网络攻击图生成方法研究

针对网络安全分析研究的要求,在已有研究的基础上,提出了一种灵活的网络攻击图生成方法.首先通过分析网络主机、用户权限、主机之间的连接关系和攻击等安全属性,建立了一个面向网络安全分析的安全模型,然后使用广度优先的正向搜索算法生成攻击路径,实现了网络攻击图的生成.通过实验和比较证明,该方法具有更高的有效性和更快的攻击图生成速度.     

基于因素神经网络理论的网络攻击态势小生境模型研究

结合因素神经网络(FNN)理论,定义了网络攻击态势小生境模型,从攻击角度对目标网络系统整体性能的变化进行了形式化分析。通过攻击态势提取、攻击态势理解和攻击态势显示这三个步骤分别得到了攻击态势因素藤、攻击态势因素网和小生境态势图。最后进行了仿真实验,并给出了网络攻击进行小生境态势图、攻击成功小生境态势图和攻击失败小生境态势图,实验结果表明了该模型可以有效地用于网络攻击仿真研究和仿真训练。     

基于支持向量回归的装备可靠性评估新方法

针对经典的装备可靠性评估方法在小样本情况下难以得到满意的评估结果的问题,提出了一种综合运用支持向量回归(SVR)算法和自适应重要抽样(AIS)算法进行评估的新方法.该方法通过分析小样本条件下装备可靠性评估原理,建立基于SVR和AIS的装备可靠性评估模型,分析小样本条件下开展装备可靠性评估的过程,给出相应的统一建模语言(...     

信任协商策略的Petri网建模分析方法

对Peer-to-Peer网络环境下信任协商和信任协商策略进行了定义和形式化描述,提出了一种基于Petri网模型的信任协商策略建模分析方法,将资源、证书和资源访问策略映射为Petri网网元进行建模,通过可达分析寻找最优资源解锁序列.和其他信任协商策略建模方法相比,该方法具有较好的性能.     

基于遗传算法的家用保安机器人路径规划方法

将领域知识与遗传算法相结合,提出了一种针对家用保安机器人的路径规划方法.该算法采用改进的栅格化方法来描述家庭环境,重新定义了路径适应度函数的评价方法,并设计有效的路径遗传算子.仿真结果表明了该算法的正确性和有效性.利用该算法实现了在实际家庭环境下保安机器人的路径规划与动态避障.     

基于细胞神经网络鲁棒性的彩色图像边缘检测

根据细胞神经网络(CNN)数学模型,提出一种新的彩色图像边缘检测方法。新方法继承了CNN的优点,解决了CNN现有算法不能直接检测彩色图像边缘的问题。该方法充分利用图像中的颜色信息,通过欧几里得距离度量像素之间的差异,使CNN方程可以在RGB彩色空间中进行运算。对CNN模板进行理论分析和鲁棒性研究,提出一个实现彩色图像边缘检测功能要求的CNN鲁棒性定理,为设计相应的CNN模板参数提供了解析判据。实验结果表明,该方法可以对彩色图像进行有效的边缘提取,定量评价验证了CNN边缘检测定位准确的优点。     

基于积因子方法的空中目标威胁排序研究

积因子方法是在层次分析法基础上改进的定性分析与定量分析相结合的决策分析方法,适用于多准则决策问题。通过实例和仿真实验用积因子方法根据威胁程度的大小对敌空袭目标进行排序,介绍了威胁评估和排序的求解过程,以此提供敌空袭目标对我保卫目标或区域所构成的威胁等级。实验结果表明,积因子方法可以解决利用传统层次分析法在威胁评估排序中产生的逆序问题。     

A novel RBF neural network for radar emitter recognition based on Rough Sets

With the spreading of radar emitter technology, it is more difficult for traditional methods to recognize radar emitter signals. In this article, a new method is proposed to establish a novel radial basis function (RBF) neural network for radar emitter recognition based on Rough Sets theory. First of all, radar emitter signals describing words are processed by Rough Sets, and the importance weight of each attribute is obtained and the classification rules are extracted. The classification rules are the basis of initial centers of Rough k-means. These initial centers can reduce the computational complexity of Rough k-means efficiently because of a priori knowledge from Rough Sets. In addition, basis functions of neural units of an RBF neural network are improved with attribute importance weights based on Rough Sets theory. The novel network structure makes the RBF neural network more effective. The simulation results show that novel RBF neural network radar emitter recognition can recognize radar emitter signals more effectively than a traditional RBF neural network, because of the improved Rough k-means and the network structure with attribute importance weights.     

