Agent-Based Design Model of Adaptive Distributed Systems

A next generation distributed system is expected to adapt to various changes of both the users' requirements and the operational conditions of environment where the distributed system operates. The aim of our research is to establish a new design model of an adaptive distributed system (ADS) to deal with various changes occurred in the system environment. In this paper, we propose an agent-based architecture of ADS, based on the agent-based computing paradigm. Then, we implement a prototype of the ADS with respect to videoconferencing applications and also evaluate the adaptive functions of the ADS realized on the basis of the proposed architecture.     

Experimental results of an adaptive controller for robot manipulators

This article presents the implementation and experimentation of a transputer-based adaptive control system on an industrial robot PUMA 560. The adaptive control problem has been extensively studied in simulations, but experimental results will highlight the performance potential and is more interesting for the practice. An experimental study of the real-time performance of an adaptive model-based controller will be demonstrated here, which is based on the Lyapunov stability theory. Especially, the effect of the unknown load dynamics is considered. A transputer network consisting of 10 transputers is employed to realize the control system, where the parallelization of the algorithms is utilized for speeding up the computation. Some practical implementation issues of such controller are considered in context of a multiprocessor environment. © 1996 John Wiley & Sons, Inc.     

一种基于免疫原理的自律机器人行为控制算法

生物体免疫系统是一个高度复杂的分布协调自适应系统。文章基于免疫学的细胞克隆选择学说和 Ｊｅｒｎｅ网络调节理论,介绍一种人工免疫系统模型及算法,并应用于自律移动机器人的行为控制研究,模拟实验结果表明,该算法能有效增强自律移动机器人在动态环境中的自适应能力。     

Distributed and autonomous sensing based on the immune network

A distributed and autonomous sensor network is proposed based on the informational features of the immune network: recognition of nonself by distributed and dynamically interacting units, recognition by a simple comparison with the units themselves, dynamic propagation of activation that would lead to system-level recognition, and memory embedded as stable equilibrium states in the dynamic network. The network is explained by an illustrative example of an eight-coin puzzle: a balance must be used only three times to identify one coin with a different weight from the other seven coins. Our network also uses a dynamic structure network rather than the fixed structures used in neural networks. Simulations show that nonself (the different coin in the eight-coin puzzle, the sensor/process fault in the monitoring example) will be identified by dynamically propagating activation through the network. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

The general architecture of adaptive robotic systems for manufacturing applications

Adaptive system often refers to the adaptive control system. In this paper, we discuss the adaptive system concept from an architectural perspective of the system. We use the robotic system as an example to illustrate the adaptive system concept, because the robotic system is a generic system underlying many manufacturing systems. Specifically, the paper presents our work on the development of a general architecture of the adaptive robotic system for manufacturing applications. The contribution of this paper includes: (i) elaboration of the concept of adaptive system and classification of means to make the system adaptive and (ii) formulation of a general architecture of the adaptive robotic system based on the criterion that the more system variants out of the architecture implies the more generality of the architecture. Throughout this paper, we use an industrial robotic system for illustration.     

基于Agent的Web Services自适应工作流模型的研究

如何让企业可以灵敏反应外部环境的变动并快速改变企业内部的流程作业,是传统工作流管理系统面临的最大挑战.提出一种基于Agent的Web Services工作流模型(AW-WFM)来解决这个难题.在这个模型中,基于Agent的技术实现一种工作流协作机制,而Web Services技术为工作流管理系统提供了计算资源.最后,介绍了AW-WFM在上海大学协同办公系统中的应用.     

基于Agent技术的复杂适应系统分析与建模

该文简单介绍了霍兰教授提出的复杂适应系统（CAS）理论,概述了agent和多agent系统（MAS）的概念及其特点,重点介绍了基于agent技术的复杂适应系统的分析方法及建模方法,对其特点及相对于传统方法的优势作了比较.对于由多个Agent组成的复杂适应系统,基于Agent技术是一种十分有效的分析工具,它不但能够对复杂适应系统的体系结构进行适当的描述,而且为复杂适应系统演化过程的建模提供了有力的工具.基于Agent技术的分析与建模是探讨复杂适应系统演化的新动向.     

