软X射线Mo/Si多层膜反射率拟合分析

由于多层膜的表界面粗糙度和材料之间的相互扩散等因素，导致多层膜的实际反射率小于理论计算的反射率，因此，多层膜结构参量的确定对镀膜工艺参量的标定具有重要意义。由于描述单个非理想粗糙界面散射的Stearns法适用于软X射线短波段区域，采用它的数学模型来描述软X射线多层膜的粗糙度，利用最小二乘法曲线拟合法对同步辐射测得的Mo／Si多层膜的反射率曲线进行拟合，得到了非常好的拟合结果，从而确定了多层膜结构参量，同时分析了多层膜周期厚度，厚度比率，界面宽度以及仪器光谱分辨率对多层膜反射特性的影响，这些工作都为镀膜工艺改进提供了一定的理论依据。     

等吸收紫外光度法同时测定槐米中的芦丁和槲皮素

以等吸收紫外光度法同时测定了槐米提取物中的芦丁和槲皮素。实验表明 ,槲皮素在波长 2 4 7nm和2 6 6 nm处有相同的吸收 ,芦丁在波长 340 nm和 370 nm处有相同的吸收。芦丁和槲皮素在 4 .32—4 8.6 5 μg/m L的浓度范围内符合比耳定律。本法测定槐米提取物中的芦丁和槲皮素 ,含量分别达到80 .6 0 %和 9.4 9% ;相对标准偏差为 1.6 1%和 1.5 3%。     

格尔木地区整层大气臭氧浓度激光外差测量与反演研究

搭建了便于外场观测的3.3μm激光外差光谱仪,实测其光谱分辨率为0.004 cm-1.利用该设备测量了青海格尔木地区整层大气臭氧的吸收光谱,并结合最优估算法反演了该地区的臭氧浓度.测量期间,格尔木地区臭氧柱浓度均值约241.7 DU,且浓度随观测时间呈上升趋势,上升速度约4 DU/h.结果表明,该激光外差光谱仪结合最优...     

Asymptotic Normality for Plug-In Estimators of Generalized Shannon’s Entropy

Shannon’s entropy is one of the building blocks of information theory and an essential aspect of Machine Learning (ML) methods (e.g., Random Forests). Yet, it is only finitely defined for distributions with fast decaying tails on a countable alphabet. The unboundedness of Shannon’s entropy over the general class of all distributions on an alphabet prevents its potential utility from being fully realized. To fill the void in the foundation of information theory, Zhang (2020) proposed generalized Shannon’s entropy, which is finitely defined everywhere. The plug-in estimator, adopted in almost all entropy-based ML method packages, is one of the most popular approaches to estimating Shannon’s entropy. The asymptotic distribution for Shannon’s entropy’s plug-in estimator was well studied in the existing literature. This paper studies the asymptotic properties for the plug-in estimator of generalized Shannon’s entropy on countable alphabets. The developed asymptotic properties require no assumptions on the original distribution. The proposed asymptotic properties allow for interval estimation and statistical tests with generalized Shannon’s entropy.     

Numerical Analysis and Comparison of Three Iterative Methods Based on Finite Element for the 2D/3D Stationary Micropolar Fluid Equations

In this paper, three iterative methods (Stokes, Newton and Oseen iterative methods) based on finite element discretization for the stationary micropolar fluid equations are proposed, analyzed and compared. The stability and error estimation for the Stokes and Newton iterative methods are obtained under the strong uniqueness conditions. In addition, the stability and error estimation for the Oseen iterative method are derived under the uniqueness condition of the weak solution. Finally, numerical examples test the applicability and the effectiveness of the three iterative methods.     

A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot Pattern

Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time–frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.     

再扩充模糊逻辑中逻辑程序的模型论语义和不动点语义研究

模糊逻辑及其扩充是处理不确定性与模糊性信息的重要数学工具,在近似推理、人工智能等领域有着广泛的应用。而逻辑程序也已经成为人工智能的研究热点之一。本文是在再扩充模糊逻辑中,对逻辑程序进行了语义的研究。给出了其语法和语义描述,并且将逻辑程序的许多主要结论推广到再扩充模糊逻辑中。首先,得到了关于模糊逻辑推论的充分必要条件以及模型交与强模型交性质。然后,通过引入一个H erbrand解释算子Pτ:LBp→LBp,给出了确定性程序P的H erbrand模型的充分条件和强H erbrand模型的充分必要条件。最后,建立了确定性程序的最小强H erbrand模型的不动点刻画定理。     

一个评价光谱估计精度的新参数

研究了对多光谱成像系统进行光谱估计的精度评价的问题。在给出光谱估计偏指数定义的基础上,提出了多光谱图像获取系统的光谱估计性能的评价的新方法——系统光谱匹配偏度指数法。该方法用一个指标来定量评价两光谱向量的光谱匹配程度,避免了以往评价参数的复杂性,同时反映了光谱反射率估计曲线实际测量曲线之间的差异程度。采用一典型多光谱相机对Macbeth色卡的24个典型光谱仿真估计所得的24个光谱对数值模拟,验证了光谱反射率估计匹配偏度指数能全面的反映光谱反射率的估计精度的设想;并拿17个多光谱估计系统的仿真估计的光谱数据,计算了它们的系统光谱估计偏度指数,计算结果有很好的一致性。结果还显示,系统光谱估计偏度指数还可用作对多光谱相机光谱估计性能进行等级划分的依据。     

基于目标和点扩展函数联合估计的点源目标图像近视解卷积

在传统的基于波前探测的解卷积方法中,由波前探测得到的点扩展函数被认为是精确的,并用维纳滤波进行复原,但是点扩展函数不可避免地存在误差,所以最终的复原目标图像质量不佳.为了解决该难题,提出了基于目标和点扩展函数联合估计的图像近视解卷积算法.它运用了点扩展函数和目标的先验信息,对点扩展函数和目标进行了规整和进一步约束,从而得到更优的恢复图像质量.对该方法的原理和实现过程进行了阐述,并将其运用于室内点源目标数据中.实验结果证明,与维纳滤波方法相比,该方法使图像恢复的效果得到明显改善.     

Stokes方程的压力梯度局部投影间断有限元法

本文对定常的Stokes方程提出了一种新的间断有限元法,通过将通常的间断Galerkin有限元法与压力梯度局部投影相结合,建立了一个稳定的间断有限元格式,对速度和压力的任意分片多项式空间P_l(K),P_m(K)的间断有限元逼近证明了解的存在唯一性,给出了关于速度和压力的L~2范数的最优误差估计.