A novel brain image enhancement method based on nonsubsampled contourlet transform

In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.     

A novel contact engineering method for transistors based on two-dimensional materials

Contact engineering is of critical importance for two-dimensional (2D) transition metal dichalcogenide(TMD)-based devices. However, there are only a few solutions to overcome this obstacle because of the complexity of the TMD-contact interface. In this work, we propose a novel method using a soft plasma treatment followed by the seamless deposition of a metal electrode to reduce the contact resistance of MoS_2 field effect transistors(FETs). The treated FETs exhibit three times higher mobility than the control FETs without plasma treatment. The soft plasma treatment can remove the facial sulfur atoms and expose the middle Mo atoms so that they come into direct contact with the metal electrode, thus greatly improving the contact behavior. First-principles calculation is also performed to support the experimental results. Our potentially scalable strategy can be extended to the whole family of TMD based FETs to provide a possible route of device processsing technology for 2D device application.     

一种基于蜡质自组装的疏水涂层制备方法

用氯仿从荷叶表面提取蜡质,氯仿挥发后蜡质能够在载玻片上自组装形成疏水涂层。扫描电镜观察表明,荷叶蜡质自组装形成的微观结构与新鲜荷叶表面纳米级的管状结构非常类似;静态接触角测试表明,载玻片上的蜡质涂层接触角高达138°±5°;GC-MS分析表明,荷叶蜡质成分主要为长链脂肪烃、脂肪醇、脂肪酸、脂和少量酮。这种疏水表面原则上能按照荷叶蜡质成分及其含量比例关系溶解混合工业原料得到。     

基于振动测试的非线性参数识别方法

研究了利用特殊的正弦扫频技术识别非线性参数的方法。该方法利用目前线性系统成熟的模态分析技术,并结合等效线性化理论,通过振动测试识别结构的非线性参数,可以建立一个更加准确的模型来反映非线性结构的动力学特性,从而提高模型的预测精度。该方法包括两部分：（1）常位移测试识别非线性刚度;（2）常速度测试识别非线性阻尼。常位移测试是在一次正弦扫频过程中,通过调整各频率下的激励力幅值使得位移响应的幅值为常数,获得一组频响函数,通过模态分析获得等效刚度;改变位移响应的幅值进行多次测试,获得多组等效刚度;对获得的一系列恒定位移响应下的等效刚度进行曲线拟合,即可获得所有线性和非线性刚度参数。常速度测试与其类似。以三自由度非线性系统为例,进行了常位移测试和     

A method of production fine layout planning based on self-organising neural network clustering

Organising and optimising production in small and medium enterprises with batch production and many different products can be very difficult due to high complexity of possible solutions. The paper presents a method of fine layout planning that rearranges production resources and minimises work and material flow transfer between production cells. The method is based on self-organising map clustering which organises the production cells into groups sharing similar product properties. The proposed method improves the internal layout of each cell with respect to a material flow diagram and a from-to matrix, and fine workspace positioning also considers various restrictions on placement, specifications and types of transportation. The method is particularly suitable for improving the existing layouts. The method was applied in the Slovenian company KGL d.o.o. and promising results were achieved. A reduction by more than 40% in the total transport length with respect to the current production layout was observed.     

基于复杂网络理论的产品结构模块划分方法

针对现有的产品结构模块划分方法的不足,将复杂网络理论中的社团结构发现方法应用于产品结构模块的划分,提出了一种新的结构模块划分方法.产品的结构单元作为网络的节点,有关联的结构单元对应的节点之间为网络的边,从而构建产品结构的网络图,使用复杂网路理论中的社团结构发现方法——GN算法实现结构模块的划分.论文阐述了基于GN算法的模块划分的方法与步骤,在此基础上以汽车发动机的结构模块划分为例验证了该方法的有效性和实用性,并对模块划分结果进行了分析,最后指出了今后进一步研究的内容与方法.     

