稀土配合物Eu(asprin)_3phen发光特性的研究

将稀土配合物Eu(asprin)_3phen掺杂到导电聚合物PVK中,制成结构分别为ITO/PVK:RE配合物/LiF/AI(1),ITO/PVK:RE配合物/PBD/LiF/AI(2)的电致发光(EL)器件。发现二者的电致发光谱存在着较大的差别:在器件(1)中,来自Eu~(3 )的位于594nm(~5D_0→~7F_1)和614nm(~5D_0→~7F_2)处的发光强度大致相当,而在器件(2)中,EL主要来自Eu~(3 )位于614nm的发光,594nm处的发光很弱,与薄膜状态下的光致发光谱(PL)一致。并针对此现象进行了初步讨论。     

Benson真有效意义下集值优化的广义最优性条件

本文引入了关于集值映射的α-阶Clarke切导数、α-阶邻接切导数及α-阶 伴随切导数的概念,借此建立了约束向量集值优化Benson真有效解导数型的Kuhn- Tucker条件.     

绿色荧光粉YAGG∶Tb,Gd用于FED的尝试

FED器件的发展迫切需要具有化学和热稳定性 ,高亮度 ,长寿命的新型荧光材料。本文合成了Gd3 +离子共掺杂的YAGG∶Tb获得了YAGG∶Tb ,Gd ,并将其用于 0～ 30 0 0V低压范围 ,对其在不同电压和电流密度下的发光特性进行了测试。结果表明 ,该种材料特性优于ZnO∶Zn并且不存在电压与电流饱和     

PHOTOIONIZATION IN AN INTENSE LASER FIELD

In this paper classical approaches are used to study the detailed processes of photoion-ization in an intense laser field. The analytic expression of the ionization rate is derived. Finally, the results obtained are compared with existing theories such as Landau's tunnel theory and Keldysh-Faisal-Reiss theory.     

A competitive algorithm in searching for many edges in a hypergraph

We give a competitive algorithm to identify all d defective edges in a hypergraph with d unknown. Damaschke did the d=1 case for 2-graphs, Triesch extended the d=1 case to r-graphs, and Johann did the general d case for 2-graphs. So ours is the first attempt to solve the searching for defective edges problem in its full generality. Further, all the above three papers assumed d known. We give a competitive algorithm where d is unknown.     

二维随机激光器的模式选择及阈值与饱和特性

基于将Maxwell方程与四能级原子系统速率方程相结合而建立起随机激光时域理论，并利用有限时域差分法，研究了二维随机介质中激光模式的输出特性与介质尺寸、外形及抽运速率等参数的关系.结果表明，与传统激光模式相似，随机激光模式的强度随抽运速率的变化不仅具有阈值特性，而且具有饱和特性.基于模式特性对介质及抽运参数的依赖关系，提出了二维随机激光器的选模方式，在很大程度上不同于传统激光器的选模方式. 关键词： 随机激光器 模式选择 无序介质中的光学特性     

KNSBN晶体猫式互抽动相位共轭光特性

在KNSBN：Ce晶体中，利用二波耦合作用，在单一光束无法获得相位共轭光的条件下，实现了“猫”式互抽运相位共轭光输出，获得较高的共轭光发射率，实验结果表明，晶体的二波耦合作用可以使晶体的自抽运相位共轭光的阈值光强降低。入射光的入射角范围增大，响应时间缩短。     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.