Clustering of rare earths in GeAs sulfide glass

Clustering of rare-earth dopants in GeAs sulfide glasses was studied by fluorescence spectroscopy of Pr-doped glasses and by EPR measurements of Gd-doped samples. The linewidth of the g  2 resonance of Gd³⁺, as well as the relative intensity of emission from the ¹D₂ level of Pr³⁺, was used as a relative measure of rare-earth clustering. Rare earths were found to have low solubility in uncodoped GeAs sulfide glasses, which also displayed poor fluorescence efficiency due to severe clustering. Codoping such glasses with Ga greatly enhanced rare-earth solubility and dispersal, particularly for Ga:rare earth ratios ≥ 10:1, as evidenced by the narrower EPR resonances and more intense luminescence of Gd- and Pr-doped glasses, respectively. In, P and Sn were also observed to ‘decluster’ rare earths, although less efficiently than Ga, whereas codoping with I was found to have no effect on clustering. These phenomena are explained by a structural model in which (1) rare-earth dopants and codopants are spatially associated and (2) rare-earth dispersal is accomplished by a statistical distribution of codopants in tetrahedral network sites.     

基于彩色结构光的自由曲面三维重建方法

彩色结构光三维重建过程中,系统的非线性耦合以及待测曲面的拓扑结构等均会对结构光解码产生影响,从而导致调制条纹漏检和颜色码误识别。为解决这一问题,提出一种基于彩色编码结构光的三维重建方法。利用YUV颜色通道对调制条纹进行滤波差分投影处理,通过调制条纹的波形分布提取中心特征线;利用颜色聚类方法精确获取调制条纹的颜色码信息值;最后,为了建立编码条纹码字与调制条纹码字间的对应关系,提出了基于序列特征组合的优化匹配方法,结合双目视觉深度感知数学模型,求解出编码码字的空间三维信息值。通过实验分析可知,本文方法的漏检率低,颜色码识别率高,具有较强的抗干扰性和鲁棒性。     

多指标面板数据融合聚类分析

针对多指标面板数据的样品分类和历史时期划分问题,从多元统计分析理论角度提出一个多指标面板数据的融合聚类分析方法。该方法改进了多指标面板数据的因子分析和系统聚类方法,依据Fisher有序聚类理论,构造了Frobenius范数形式的离差平方和函数,提出了多指标面板数据的有序聚类方法。实证结果表明,该方法能够满足系统分析的统一性要求,保证指标之间的不相关;能够克服时间维度上均值处理造成的偏误,信息损失较少;能够解决面板数据有序聚类的问题;弥补了单一分析的片面性和局限性。     

Fault Recognition of Rolling Bearings Based on Parameter Optimized Multi-Scale Permutation Entropy and Gath-Geva

To extract fault features of rolling bearing vibration signals precisely, a fault diagnosis method based on parameter optimized multi-scale permutation entropy (MPE) and Gath-Geva (GG) clustering is proposed. The method can select the important parameters of MPE method adaptively, overcome the disadvantages of fixed MPE parameters and greatly improve the accuracy of fault identification. Firstly, aiming at the problem of parameter determination and considering the interaction among parameters comprehensively of MPE, taking skewness of MPE as fitness function, the time series length and embedding dimension were optimized respectively by particle swarm optimization (PSO) algorithm. Then the fault features of rolling bearing were extracted by parameter optimized MPE and the standard clustering centers is obtained with GG clustering. Finally, the samples are clustered with the Euclid nearness degree to obtain recognition rate. The validity of the parameter optimization is proved by calculating the partition coefficient and average fuzzy entropy. Compared with unoptimized MPE, the propose method has a higher fault recognition rate.     

