一类约化梯阵的RaRb表示

本文首先阐明线性R_aR_b变换之间的关系，并提出了算法MR_ab，再引用标准线性R_aR_b变换，证明了R_aR_b变换与算法MR_ab求解方程组的能力是等价的．然后讨论MR_ab与算法ALT之间的关系，进而说明受ALT攻击的那些有限自动机包含     

不确定系统鲁棒容错H∞控制的LMI设计方法

针对不确定线性系统,研究了执行器失效情况下鲁棒容错H_∞控制问题.基于连续增益故障模式.利用线性矩阵不等式LMI推导了系统H_∞指标约束下鲁棒容错镇定的充要条件,分别给出了输出反馈和状态反馈H_∞控制器的设计方法.通过引入变量代换,将求解输出反馈H_∞指标约束的鲁棒容错控制器的可解条件转化为标准的LMI所获得的控制器不仅能使故障系统鲁棒定,并且能达到给定的H_∞性能指标.仿真实例验证了所提出设计方法的有效性.

     

线性控制系统窗口频域分析与设计

针对局部频率范围提出了窗口H_∞范数的新概念,指出传统H_∞范数是窗口H_∞范数的特例.利用GKYP引理证明了广义界实定理,研究了线性控制系统在窗口频域的性能分析问题.基于近似模型匹配原则和广义界实定理,将控制器设计问题转化为窗口H_∞范数优化问题.仿真实例表明,窗口H_∞范数适于窗口频域的线性控制系统分析和设计.

     

模态K4、D4系统的归结推理

本文将Ｐ．Ｅｎｊａｌｂｅｒｔ和Ｌ．ＦａｒｉｎａｓｄｅｌＣｅｒｒｏ提出的模态归结推理方法推广到命题模态逻辑K₄和D₄系统，建立了K₄逻辑的归结推理ＲK₄；D₄逻辑的归结推理R D₄，分别证明了ＲK₄和ＲD₄关于K相似文献   

具有非线性扰动的广义系统的鲁棒H∞控制

研究具有非线性结构扰动广义系统的鲁棒H_∞控制和鲁棒H_∞保性能控制问题,该不确定性为时间和状态的函数,且满足Lipschitz条件.目的是分别设计系统的鲁棒H_∞控制器和鲁棒H_∞保性能控制器.应用线性矩阵不等式方法,分别给出了系统的鲁棒H_∞控制器和鲁棒H_∞保性能控制器存在的充分条件.当这些条件可解时,分别给出了鲁棒H_∞控制器和鲁棒H_∞保性能控制器的表达式.最后通过一个仿真算例说明了所给出方法的应用.

     

基于B／S模式数据库设计的优化

中国3G之路 5月10日中午,北京居民陈立勤接到一个奇怪的手机来电,号码显示是“189××××××××”。     

基于观测器的非线性系统H∞模糊可靠控制

研究了基于观测器的非线性系统H_∞模糊可靠控制问题.采用T-S模糊模型对非线性系统进行建模,用模糊观测器重构系统状态.在系统发生故障时满足给定H_∞性能的约束下,最小化正常情况下的H_∞性能,实现次优H_∞模糊可靠控制.提出了两种应用线性矩阵不等式(LMI)的H_∞模糊可靠控制器设计方法.分别采用两步法和相似变换法将双线性矩阵不等式问题转化为LMI问题.仿真示例验证了所提出方法的有效性.

     

信捷电子-全新XCM系列运动控制型PLC

特点： ·运动控制专用PLC; ·直接在XCP Pro软件中调用控制代码; ·容量更大,处理速度更快; ·可扩展模块和BD板,支持MODBUS通讯。     

基于opencv的单目视觉测量系统的研究

_{介绍一种基于机器视觉的测量工件宽度的新方法,在开源计算机视觉库OpenCV的支持下,设计并实现一种对工件宽度进行动态实时测量的系统。为了降低其它光的影响,在CCD镜头下面固定一块红光滤光片,系统通过采集经红光线激光器投射的工件图像,对图像进行二值化、截取感兴趣区域、轮廓提取等处理,最后实现工件宽度的自动测量。对不同尺寸的工件进行了自动测量实验,实验结果表明,采用的测量方法在实际工件测量中是可用的,整个测量过程用时在100ms左右,最大相对误差不超过1.5%。}     

多输入多输出离散系统的量化H∞滤波器设计

研究多输入多输出线性离散系统的量化H_∞滤波器设计问题.每路输出采用一个独立的静态对数量化器.采用扇形界方法描述量化误差,并将量化滤波问题转化为范数不确定系统的滤波问题.进而采用线性矩阵不等式方法设计线性H_∞滤波器,使滤波误差系统渐近稳定并具有给定的H_∞性能.数值仿真表明了设计方法的有效性.

