远程勘验取证分析软件开发与实现

远程勘验分析技术是计算机取证技术的一个重要分支,而如何实现自动、智能远程勘验分析目标网站是一项重要研究课题。文章简要介绍了远程勘验取证分析软件＂网际无痕特种兵＂基本原理和功能,进行了实证性分析并取得了积极的成果。     

基于带有惩罚因子的阴性选择算法的恶意程序检测模型

提出了一个基于带有惩罚因子的阴性选择算法的恶意程序检测模型.该模型从指令频率和包含相应指令的文件频率两个角度出发,对指令进行了深入的趋向性分析,提取出了趋向于代表恶意程序的恶意程序指令库.利用这些指令,有序切分程序比特串,模型提取得到恶意程序候选特征库和合法程序类恶意程序特征库.在此基础上,文中提出了一种带有惩罚因子的阴性选择算法(negative selection algorithm with penalty factor,NSAPF),根据异体和自体的匹配情况,采用惩罚的方式,对恶意程序候选特征进行划分,组成了恶意程序检测特征库1(malware detection signature library 1,MDSL1)和恶意程序检测特征库2(MDSL2),以此作为检测可疑程序的二维参照物.综合可疑程序和MDSL1,MDSL2的匹配值,文中模型将可疑程序分类到合法程序和恶意程序.通过在阴性选择算法中引入惩罚因子C,摆脱了传统阴性选择算法中对自体和异体有害性定义的缺陷,继而关注程序代码本身的危险性,充分挖掘和调节了特征的表征性,既提高了模型的检测效果,又使模型可以满足用户对识别率和虚警率的不同要求.综合实验...     

基于晶格的人工内分泌系统模型及在多机器人系统中的应用

为了解决多机器人系统分布式控制问题,借鉴人体内分泌系统研究的理论成果,提出基于晶格的人工内分泌系统模型.该模型以环境晶格化为基础,以细胞智能化为依托,以累加激素为纽带,以靶细胞为导向,能够适应外环境的持续变化,保持内环境的相对稳定,充分体现人体内分泌系统的自组织和自修复特性.实验结果表明,无需复杂的集中控制策略,基于该...     

基于冯•诺依曼邻域结构的人工鱼群算法

提出了一种基于冯¢ 诺依曼邻域结构的人工鱼群算法. 每条人工鱼只和与自己相连的上下左右的人工鱼进行信息交换, 从而减少了计算邻域中心位置和极值位置的计算量, 有效地维持了种群的多样性, 加快了算法的运行速度. 在觅食行为中, 人工鱼通过直接移动到搜索到的较好位置, 来加快搜索速度. 在随机游动行为中, 人工鱼以小半径进行搜索, 因此算法的优化精度得到了提高. 采用动态调整人工鱼视野和步长的方法, 较好地平衡了全局搜索能力和局部搜索能力. 仿真和实例计算结果表明, 该算法具有更好的优化性能.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

基于正交试验设计的克隆选择函数优化

将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(I)算法和串联式的CSO+CSO-OED(II)算法.将新设计的算法用于9个经典的测试函数和6个复杂的测试函数进行对比测试,实验结果表明,CSO-OED能够有效地保持种群的多样性,避免算法不成熟收敛.CSO+CSO-OED(I)和CSO+CSO-OED(II)将全局搜索和局部搜索分开进行优化,对比实验表明,这种搜索策略不但能够保证算法的收敛性,还能有效地提高搜索解的精度,增强算法的鲁棒性.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

基于优化目标可调控的免疫网络算法的分析与应用

针对目前几种免疫网络模型在数据聚类方面应用的一些不足,在aiNet免疫算法的基础上结合函数优化的思想提出一种基于目标可调控的免疫模型。并在算法中给出目标控制函数,和细胞记忆库的概念。本算法提高了免疫学习质量并从整体上对免疫网络进行优化。     

T–S fuzzy model-based tracking control of a one-dimensional manipulator actuated by pneumatic artificial muscles

Pneumatic artificial muscle (PAM) has highly nonlinear and time-varying behavior due to gas compression and nonlinear elasticity of the bladder containers. Hence, it is difficult to achieve excellent tracking performance when using classical control methods. This study proposes a Takagi–Sugeno (T–S) fuzzy model-based control for improving control performance. The proposed approach decomposes the model of a nonlinear system into a set of linear subsystems. This allows, the T–S fuzzy model-based controller to use simple linear control techniques providing a systematic framework for the design of a state feedback controller. Stability analysis is carried out using Lyapunov direct method. The powerful LMI Toolbox in MATLAB is employed to solve linear matrix inequalities (LMIs) to obtain the controller gains. Experimental results verified that the proposed controller can achieve excellent tracking performance under different disturbances.     

一种人工鱼算法与捕鱼算法相结合的优化方法

在分析人工鱼群算法和捕鱼算法存在不足的基础上,提出了一种人工鱼群算法(AFSA)与采用捕鱼策略的优化算法(FSOA)相结合的混合算法。该算法在优化初期使用AFSA算法搜索局部最优域,而在优化后期则使用FSOA算法在优化前期所初步确定的局部最优域中搜索最优解。实验计算结果表明,该算法具有优化精度高、收敛速度快的特点。