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。     

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.     

FeSiB薄膜的巨磁阻抗和应力阻抗效应研究

软磁材料中存在巨磁阻抗 (giantmagneto impedance ,GMI)效应以及与之相同来源的应力阻抗 (stress impedance ,SI)效应 ,利用这两种效应可以制成具有高灵敏度的微型化的磁场和应力 应变传感器。本文基于传感器的实际应用 ,对图形化的、较大磁致伸缩的FeSiB单层和多层薄膜的巨磁阻抗和应力阻抗效应中频率和退火的影响进行了研究。结果表明 ,对于两种效应 ,经过退火处理的单层和多层膜均可在较低的频率下得到较高的灵敏度 ,而多层膜中的应力阻抗效应将为新型高灵敏传感器的设计和研制开辟一条崭新的途径     

数字水印攻击及对策分析

数字水印技术作为数字媒体版权保护的重要手段越来越引起人们的重视。文章讨论了数字水印的概念、特征,重点介绍了数字水印的鲁棒性及影响数字水印鲁棒性的因素,总结和分析了数字水印主要攻击方式并提出相应的应对措施。     

LDAP认证技术在油田生产系统中的设计与实现

根据采用单一用户登录应用系统的要求,提出一种基于LDAP的双重认证方式。以用户认证为背景,首先对LDAP基本原理及体系结构进行简单描述,然后设计并实现基于LDAP验证与本地验证相结合的双重认证方式。本文对认证方法的特点进行阐述,并成功应用于青海油田油气水井远程计量传输发布系统。     

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.