支持产品协同设计的AD技术与服务层项目人员管理的实现

在产品的协同设计过程中，每个项目都要涉及到众多的工作人员和大量的网络资源。协调好各个人员的工作并且有效、安全地使用网络资源是保证产品设计顺利进行的关键。本文提出并研究了采用活动目录技术来实现协同设计系统的服务层上的项目人员管理需求模式，开发了相应的项目人员管理工具，并且在基于知识的注塑模具和冲压模具设计系统中得到应用。     

Modularized design for cooperative control and plug-and-play operation of networked heterogeneous systems

In this paper, a cooperative control analysis and design method is investigated for heterogeneous dynamical systems that may be of arbitrary relative degree or nonminimum-phase or both. To achieve consensus or cooperative stability, a negative value of input-feedforward passivity index is used to accommodate and analyze such systems, and the magnitude of the index value is also used as the impact coefficient to quantify the impacts of heterogeneous dynamics of these systems on their networked operations. Physical-system-level designs are explicitly carried out to make individual linear and nonlinear systems (which are either feedback linearizable or nonminimum phase of certain form) become passivity-short and to embed one pure integrator into their input–output dynamics. The network-level distributed control can simply be chosen without any knowledge of the heterogeneous dynamics but with only information of an upper bound on their impact coefficients. It is shown, using the impact equivalence principle, that these controls separately designed but implemented together always ensure either local or global consensus and that a global non-trivial consensus emerges if and only if the information network has at least one globally reachable node or is varying but cumulatively connected. The proposed methodology of fully modularized designs unravels complexity of analyzing and designing cyber–physical systems and enables their plug-and-play into networked operations.     

单向通信拓扑领航者动态未知线性多智能体系统的协同迭代学习控制（英文）

This paper considers the cooperative tracking of linear multi-agent systems with a dynamic leader whose input information is unavailable to any followers. Cooperative iterative learning controllers, based on the relative state information of neighboring agents, are proposed for tracking the dynamic leader over directed communication topologies. Stability and convergence of the proposed controllers are established using Lyapunov-Krasovskii functionals. Furthermore, this result is extended to the output feedback case where only the output information of each agent can be obtained. A local observer is constructed to estimate the unmeasurable states. Then, cooperative iterative learning controllers, based on the relative observed states of neighboring agents,are devised. For both cases, it is shown that the multi-agent systems whose communication topologies contain a spanning tree can reach synchronization with the dynamic leader, and meanwhile identify the unknown input of the dynamic leader using distributed iterative learning laws. An illustrative example is provided to verify the proposed control schemes.     

Internal structure of coalitions in competitive and altruistic graphical coalitional games

This paper introduces a certain graphical coalitional game where the internal topology of the coalition depends on a prescribed communication graph structure among the agents. The game Value Function is required to satisfy four Axioms of Value. These axioms make it possible to provide a refined study of coalition structures on graphs by defining a formal graphical game and by assigning a Positional Advantage, based on the Shapley value, to each agent in a coalition based on its connectivity properties within the graph. Using the Axioms of Value the graphical coalitional game can be shown to satisfy properties such as convexity, fairness, cohesiveness, and full cooperativeness. Three measures of the contributions of agents to a coalition are introduced: marginal contribution, competitive contribution, and altruistic contribution. The mathematical framework given here is used to establish results regarding the dependence of these three types of contributions on the graph topology, and changes in these contributions due to changes in graph topology. Based on these different contributions, three online sequential decision games are defined on top of the graphical coalitional game, and the stable graphs under each of these sequential decision games are studied. It is shown that the stable graphs under the objective of maximizing the marginal contribution are any connected graph. The stable graphs under the objective of maximizing the competitive contribution are the complete graph. The stable graphs under the objective of maximizing the altruistic contribution are any tree.     

