Identification and Classification of Iridescent Glass Artifacts with XRF and SEM/EDX

 Art Nouveau (Tiffany, Loetz) and modern (Jack Ink, Strini Art Glass) iridescent glass fragments were characterized using energy dispersive X-ray fluorescence analysis (EDXRF) and energy dispersive X-ray microanalysis in a scanning electron microscope (SEM/EDX) in combination with factor analysis in order to obtain clustring. A character istic of Tiffany glass fragments is leaded bulk glass, whereas in the case of Loetz K-Ca-Si bulk glass could be determined. Modern glass fragments show a high amount of Na (7 wt% in the bulk of Jack Ink) and 0.6–1.5 wt% Sr in the bulk of Strini Art Glass. The contents of Si and Ca are similar to Loetz glass. Furthermore, the differences in the structure of the glass artifacts could be determined. The cross-sections of Tiffany show a layered structure of the bulk without a specific surface layer whereas the cross-sections of Loetz glass reveal a homogeneous bulk material with one or two homogeneous surface layers in the BE-image. In the case of the Jack Ink a homogeneous bulk glass and an inhomogeneous multilayered surface could be determined. Strini Art Glass show a homogeneous bulk glass and a homogeneous surface layer in the backscattered electron image.     

Monitoring of water quality in South Paris district by clustering and SIMCA classification

With the aim of obtaining a monitoring tool to assess the quality of water, a multivariate statistical procedure based on cluster analysis (CA) coupled with soft independent modelling class analogy (SIMCA) algorithm, providing an effective classification method, is proposed. The experimental data set, carried out throughout the year 2004, was composed of analytical parameters from 68 water sources in a vast southwest area of Paris. Nine variables carrying the most useful information were selected and investigated (nitrate, sulphate, chloride, turbidity, conductivity, hardness, alkalinity, coliforms and Escherichia coli). Principal component analysis provided considerable data reduction, gathering in the first two principal components the majority of information representing about 92.2% of the total variance. CA grouped samples belonging to different sites, distinctly correlating them with chemical variables, and a classification model was built by SIMCA. This model was optimised and validated and then applied to a new data matrix, consisting of the parameters measured during the year 2005 from the same objects, providing a fast and accurate classification of all the samples. The most of the examined sources appeared unchanged during the 2-year period, but five sources resulted distributed in different classes, due to statistical significant changes of some characteristic analytical parameters.     

Generation of linear and nonlinear waves in numerical wave tank using clustering technique-volume of fluid method

A two-dimensional(2D) numerical model is developed for the wave simulation and propagation in a wave flume.The fluid flow is assumed to be viscous and incompressible,and the Navier-Stokes and continuity equations are used as the governing equations.The standard k-ε model is used to model the turbulent flow.The NavierStokes equations are discretized using the staggered grid finite difference method and solved by the simplified marker and cell(SMAC) method.Waves are generated and propagated using a piston type wave maker.An open boundary condition is used at the end of the numerical flume.Some standard tests,such as the lid-driven cavity,the constant unidirectional velocity field,the shearing flow,and the dam-break on the dry bed,are performed to valid the model.To demonstrate the capability and accuracy of the present method,the results of generated waves are compared with available wave theories.Finally,the clustering technique(CT) is used for the mesh generation,and the best condition is suggested.     

基于新直觉模糊相似度的大规模群决策方法

研究了群体规模较大情况下基于直觉模糊评价信息的决策方法。提出一种新的直觉模糊相似度公式,研究了一种新的直觉模糊聚类方法,设计了一种基于新的聚类方法的核心决策者权重确定方法。实例分析说明了该方法的合理性和可行性。     

基于K-means算法的学生试卷成绩分析

目前在教学管理中,通常采用算术平均线性划分法对学生成绩进行分析评价,难以准确地反映学生真实的学习情况.笔者运用K-means算法对上海市某高级中学某班的一次试卷成绩进行聚类,并对聚类结果进行了详细分析,为制定有效的教学及学习策略提供相关依据.     

Effect of particle clustering on the gravitational settling velocity in homogeneous turbulence

Astatistical model of the gravitational settling velocity of clusters in homogeneous isotropic turbulence is developed. The effects of particle hydrodynamic interaction, inertia, size, and volume fraction on the particle settling velocity component attributable to particle clustering are analyzed.     

