无线移动感知网络上的数据聚集传输规划

随着城市交通日益增多,智能交通系统研究受到工业界和学术界的极大关注.智能交通系统上的实时导航、交通监控等应用都需要大量实时车辆速度、位置等信息.由智能车组成的无线移动感知网络的链路变化频繁并且无线干扰严重,现有的车辆网络上的数据聚集算法没有考虑无线链路变化的具体情况,就进行数据聚集和传输的规划.文中分析并求解了移动感知网络上的无冲突数据聚集传输规划问题,将该问题形式化为移动通信拓扑图上的单传聚集传输规划问题,并证明了该问题是NP完全的;提出了移动聚集路由树,将无线冲突分为树内冲突和树间冲突,首先构建树间冲突图描述树间冲突,过滤节点的传输候选时刻集以消除树间冲突,然后利用动态规划思想进行聚集路由树上的数据聚集传输时刻规划来消除树内冲突.并且在真实出租车移动轨迹数据集上进行了实验,实验结果表明文中的算法比现有的算法在数据收集率上提高了1/4左右,并且收集上来的数据的平均延迟也更小.     

移动定位技术的现状与发展趋势

基于位置服务为人们的日常生活带来了极大的方便,同时也推动了移动定位技术行业的快速发展。针对移动定位技术发展状况,分析目前主流定位技术的优劣,并对取得的突破作简单的总结归纳。     

基于混合遗传算法的去耦电容网络设计

结合基于目标阻抗的设计方法,将混合遗传算法应用于PDN(电源分配网络)的去耦电容网络设计,对所需去耦电容器的种类和数目进行优化计算,同时与标准遗传算法和传统设计方法进行对比,借助Cadence电源完整性仿真工具验证设计结果的有效性和合理性。避免了传统设计需要借助EDA工具进行反复修改和验证的繁琐过程,同时减少了设计冗余,节省了PCB布局布线空间,为高频电路的PDN设计提供参考。     

Case‐based approach for supporting strategy decision making

The practice of strategy decision making proves that when the management board is strongly limited in its capacity to take rational actions, specifically in the context of great decision complexity and uncertainty, it is considered good practice to refer to experience through reasoning by analogy. In this paper, we would like to concentrate on supporting strategic decisions in small and medium enterprises (SMEs). The complexity of analogy‐based reasoning has its roots in an attempt to solve new problems based on past cases from a different domain, while we will focus on case‐based approach for a single domain. Additionally, we have chosen case‐based reasoning as a suitable decision‐making paradigm because it is corresponds to managers’ decision‐making behaviour. We present the STRATEGOS case‐based reasoning system for supporting strategic decision making by SMEs management boards and then the system evaluation by the dozens of chief executive officers (CEOs) from SMEs is presented. The results of the survey are promising and show the remarkable correspondence of the proposed solution with expectations and strategic behaviour of CEOs.     

具备反向学习和局部学习能力的粒子群算法

为解决粒子群优化（Particle Swarm Optimization,PSO）算法中存在的种群多样性和收敛性之间的矛盾,该文提出了一种具备反向学习和局部学习能力的粒子群优化算法（Reverse-learning and Local-learning PSO,RLPSO）。该算法保留了初始种群中满足排异距离要求的多个较差粒子以及每个粒子的历史最差位置。当检测到算法陷入局部最优时,利用这些较差粒子的位置信息指导部分粒子以较快飞行速度进行反向学习,将其迅速牵引出局部最优区域。反向学习过程可改善粒子种群的多样性,保证了算法的全局探测能力;同时,利用较优粒子间的差分结果指导最优粒子进行局部学习与搜索,该过程可与粒子群的飞行过程并行执行,且局部学习的缩放因子可随进化过程动态调节。局部学习可提高算法的求解精度,保证算法的迅速收敛。实验结果表明,RLPSO 算法同其他 PSO 算法相比,在高维函数优化中具有收敛速度快、求解精度高的特点。     

Infra-chromatic bound for exact maximum clique search

Many efficient exact branch and bound maximum clique solvers use approximate coloring to compute an upper bound on the clique number for every subproblem. This technique reasonably promises tight bounds on average, but never tighter than the chromatic number of the graph.Li and Quan, 2010, AAAI Conference, p. 128–133 describe a way to compute even tighter bounds by reducing each colored subproblem to maximum satisfiability problem (MaxSAT). Moreover they show empirically that the new bounds obtained may be lower than the chromatic number.Based on this idea this paper shows an efficient way to compute related “infra-chromatic” upper bounds without an explicit MaxSAT encoding. The reported results show some of the best times for a stand-alone computer over a number of instances from standard benchmarks.     

Can technologies make a difference for hospitalized youth: Findings from research

Children and youth who are hospitalized for a short or long term become socially isolated from their family, school and classmates. As their isolation increases, so does their vulnerability as a result of disrupted schooling. Research studies suggest different ways of using technologies to overcome this isolation and support children during this critical time of their lives. With technology for children in hospitals becoming increasingly commonplace, evaluating how it is used and its impact on children's learning and well‐being is necessary. In this paper, we systematically evaluated existing research on use of technologies for communication, support and educational purposes and its contribution to the well‐being of hospitalized children. A rigorous selection process yielded 14 research articles meeting the following criteria: empirical studies, using technologies for network of support, targeting hospitalized children and adolescents and targeting learning or social and emotional well‐being in the last decade. This review indicates that the use of technologies with children and youth in hospitals generally increased their potential for learning and improved connectivity with school. Our findings revealed that implementing digital pedagogies and creating online communities were helpful but underutilized features in the research.     

Toward Self‐Motivated,Cognitive, Continually Planning Agents

We present a flexible initial framework for defining self‐motivated, self‐aware agents in simulated worlds, planning continuously so as to maximize long‐term rewards. While such agents employ reasoned exploration of feasible sequences of actions and corresponding states, they also behave opportunistically and recover from failure, thanks to their continual plan updates and quest for rewards. Our framework allows for both specific and general (quantified) knowledge and for epistemic predicates such as knowing‐that and knowing‐whether. Because realistic agents have only partial knowledge of their world, the reasoning of the proposed agents uses a weakened closed‐world assumption; this has consequences for epistemic reasoning, in particular introspection. The planning operators allow for quantitative, gradual change and side effects such as the passage of time, changes in distances and rewards, and language production, using a uniform procedural attachment method. Question answering (involving introspection) and experimental runs are shown for our particular agent ME in a simple world, demonstrating the value of continual deliberate, reward‐driven planning. Though the primary merit of agents definable in our framework is that they combine all of the aforementioned features, they can also be configured as single or multiple goal‐seeking agents and as such perform comparably with some recent experimental agents.