求解高维函数优化问题的交叉熵蝙蝠算法

为改善蝙蝠算法求解高维函数优化问题的全局搜索能力,提高其搜索精度,将交叉熵方法和蝙蝠算法相结合,提出一种交叉熵蝙蝠算法。该算法将基于重要度抽样和Kullback-Leibler距离的交叉熵全局随机优化算法应用于蝙蝠算法中,采用自适应平滑技术提高算法的收敛速度,利用交叉熵方法的遍历性、自适应性和鲁棒性,有效抑制蝙蝠算法的早熟收敛现象。对经典测试函数和CEC2005测试函数的仿真结果表明,该算法具有全局搜索能力强、求解精度高和鲁棒性好等特性。     

生态位理论在大学生党员核心竞争力建设中的应用

党的基层组织文化建设是加强党的建设中的一项战略任务和系统工程。从党员核心竞争力与党支部生态位的内在关联性入手,在尊重党员个体差异性、满足党员发展需求的基础上,提出了提升学生党员核心竞争力的具体措施,以期达到增强学生党支部凝聚力和提升学生党员竞争力的统一。     

基于双策略协同进化的QPSO算法及其应用

为更好地提升量子粒子群优化算法（QPSO）的局部挖掘和全局搜索能力,提出了一种改进的QPSO算法（DSQPSO）。在改进算法中引入了双策略协同进化的思路调整粒子的位置更新公式。为充分体现个体粒子挖掘的优势和群体共同引导的特点,提出了两种吸引点构造的思路,做到个体和种群更好地融合以及信息的互通;分别考虑了最优平均位置与全局最优和粒子的历史最优之间的联系,对粒子搜索范围作出了重新定义;此外,在迭代过程中,借助随机扰动机制对全局最优位置进行调整,以保持种群的多样性。通过18个测试函数将DSQPSO算法与PSO、QPSO、RQPSO和LQPSO四种算法在收敛精度和鲁棒性方面进行对比;进而在两个具体的工程优化问题上,应用改进算法与八个智能算法进行了寻优结果比较。实验表明DSQPSO算法无论在基准测试中还是在工程应用上,其计算精度和收敛效果均有明显优势。     

大学英语课堂教学中合作学习有效性探讨

合作学习已经越来越多的被大学教师引入到英语教学中。文章探讨了合作学习中遇到的问题,分析合作学习受阻的原因,提出解决问题的对策。     

A case study of developing ICT-supported pedagogy through a collegial practice transfer process

A collegial practice transfer process between more- and less-experienced teachers was examined in two cases that sought to encourage teachers to use information and communication technology (ICT) in a pedagogically meaningful way. Collegial practice transfer aims to offer new models for in-service teacher training. The process included guiding sessions, training materials presenting authentic pedagogical examples, and try-outs in the classroom. The success of practice transfer was examined through comparing the classroom practices of more- and less-experienced teachers applying the Pedagogical Infrastructure Framework, and through analyzing the tutored teachers' self-reported experiences in interviews. The process provided flexible support and gave self-confidence to the less-experienced teachers. Technology use was successful, but the tutored teachers designed less coherent tasks and weaker support for pupils' collaboration, knowledge construction and metacognition than their more-experienced colleagues. When tutoring, underlying educational ideologies and reasons should be explicated in addition to practical issues of classroom orchestration and technology use.     

面向不确定目标的多无人机协同搜索控制方法

目标搜索是多无人机协同控制的重要研究内容。多架UAV(Unmanned Aeiral Vehicle)同时对一个未知区域进行搜索,目的在于获取搜索区域的信息,尽可能多地发现目标。针对不确定目标的搜索问题,研究多无人机协同搜索控制的新方法。建立多UAV运动模型,用目标存在概率对搜索环境进行描述,给出基于Bayesian准则的搜索环境更新方法,考虑了环境探测回报、目标发现回报和无人机协同回报,采用MPC实现对多目标优化问题的迭代求解。通过仿真实验和对比分析,证明了该方法具有更好的搜索性能。