基于前馈神经网络的自适应回声消除方法

回声消除常用的LMS算法收敛性差，而收敛性好的RLS算法计算量大。文章中提出一种全新的求解方法：基于前馈神经网络的自适应回声消除方法。把回声消除模型中求解滤波器系数的问题转化为前馈神经网络神经元权值的训练问题，并运用误差反向传播算法(BP算法)得出神经元权值的递推公式。经仿真计算，能较好地实现回声消除，与原传统算法LMS和RLS计算比较：该方法能得到非常高的计算精度和明显优越的收敛性能，而计算量只有RLS算法的一半。     

A novel multi-modal medical image fusion method based on shift-invariant shearlet transform

Abstract

Medical image fusion plays an important role in clinical applications, such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis and treatment planning. Shearlet is a novel multi-scale geometric analysis (MGA) tool proposed recently. In order to overcome the drawback of the shearlet-based fusion methods that the pseudo-Gibbs phenomenon is easily caused around the singularities of the fused image, a new multi-modal medical image fusion method is proposed in shift-invariant shearlet transform domain. First, the original images are decomposed into lowpass sub-bands and highpass sub-bands; then, the lowpass sub-bands and high sub-bands are combined according to the fusion rules, respectively. All the operations are performed in shift-invariant shearlet domain. The final fused image is obtained by directly applying inverse shift-invariant shearlet transform to the fused lowpass sub-bands and highpass sub-bands. Experimental results demonstrate that the proposed method can not only suppress the pseudo-Gibbs phenomenon efficiently, but perform better than the popular wavelet transform-based method, contourlet transform-based method and non-subsampled contourlet transform-based method.     

基于深度置信网络与信息融合的齿轮故障诊断方法

针对齿轮在复杂运行工况下故障特征提取困难,传统故障诊断方法的识别精度易受人工提取特征的影响,以及单传感器获取信息不全面等问题,提出基于深度置信网络(DBN)与信息融合的齿轮故障诊断方法.通过多传感器信息融合技术对每个传感器采集的振动信号进行数据层融合;利用DBN进行自适应特征提取从而实现故障分类.为了避免因人为选择DB...     

一种基于VGGish神经网络的水声目标识别方法

水声目标智能识别是水声装备智能化的重要组成部分,深度学习则是实现水声目标智能识别的重要技术手段之一。当前水声目标智能识别经常面临数据集较小带来的训练样本量不足的情况,针对小数据集识别中存在的因过拟合导致模型泛化能力不足,以及输入的水声信号二维谱图样式不统一的问题,文章提出了一种基于VGGish神经网络模型的水声目标识别方法。该方法以VGGish网络作为特征提取器,并在VGGish网络前部加入了信号预处理模块,同时设计了一种基于传统机器学习算法的联合分类器,通过以上措施解决了过拟合问题和二维谱图样式不统一问题。实验结果显示,该方法应用在ShipsEar数据集上得到了94.397%的识别准确率,高于传统预训练-微调法得到的最高90.977%的准确率,并且在相同条件下该方法的模型训练耗时仅为传统预训练-微调方法的0.5%左右,有效提高了识别准确率和模型训练速度。     

基于可视切线图的未知环境建模新方法研究

针对移动机器人路径规划的环境建模问题,提出了一种基于可视切线图的未知环境建模新方法。首先给出可视切线图的定义,其节点为障碍物边界上的可视切点,边为视点与可视切点间的切线段。其次为避免机器人与障碍物相碰,对可视切线图进行了扩展。最后在建立可视切线图的基础上,采用两种启发函数搜索全局目标指导下的局部最优路径。仿真分析证明,可视切线图的数据结构简单,所需存储空间小,具有较好的环境适应能力,是一种有效的路径规划工具。     

An approximate method for non-repairable systems based on oppurtunistic replacement policy

An opportunistic replacement policy for a multi-unit system with various failure rates is developed. The objective function is to minimize the total maintenance cost due to preventive and failure replacement. Failure rate is used as replacement criterion for each unit. Units are replaced at failure or at a failure rate limit, L, whichever occurs first. A unit is also replaced at replacement time of another unit if its failure rate falls in a preventive replacement interval, (L-u, L). A solution method to obtain the value of and u is presented. The solution procedure is demonstrated through an algorithm followed by a numerical example.