     

Collaborative team formation using brain drain optimization: a practical and effective solution

In the area of professional social networks, collaborative team formation with its NP-hard nature, has attracted the attention of many researchers. The purpose of this study is to find a collaborative team which covers required skills and minimizes the communication cost among team members. To solve this problem, BRADO (BRAin Drain Optimization), a recently-proposed meta-heuristic swarm-based algorithm which simulates the brain drain phenomenon, has been utilized. In order to evaluate BRADO, it has been applied in extensive experiments to the DBLP and IMDb datasets. Results demonstrate the effectiveness and superiority of the BRADO algorithm in comparison with PSO, GA, ICA, RarestFirst and EnhancedSteiner algorithms. Our findings lead us to believe that the BRADO algorithm can be a promising method in the context of team formation problem.     

Team formation in social networks based on collective intelligence – an evolutionary approach

The tremendous growth of the social web has inspired research communities to discover social intelligence, which encompasses a wide spectrum of knowledge characterized by human interaction, communication and collaboration, thereby exploiting collective intelligence (CI) to support the successful existence of social communities on the Web. In this work, we address the team formation problem for generalized tasks where a set of experts is to be discovered from an expertise social network that can collaborate effectively to accomplish a given task. The concept of CI that emerges from these collaborations attempts to maximize the potential of the team of experts, rather than only aggregating individual potentials. Because the team formation problem is NP-hard, a genetic algorithm-based approach is applied to optimize computational collective intelligence in web-based social networks. To capture the essence of CI, a novel quantitative measure Collective Intelligence Index (CII) is proposed that takes two factors into account –the “enhanced expertise score” and the “trust-based collaboration score”. This measure relates to the social interactions among experts, reflecting various affiliations that form a network of experts that help to drive creativity by deepening engagements through collaboration and the exchange of ideas and expertise, thereby enriching and enhancing the knowledge base of experts. The presented model also captures the teams’ dynamics by considering trust, which is essential to effective interactions between the experts. The computational experiments are performed on a synthetic dataset that bears close resemblance to real-world expertise networks, and the results clearly establish the effectiveness of our proposed model.     

Team formation with influence maximization for influential event organization on social networks

Online event-based social services allow users to organize social events by specifying the themes, and invite friends to participate social events. While the event information can be spread over the social network, it is expected that by certain communication between event hosts, users interested in the event themes can be as more as possible. In this paper, by combining the ideas of team formation and influence maximization, we formulate a novel research problem, Influential Team Formation (ITF), to facilitate the organization of social events. Given a set L of required labels to describe the event topics, a social network, and the size k of the host team, ITF is to find a k-node set S that satisfying L and maximizing the Influence-Cost Ratio (i.e., the influence spread per communication cost between team members). Since ITF is proved to be NP-hard, we develop two greedy algorithms and one heuristic method to solve it. Extensive experiments conducted on Facebook and Google+ datasets exhibit the effectiveness and efficiency of the proposed methods. In addition, by employing the real event participation data in Meetup, we show that ITF with the proposed solutions is able to predict organizers of influential events.     

Using team cognitive work analysis to reveal healthcare team interactions in a birthing unit

Cognitive work analysis (CWA) as an analytical approach for examining complex sociotechnical systems has shown success in modelling the work of single operators. The CWA approach incorporates social and team interactions, but a more explicit analysis of team aspects can reveal more information for systems design. In this paper, Team CWA is explored to understand teamwork within a birthing unit at a hospital. Team CWA models are derived from theories and models of teamwork and leverage the existing CWA approaches to analyse team interactions. Team CWA is explained and contrasted with prior approaches to CWA. Team CWA does not replace CWA, but supplements traditional CWA to more easily reveal team information. As a result, Team CWA may be a useful approach to enhance CWA in complex environments where effective teamwork is required.

Practitioner Summary: This paper looks at ways of analysing cognitive work in healthcare teams. Team Cognitive Work Analysis, when used to supplement traditional Cognitive Work Analysis, revealed more team information than traditional Cognitive Work Analysis. Team Cognitive Work Analysis should be considered when studying teams.     

Sign prediction in sparse social networks using clustering and collaborative filtering

Today, social networks have created a wide variety of relationships between users. Friendships on Facebook and trust in the Epinions network are examples of these relationships. Most social media research has often focused on positive interpersonal relationships, such as friendships. However, in many real-world applications, there are also networks of negative relationships whose communication between users is either distrustful or hostile in nature. Such networks are called signed networks. In this work, sign prediction is made based on existing links between nodes. However, in real signed networks, links between nodes are usually sparse and sometimes absent. Therefore, existing methods are not appropriate to address the challenges of accurate sign prediction. To address the sparsity problem, this work aims to propose a method to predict the sign of positive and negative links based on clustering and collaborative filtering methods. Network clustering is done in such a way that the number of negative links between the clusters and the number of positive links within the clusters are as large as possible. As a result, the clusters are as close as possible to social balance. The main contribution of this work is using clustering and collaborative filtering methods, as well as proposing a new similarity criterion, to overcome the data sparseness problem and predict the unknown sign of links. Evaluations on the Epinions network have shown that the prediction accuracy of the proposed method has improved by 8% compared to previous studies.

