Immune multi-agent model using vaccine for cooperative air-defense system of systems for surface warship formation based on danger theory

Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.

Multi-Agent Network Intrusion Active Defense Model Based on Immune Theory

Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced, and its running mechanism is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is presented. This model implements the multi-layer and distributed active defense mechanism for network intrusion. The experiment results show that this model is a good solution to the network security defense.

A Multi-Agent Immune Network Algorithm and Its Application to Murphree Efficiency Determination for the Distillation Column

Artificial Immune Network （aiNet） algorithms have become popular for global optimization in many modem industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network （Ma-aiNet） algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The perform- ance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.

A Network Intrusion Detection Model Based on Immune Multi-Agent

A new network intrusion detection model based on immune multi-agent theory is established and the concept of multi-agents is advanced to realize the logical structure and running mechanism of immune multi-agent as well as multi-level and distributed detection mechanism against network intrusion, using the adaptability, diversity and memory properties of artificial immune algorithm and combing the robustness and distributed character of multi-agents system structure. The experiment results conclude that this system is working pretty well in network security detection.

基于免疫多智能体的舰艇编队协同防空体系模型

借鉴生物免疫系统的性质和机制,结合多智能体理论与方法,提出了一种新的免疫多智能体网络(Immune Multi-Agent Network,IMAN)模型。根据舰艇编队协同防空体系和生物免疫系统在防御功能、自适应和自组织特性上相似的特点,将舰艇编队协同防空体系映射到生物免疫系统之中,设计了舰艇编队协同防空免疫智能体功能结构模型,并综合上述免疫多智能体网络模型IMAN及其免疫防御原理,构建了一种新颖的舰艇编队协同防空体系免疫多智能体网络模型,分析了舰艇编队协同防空体系免疫多智能体的免疫防御机制及其性质,旨在为舰艇编队协同防空体系研究开拓一种新思路,并为建立适合现代实战需要的作战仿真系统提供理论模型。

基于meta-种群理论的免疫遗传算法

自然meta-种群中局部种群之间存在相对隔离和种群个体冒险迁徙的机制,提出了一种基于meta-种群理论的免疫遗传算法。该算法模拟了自然meta-种群中局部种群克隆、生殖、变异和自然灭绝等自然过程。其主要步骤包括初始种群,种群及其个体适应度计算,选择,克隆变异、交叉生殖、含记忆B细胞个体克隆和局部种群灭绝及其最优个体的冒险迁徙等。该算法的特点是模拟了meta-种群的自然机制,具有并行性,能够产生高适应能力的个体并不断地更新,直到最优个体的出现。对两种问题进行了模拟实验,并与普通遗传算法IMA进行了比较,结果表明所提出的算法能以较少的迭代次数完成最优解的寻找。

AIDS相关性颅内病变MRI分析

目的:探讨获得性免疫缺陷综合症(AIDS)颅内病变的MRI表现,总结其影像学特点。方法:回顾性分析经我院公共卫生救治中心确诊的32例神经系统型AIDS患者脑部MRI表现。结果:6例HIV脑炎表现为半卵圆中心脑白质区对称性多发点片状长T1长T2信号影,4例有脑萎缩;9例弓形体脑炎表现为基底节区和额顶叶、小脑半球多发或单发片状长T1长T2信号,增强T1WI呈多发环状、结节状及不规则靶形强化;8例隐球菌性脑炎中6例表现脑灰质内多发小片状长T1长T2信号影,2例表现为类圆形稍长T1长T2信号影,DWI结节中心可见片状高信号;5例结核性脑膜炎增强T2WIFlair示鞍上池及环池、脑干周围脑膜明显线状强化;4例脑淋巴瘤为大块状及不规则性长T1长T2信号影,占位效应明显,增强T1WI呈明显强化。结论:AIDS相关性颅内病变由于病因及病理的多样性,其MRI表现多样,在诊断时要密切结合临床。

舰艇编队协同防空体系免疫多智能体模型

针对舰艇编队协同防空问题,根据舰艇编队协同防空体系与生物免疫系统防御机制和性质相似的特点,实现了二者在结构和功能上的映射.在此基础上,结合多智能体理论与方法,设计了舰艇编队协同防空体系的监测、调节和抗体等免疫智能体的功能结构模型,构建出一种新颖的舰艇编队协同防空体系免疫多智能体(IMA)系统模型,提出了免疫多智能体系统的形式化描述,分析了舰艇编队协同防空体系免疫多智能体系统的免疫应答过程和机制.该模型的防御机制具有自学习、自适应、自组织的特性,充分体现了舰艇编队协同防空体系的协同防御机制和性质,为舰艇编队协同防空体系研究开拓了一种新思路.