HK网络演化模型的研究和改进

研究一种高集聚度的无标度网络模型,以Holme和Kim（HK）模型为基础,通过引进新增节点所应该具备的连接动态性,改进HK模型的局部特性。分析和仿真表明,改进模型HK’不仅继承了HK模型的高聚类效应和无标度特性,且它的幂律指数可在2～3之间变化,网络成员间的竞争机制也与现实社会网络较接近。     

基于传感器数据的配电网设备能效评估

在深入分析配电网设备上加装的各类传感器装置中数据获取情况的基础之上,综合考虑设备的能效影响因素,构建了基于传感器数据的配电网设备能效评估指标体系。根据每个指标在节能潜力、成本和难度方面对配电网设备能效的影响程度不同,构建了递阶层次结构模型,并采用层次分析法（ AHP）进行指标权值计算。根据定量指标易于量化的特征,构建了对标函数,完成了定量指标能效指数的计算。从而实现对配电网设备的能效评估。并以某实际的配电变压器算例验证了所提方法的实用性和有效性。     

支持策略隐藏的加密云存储访问控制机制

使用密码技术对云存储数据实施机密性保护和访问控制,是当前云计算安全研究的重要内容.选择加密(Selective Encryption)技术根据访问控制策略产生密钥推导图来分发密钥,在保证云存储数据机密性和细粒度访问控制的前提下,具有简化文件存储加密、系统密钥量少的优势.然而,已有选择加密方案需要完全或部分地公开访问控制策略,以用于密钥推导;该信息反映了用户/文件之间的授权访问关系,泄露用户隐私.基于现有的研究工作,本文提出了一个新的访问控制策略隐藏机制,在支持加密云存储数据的细粒度访问控制和高效密钥分发的前提下,能更好地隐藏访问控制策略信息;而且在密钥获取计算速度上有明显优势.     

基于联合特征直方图的均值漂移目标跟踪算法

针对单一颜色特征跟踪性能差的缺点, 提出一种基于联合特征直方图的均值漂移目标跟踪算法. 采用颜色、边缘、运动3 个具有一定互补性的信息进行联合概率跟踪, 以提高目标模型的描述能力; 利用运动信息修正颜色和边缘模型核函数, 用以克服背景颜色、轮廓对跟踪效果的影响; 根据各特征所占权重, 运用自适应融合法生成关联特征直方图, 并将所构建的目标模型融合到mean shift 跟踪框架中, 实现目标跟踪. 实验结果表明, 所提出的算法具有较高的准确性.

     

Analysis of weight distribution strategies in unilateral transtibial amputees during the stand-to-sit activity.

Current methods of quantifying the stand-to-sit activity (StTS) are resource intensive and have not been applied to unilateral transtibial amputees (TTAs). The purpose of this study is to define five phases of arm-rest assisted and unassisted StTS using simple instrumentation and implement this method for assessing TTA movement patterns. Twelve TTAs and 12 age-matched non-amputees performed StTS with and without arm-rest support. Symmetry of weight distribution between lower limbs was calculated for five StTS phases: Descent Initiation; Descent Deceleration; Seat-Contact; Stabilisation and Sitting. TTAs demonstrated an asymmetrical weight distribution pattern and a tendency to transfer weight to the intact limb during the course of the activity. Non-amputees had relatively higher symmetry and did not exhibit substantial weight shifts during the activity. Symmetry indices were similar for assisted and unassisted sitting in both subject groups. These results highlight a need for therapeutic interventions in TTAs for reducing loading asymmetries and associated co-morbidities.     

A skewed unscented Kalman filter

State estimation of nonlinear systems is a challenging task, especially when the Gaussian approximation fails. The unscented Kalman filter was proposed to deal with state estimation of nonlinear systems. We modify the traditional unscented Kalman filter to capture the third-order moment (skewness) of the state vector. Methods are also proposed to reduce the computation time of the suggested approach, and showing that the proposed algorithm is as fast as the unscented Kalman filter. Simulation results confirm that the method is better than, or at least as good as, the unscented Kalman filter.     

Synchronisation of complex dynamical networks with additive stochastic time-varying delays

In this letter, a class of complex dynamical networks with additive stochastic time-varying delays is investigated. Two kinds of delays in complex dynamical networks are taken into consideration, one is called the nodes-induced delay in this paper, and the other is the network-induced delay. Both of the delays are assumed to obey different stochastic distributions. By utilising a novel Lyapunov–Krasovskii functional and stochastic analysis techniques, a sufficient criterion is obtained in the form of linear matrix inequalities to ensure the synchronisation of the complex dynamical networks. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

Online collaborative learning in dyads: Effects of knowledge distribution and awareness

Research on collaborative learning traditionally assumes a certain degree of symmetry between the learning partners in terms of both their learning-relevant traits and their individual learning outcomes. However, if one collaborative partner is clearly more able, skilled, or knowledgeable than the other partner, then it remains unclear who profits more from the collaboration. The present study aimed to explore this issue by manipulating symmetry in prior knowledge within small groups of online learners (dyads) and measuring their problem-solving efficiency and incidental learning gain on an individual and dyad level. Awareness of this symmetry/asymmetry was manipulated, too, to discern it as a potential moderator. Dyads with symmetrical and asymmetrical prior knowledge performed equally well on most measures. Moreover, on average, the more and the less knowledgeable partners in the asymmetrical conditions had equal learning gains. However, while in dyads with symmetrical knowledge learning gains were correlated between the partners, in the asymmetrical dyads they were not. Awareness of symmetry/asymmetry did not act as a moderator, but, overall, dyads with awareness of each other's knowledge learned more from each other than dyads without such awareness. The benefit of awareness was, however, specific to the learning content exposed via awareness. We conclude that researchers and practitioners should be careful when choosing or assigning collaborative partners to each other, as only for partners with symmetrical prior knowledge can a symmetrical increase in knowledge be expected. We further discuss the implications of these findings for research on knowledge awareness and collaboration.     

Collaborative distribution between two logistics service providers

Mathematical models are described for collaborative distribution between two logistics service providers (LSPs). We find that (a) collaborative distribution is conditionally profitable for LSPs, and the conditions for cooperation are given; (b) LSPs save more costs primarily by shortening the delivery distance and decreasing delivery speed; (c) collaborative distribution makes the delivery time either shorter or longer; (d) if one LSP changes its equilibrium price, the total profit conditionally increases, and this condition is related to demand structure and delivery cost structure; and (e) collaborative distribution can solve the personalization of delivery time and reduce pollution to some extent. For the companies sharing the incremental profit, we characterize a transfer payment contract that includes fixed and variable fees. Our findings suggest that LSPs should adopt collaborative distribution to formulate an optimal distribution plan to save costs and shorten the delivery time, and should select partners with smaller cost parameters and both different and moderate speeds. In addition, warehouse and vehicle sharing are beneficial to the implementation of collaborative distribution.     

面向新媒体广播的云CDN系统

互联网用户的快速增长使得流媒体访问需求不断增加,庞大的流媒体访问需求和有限的服务器分发能力,造成了系统响应慢、用户体验下降等问题.针对以上问题,结合新媒体广播项目中流媒体分发的实际应用,提出了一种基于云计算的流媒体分发系统,主要利用云计算的分布式处理和CDN（Content Distributed Network）分发系统来使用户能够就近访问所需资源,在保证流媒体分发质量的同时提高分发速度,降低企业成本改善用户访问体验.本文主要阐述了面向新媒体广播的云CDN系统的总体框架和主要功能,具体介绍了借助于Hadoop实现的客户端测速技术以及基于用户访问行为的缓存策略.