a nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data, which is the combination of sparse representation and data interpolation. This method need not preset sparse degrees and dictionary dimensions, and our dictionary atoms are drawn from a multivariate normal distribution. In this case, the dictionary size will be learned adaptively by the nonparametric Bayesian method. In addition, we implement the Dirichlet process to exploit the spatial similarity of the sensing data in WSNs, thus to improve the interpolation accuracy. The interpolation model parameters, the optimal dictionary and sparse coefficients, can be inferred by the means of Gibbs sampling. The missing data will be estimated commendably through the derived parameters. The experimental results show that the data interpolation method we proposed outperforms the conventional methods in terms of interpolation accuracy and robustness.     

基于快速张量分解的波束空间MIMO雷达二维DOA估计算法

传统MIMO雷达由于采用全向发射模式导致目标增益损失严重,致使DOA估计算法性能较差.因此,本文提出基于波束空间MIMO雷达的张量模型和快速张量分解的二维DOA估计算法.波束空间MIMO雷达能够通过发射波束成形技术将发射能量集中到指定空域,弥补传统MIMO雷达的发射增益损失.通过高阶张量模型应用MIMO雷达多脉冲接收数...     

Image completion with approximate convex hull tensor decomposition

Many image completion methods are based on a low-rank approximation of the underlying image using matrix or tensor decomposition models. In this study, we assume that the image to be completed is represented by a multi-way array and can be approximated by a conical hull of subtensors in the observation space. If an observed tensor is near-separable along at least one mode, the extreme rays, represented by the selected subtensors, can be found by analyzing the corresponding convex hull. Following this assumption, we propose a geometric algorithm to address a low-rank image completion problem. The extreme rays are extracted with a segmented convex-hull algorithm that is suitable for performing noise-resistant non-negative tensor factorization. The coefficients of a conical combination of such rays are estimated using Douglas–Rachford splitting combined with the rank-two update least-squares algorithm. The proposed algorithm was applied to incomplete RGB images and a hyperspectral 3D array with a large number of randomly missing entries. Experiments confirm its good performance with respect to other well-known image completion methods.     

大数据分析技术的海量网络流量建模与预测分析

为了有效避免网络拥堵,文中提出基于大数据分析技术的海量网络流量建模与预测分析方法,准确预测网络流量数据.首先利用经验模态分解处理网络流量数据,获取模式函数分量,分析网络流量的短相关性,然后通过确定数据平稳性、计算平稳时间网络流量序列的自相关系数和偏自相关系数,建立网络流量的预测模型,最后进行了网络流量的仿真实验,实验结...     

NetCube: a comprehensive network traffic analysis model based on multidimensional OLAP data cube

Network traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. The NetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposed NetCube traffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.     

基于反射率统计模型填充MODIS AOD中国陆地缺值区域

针对MODIS气溶胶产品受算法局限性、厚云层造成缺值问题,提出基于反射率统计模型的普通克里金-自然邻近插值方法,利用6S辐射传输模型模拟MODIS蓝光波段表观反射率随地表反射率、气溶胶光学厚度(aerosol optical depth, AOD)的响应变化,建立反射率与AOD的统计模型。采用2017年11月的中国区域气溶胶产品MOD04_L2、反射率数据做实验,并利用时空匹配的AERONET地基站数据做交叉验证,结果表明：利用该方法填充MOD04_L2中国缺值区域精度较好,60%以上的插补结果处于期望误差界限内,不仅能有效解决低反射率地区AOD有效值缺失问题,而且不受气溶胶类型假设不当影响。     

5G系统中基于解调参考信号的信道估计方法

5G系统使用解调参考信号(Demodulation Reference Signal,DMRS)用于下行信道估计,工程中通常采用插值法得到数据位置信道响应,考虑实际信道中噪声的影响,频域采用线性最小均方误差(Linear Minimum Mean Squared Error,LMMSE)插值算法.针对LMMSE需要获取...     

基于张量分解的双基地MIMO 雷达目标数估计

针对双基地多输入多输出(MIMO)雷达目标数估计问题,提出一种基于三阶张量分解的目标数估计算法。该算法首先将匹配滤波输出转化为三阶张量;然后,从张量的三个维度分别计算特征值,并通过迭代方法估计全局特征值;最后,利用全局特征值估计目标数。与传统基于矩阵分解算法相比,该算法充分利用了回波数据的多维结构特征,可有效改善特征值的估计精度,从而提高目标数的正确估计概率。仿真结果证明了所提算法的有效性和优越性。     