基于对象多属性差异的灰色发展聚类方法及应用

在现实决策问题中,决策对象在不同时期行为状态和所属类型往往呈现一定的发展规律,而现有聚类方法难以充分挖掘聚类对象的发展信息、对象间的关系信息和发展属性的差异信息。为有效处理此类问题,考虑到研究对象的发展趋势、发展行为和发展绝对量与增长量的属性差异,采用GM(1,1)和灰色定权聚类方法,构建了基于对象多属性差异的灰色发展聚类方法,并以我国区域高新技术产业化聚类评估问题为例验证了模型的有效性与合理性。结果表明,所构建模型能够有效描述研究对象呈现发展趋势或未来行为,并实现对研究对象的有效聚类。     

Clustering Categorical Data via Ensembling Dissimilarity Matrices

We present a technique for clustering categorical data by generating many dissimilarity matrices and combining them. We begin by demonstrating our technique on low-dimensional categorical data and comparing it to several other techniques that have been proposed. We show through simulations and examples that our method is both more accurate and more stable. Then we give conditions under which our method should yield good results in general. Our method extends to high-dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables. In this context, we compare our method with two other methods. Finally, we extend our method to high-dimensional categorical data vectors of unequal length by using alignment techniques to equalize the lengths. We give an example to show that our method continues to provide useful results, in particular, providing a comparison with phylogenetic trees. Supplementary material for this article is available online.     

Sparse Convex Clustering

Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex clustering has not yet been investigated in the high-dimensional clustering scenario, where the data contains a large number of features and many of them carry no information about the clustering structure. In this article, we demonstrate that the performance of convex clustering could be distorted when the uninformative features are included in the clustering. To overcome it, we introduce a new clustering method, referred to as Sparse Convex Clustering, to simultaneously cluster observations and conduct feature selection. The key idea is to formulate convex clustering in a form of regularization, with an adaptive group-lasso penalty term on cluster centers. To optimally balance the trade-off between the cluster fitting and sparsity, a tuning criterion based on clustering stability is developed. Theoretically, we obtain a finite sample error bound for our estimator and further establish its variable selection consistency. The effectiveness of the proposed method is examined through a variety of numerical experiments and a real data application. Supplementary material for this article is available online.     

基于FCM算法的小脑基因模糊聚类分析

探讨基因表达数据的聚类分析方法,结合一种聚类结果的评判准则,应用于胎儿小脑基因表达数据,得到了最优的聚类结果,并做出了生物学解释.利用Matlab软件进行了仿真,利用模糊聚类Xie-Beni指数得到了最优聚类数,并把每一类对应的基因标号输出到txt文件,最后进行生物学解释.得到的小脑基因最优聚类数为3类,与生物学意义比较吻合,各类中的基因功能接近.基于FCM算法的基因模糊聚类是有效的,结果具有一定生物学意义,能对生物学基因聚类有一定指导作用.     

Edge-Weighted Centroidal Voronoi Tessellations

<正>Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy—a sum of the classic CVT energy and the weighted length of cluster boundaries.To distinguish it with the classic CVTs,we call it an Edge-Weighted CVT(EWCVT).The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries.The EWCVT method is easy in implementation,fast in computation,and natural for any number of clusters.     

A Clustering Approach for the Separation of Touching Edges in Particle Images

The occurrence of touching objects in images of particulate systems is very common especially in the absence of dispersion methods during image acquisition. The separation of these touching particles is essential before accurate estimation of particle size and shape can be achieved from these images. In the current work, clustering approaches based on the fuzzy C‐means algorithm are employed to identify the touching particle regions. Firstly, clustering in the multidimensional space of image features, e.g., standard deviation, gradient and range calculated in a certain neighborhood of each pixel, is performed to trap the touching regions. Then, in a novel proposed method, the clustering of pixel intensity itself into two fuzzy clusters is performed and a feature, referred to as the ‘Fuzzy Range', is calculated for each pixel from its membership values in both clusters and is presented as a distinguishing feature of the touching regions. Both approaches are compared and the superiority of the latter method in terms of the non‐necessity of neighborhood based calculations and minimum disfiguration is elucidated. The separation methods presented herein do not make any assumption about the shape of the particle as is undertaken in many methods reported elsewhere. The technique is proven to minimize greatly the deleterious effects of over‐segmentation, as is the case with traditional watershed segmentation techniques, and consequently, it results in a superior performance.