基于Filter-Hook技术的防火墙软件设计与实现

为了解决网络安全问题,设计了一个基于Filter-Hook技术的防火墙软件,该软件能够根据数据包过滤规则实现防火墙的功能。实验结果显示,该防火墙软件有效地改善了网络的安全性能。

Cubature Kalman filters: Derivation and extension

This paper focuses on the cubature Kalman filters （CKFs） for the nonlinear dynamic systems with additive process and measurement noise. As is well known, the heart of the CKF is the third-degree spherical–radial cubature rule which makes it possible to compute the integrals encountered in nonlinear filtering problems. However, the rule not only requires computing the integration over an n-dimensional spherical region, but also combines the spherical cubature rule with the radial rule, thereby making it difficult to construct higher-degree CKFs. Moreover, the cubature formula used to construct the CKF has some drawbacks in computation. To address these issues, we present a more general class of the CKFs, which completely abandons the spherical–radial cubature rule. It can be shown that the conventional CKF is a special case of the proposed algorithm. The paper also includes a fifth-degree extension of the CKF. Two target tracking problems are used to verify the proposed algorithm. The results of both experiments demonstrate that the higher-degree CKF outperforms the conventional nonlinear filters in terms of accuracy.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks （WSNs）, a new signal reconstruction algorithm based on a cubature Kalman particle filter （CKPF） is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter （CKF） to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter （UKPF） and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

Generalized cubature quadrature Kalman filters:derivations and extensions

A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.

Adaptive “Cubature and Sigma Points” Kalman Filtering Applied to MEMS IMU/GNSS Data Fusion during Measurement Outlier

In this paper, adaptive sensor fusion INS/GNSS is proposed to solve specific problem of non linear time variant state space estimation with measurement outliers, different algorithms are used to solve this specific problem generally occurs in intentional and non-intentional interferences caused by other radio navigation sources, or by the GNSS receiver’s deterioration. Non linear approximation techniques such as Extended Kalman filter EKF, Sigma Point Kalman Filters such as UKF and CDKF are computed to estimate the navigation states for UAV flight control. Several comparisons are conduced and analyzed in order to compare the accuracy and the convergence of different approaches usually applied in navigation data fusion purposes. The last non linear filter algorithm developed is the Cubature Kalman Filter CKF which provides more accurate estimation with more stability in Tracking data fusion application. In this work, CKF is compared with SPKF and EKF in ideal conditions and during GNSS outliers supposed to occur during specific interval of time, innovation based adaptive approach is selected and used to modify the covariance calculation of the non linear filters performed in this paper. Interesting results are observed, discussed with real perspectives in navigation data fusion for real time applications. Three parallel modified algorithms are simulated and compared to non-adaptive forms according to Root Mean Square Error (RMSE) criteria.

An Approach towards Eco-labeling of Compressed Air Filters Based on Carbon Footprint

Compressed air--a major industrial energy carrier, its filters are used to reach the required cleanliness level of compressed air. These filters, however, introduce a pressure drop which results in a loss of energy. It is shown that over the life cycle of a compressed air filter, the pressure drop dominates the total energy consumption and subsequently, the carbon footprint. From an economic as well as ecologic point of view, a reduction of the pressure drop is hence of utmost importance. Based on this finding and structuring, the wide range of applications and operational parameters, a pragmatic and technically feasible eco-labeling system for compressed air filters was developed as a decision supporting tool. Using this tool, procurement managers as well as the process engineers and environmental officers are able to steer the selection of suitable compressed air filters. The approach has been proposed to and discussed with various representatives of the compressed air filter industry nevertheless there is probably a long way to go to establish such a labeling procedure in industry.

无线局域网应用多重USG防火墙的安全策略研究

无线网络是一种方便灵活的网络接入方式,同时也存在一系列的安全威胁。通过对无线网络中可能产生的攻击和入侵方式进行详细的研究,确定基于华为USG5500防火墙的多重综合防御措施,包括身份验证、访问控制、数据加密、监测防御等多个方面,重点放在无线网络的身份验证和访问控制上。基于eNSP平台部署仿真拓扑,规划IP地址和防火墙区域,在无线网络中采用WPA2-PSK加密技术和VLAN技术,在多重防火墙中设置访问控制策略和流量过滤规则。仿真结果表明,在正确设置策略规则的情况下,多重USG5500防火墙能够及时监控和识别网络中的异常行为,有效检测和阻止网络攻击,提升无线网络的安全性和可靠性。

