基于免疫危险理论的网络安全态势评估

为实时定量评估网络安全态势,提出了一种基于免疫危险理论的网络安全态势评估方法.通过研究免疫运行机制,定义了网络安全问题中的抗原、抗体和免疫细胞,描述了危险信号的判断规则,准确识别出了抗原.在分析免疫应答机制和免疫平衡机制中抗体浓度变化原因的基础上,给出了抗体浓度的计算方法.最后,结合抗体浓度与危险程度的关系,建立了基于抗体浓度的危险感知模型以实时定量评估网络安全态势.仿真实验表明,所提方法计算出的抗体浓度准确地反映了系统面临的危险程度,能够为网络管理提供有效的决策支持.     

基于免疫网络的遥感影像分类算法

基于独特型免疫网络原理,提出了一种新型的分区记忆模式人工独特型网络模型,并利用其对卫星遥感数据进行了分类。该模型在结构上将免疫网络的记忆抗体划分为特异记忆抗体区和自由记忆抗体区。前者的主要功能是记忆各类别抗原的特异特征,后者为前者提供各种类型的抗体源。记忆抗体间按照亚动力学原理进行调节,实现免疫网络的寻优过程。基于上述分区,它在初次免疫响应过程中实现网络的搭建和训练,在二次免疫响应过程中实现信息提取。最后利用该模型对ETM数据进行地物分类,并与传统分类方法进行对比。结果表明：该模型的总分类精度和Kappa系数分别是92.6%和0.91,优于传统分类方法。     

混合编码免疫算法在船舶载重计量的应用

提出一种混合编码免疫辨识算法, 并应用于船舶载重货物计量的模型辨识. 该算法结合免疫算法优越的全局搜索性能与GP算法简洁的结构树编码方法, 将抗体编码为模型结构树编码与模型参数编码的组合, 通过对抗体结构与参数的免疫操作, 实现全局寻优及非线性模型的结构与参数的一体化辨识. 算法不依赖对象的先验知识, 辨识的模型结构简单、易于理解. 仿真及船舶载模型的辨识应用验证了本算法的有效性及较强的非线性逼近能力.     

乙肝肝硬化的突变演化研究

为了研究乙型肝炎肝硬化的演化规律,从而制定出有针对性的治疗方案,提出了一种基于突变理论的乙型肝炎肝硬化演化模型。研究了乙肝病毒免疫模型中抗原、抗体、和B细胞作用机理,以及免疫模型中的突变特性。利用肝脏边缘粗糙度作为模型状态变量,抗原和抗体作为模型控制变量,建立了尖点型突变模型,并根据临床数据对模型参数进行了拟合。通过历史临床数据验证了模型的有效性。     

质量放大石英晶体微天平传感器用于人血清中IgM的测定

本文报道了一种质量放大石英晶体微天平免疫分析。抗体包被的石英晶振在待测怕溶液中孵育时,加入相应的抗体,待测抗原与抗体以复合物的形式结合到晶振上。这种抗原抗体多分子层与通常抗原单仓层结合相比克服好单分子层结合的抗原分子数较少的限制。应用该方法测定了人血清中的免疫球蛋白,在１０．２￣１６１．０μｇ／ｍｌ范围,响应频率与浓度有较好的线性关系,比较常规的石英晶体微天平免疫传感器方法,灵敏度提高了约３倍。     

基于免疫神经网络的故障诊断方法研究

设计了一种免疫神经网络故障诊断方法,将神经网络权值进行编码作为抗体,将网络误差作为抗原,算法首先利用免疫算法的全局收敛特性,对网络权值进行全局搜索,然后再利用BP算法的局部搜索性能对网络权值进行局部搜索;以抽油机井为对象进行了仿真研究,并与BP神经网络故障诊断方法进行比较分析,表明免疫神经网络故障诊断方法能够有效避免陷入局部极小值的现象,并且在一定程度上加快了算法的运行速度。     

基于人工免疫系统的僵尸短信入侵检测

针对移动僵尸网络日益活跃的现状,提出一种基于人工免疫的僵尸短信入侵检测模型。该模型包含两个核心模块,短信过滤模块提取短信号码与黑名单信息相匹配初步过滤垃圾短信和广告;短信识别免疫模块量化短信的签名信息生成抗原,采用实值否定选择算法生成抗体,通过抗原与抗体的亲密程度识别僵尸短信,最后根据用户反馈结果更新抗体。实验结果表明:该模型具有较高的检测率,证明了其可行性。     

纳米金修饰铂金电极固载白喉抗原免疫传感器的研究

该文报道了一种制作电流型白喉抗原免疫传感器的方法.用金纳米颗粒吸附白喉抗原,采用聚乙烯醇缩丁醛(PVB)为辅助固定抗原膜基质将其固定在铂金电极的表面,制成白喉抗原免疫传感器.根据抗原抗体特异性结合形成的免疫复合物使电极敏感膜有效扩散面积减小,电流减少的特性,实现对白喉抗体的定量检测.该传感器对白喉抗体检测的线性范围是2...     

