氧化锆增韧陶瓷的相变及相变增韧

本文阐述了氧化锆增韧陶瓷（ＺＴＣ）的相变增韧机理，并探讨了热处理工艺对ＺＴＣ的相变及显微组织的影响规律。     

Hg_（1－x）Cd_xTe的扫描光致发光研究

用傅里叶变换红外扫描光致发光方法研究了Ｈｇ１－ｘＣｄｘＴｅ体单晶样品，该方法可直接得到ＨｇＣｄＴｅ晶片组分的二维平面分布，并可得到辐射复合在复合机制中所占比重的平面分布，以及晶体中非平衡载流子寿命的分布     

特大型油罐整体一次性控爆拆除

本文介绍了特大型油罐整体一次性控制爆破拆除方案及其实施方法，这一方法与水压爆破拆除和常规钻孔爆破拆除方法相比，具有工效高，安全性好的突出优点。     

钛酸酯催化环氧树脂固化动力学研究

用FTIR“原位”技术,研究了钛酸酯(Tc-114)偶联剂对环氧E-44和二氨基二苯基甲烷(DDM)固化反应的催化作用,得出固化程度与时间的关系曲线。又根据DSC曲线,分别算出加有Tc-114和不加Tc-114的两个固化体系的反应表观活化能和反应级数。结果表明,Tc-114的存在使环氧固化反应活化能降低,催化作用明显。     

一种8毫米波变张角,半径突变多模圆锥馈源

我们研制了一种8毫米波变张角、半径突变的多模圆锥馈源。计算出其模比并推导出其辐射方向图公式;给出其在两个主平面——E平面与H平面上的辐射方向图和波束场分布特性的测试结果以及电压驻波比VSWR的测量结果;同时给出频带和旁瓣特性。分析与测试共同表明,在34.5GHz～37GHz频带里,该多模馈源的辐射图在大于24dB范围内获得幅度等化且不出现旁瓣,因此具有一定的应用价值。     

10Gbit/s甚短距离传输系统接收模块研究

本文研究了符合OIF—VSR4—01．0规范的甚短距离（VSR）并行光传输系统转换器集成电路，实现了接收部分转换器集成电路中帧同步、8B／10B解码、12路通道去斜移、检错纠错等模块的设计。在Altera的Stratix GX系列的FPGA上实现了接收部分转换器集成电路。仿真分析的结果表明所设计的各个模块能正确的实现接收部分转换器集成电路的功能，给出了仿真结果。     

Monolithic Catalysts with Low Noble-Metal Content for Exhaust Purification of Small Gasoline Engines

A series of low noble-metal coment monolithic catalysts for exhaust purification of small gasoline engines was investigated, and it was found that the Pt/Rh-OSM/Al2O3 （where OSM was oxygen storage material） catalyst with Ce0.5Zr0.5-MnOx（3%MnOx） OSM held low light-off temperature for CO, HC, and NO; quite wide three-way window, and outstanding thermal stability. The catalyst could efficiently comrol exhaust emission of small gasoline engines.     

松材线虫病害高光谱时序与敏感特征研究

采用高光谱仪ASD FieldSpec Pro FR,连续观测了健康和发病马尾松针叶的时序高光谱,分析了松材线虫病害光谱的时序特征、敏感时期和敏感特征。结果表明:与健康马尾松相比,病害马尾松时序光谱差异较大;病害首先造成红边区域内光谱反射率减低,然后再出现红边蓝移的2阶段光谱变化规律;感染松材线虫的马尾松9月初已经出现了病态植被典型的光谱特征;近红外平台内最大的一阶微分值、红边内一阶微分的总和(SDr)与蓝边内一阶微分的总和(SDb)的比值等是指示病害发生的显著性高光谱特征。     

图形图像制作软件中的色彩模式分析

色彩模式是在数字处理过程中处理色彩的基本标准,其是以颜色模型为基础,利用数字定量对颜色进行描述的方法。针对图形图像制作软件中的色彩模式进行分析。     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.