基于构件的分布式多媒体创作环境初探

以构件技术为基础,介绍了一个分布式多媒体创作环境的设计与实现过程,给出了该分布式多媒体创作环境的系统构成、实现策略,并就协同创作及相关技术进行了讨论。     

混合文化优化算法及在车间调度中的应用

针对文化算法收敛速度慢、易陷入局部最优解以及种群多样性少的问题,本文对文化算法进行优化设计,提出一种将带有精英保留策略的遗传算法(GA)和模拟退火算法(SA)纳入文化算法(CA)框架的混合优化算法.此算法基于协同进化的思想,算法分为下层种群空间和上层信念空间,两个空间采用了相同的进化机制,但使用不同的参数.在文化算法的基础上加入带有精英保留策略的遗传算法,使种群中的优秀个体直接进入下一代,以此提高收敛速度;加入模拟退火算法,利用其具有突变的特点,概率性的跳出局部最优并接受劣质解,以此增加种群多样性.函数优化结果证明了算法的有效性,将此算法用于求解最小化最大完工时间的流水车间调度问题,仿真结果显示,此算法在收敛速度和精度方面都优于其他几个具有代表性的算法.     

Towards probabilistic synchronisation of local controllers

The traditional use of global and centralised control methods fails for large, complex, noisy and highly connected systems, which typify many real-world industrial and commercial systems. This paper provides an efficient bottom-up design of distributed control in which many simple components communicate and cooperate to achieve a joint system goal. Each component acts individually so as to maximise personal utility whilst obtaining probabilistic information on the global system merely through local message-passing. This leads to an implied scalable and collective control strategy for complex dynamical systems, without the problems of global centralised control. Robustness is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, can be implemented adaptively and opens a systematic rich way to information sharing. This paper opens the foreseen direction and inspects the proposed design on a linearised version of coupled map lattice with spatio-temporal chaos. A version close to linear quadratic design gives an initial insight into possible behaviours of such networks.     

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.     

支持计算机协同操作的虚拟教室的研究开发

基于WWW的现代远程教学是远程教育的重要方向,网上虚拟教室是提高远程教学的重要手段。文章概述了网上教学的交互性、虚拟教室(VirtualClassroom)、计算机支持协同操作(CSCW)的原理,设计了支持协同操作、交互性良好的网上虚拟教室的基本结构,并研究了利用协同操作来解决网上虚拟教室中学习者与教师、学习者与学习者之间的进行交互的关键技术,实现了一个虚拟教室系统。     

基于语义的多代理协同数据库平台

提出了协同数据库平台CDP的体系结构、模型和实现。该平台可支持协同活动和管理多数据源。讨论了包括应用、支持和数据库的3层多Agent体系结构，基于语义的Agent模型以及语义联系的实现，从而屏蔽了异步和同步工作方式。协同用户通过应用语义来进行协同操作和信息共享。     

协同商务平台的访问控制服务研究与实现

访问控制服务目前已成为安全研究的重要方向，文章分析了访问控制服务在电子商务网站中的重要性，介绍了与访问控制有关的技术方法，通过一个具体的协同电子商务平台，详细说明了如何设计及实现访问控制系统。     

面向VANET可靠广播的协同背驮算法研究*

道路信号干扰造成的非可靠链路使得车载自组织网络Vehicular Ad-hoc Network (VANET) 中的广播消息频繁发生数据丢包。由于广播消息中包含与车辆安全相关的信息,任何数据丢包都可能对车辆的安全驾驶造成严重影响。为增强车辆之间信息交互的可靠性,提出了一种贪心背驮算法(Greedy Piggyback Algorithm, GPA)。允许所有车辆在发送自身丢包请求的同时,从接收缓存中选取若干消息进行背驮式附带广播,对其他车辆的数据丢包进行合作恢复。针对由非对称信道引起的车辆信息孤岛问题,在贪心背驮算法的基础上提出了一种基于策略学习的交互背驮算法(Mutual Learning and Piggybacking Algorithm, MLPA),通过车辆之间背驮策略的交互、学习以提高VANET中消息广播的可靠性。仿真结果表明,GPA和MLPA不仅将车辆广播中数据接收的可靠度分别提高到0.85和0.95,还能获得较低的网络负载和较短的传输延迟。     

Effects of face-to-face versus chat communication on performance in a collaborative inquiry modeling task

In many contemporary collaborative inquiry learning environments, chat is being used as a means for communication. Still, it remains an open issue whether chat communication is an appropriate means to support the deep reasoning process students need to perform in such environments. Purpose of the present study was to compare the impact of chat versus face-to-face communication on performance within a collaborative computer-supported modeling task. 44 Students from 11th-grade pre-university education, working in dyads, were observed during modeling. Dyads communicated either face-to-face or through a chat tool. Students’ reasoning during modeling was assessed by analyzing verbal protocols. In addition, we assessed the quality of student-built models. Results show that while model quality scores did not differ across both conditions, students communicating through chat compressed their interactions resulting in less time spent on surface reasoning, whereas students who communicated face-to-face spent significantly more time on surface reasoning.     

Combination framework of rendezvous algorithm for multi‐agent systems with limited sensing ranges

In this paper, we present a novel rendezvous algorithm framework ‐ the combination framework, which is named after the term convex combination in computational geometry. The convergence of the proposed algorithms is proved based on the novel tools from the recent results in graph theory and consensus study. It has been observed that the circumcenter algorithm can be considered as a special case of the combination framework. Moreover, the relevant analysis provides us a novel viewpoint on designing and the rendezvous rate of rendezvous algorithms for networked multi‐agent systems with limited sensing ranges. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society