茶叶傅里叶红外光谱的可能模糊K调和均值聚类分析

茶叶的品种不同,其有机化学成分含量往往不同,其功效也是不尽相同的,因此,研究出一种简单、高效、识别率高的茶叶品种鉴别技术方法是十分有必要的。中红外光谱技术是一种快速检测技术,在用中红外光谱仪采集得到的茶叶中红外光谱中含有噪声信号。为了对含噪声茶叶中红外光谱的准确分类以实现茶叶品种分类,将可能模糊C-均值聚类(PFCM)思想应用到K调和均值(KHM)聚类,设计出一种可能模糊K调和均值(PFKHM)聚类算法,计算出PFKHM的模糊隶属度、典型值和聚类中心。可能模糊K调和均值聚类能有效解决K调和均值聚类的噪声敏感性问题。用傅里叶红外光谱分析仪(FTIR-7600型)分别对三种茶叶(优质乐山竹叶青、劣质乐山竹叶青和峨眉山毛峰)进行扫描以获取它们的傅里叶中红外光谱。光谱波数区间是4 001.569～401.121 1 cm-1。先采用主成分分析法(PCA)将光谱数据压缩到20维,再采用线性判别分析(LDA)将光谱数据压缩到两维并提取鉴别特征信息。最后分别用K调和均值聚类和可能模糊K调和均值聚类实现茶叶品种分类。实验结果：当权重指数m=2,q=2和p=2时,KHM具有91.67%的聚类准确率,PFKHM聚类准确率达到94.44%;KHM迭代12次达到收敛,而PFKHM迭代11次就可以达到收敛。采用傅里叶红外光谱技术检测茶叶,用主成分分析和线性判别分析压缩光谱数据,再用可能模糊K调和均值聚类进行品种分类可快速、准确地实现茶叶品种的鉴别。     

Short-range order clustering in BCC Fe–Mn alloys induced by severe plastic deformation

The effect of severe plastic deformation, namely, high-pressure torsion (HPT) at different temperatures and ball milling (BM) at different time intervals, has been investigated by means of Mössbauer spectroscopy in Fe_100–xMn_x (x = 4.1, 6.8, 9) alloys. Deformation affects the short-range clustering (SRC) in BCC lattice. Two processes occur: destruction of SRC by moving dislocations and enhancement of the SRC by migration of non-equilibrium defects. Destruction of SRC prevails during HPT at 80–293 K; whereas enhancement of SRC dominates at 473–573 K. BM starts enhancing the SRC formation at as low as 293 K due to local heating at impacts. The efficiency of HPT in terms of enhancing SRC increases with increasing temperature. The authors suppose that at low temperatures, a significant fraction of vacancies are excluded from enhancing SRC because of formation of mobile bi- and tri-vacancies having low efficiency of enhancing SRC as compared to that of mono vacancies. Milling of BCC Fe_100–xMn_x alloys stabilises the BCC phase with respect to α → γ transition at subsequent isothermal annealing because of a high degree of work hardening and formation of composition inhomogeneity.     

Effect of particle shape on micro- and mesostructure evolution of granular assemblies under biaxial loading conditions

Discrete element method (DEM) numerical biaxial tests on samples with different particle shapes are performed to investigate how the multiscale evolves with varying particle shape. The samples used in such simulations are composed of circular, square, and elongated particles, respectively. For the numerical results, analyses are conducted in terms of microscopic evolution, i.e. particle rotation and evolution of fabric, and mesoscopic evolution, i.e. the evolution of loops and improved clustering coefficient. At the microscale, the mean particle rotation of circular particles is remarkably larger than those of square and elongated particles, and the shear band localization phenomenon is more obvious when the aspect ratio (AR) decreases. Considering the fabric evolving with particle shape, the value of anisotropy gradually increases when particle shape becomes irregular, and contacts of circular particles are pronouncedly less than those of irregular particles from the coordination number and curves of degree distribution. At the mesoscale, when the particle relationship is considered, the isotropic particles (i.e. circular and square particles) have similar evolutions of loops and modified clustering coefficient, whereas the elongated particles have remarkable three loops and modified clustering coefficient, which are both larger than those of isotropic particles.