构件行为协议实时性扩展及相容性验证

对复杂实时构件系统行为进行形式化描述和相容性验证,可以有效提高系统的正确性、可靠性。分析了学术界和工业界的主流构件模型及常见时间行为的形式化描述方法,对构件行为协议BP(Behavior Protocol)进行了扩展,提出了时间行为协议TbP(Timed Behavior Protocol),分析了构件组合中常见的相容性错误类型,给出了基于时间行为协议的构件组合相容性验证算法。TBP应用简洁、方便、易于验证。结合具体例子给出了应用示例。     

社区问答系统研究综述

作为一种新兴的知识共享模式,社区问答系统(CQA)具有交互性、开放性的特点,能够更好地满足为用户提供个性化的信息服务的需求。对社区问答系统的研究及应用现状进行综述,系统阐述了用户行为模式、内容质量检测、问题检索等CQA中主要问题的研究以及CQA在其他媒体中的应用。最后展望了CQA中下一步值得研究的问题。本讨论有助于进一步丰富和拓展CQA的研究。     

基于用户行为的网络管理模型能力分析

现有的网络管理模型一般都侧重网络设备本身的管理,不太适应现在越来越复杂的网络管理。基于用户行为的网络管理模型从用户行为管理的角度出发,关联分析出网络中的用户行为,并根据用户行为管理策略规范用户的网络行为,从而消除不良网络行为对网络性能和安全的影响,使网络能安全稳定地运行,并可大幅度减轻网络管理员的负担。本文讨论基于用户行为的网络管理模型要解决的关键问题,提出并分析模型应具备的4个能力：感知能力、控制能力、取证能力和告知能力,最后讨论它们在实际网络管理中的应用情况,并证明它们的可用性。     

信息网络安全预警技术研究

随着互联网络的发展,为了提高网络对各种安全事件的及时检测能力以及总体控制能力,本文提出了一种信息网络安全预警系统,实现对网络行为的管理与监控,有效地实现了对网络行为的约束,为建立网络信息安全保障体系提供了有力支撑。     

AOP在虚拟行为建模中的应用研究

该文介绍了对面向方面的思想和编程方法以及目前虚拟现实中各种行为造型的方法,并研究了AOP（Aspect Oriented Pro—gramming）思想应用到行为造型中的方法,将行为视为虚拟现实实体对象的方面,动态添加到实体对象中。最后阐述了在X3D中实现基于AOP的行为造型的方法和关键技术。还阐述了基于AOP的行为造型方法的在实际项目中的应用情况,充分验证了该方法的实用性和有效性。     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

基于Agent的虚拟抢修任务系统的设计与实现

针对复杂装备抢修现实问题,提出了构建虚拟抢修任务系统概念模型,建立了其系统框架,并搭建了基于多Agent的系统结构,分析确定了系统运行的事件驱动机制.在完成该Agent系统实现方法、系统行为及Agent行为推理的Petri网模型研究之后,设计实现了该虚拟抢修任务系统.该系统对于优化复杂装备的抢修性设计,提高装备使用单位抢修水平都有一定的借鉴和指导意义,可以有效地解决复杂装备现实抢修面临的诸多问题.     

Simulations of the dynamic behavior of a bipedal robot with trunk and arms subjected to 3D external disturbances in a vertical posture, during walking and during object handling

This paper provides a new approach to the bipedal robot stability problems in presence of external disturbances in vertical posture of the robot, during walking and during object handling. This approach is based on synergy between the dynamic motions of balancing masses and arms to reject large perturbations applied to the upper part of ROBIAN robot. In these cases, the stabilization is carried out in the first time with a trunk having 4 degrees of freedom (dof): one rotational and three translational movements. In the second time, the stabilization is performed with a system with arms and having 10 dof. During the walk, the trunk elements of ROBIAN reproduce necessary movements to perform the dynamic walking gait of the robot. The compensation of external three-dimensional efforts applied to the robot is achieved firstly by the trunk and secondly with the arms. This study allows us to determine on-line the required movements and accelerations of the trunk elements in order to maintain the robot stability and shows the importance of the arms for the robot stability.     

Large-scale network intrusion detection based on distributed learning algorithm

As network traffic bandwidth is increasing at an exponential rate, it’s impossible to keep up with the speed of networks by just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the pressure on network intrusion detection (NIDS) platforms, so the continuous increasing speed and throughput of network poses new challenges to NIDS. To make NIDS usable in Gigabit Ethernet, the ideal policy is using a load balancer to split the traffic data and forward those to different detection sensors, which can analyze the splitting data in parallel. In order to make each slice contains all the evidence necessary to detect a specific attack, the load balancer design must be complicated and it becomes a new bottleneck of NIDS. To simplify the load balancer this paper put forward a distributed neural network learning algorithm (DNNL). Using DNNL a large data set can be split randomly and each slice of data is presented to an independent neural network; these networks can be trained in distribution and each one in parallel. Completeness analysis shows that DNNL’s learning algorithm is equivalent to training by one neural network which uses the technique of regularization. The experiments to check the completeness and efficiency of DNNL are performed on the KDD’99 Data Set which is a standard intrusion detection benchmark. Compared with other approaches on the same benchmark, DNNL achieves a high detection rate and low false alarm rate.