基于蚁群计算的自适应Web检索算法设计

受蚁群觅食行为仿生研究和蚁群系统模型理论所启发,提出了一种基于蚁群计算模型的分布、协作多主体（multi-agent）反应架构的自适应、可伸缩的Web搜索系统模型（MASAIR）,其由大量智能主体组成,利用智能主体架构的优异特性,旨在从巨型超文档集合（Web）中自治地搜索特定主题的信息,从而为用户提供迅捷的信息检索服务。详细描述了MASAIR的计算模型及其算法,通过对标准Web文档集的检索仿真实验结果显示：该架构具有对环境改变的鲁棒性和对用户信息需求变更的自适应性。     

传感器自适应跟踪算法研究及仿真系统实现

雷达和红外作为目标跟踪常用的两种探测手段,各有其优缺点,利用雷达高精度的距离测量和红外高精度的角度测量,通过信息融合技术充分实现二者的优势互补,并结合交互式多模型(IMM)跟踪思想,给出对目标位置的精确估计;设计基于雷达/红外多传感器跟踪平台的自适应融合跟踪算法,实现根据目标不同运动特性进行跟踪模型灵活、合理切换的自适应目标跟踪,改善对目标的综合识别,达到更好的跟踪效果;选取当前工程实践中广泛应用的目标运动模型,设计基于VC++环境的目标跟踪仿真系统软件,并利用MFC界面制作技术创建可视化目标跟踪仿真软件平台,对跟踪算法性能进行验证。     

一类通用的适应性软件体系结构风格研究

研究并提出一类通用的适应性软件体系结构风格.通过对适应性软件体系结构的基本特性分析,抽取适应性软件体系结构的通用框架,建立基于多视图建模理论的集成化适应性软件体系结构参考模型,给出多视图模型的演化与映射规律.基于元建模和图转换理论,提出模型映射一致性算法.最后,建立了适应性软件体系结构支撑环境.适应性软件体系结构适用于复杂软件系统,特别是网络环境下大型、开放式软件系统的开发和实施.     

Organizational information security as a complex adaptive system: insights from three agent-based models

The management of information security can be conceptualized as a complex adaptive system because the actions of both insiders and outsiders co-evolve with the organizational environment, thereby leading to the emergence of overall security of informational assets within an organization. Thus, the interactions among individuals and their environments at the micro-level form the overall security posture at the macro-level. Additionally, in this complex environment, security threats evolve constantly, leaving organizations little choice but to evolve alongside those threats or risk losing everything. In order to protect organizational information systems and associated informational assets, managers are forced to adapt to security threats by training employees and by keeping systems and security procedures updated. This research explains how organizational information security can perhaps best be managed as a complex adaptive system (CAS) and models the complexity of IS security risks and organizational responses using agent-based modeling (ABM). We present agent-based models that illustrate simple probabilistic phishing problems as well as models that simulate the organizational security outcomes of complex theoretical security approaches based on general deterrence theory (GDT) and protection motivation theory (PMT).     

Collaborative RFID intrusion detection with an artificial immune system

The current RFID systems are fragile to external attacks, due to the limitations of encryption authentication and physical protection methods used in implementation of RFID security systems. In this paper, we propose a collaborative RFID intrusion detection method that is based on an artificial immune system (AIS). The new method can enhance the security of RFID systems without need to amend the existing technical standards of RFID. Mimicking the immune cell collaboration in biological immune systems, RFID operations are defined as self and nonself antigens, representing legal and illegal RFID operations, respectively. Data models are defined for antigens’ epitopes. Known RFID attacks are defined as danger signals represented by nonself antigens. We propose a method to collect RFID data for antigens and danger signals. With the antigen and danger signal data available, we use a negative selection algorithm to generate adaptive detectors for self antigens as RFID legal operations. We use an immune based clustering algorithm aiNet to generate collaborative detectors for danger signals of RFID intrusions. Simulation results have shown that the new RFID intrusion detection method has effectively reduced the false detection rate. The detection rate on known types of attacks was 98% and the detection rate on unknown type of attacks was 93%.     

