基于MMSE的大气激光通信中LDPC码BP译码算法研究

针对大气激光通信中低密度奇偶校验码(LDPC)置信传播(BP)译码算法复杂度高的问题,对几类BP-Based简化译码算法进行了分析,并基于最小均方误差准则(MMSE)对Scaled BP-Based和Offset BP-Based两类改进译码算法的优化设计进行了探讨,得出两类算法的最优校正因子,并给出了数值计算.在不同的湍流强度下,对码长1008的(6,3)比特填充LDPC码进行了仿真实验,结果表明,对于短码长的LDPC码,当译码BER=10-5时,最小和算法(UMP BP-Based)相对于BP算法有0.1～0.15dB的译码性能损失;基于MMSE设计的两类算法相比BP算法大大降低复杂度的同时,译码性能与BP算法相当,甚至优于BP算法,优于UMP BP-Based算法0.075～0.15dB.     

酸及镧盐对钙测定的影响

用空气-乙炔原子吸收光谱法(AAS)对不同溶液中钙元素进行检测,研究了盐酸、硝酸、镧盐对Ca测定的影响.结果表明,在使用原子吸收光谱法测定Ca时,测定结果受到盐酸、硝酸、镧盐严重的负干扰.     

电势法测定KI在CH3OH-H2O混合溶剂中的活度系数

用无液接电势电池测定了303.15 K时,KI在甲醇-水混合溶剂中的电动势,用扩展的Debye-Hückel方程及Pitzer方程关联不同溶剂组成下电动势的实验值,得到电池的标准电动势E°和KI在混合溶剂中的活度系数.结果表明,在一定的溶剂比例下,随KI浓度的增大,其平均活度系数先随它的浓度增加而下降,经过一个最低点后又上升;或者随浓度增加而单调递减.当KI浓度固定时,它的平均活度系数随溶剂中甲醇的含量增加而下降.表明KI在甲醇中有微弱的缔合.     

线性回归模型中方差变点的连续监测

The paper investigates the sequential observations’ variance change in linear regression model. The procedure is based on a detection function constructed by residual squares of CUSUM and a boundary function which is designed so that the test has a small probability of false alarm and asymptotic power one. Simulation results show our monitoring procedure performs well when variance change occurs shortly after the monitoring time. The method is still feasible for regression coefficients change or both variance and regression coefficients change problem.     

用自相关法检测壁湍流的平均猝发周期

本文通过研究湍流脉动速度信号的自相关函数，提出了用自相关函数检测壁湍流平均猝发周期的方法。     

磁过滤真空弧源沉积技术制备C/C多层类金刚石膜及其摩擦磨损性能研究

采用磁过滤直流阴极真空弧源沉积技术在Si基体和GCr15基体表面制备了C/C多层DLC膜,通过X射线光电子能谱仪分析薄膜结构特征;用原子力显微镜观察C/C多层DLC膜的表面形貌;采用台阶仪测试薄膜厚度;利用纳米硬度仪测试薄膜纳米硬度;在销盘式摩擦磨损试验机上进行C/C多层DLC膜在大气下的摩擦性能评价,同时比较了单层DLC膜、TiN膜和C/C多层DLC膜的耐磨性能.结果表明:C/C多层DLC膜表面光滑、致密,厚度达0.7 μm,硬度高达68 GPa,与SiC球对摩时的摩擦系数为0.10左右,耐磨性明显优于单层DLC膜和TiN膜.     

番茄果肉果芯固形物的分离提取及对大鼠血栓形成的影响

分别对番茄果肉及果芯的固形物进行低温分离提取,并分别采用这二种提取物对大鼠血栓形成的影响进行了研究.实验结果发现,这种提取物对大鼠的血栓形成有明显的抑制作用,其中,果芯固形物对大鼠血栓抑制效果优于果肉固形物.     

Structured Eigenvalue Problems in Electronic Structure Methods from a Unified Perspective?

In (relativistic) electronic structure methods, the quaternion matrix eigenvalue problem and the linear response (Bethe-Salpeter) eigenvalue problem for excitation energies are two frequently encountered structured eigenvalue problems. While the former problem was thoroughly studied, the later problem in its most general form, namely, the complex case without assuming the positive definiteness of the electronic Hessian, was not fully understood. In view of their very similar mathematical structures, we examined these two problems from a unified point of view. We showed that the identification of Lie group structures for their eigenvectors provides a framework to design diagonalization algorithms as well as numerical optimizations techniques on the corresponding manifolds. By using the same reduction algorithm for the quaternion matrix eigenvalue problem, we provided a necessary and sufficient condition to characterize the different scenarios, where the eigenvalues of the original linear response eigenvalue problem are real, purely imaginary, or complex. The result can be viewed as a natural generalization of the well-known condition for the real matrix case.     

地表反射太阳辐射的参数化计算方法

 从能量守恒定律出发,建立了朗伯地表对下行辐射的反射作用引起天空亮度变化的解析计算模型。利用射线追踪原理,提出了地表与大气之间多次反射作用的理论模型。引入了一个多次散射的影响因子,并对附加的天空亮度进行了修正。利用混合修正的d-Eddington近似和互易性原理计算了大气的透射率、反照率以及半球平均反照率。最后计算了地表反照率分别为0.15,0.25和0.35情形下的天空亮度增量,实验测量数据证实了这些结果。结果表明：当地表的反照率不为0时,存在“临边增亮”效应,并且随着地表反照率的增大,这种效应更加明显。     

Application of Statistical K-Means Algorithm for University Academic Evaluation

With the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for various indicators, which can be subjective and limited. This paper investigates the evaluation of academic performance by using the statistical K-means (SKM) algorithm to produce clusters. The core idea is mapping the evaluation data from Euclidean space to Riemannian space in which the geometric structure can be used to obtain accurate clustering results. The method can adapt to different indicators and make full use of big data. By using the K-means algorithm based on statistical manifolds, the academic evaluation results of universities can be obtained. Furthermore, through simulation experiments on the top 20 universities of China with the traditional K-means, GMM and SKM algorithms, respectively, we analyze the advantages and disadvantages of different methods. We also test the three algorithms on a UCI ML dataset. The simulation results show the advantages of the SKM algorithm.