A redundant subspace weighting procedure for clock ensemble

A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Persymmetric adaptive polarimetric detection of subspace range-spread targets in compound Gaussian sea clutter

This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.

DOA estimation of high-dimensional signals based on Krylov subspace and weighted l_(1)-norm

With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

The Study of Root Subspace Decomposition between Characteristic Polynomials and Minimum Polynomial

Let Abe the linear transformation on the linear space V in the field P, Vλibe the root subspace corresponding to the characteristic polynomial of the eigenvalue λi, and Wλibe the root subspace corresponding to the minimum polynomial of λi. Consider the problem of whether Vλiand Wλiare equal under the condition that the characteristic polynomial of Ahas the same eigenvalue as the minimum polynomial (see Theorem 1, 2). This article uses the method of mutual inclusion to prove that Vλi=Wλi. Compared to previous studies and proofs, the results of this research can be directly cited in related works. For instance, they can be directly cited in Daoji Meng’s book “Introduction to Differential Geometry.”

Modeling One Dimensional Two-Cell Model with Tumor Interaction Using Krylov Subspace Methods

A brain tumor occurs when abnormal cells grow, sometimes very rapidly, into an abnormal mass of tissue. The tumor can infect normal tissue, so there is an interaction between healthy and infected cell. The aim of this paper is to propose some efficient and accurate numerical methods for the computational solution of one-dimensional continuous basic models for the growth and control of brain tumors. After computing the analytical solution, we construct approximations of the solution to the problem using a standard second order finite difference method for space discretization and the Crank-Nicolson method for time discretization. Then, we investigate the convergence behavior of Conjugate gradient and generalized minimum residual as Krylov subspace methods to solve the tridiagonal toeplitz matrix system derived.

Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

聚氨酯改性环氧树脂/聚苯乙烯SIPNs固化动力学 (Ⅰ)环氧树脂固化剂含量对体系固化过程的影响

用化学滴定，粘度测定等方法，研究了环氧树脂固化剂（Ｔ３１）用量对聚醚型聚氨酯改性双酚Ａ型环氧树脂（ＰＵＤＧＥＢＡ）／聚苯乙烯（ＰＳｔ）室温同步ＩＰＮｓ体系固化动力学行为的影响。研究结果表明，增加Ｔ３１的用量，可以大大地缩短上述体系的凝胶时间。两个网络固化速度的峰值相对位置未改变。苯乙烯的固化受扩散控制，反应级数为２．０级；ＰＵＤＧＥＢＡ的固化为自动催化机理，反应级数为３．０级。

聚氨酯改性环氧树脂/聚苯乙烯室温同步固化IPNs的结构特征

用ＩＲ、ＤＳＣ及ＴＥＭ等手段，表征了聚醚型聚氨酯改性双酚－Ａ型环氧树脂（ＰＵＤＧＥＢＡ）／聚苯乙烯（ＰＳｔ）室温同步固化ＩＰＮ的结构。研究结果表明，所形成的固化产物，在３４２７ｃｍ－１处有强而宽的吸收，说明形成强的分子间氢键和较弱的分子内氢键。ＤＳＣ谱图只出现单一的转变，说明体系内两个组分的共混相容性较好；由于两个网络的固化速度基本同步，所形成的聚合物呈现界面较为模糊的双连续层状微相分离结构，层带宽度约１．０～２．０μｍ，层带中还有与之垂直重叠的亚片层结构，宽约０．１～０．２μｍ。

基于改进的Random Subspace 的客户投诉分类方法

电信业的客户投诉不断增多而又亟待高效处理。针对电信客户投诉数据的特点,提出了一种面向高维数据的改进的集成学习分类方法。该方法综合考虑客户投诉中的文本信息及客户通讯状态信息,基于Random Subspace方法,以支持向量机(Support Vector Machine,SVM)为基分类器,采用证据推理(Evidential Reasoning,ER)规则为一种新的集成策略,构造分类模型对电信客户投诉进行分类。所提模型和方法在某电信公司客户投诉数据上进行了验证,实验结果显示该方法能够显著提高客户投诉分类的准确率和投诉处理效率。