     

Perspectives on the evolution of online communities

The rapid growth of social networks opens interesting research opportunities to make use of the massive information exchanged in day-to-day communication. One of the active research issues related to this aspect is the study of online community formation and evolution in dynamic social networks. As community structure is usually ambiguous, then defining how it evolves over time becomes a challenge in terms of tracking mechanism and evaluation method. In this study, we review the online communities and their evolution tracking mechanisms and discuss the main categories of approaches for tracking community evolution and how they work. We analyse the different solutions proposed under each community evolution tracking category and provide an assessment of their projected performance. Finally, a discussion of analysis insights concerning community evolution and its influence is introduced.     

广域复杂流体系统中基于无线传感网的数据保存关键技术研究进展

流体系统包括城市供水管网、天然气供给管网等,是具有经济和社会价值的重要基础设施。它们具有分布地域广、结构复杂、规模庞大、难以检测等特点,在出现泄漏、污染等异常时难以快速发现和准确定位。随着传感器技术、通信技术、微机电技术等的发展,利用无线传感网来对系统进行监测成为研究热点。由于在流体系统中通信困难,数据被监测到后很难实时传送给用户,只能暂时保存在传感器节点(简称节点)上,等待适当的时刻再进行上传。但是,节点具有体积微小、易损坏、存储容量小、通信能力弱、能量有限等特点,如何可靠地存储大量的数据是一个难点问题。目前,已有部分工作对这个问题进行了研究。为了解该领域研究的进展,文中对相关工作进行了细致地分析、对比、归纳和总结,介绍了它们的优缺点,并对未来的研究方向进行了探讨。     

模糊多目标进化的社会团队形成方法

目前,基于社会网络的团队形成问题研究大多采取0-1规则度量专家技能。针对人们通常使用自然语言描述专家技能的情况,提出模糊多目标进化的社会团队形成方法。该方法研究模糊环境下如何从专家社会网络中查询出合适的个体并组成规模一定的团队,实现最小的通信代价和最优的团队绩效。其采用模糊语言变量代替以0-1规则为代表的精确参数来描述专家技能,使用团队绩效的概念衡量团队对任务P的技能表现力。鉴于标准SPEA2算法在进化初期收敛速度慢的缺点,引入档案精英学习策略生成优良个体。另外,考虑到专家技能的模糊性,文中提出了细粒度Dominance判断作为 判断 个体间支配关系的新准则。仿真实验结果证明,改进算法的收敛速度快,获得的近似Pareto前沿更加逼近真实解集,可有效求解团队形成问题。     

Distributed-information neural control: the case of dynamic routingin traffic networks

Large-scale traffic networks can be modeled as graphs in which a set of nodes are connected through a set of links that cannot be loaded above their traffic capacities. Traffic flows may vary over time. Then the nodes may be requested to modify the traffic flows to be sent to their neighboring nodes. In this case, a dynamic routing problem arises. The decision makers are realistically assumed 1) to generate their routing decisions on the basis of local information and possibly of some data received from other nodes, typically, the neighboring ones and 2) to cooperate on the accomplishment of a common goal, that is, the minimization of the total traffic cost. Therefore, they can be regarded as the cooperating members of informationally distributed organizations, which, in control engineering and economics, are called team organizations. Team optimal control problems cannot be solved analytically unless special assumptions on the team model are verified. In general, this is not the case with traffic networks. An approximate resolutive method is then proposed, in which each decision maker is assigned a fixed-structure routing function where some parameters have to be optimized. Among the various possible fixed-structure functions, feedforward neural networks have been chosen for their powerful approximation capabilities. The routing functions can also be computed (or adapted) locally at each node. Concerning traffic networks, we focus attention on store-and-forward packet switching networks, which exhibit the essential peculiarities and difficulties of other traffic networks. Simulations performed on complex communication networks point out the effectiveness of the proposed method.     

Trajectory design and control for aggressive formation flight with quadrotors

In this work we consider the problem of controlling a team of micro-aerial vehicles moving quickly through a three-dimensional environment while maintaining a tight formation. The formation is specified by shape vectors which prescribe the relative separations and bearings between the robots. To maintain the desired shape, each robot plans its trajectory independently based on its local information of other robot plans and estimates of states of other robots in the team. We explore the interaction between nonlinear decentralized controllers, the fourth-order dynamics of the individual robots, time delays in the network, and the effects of communication failures on system performance. Simulations as well as an experimental evaluation of our approach on a team of quadrotors suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.