基于NDIS中间层的网络数据包拦截技术及实现

该文分析了Windows平台下各种包拦截过滤的方法,在分析各种方法的基础上,采取基于NDIS中间层来拦截网络数据包。设计并实现了Windows 2000/xp平台下的网络数据包的拦截与处理。     

一种针对规则集不一致性的测试数据包选取算法

在防火墙规则集正确性测试中,现有的测试数据包选取算法大多随机选取数据包和从规则顶点选取数据包。然而,这种做法忽略了存在规则不一致性的区域,从而导致不能检测出所有因规则不一致性而产生的配置错误。针对这一情况,提出了一种针对规则集不一致性的测试数据包选取算法。该算法以两条规则为基本单位,计算其不一致性区域。算法不但从规则顶点选取数据包,而且从规则集不一致性区域选取数据包。测试表明,与常见测试数据包选取算法相比,该算法只需增加少量测试数据包,就能检测出所有因规则不一致性而带来的配置错误。     

基于线性回归机制的带宽测量方法设计及实现

拓展了变包模型以获取某个网段内主干链路的带宽.分析了带宽测量的基本原理,之后讨论了获取网络链路带宽的方法,解决了路由器判定、路径分解以及探测结果组织获取等3个问题.着重讨论了减少探测量的方法、并行调度机制以及回归步骤等关键问题,最后给出了带宽测量系统的实现及实验结果.     

基于奇异值分解和小波包分析的液压泵振动信号特征提取方法

针对液压泵故障特征提取问题,提出了一种基于奇异值分解和小波包变换的液压泵振动信号特征提取方法.通过奇异值分解将噪声非均匀分布的液压泵振动信号正交分解为噪声分布相对均匀的分量,对各分量进行小波包阈值去噪,重构去噪后分量,对去噪后信号进行小波包分解,提取各频带能量特征.以齿轮泵为例,将该方法对齿轮泵的气穴故障、齿轮磨损和侧板磨损3种常见故障和正常状态的振动信号进行特征提取分析,结果表明,该方法可有效提取齿轮泵故障特征.     

Output-feedback control design for NCSs subject to quantization and dropout

In this paper, the output-feedback control problem is considered for networked systems involving in signal quantization and data packet dropout. The states of the controlled system are unavailable and the output signals are quantized before being communicated. An estimation method is introduced to cope with the effect of random packet loss that is modelled as a Bernoulli process. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. The output-feedback controller is designed which guarantees the closed-loop system is exponentially mean-square stable. The simulation example is given to illustrate the proposed method.     

基于多频带分析的自适应配电网故障选线研究

该文应用小波包具有良好的领域分频特性，以适当频率带宽对配电网发生单相接地故障后备条线路的暂态零序电流进行分解，得到其在不同频段下的输出。按照能量最大的观点确定各条线路暂态电容电流分布最集中的特征频段，并根据故障线路暂态电容电流的极性与非故障线路不同的原理，通过比较所有线路在各自特征频段下的小波包分解系数的极性来自适应地实现故障选线。理论分析及大量的ATP仿真结果表明：该选线方法可以准确、可靠地实现配电网单相故障的选线。     

一种IP分组重组的集群并行计算的方法

基于N元非合作模型的路由切割调度算法的基础,利用集群来解决大规模的IP分组重组问题是一个可行的办法。论文设计实现了IP分组重组（又称网络地址转换NAT,Network Address Transfer）的集群并行计算方法。使用普通PC构建了基于MPI用于IP分组重组计算的SMP集群,研究了在典型的校园网环境下的较大规模的IP分组重组环境并讨论了集群内部计算节点上的两种负载平衡方法。     

一维卷积神经网络超宽带天线建模方法

为加快天线建模优化速度,提出了一种改进的一维卷积神经网络(1D-MCNN)模型。此一维神经网络的卷积核大小为2,将ReLU函数作为激活函数降低梯度弥散;利用Adam优化器与dropout技术结合,提高模型的特征学习能力和非线性函数逼近能力。本文使用1D-MCNN模型对超宽带微带单极子天线几何参数建模,以天线的8个几何参数作为特征输入,对天线的回波损耗值进行预测。实验表明,本文所提1D-MCNN模型与深层MLP网络模型、MLP网络模型、RBF神经网络模型相比,回波损耗值的平均误差分别减小了1.95%,120.27%,125.71%,拥有更高的准确度,预测能力更强,对优化超宽带天线建模可行且性能具有一定优越性。     

Iterative Learning Control With Incomplete Information: A Survey

This paper conducts a survey on iterative learning control (ILC) with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects: the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory.      

一种不完全信息下递推辨识方法及收敛性分析

针对信号在网络环境下传输带来不完全信息使得在线参数辨识算法和收敛性困难的问题, 不同于传统递推最小二乘方法, 本文提出了一种不完全信息下递推辨识方法并分析其收敛性. 首先运用伯努利分布刻画引起不完全信息的数据丢包特性, 然后基于辅助模型方法补偿不完全信息并构造了新的数据信息矩阵, 并运用矩阵正交变换性质对数据信息矩阵进行QR分解, 推导了融合网络参数的递推辨识新算法, 理论证明了在不完全信息下递推参数辨识算法的收敛性. 最后仿真结果验证了所提方法的可行性和有效性.