蓖麻油型聚氨酯/PS同步IPNs的协同效应研究

采用同步互穿网络技术,制备了一系列蓖麻油型聚氨酯/聚苯乙烯(COPU/PS)互穿网络聚合物(IPNs),研究了它们的力学性能和溶胀性能.结果表明,当PS/COPU、BPO(过氧化二苯甲酰)/St(苯乙烯)质量分数分别为30%和1.5%时,三羟甲基丙烷(TMP)和蓖麻油羟基的摩尔比、NCO与OH的摩尔比分别为0.2和1.3时,互穿网络材料的相容性好,协同效应明显,此时力学性能最佳.当COPU/PS的IPNs材料中PS质量分数为20%～40%时,由于两相的互穿作用,使二甲苯对IPNs材料的溶胀趋缓;并且随着交联剂TMP的增加,平衡溶胀增重率增幅变小,到TMP和蓖麻油的羟基摩尔比值大于0.2后,平衡溶胀增重率基本不变.

CIIR/PMAcIPNs阻尼性能

通过以氯化丁基橡胶(CIIR)与聚丙烯酸酯(PMAc)制备互贯聚合物网络(IPNs),实现了将CIIR阻尼功能区向高温区的拓展. DMA测试结果表明,第1网络CIIR的交联剂用量,第2网络聚丙烯酸酯(PMAc)的类型和比率、交联剂种类及用量对阻尼性能有较大影响. 当m(CIIR)∶m(PBMA)为(80～60)∶m(20～40)时,CIIR/PBMA IPNs可形成阻尼平台区(10 Hz下,tanδmax≥0.65,tanδ＞0.3的有效阻尼功能区可从-50 ℃持续至60 ℃);同时CIIR/PEA IPNs具有较高的阻尼值(tanδmax≥1.24);样品的TEM形貌分析表明,CIIR/PMAc IPNs的相畴尺寸在100～200 nm之间,2组分之间具有较好的相容性.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

BaTiO_3/(PU/UP-IPNS)复合材料的相容性、力学性能和阻尼性能研究(英文)

以水热法合成的BaTiO3纤维和同步法制备的互穿聚合物网络为原料,采用原位分散聚合法获得了一系列BaTiO3/(PU/UP-IPNs)复合材料。采用傅立叶交换红外分光光度计跟踪考察了IPNs的聚合过程,用透射电镜观测了IPNs及其复合物的形貌。结果表明,IPNs中两相相畴尺寸在纳米级范围内,在此基础上,实现了BaTiO3纤维状的复合。动态力学性能的检测结果表明,相较纯IPNs,复合材料的阻尼损耗模量和阻尼损耗因子值均有所提高,且在低温区均出现了肩峰。复合物的最大损耗因子值均大于0.4,在约50℃范围内,E″值提高100MPa。力学性能检测结果表明,IPNs中的连续相是决定材料力学性能的主要因素;有机/无机组分间混溶性的降低,使BaTiO3/IPNs复合材料的抗张强度和断裂伸长率均下降。

交联聚合物微粒复合COPU/PS-IPNs的研究

为研究交联聚合物微粒（CPMB）在蓖麻油聚氨酯（COPU）／聚苯乙烯（PS）五穿网络聚合物（IPNs）中的增强和阻尼作用，将CPMB溶胀在蓖麻油中，与过量甲苯二异氰酸酯反应生成预聚体，再通过同步互穿网络聚合，制备了CBMB复合COPU／PS—IPNs。研究发现，添加少量的CPMB使COPU／PS—IPNs的扛伸强度有较大提高。DMA测试表明，COPU／PS-IPNs只存在一个窄的阻尼峰，而加入不同的CPMB，可以使COPU／PS—IPNs材料在不同温域出现阻尼峰，从而加宽阻尼温域。TGA揭示，不同CPMB改性COPU／PS—IPNs的开始和30％的热分解温度分别比不加CPMB的增加了2℃～5℃和4℃～7℃。磨损研究结果显示，随COPU／PS—IPNs中PU组分的增加，摩擦系数增加，磨损量减少；随CPMB的增加，摩擦系数和磨损量都增加。