网络防火墙技术应用分析

介绍了防火墙的概念、组成原理及几种典型的防火墙系统,对如何加强防火墙的防护作用进行了分析。     

基于Web日志和商品分类的协同过滤推荐系统

传统的协同过滤算法一般以用户对商品的评分作为研究,考虑到显式的评分数据一般很少能够取到,本文从记录用户浏览历史的服务器端日志文件以及客户购买历史数据作为数据源,根据商品分类进行评分矩阵设计,最后由此得到最近邻居从而给予个性化推荐,试验证明推荐效果比较显著。     

动态定位中机动和粗差检测延迟的探讨

动态定位数据处理中,判定运动载体何时开始机动,测量数据何时出现粗差是一个困难的问题。为了在实时动态定位中尽可能快地检测出运动载体机动和观测数据何时出现粗差,文中提出了一种对机动和粗差检测的新方法。该检测算法是基于机动和粗差检测延迟最小的准则。通过分析,对不同的显著水平和机动幅度存在一个最优窗口长度。     

IP Filter包过滤防火墙在校园网上的实现

介绍了利用IP Filter包过滤功能在网络出口组成防火墙实现网络安全,并通过一个校园网作为实例,说明包过滤规则的制定和在IP Filter上的配置。     

改善全息圆锥准直线及提高准直精度的一种方法

用一张计算全息图可产生锥面波,锥面波衍射图样的中心在空间是连续的,可作为直线基准。本文提出了提高锥面波直线准直精度的一种方法即空间滤波法。实验结果与理论分析一致。     

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast? τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.     

Several least-squares problems related to the Hodrick–Prescott filtering

The Hodrick–Prescott (HP) filtering is frequently used in macroeconometrics to decompose time series, such as real gross domestic product, into their trend and cyclical components. Because the HP filtering is a basic econometric tool, it is necessary to have a precise understanding of the nature of it. This article contributes to the literature by listing several (penalized) least-squares problems that are related to the HP filtering, three of which are newly introduced in the article, and showing their properties. We also remark on their generalization.     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

A small but practically useful modification to the Hodrick–Prescott filtering: A note

The Hodrick–Prescott (HP) filtering is widely applied to decompose macroeconomic time series, such as real Gross Domestic Product, into cyclical and trend components. This paper presents a small but practically useful modification to this approach. The reason why this modified filtering is of practical use is that it provides not only identical trend estimates as the HP filtering but also extrapolations of the trend. We provide a proof based on a ridge regression representation of the modified HP filtering. This is mainly because it enhances our understanding of the approach.     

The Frisch–Waugh–Lovell theorem for the lasso and the ridge regression

The Frisch–Waugh–Lovell (FWL) (partitioned regression) theorem is essential in regression analysis. This is partly because it is quite useful to derive theoretical results. The lasso regression and the ridge regression, both of which are penalized least-squares regressions, have become popular statistical techniques. This article describes that the FWL theorem remains valid for these penalized least-squares regressions. More precisely, we demonstrate that the covariates corresponding to unpenalized regression parameters in these penalized least-squares regression can be projected out. Some other results related to the FWL theorem in such penalized least-squares regressions are also presented.