免疫原理在网络攻击检测系统中的应用研究

针对网络攻击方式具有动态性和多样性等特点,提出一种基于免疫原理的网络攻击检测系统;系统根据免疫原理将网络正常行为定义为自体集,网络攻击行为定义为非自体集,通过免疫机制来检测自体集和非自体,从而实现对网络系统的保护;仿真结果表明,该系统弥补了传统网络攻击检测系统的缺陷,提高了网络攻击检测的正确率,降低了误报率低。     

EISEval,a generic reconfigurable environment for evaluating agent-based interactive systems

The evaluation of interactive systems has been an active subject of research for many years. Many methods have been proposed, but most of them do not take the architectural specificities of an agent-based interactive system into account, nor do they focus on the link between architecture and evaluation. In this paper, we present an agent-based architecture model for interactive systems. Then, based on this architecture, we propose a generic, reconfigurable evaluation environment, called EISEval, designed and developed to help evaluators analyze and evaluate certain aspects of interactive systems in general and of agent-based architecture interactive systems in particular: User Interface (UI), non-functional properties (e.g., response time, complexity) and user characteristics (e.g., abilities, preferences, progress). System designers can draw useful conclusions from the evaluation results to improve the system. This environment was applied to evaluate an agent-based interactive system used to supervise an urban transport network in a study organized in laboratory.     

An adaptive neuro-fuzzy system for automatic image segmentation andedge detection

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels     

模糊自适应运动目标跟踪算法

运动目标在跟踪过程中往往伴随着尺度、形状的变化,Mean shift跟踪算法由于采用固定的核窗宽度进行运动目标跟踪,因而它本身不能适应这种变化。针对Mean shift算法存在的缺点,提出一种基于模糊推理的自适应Mean shift跟踪算法,该算法利用卡尔曼滤波算法对目标当前位置进行预测;设计模糊判定准则在线调整目标尺度值,利用Mean shift迭代运算逐步逼近目标完成跟踪;利用相似度和置信度系数设计模型更新准则,以实现模板的自适应更新。实验结果证明,该算法能够适应目标尺度和背景的变化,较普通的Mean shift跟踪算法不仅跟踪精度提高,而且鲁棒性更强。     

一种基于移动Agent的分布式应用系统架构

拒绝服务（Dos）攻击对分布式应用系统的安全威胁日益严重。文章提出了一种基于移动Agent的分布式应用系统架构,用以保护系统中的应用服务器,提高其抗DoS攻击的能力。为进一步改善系统抗DoS攻击性能,提出了一种基于遗传算法的代理主机分组算法。最后定性评估了系统的抗DoS攻击能力。     

基于STM32F107的暖通空调混合通风自适应控制系统设计

针对暖通空调混合通风自适应控制系统受到室内空气环境的影响,导致暖通空调的通风控制效果变差,增加了空调能耗的问题,提出了基于STM32F107的暖通空调混合通风自适应控制系统设计。在STM32F107控制平台上,通过暖通空调混合通风自适应控制电源柜设计和暖通空调混合通风自适应控制器设计,完成了系统的硬件设计,结合暖通空调混合通风换气量的计算和暖通空调混合通风控制算法设计,完成了系统的软件设计,实现了暖通空调混合通风的自适应控制。测试结果表明,基于STM32F107的暖通空调混合通风自适应控制系统不仅可以控制室内的温度环境,还可以控制室内的湿度环境,有效提高了暖通空调混合通风的控制效果。     

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.     

增加检测器覆盖非自体区域的简单方法

免疫启发式否定选择算法应用于数据集中的异常检测。这种检测基于自体/非自体的识别,他的特征是具有通过自体或规则样本信息检测非自体样本的能力。所以,问题空间被分成两个不相交的子空间：一个子空间包含自体样本,另一个子空间包含促使通过否定选择算法生成检测器的样本。因此,否定选择算法的效率与检测器覆盖非自体空间范围成正比。在文章中,提出了一种扩大检测器覆盖范围的简单方法。