基于改进SPRINT分类算法的数据挖掘模型

为解决目前数据挖掘模型分类时间长、挖掘准确率不高的问题,提出基于改进决策树分类算法(SPRINT)的数据挖掘模型。先采用最大-最小规范化公式完成原始数据线性变换,利用改进后的SPRINT分类算法按照输入数据特性进行分类,使用协同过滤技术生成与数据相近的属性集,计算数据属性相似度,生成语义规则集,为用户提供更优的数据服务。选取某公司营销数据集作为样本进行对比实验,结果表明,与对比模型相比,所提出的数据挖掘模型分类时间更短,挖掘准确率更高,能为用户提供更优质的数据服务。

算法推荐背景下网络服务平台过滤义务负担研究

算法推荐技术使得传统版权法体系受到冲击,“通知删除”规则在算法推荐背景下逐渐失灵,无法有效平衡版权人与传播者之间的利益关系,由此所形成的价值差将有损版权人的创作积极性进而严重阻碍互联网文化产业的发展。过滤义务既不是注意义务也不是普遍审查义务,而是一种特殊的版权审查义务,引入网络服务平台的版权过滤义务既有利于缓解现实矛盾,也符合法经济学、公共政策学及知识产权基本原则的基本理论。为了有效平衡版权人与传播者等相关主体的利益,过滤义务负担的标准不宜过高,应根据网络服务平台提供的不同服务类型,对版权人上传的作品类型及网络服务平台的预见能力进行综合设定,网络服务平台对于侵权行为的判断难度越高则其负担的过滤义务越低,反之亦然。

基于CNN和关联规则的协同过滤混合推荐

针对传统协同过滤算法在药品推荐领域中对用户评价过于依赖、数据稀疏性严重等问题,提出一种基于卷积神经网络(CNN)和关联规则的协同过滤混合推荐算法。首先利用CNN从药品文本数据中获取深层功效特征向量,然后使用Apriori算法发掘药品间的关联规则。在此基础上,从药品功效特征和关联性两个角度结合对应的相似度计算公式计算药品相似度,进而预测评分缺失值,最终对稀疏矩阵进行叠加填充实现药品推荐的优化。经试验对比,论文算法相比传统的协同过滤算法在MAE和RMSE指标上下降了3%~4%,在数据较稀疏的情况下具有良好的推荐效果。

Web3.0时代短视频产业版权过滤义务规则探析

Web3.0时代背景下,短视频平台凭借数字技术的发展已由简单服务中立者转变为内容服务辅助者。从规范角度检视,传统的“通知—删除”规则不仅成为平台滥用并逃避责任的借口,还因被动等待、事后保护的特征导致短视频产业发展同规则治理错位明显。因此,尝试引入内容过滤义务是解决该冲突的关键。通过深入对过滤义务规则进行正当性论证,建议不仅需引入事前过滤义务还应结合事后保护规则,形成产业保护链,充分发挥事前、事后双向保障之功效。最后,在借鉴欧盟立法的同时应立足我国实际,重新审视产业职责以营造数字营商环境。提出分层级过渡适用事前过滤方案,不仅施以“硬法”保护也加以“软法”塑造,以期发掘短视频产业治理的新路径。

智能媒介内容过滤义务的理论基础与规范构造——以短视频平台为考察

算法推荐技术使传统避风港规则饱受质疑,影响着平台权责适配的逻辑起点。短视频平台对用户内容的推送无疑解决了信息过载问题,却也导致了现阶段用户权利的事前保护阙如和事后救济失范的局面。由此产生了实践上注意义务的扩张和学理上针对平台应然义务的争论。而施以平台过滤义务是解决该问题的关键。从侵权法的预防功能来看,过滤义务属于较高程度的注意义务,是民事注意义务内涵的延伸。同时,守门人理论是引入过滤义务正当性的支撑,具有缓解现行法对平台类型不足和过错归责解释受限的功能。有鉴于此,应以守门人理论为基础对平台的应然义务进行教义学的归位。通过对事前过滤机制的规范构造,增设或解释过滤义务条款,将过滤义务严格限缩于特定情形与用户申请两种模式;在构造时也应做好同避风港规则的相衔接和搭建错误过滤的救济程序,以期在私法上为“平台-用户”的权益分配提供新方案。

轨道运输车辆液压制动系统车轮防抱死冗余控制

为了提高轨道运输车辆制动期间的安全性、避免车辆侧翻事件的发生,提出轨道运输车辆液压制动系统车轮防抱死冗余控制方法。分析液压制动系统的制动方式,并初步建立液压制动防抱死策略,在经典卡尔曼滤波算法的基础上,结合贝叶斯法则与先验估计法,实现液压制动系统的状态跟踪,得到车辆质心实时偏移测量结果,当测量结果超出偏移量正常波动范围时,判定液压制动防抱死策略失效,即刻启动防抱死冗余控制策略。防抱死冗余控制作为液压制动系统控制失效时的备份控制方法,将抗积分饱和算法与滑模控制算法相结合,避免因滑模控制器自身控制率饱和造成的制动系统超调及振动,有效实现了车轮制动力的调节分配,保证车辆制动期间的行驶安全。实验证明,所提方法在轨道运输车辆制动过程中,能够实现车轮滑移率的稳定控制,有效避免车辆脱轨事件的发生,为轨道运输车辆的制动安全提供重要保障。