基于张量四元数极化平滑的极化-DOA估计

为了解决相干信号的极化平滑算法在小快拍数和低信噪比条件下估计性能较差的问题,结合四元数的正交特性和协方差张量方法,提出了一种基于张量四元数的极化平滑多重信号分类（Multiple Signal Classification,MUSIC）解相干算法。首先,为了充分利用接收数据样本中的多维结构信息,建立了由张量四元数表示的柱面共形阵列极化平滑信号模型;其次,将平滑后的张量协方差矩阵通过高阶奇异值分解得到信号子空间;最后,通过极化秩亏MUSIC算法对入射相干信号分别进行二维波达方向（Direction of Arrival,DOA）估计和极化参数估计。仿真结果表明,该算法在小快拍数和低信噪比条件下具有更高的估计精度和分辨能力。     

基于改进的集成经验模态分解的海杂波去噪

针对海杂波信号因混有噪声而难以提取的特点,提出基于改进的集成经验模态分解(MEEMD)的海杂波去噪方法。文中提出的MEEMD 在补充的集成经验模态分解(CEEMD)的基础上,利用排列熵和Savitzky-Golay 滤波对CEEMD 分解后的固有模态函数进行处理,最后在经验模态分解分解重构后得到削噪后的信号。以IPIX 雷达实测得到的海杂波数据进行仿真实验,结合最小二乘支持向量机建立混沌序列的单步预测模型,从预测误差中检测淹没在海杂波背景中的微弱信号,并用均方根误差判断去噪效果。仿真结果表明,文中所提出的MEEMD 算法对模式混淆有很好的抑制效果,去噪后得到的均方根误差为0. 000 847,比去噪前的均方根误差0. 012 2 降低了两个数量级。     

Joint recommendation algorithm based on tensor completion and user preference

Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average.     

车联网中基于贝叶斯决策的垂直切换方法研究

车辆需要在车联网的异构无线网络环境下进行垂直切换,针对垂直切换技术普遍不能支持 WAVE, WiMAX和3G cellular间的垂直切换这一问题,提出了一种基于贝叶斯决策的垂直切换算法。首先,根据接入网络的信号强度、传输速率、误码率和网络阻塞率以及车辆终端的速度和运动趋势建立多条件相关的切换概率分布,计算出切换先验概率;然后通过贝叶斯决策算法计算后验概率并进行决策分类,从而选取最优网络接入。仿真实验结果表明,该算法不仅有效地实现WAVE,WiMAX和3G cellular无线接入技术之间的垂直切换,而且避免了乒乓效应,保证了网络及时更新。     

An efficient modeling and dimensioning approach for integrated streaming and elastic traffic

Recent studies show that both data traffic and real-time traffic grow very fast in wired and wireless networks. To provide better performance guarantee, these applications need efficient network modeling and planning. In this paper, the problem where the total bandwidth of a link is shared by streaming traffic (real time traffic such as voice or video etc.) and elastic traffic (such as data) is studied. Integrating streaming traffic and elastic traffic presents a unique dimensioning problem. This paper considers dimensioning a link to satisfy both quality of service (QoS) requirements for streaming traffic, such as loss probability, and elastic traffic, such as mean waiting (delay) time. The Erlang loss model is applied to streaming traffic and a bursty traffic model is applied to the elastic traffic. Efficient dimensioning algorithms based on classical Markovian models and time-scale decomposition are then proposed. Numerical results show that the proposed methods have good accuracy.     

Blind signal separation for coprime planar arrays: An improved coupled trilinear decomposition method

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.     

基于四阶累积张量方法的欠定盲源信号分离

针对瞬时欠定盲源信号分离问题,提出一种四阶累积张量分解算法.首先构建观察信号四阶累积协方差,依据源信号具有相互独立且均值为零的性质,对累积协方差化简并扩展到张量域,得到四阶累积张量.采用分层交替最小二乘算法对四阶累积张量进行非负库克分解,求得非负库克模型的参数,同时获得非负混合矩阵并求其伪逆,最终估计出源信号.选用真实的语音信号和生物信号进行仿真实验,结果表明该方法提高了源信号和非负混合矩阵的估计性能.     

基于帧间DBSCAN聚类的毫米波雷达交通多目标跟踪方法

毫米波雷达已成为车联网中的主流传感器之一,可用于交通场景的多目标跟踪。本文将毫米波雷达安装于道路上方进行交通目标跟踪,针对基于帧内DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类的多目标跟踪中,在该安装场景下多径噪点难以去除和纵向的交通目标点云难以区分的问题,提出了基于帧间DBSCAN聚类的毫米波雷达交通多目标跟踪方法。该算法使用多帧合并处理的方式,利用帧序特征用于解决多径噪点问题,并利用空间纵向分段的方法改善了原算法在纵向上目标区分度不足的缺点。本文通过六组不同的实际场景实验,证明了本方法在不同场景下,均相比原方法对跟踪结果有不同程度的改善。