对于僵尸网络传播特性的研究已有一定进展，无尺度网络传播模型更加符合实际网络特征，基于KSC算法对僵尸程序在无尺度网络中的传播特性进行研究，研究发现，模型基本能够体现僵尸程序在无尺度网络的传播特性和感染特征。

For some research progress of the existing botnet propagation, no communication scale network model accords with the actual network characteristics, studies the KSC algorithm of bots in the absence of propagation characteristics of scale network, based on the research findings, the model can reflect the bots in the propagation and infection characteristics of scale free networks.

目标特性分析是研究目标探测性能的一个重要环节,包括目标(和背景)的辐射特性、辐射的大气传输特性、探测器的成像特性、目标自身特征等。目前对目标特性分析的图像融合算法的研究很少,本文针对这一不足,分析了红外与微光情况下的目标特性,在此基础上,确定了图像融合算法中的加权系数和最终融合图像。对实验结果进行了定性的主观分析,结果表明:基于目标特性分析的红外与微光图像融合算法能够更有效、准确地提取图像中的目标特征,更有利于目标的探测识别。

Target characteristic analysis is an important part of studying target detection performance,which includes the radiation characteristics of target (and background),the atmosphere transmission characteristics of the radiation, the imaging characteristics of the detector and target’s own characteristics and so on.At present,the image fusion al-gorithm of target characteristic analysis is scanty.Aiming at this flaw,the target characteristic in the case of infrared and low-light-level(LLL)is analyzed.Based on this,weighting coefficient of the image fusion algorithm is deter-mined.The experimental results are analyzed qualitatively,which shows:infrared and LLL image fusion algorithm based on the target characteristic can extract the target characteristic efficiently and accurately,and it is more benefi-cial to target detection and recognition.

该研究考察了网络交易会话所具有的一般性特征及其独有的网络话语特性,发现网络交易会话既具有口语交流特征,也包含书面语特点,同时还有口语和书面语所不具备的网络媒介话语的独有特征,是一种融合了口语、书面语和网络媒介语体特性的新型语体。

This study investigates the conversation’s general features and unique features in networked transaction, and discovers that it has not only the features of spoken communication and written language, but also the unique feature of network media discourse, which is a kind of new linguistic style with the fusion of the spoken language, written language and network media language.

充分考虑到人脸图像的整体和细节特征，进而将人脸的眉毛、眼睛、鼻子和嘴部细节部分的水平方向特性引入到特征提取环节中；将小波变换后的低频近似分量、表达上述水平特性的水平细节分量以及眉毛、眼睛、鼻子和嘴部细节区域分别进行奇异值分解，并对得到的3组奇异值进行排列组合，最终作为该图像的有效识别特征。结果表明，基于人脸细节方向特性的识别特征提取方法的识别率高于在原图上的基本奇异值分解等方法。

This paper gives full consideration to the overall and detail features , and thus introduces the horizontal direction fea-tures to the feature extraction , such as the details of the face , eyebrows , eyes , nose and mouth . Then , it takes the singular value decomposition respectively on the areas of the low frequency approximation component and the level of the detail component which expresses above levels the characteristics after wavelet transform , and the eyebrows , eyes , nose and mouth , array and assemble the obtained three sets of singular values as the effective identification feature finally . The results show that the recognition rate is high-er than the general singular value decomposition method which based on the directional characteristics of the face recognition feature extraction .

火电机组的能耗特性(发电煤耗率与各能耗参数的特性关系)反映着机组的能耗特征，它是机组节能研究的重要组成内容。机组的能耗特性等重要知识蕴含于机组的运行数据中，理论上机组的能耗特性可以从其历史数据中回归挖掘出来，然而机组的能耗指标不仅数量多，而且相互之间具有多重共线性特点，这给回归分析带来了困难。运用改进主成分分析法的特征提取和降维的功能，以损失少量信息为代价，解决了能耗变量维数过高及其多重相关性给能耗特性方程的回归带来的困难。然后，从能耗特性方程出发，辨识出了各重要能耗指标的灵敏系数。最后，通过对国内某火电厂的600 MW机组进行能耗特征分析，验证了该方法的有效性。

The energy consumption characteristics of thermal generation unit, namely the characteristic relation among coal consumption rate for power generation and energy consumption parameters, reflects energy consumption feature of the unit, so it is an important part in the research on energy-saving of generation unit. Important information such as the energy consumption characteristics of generation unit is implied in operational data, thus theoretically the energy consumption characteristics of the unit can be attained by regression in data mining (DM) technique. However, various energy consumption indices of a unit and the multiple collinearities among them make the regression analysis difficult. A method to solve this problem is proposed: firstly, utilizing the functions of feature extraction and dimension reduction in principal component analysis (PCA) and at the cost of losing bits of information, the difficulty in the regression of energy consumption characteristic equation due to too