解优先级约束并行机调度问题的人工免疫算法

针对家纺企业车间调度的实际情况,建立了优先级特殊工艺约束下并行多机拖后调度模型,并提出一种新颖的人工免疫算法对其求解。该算法是依据生物的免疫机理,将目标函数作为抗原,将问题的解作为抗体,对抗体采用向量组编码的方式进行编码,通过克隆、变异及一种新颖的基于浓度的种群多样性更新选择方法,提高了种群多样性,并通过局部搜索改善了种群质量,加快了收敛速度。仿真结果表明,与遗传算法相比较,该算法能更快更准确地收敛到全局最优解。     

基于人工免疫算法的多属性谈判模型

在基于多Agent的多属性谈判过程中,谈判Agent间常因追求自身最大利益导致谈判陷入僵局。为此,将谈判目标视为抗原,将候选提议视为抗体,建立基于人工免疫算法的多属性谈判模型。该模型通过抗原与抗体的免疫过程,取得谈判双方整体利益的最大化。仿真实验表明,该模型可有效解决谈判僵局问题,提高谈判的适应性及效率。     

分级变异的动态克隆选择算法

基于浮点数编码,提出一种分级变异的动态免疫克隆选择优化算法．根据抗体的亲和度将种群分解为3个子种群,分配以不同的搜索任务,实施不同的变异策略．在进化过程中动态改变种群规模、克隆规模和变异参数,从而加快了全局搜索速度,提高了局部搜索精度．对5个复杂函数的优化仿真实验表明了该算法的有效性。     

Symbolic reinterpretation of HLA gene products--impact on interpretation of HLA data at the molecular level

In the HLA system genes are defined by antibody/antigen reactions and are denoted by single symbolic identifiers. This symbolization assumes a one-to-one correspondence between antibodies, antigens and genes. It is important, however, to label each reagent with symbols corresponding to all genes coding for antigens with which the reagent will react. The problems of cross-reactive groups and unexplained linkage relations may be elucidated by the redefinition and clarification of certain HLA antigens. A computer program can suggest such labelling schemes using input given by phenotype reaction patterns with a panel of reagents. When this program was applied to data on the class I HLA antigens a genetic model was suggested that differs somewhat from the currently accepted view. The new model is consistent with applicable and available family data on recombinants and has implications for the interpretation of data at the DNA level.     

模块化免疫神经网络平衡态检测模型*

基于神经网络原理、免疫系统和遗传算法的相关机理,构造了一个网络安全平衡器。该平衡器建立了抗原与抗体平衡态检测的数学模型、抗原与抗体的促进和抑制函数,提出了安全平衡态的概念,给出了抗原与抗体浓度的计算公式和模块化多层分类处理模型,利用促进、抑制函数和遗传算法适应度函数使已受攻击的网络再次达到一种新的安全平衡状态,这为网络安全提供了一个新的途径。理论证明网络安全方案是有效的。     

An immunity based network security risk estimation

There are two kinds of risk-estimation methods for the network security: static and real-time. The static methods estimate the network risk through statically evaluating the network value, security holes, and the occurring frequency of security events[1], e.g., COBRA1), OCTAVE[2], etc. Focusing on the static factors of the target system, the static methods can only make a rough estimation of the security risk that the network faced in the past, and cannot in real-time evaluate the risk of …     

Predicting the distance between antibody’s interface residue and antigen to recognize antigen types by support vector machine

In this paper, a machine learning approach, known as support vector machine (SVM) is employed to predict the distance between antibody’s interface residue and antigen in antigen–antibody complex. The heavy chains, light chains and the corresponding antigens of 37 antibodies are extracted from the antibody–antigen complexes in protein data bank. According to different distance ranges, sequence patch sizes and antigen classes, a number of computational experiments are conducted to describe the distance between antibody’s interface residue and antigen with antibody sequence information. The high prediction accuracy of both self-consistent and cross-validation tests indicates that the sequential discovered information from antibody structure characterizes much in predicting the distance between antibody’s interface residue and antigen. Furthermore, the antigen class is predicted from residue composition information that belongs to different distance range by SVM, which shows some potential significance.     

基于Lagrange的H．264率失真编码优化算法

为了进一步提高编码速度，促进视频编码技术在多媒体通信领域的实际应用，本文对率失真视频编码的关键算法进行了深入的研究并提出了其优化方法，并从信息论理论出发，着重围绕图像压缩的理论，利用率失真特征数学模型、拉格朗日乘子法，对目前新一代的H．264视频标准进行编码算法模式选择，试图从最根本的理论角度探讨信源编码，以便得到更高的压缩比、更好的图像质量和更快的编码速度。最后，利用本文提出的编码模式优化算法，对H．264的JM61e（JUT Test Model）系统测试模型的性能进行全面测试。     

Numerical analysis of a magnetic nanoparticle-enhanced microfluidic surface-based bioassay

An innovative magnetic nanoparticle (MNP)-based strategy for enhancing the dynamics and kinetics of surface-based antigen–antibody binding in a microfluidic platform is presented in this study. Finite element technique was employed for quantifying the effect of convection, diffusion, reaction and magnetic field on the detection performance of surface-based bio-assay. It was identified that diffusion is rate limiting when compared with reaction and convection. In order to reduce the detection time, increasing diffusion transport or in general bringing more target antigen towards the surface-bound antibody will be most effective. A novel and simple strategy based on tagging the antigen with MNPs was demonstrated using the numerical model. It was found that local concentration of antigen–MNP complex in the vicinity of sensing surface was increased when magnetic field was used. Different configurations of magnetic field around the microchannel for focusing target antigen towards the sensing surface were simulated and the most optimized configuration was identified. Furthermore, it was quantitatively demonstrated that MNP enhanced the surface-binding kinetics and reduced the detection time of target antigen by almost 42%. Moreover, when compared with physical means of reducing diffusion barrier, MNP-based detection was 35% more efficient. Overall, MNPs enhanced the mass transport of target antigen towards sensing surface which resulted in considerable reduction in detection time. The simulations performed using the developed model will not only help to investigate a wide range of design parameters but also provide generic strategy that can be exploited at the concept stage for designing, optimizing and developing efficient and fast small-scale surface-based bioassays.