遗传算法在抽取和过滤模糊控制规则中的应用研究

本文分析了模糊控制规则抽取中存在的困难，提出了用遗传算法（GA）解决模糊控制中模糊规则的抽取和过滤问题，给出了具体的解决方法．仿真结果表明该方法有效地解决了模糊规则的抽取和过滤问题，而且结果可以直接放入计算机内存中进行控制．该方法具有推广应用的价值．

可扩展的网络安全设备内策略冲突检测算法

从研究网络安全设备内策略冲突检测方法入手,针对当前设备内策略冲突检测算法不具有扩展性的缺点,采用规范化和离散化技术,将策略域的属性数据统一映射到实数区间;通过定义实数区间的关系运算判断策略域之间的关系;在此基础上设计了一种可扩展的网络安全设备内冲突检测算法;该算法通过规则过滤处理,提高了安全策略冲突检测算法的执行效率。实验验证表明该算法正确高效,具有实用价值。

一种基于模糊规则的非线性系统快速模糊辨识方法

针对以往模糊建模方法中算法过于复杂的问题,提出了一种简单而有效的复杂系统模糊建模新方法。该方法是基于输入空间的模糊划分,计算给定样本在各模糊子空间的隶属度,并利用卡尔曼滤波算法辨识模糊模型的结论参数。整个辨识过程与模糊聚类方法[8]和误差反馈学习方法[5]相比所需的CPU时间最短。最后通过著名的Box-Jenkins煤气炉数据[7]仿真结果证明了该方法的有效性与实用性。

信息安全中的变形关键词的识别

互联网中的不法分子为了逃避安全过滤,将不良信息中的文本进行变形,并在在网络中散布。为了识别和过滤这些不良文本,该文分析了其变形的特征,根据词同现和字符编码规则的不同对文本进行预处理,从文本中抽出包含有变形特征的有害词串。针对这些有害词串中各字符相邻、有序频繁出现的特点,提出采用基于关联规则自学习算法提取具有安全特色的关键词。实验表明,该方法可以改善传统方法在安全过滤过程中无法识别变形关键词的现状,对主题过滤提供补充,提高基于内容的安全过滤的效率。

雷达/红外双模制导背景下的模糊目标跟踪器

在雷达/红外双模制导背景下,对雷达/红外传感器的测量数据进行融合并提出了一种跟踪机动目标的模糊跟踪器。该跟踪器能够精确的获取目标机动的动态信息,应用一系列的模糊规则自适应的调整卡尔曼滤波器的过程噪声协方差矩阵,从而更适合用于实际的跟踪系统。通过对红外成像和雷达多传感器对目标跟踪系统的数字仿真表明所提出的模糊跟踪器性能良好。

基于Android系统的KOA运动监测系统研究

本文设计了一种便捷式的膝骨性关节炎(KOA)运动监测系统,KOA患者可以通过该监测系统了解自身运动规范性并作出适当的调整。该监测系统将装有MMA7361加速度传感器的蓝牙模块穿戴在人体的下肢进行三轴加速度信号的采集,并将三轴加速度信号通过蓝牙发送至Android智能手机上。Android手机通过扩展Kalman滤波算法将采集到的加速度信号进行滤波,并通过规则归纳法对动作规范性进行识别。经过大量实验测试结果统计,对保持时间太短、运动腿弯曲、抬腿速度太快这三类动作训练的平均识别率分别为89.6%,89.3%,89.5%。结果表明:基于Android智能手机平台构建的KOA运动监测系统具有便捷、成本低等优点,而且能够较好的识别患者运动中出现的不规范动作,满足运动监测的应用要求。

均方根嵌入式容积卡尔曼滤波

传统容积卡尔曼滤波(CKF)的基础是三阶球面–径向容积准则,该准则不仅要求计算n维超球体上的面积分,还需将容积准则与扩展高斯–拉盖尔准则配合使用,不易推导出高阶CKF滤波算法.此外,CKF推导所采用的三阶球面容积准则也存在缺陷,这极大地限制了CKF的滤波精度.为避免以上问题,本文基于嵌入式容积准则和均方根滤波技术,提出一种加性噪声环境下,用于非线性动态系统状态估计的全新容积卡尔曼滤波算法—–三阶均方根嵌入式容积卡尔曼滤波(SICKF).SICKF具有滤波精度高、数值稳定性强等诸多优点,适用于动态目标跟踪、非线性系统控制等.仿真结果表明,SICKF的滤波精度显著优于传统的非线性滤波算法.