ASTM clustering for improving coal analysis by near-infrared spectroscopy

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.     

面心立方金属铈的高压电子结构研究

本文简要介绍了我们开发的基于密度泛函理论和动力学平均场理论(DFT+DMFT)的全自洽的的计算程序,并运用该程序研究了面心立方金属铈的等结构相变.动力学平均场采用的是目前公认的最为准确和高效的连续时间蒙特卡罗方法作为杂质求解器.我们首先分别计算了γ和α相的电子态密度,该结果可以正确的描述两个相的电子结构特征.然后计算了不同温度下的相变前后的电子结构,从中可以得到电子结构在不同温度下的相变过程,与之前的实验结果一致.这一工作为以后进一步的金属铈的等结构相变研究打下了基础.     

支持向量鉴别分析及在人脸表情识别中的应用

模式识别一般首先要对数据进行降维,PCA和LDA及其对应的核化算法是其中应用广泛的方法,但这些算法的应用前提是假设样本数据为高斯分布,在少样本训练时它们的推广性能有很大局限.本文提出了一种基于支持向量机的鉴别分析算法,该算法首先寻找有限样本情况下最优分类面,以其法线方向为投影轴对数据进行投影降维,在多类情况下提供了极其丰富的方案选择投影轴.该算法体现了支持向量机的内在优良推广性能,克服了PCA和LDA等算法的局限性.本文将所提算法应用于人脸表情特征提取,并与PCA、LDA、KPCA、GDA等算法进行了比较,结果表明该算法的有效性.