业务自相似性分析方法研究

自相似性是真实网络业务数据中的一个主要特性,其强度大小对网络性能、流量控制及排队分析结果有重要影响,因此,准确计算表征自相似性强弱的参数Hurst值是一项很有意义的研究课题.从时域与频率域两个方面出发,综述了各种计算方法的基本原理,介绍了各自统计量定义方法,并对各种方法的结果进行横向比较,分析了各自的影响因素,最后对下一步研究方向进行了预测.

一种基于Hurst参数的FFSN实时检测方法

为控制速变服务网络(Fast Flux Service Network,FFSN)对恶意程序的隐藏,在研究已有FFSN检测方法的基础上,根据FFSN的本质特征,提出相应的检测方法:采用被动方法提取网络域名的响应延迟时间,根据合法域名响应延迟时间的自相似性及与FFSN响应延迟时间的差异,使用基于Hurst参数的方差-时间图(Variance-Time Plots,VTP)分析法检测FFSN;为避免网络误报,对于疑似FFSN采用主动方法进行速变属性判断,以确认FFSN的存在,给出了算法流程和序列构建方法。实验验证结果表明,基于Hurst参数的FFSN检测法不仅能实时检测FFSN的存在,还能给出具体的FFSN域名,快速有效,达到预期目标。

网络时延的自相似性研究

网络时延是评价网络性能的重要指标之一,针对网路时延的自相似特性展开研究。首先分析网络时延的组成,并通过Ping测量方法,对局域网、广域网的不同目标主机进行Ping测量网络时延试验。给出自相似过程的定义、判别方法,采用方差时间法对网络时延进行自相似性判别,分析结果表明,网络时延具有很强的非线性及自相似特征,与局域网相比,广域网的长程依赖性更大。

Finite difference time domain method forward simulation of complex geoelectricity ground penetrating radar model

The ground penetrating radar(GPR) forward simulation all aims at the singular and regular models, such as sandwich model, round cavity, square cavity, and so on, which are comparably simple. But as to the forward of curl interface underground or “v” figure complex model, it is difficult to realize. So it is important to forward the complex geoelectricity model. This paper takes two Maxwell’s vorticity equations as departure point, makes use of the principles of Yee’s space grid model theory and the basic principle finite difference time domain method, and deduces a GPR forward system of equation of two dimensional spaces. The Mur super absorbed boundary condition is adopted to solve the super strong reflection on the interceptive boundary when there is the forward simulation. And a self-made program is used to process forward simulation to two typical geoelectricity model.

Slope displacement prediction based on morphological filtering

Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.

Influence of Synchronization Impairments on an Experimental TDOA/FDOA Localization System

In Electronic Warfare, and more specifically in the domain of passive localization, accurate time synchronization between platforms is decisive, especially on systems relying on TDOA （time difference of arrival） and FDOA （frequency difference of arrival）. This paper investigates this issue by presenting an analysis in terms of final localization performance of an experimental passive localization system based on off-the-shelf components. This system is detailed, as well as the methodology used to carry out the acquisition of real data. This experiment has been realized with two different kinds of clock. The results are analyzed by calculating the Allan deviation and time deviation. The choice of these metrics is explained and their properties are discussed in the scope of an airborne bi-platform passive localization context. Conclusions are drawn regarding the overall localization performance of the system.

The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability

Based on the analysis of the satellite DCB data estimated by our method and the Center for Orbit Determination in Europe(CODE)from 1999 to 2011,the features of the temporal variation of differential code biases(DCB)are studied.Summarily,there are three types of variations in DCB on different time scales.The first one is the day-to-day variation that exhibits more obviously in solar maximum years.The second one is the variation with about one year periodic variation that behaves more obviously from 1999 to 2004.The last one is the monotonously descending tendency from 1999 to 2010.Considering the basic ionospheric approximation in DCB estimation method,the features of the variability of the ionospheric morphology from 1999to 2010 are also displayed based on the ionospheric characteristic parameters.It can be concluded that the day-to-day and annual variation of the estimated global positioning system(GPS).DCB is related to the ionospheric variability.The variation of DCBs on solar cycle time scale includes the real hardware DCBs and pseudo-DCBs induced by ionospheric variation.No doubt,these kinds of"pseudo"variations of DCB will affect the precision of ionospheric total electron content(TEC)derived from the GPS data.In addition,this study is helpful for evaluating the influence of ionospheric weather on TEC derivation and is also useful for developing one estimation method of DCB with more stability and precision through introducing a more practical ionospheric model.