用改进扩展有限元法研究重力坝强震断裂过程

采用改进的扩展有限元法(Extended Finite Element Method,XFEM),即引入虚结点的XFEM,在静力功能基础上实现了动力断裂分析功能,并用于强震中结构多条裂缝的同时扩展分析。采用混凝土Ⅰ型和Ⅰ-Ⅱ混合型动力断裂试验进行了精度验证,然后将该方法应用于Koyna坝的地震开裂分析。结果表明,这一方法在求解混凝土Ⅰ型和Ⅰ-Ⅱ混合型动力断裂问题方面具有良好精度,其避免了网格不断重剖分的问题,也无需采用开裂单元的子域积分来描述非连续位移场,即可实现单元内连续断裂过程的模拟。     

SAM:一种容错的末级缓存可扩展地址映射方法

随着半导体工艺进步,多核处理器超过60%的片上面积由片上缓存占据.由于特征尺寸缩小及供电电压下降,片上缓存较以往更容易发生错误.缓存错误包括可恢复的软错误(soft error)及不可恢复的不稳定位(erraticbit)失效.传统容错技术主要研究针对单个缓存模块的保护.当缓存中包含成百上千个模块时,即使单个缓存模块出错的概率很低,系统中有一个或多个缓存出错的概率也相对较高.文中提出可扩展地址映射(SAM)方法,支持对可缓存地址空间灵活高效的映射,提高末级缓存的可靠性.通过对末级缓存地址空间进行重构,只要有末级缓存模块可以工作,SAM就能够保证系统正确运行.SAM可应用于共享或集群缓存组织方式.文中提出的算法能根据末级缓存中出错缓存模块的数目变化,动态调整集群缓存组织方式下的集群大小.实验结果表明,SAM方法可在多种出错环境下保证系统功能正确,且性能平滑下降.     

鉴别式多核手跟踪

快速运动、背景混杂及手的特殊性为传统的核目标跟踪技术提出了挑战.文中提出一种鉴别式颜色子空间选择和多核校正机制的核跟踪方法.首先基于前景背景建模,通过鉴别函数选取最优鉴别式颜色子空间集,再基于训练的肤色模型保留高置信子空间表征目标.其次,为适应尺度变化和快速运动,基于多核快速估计相似度表面以校正初始核位置,迭代定位目标.实验表明,融合二者的跟踪器在有效性和速度上都实现了满意的性能,满足实时性需要.     

一种基于IPv6的物联网分布式源地址验证方案

作者在融合物联网的新一代互联网网络环境下,提出了基于IPv6的源地址验证整体架构.基于该架构,考虑物联网节点资源受限特点,并结合物联网末梢网络的拓扑形态及其路由方式上的特征,设计了基于IPv6的物联网末梢网络分布式源地址验证方案.分别讨论了静态指定、SLAAC(Stateless Address AutoConfiguration)、DHCPv6(Dynamic Host Configuration Protocol Version 6)以及DHCPv6与SLAAC混合情况下的物联网节点IP地址分配及其验证机制.模拟实验表明,该方案仅以微小的代价实现了物联网节点IP地址的分配,同时还保证了物联网节点之间、物联网节点与互联网端系统之间端到端通信时双方IP地址的真实可靠性,从而整体上增强了物联网的安全性.     

Hidasav:一种层次化的域间真实源地址验证方法

可信任是下一代互联网的重要特征,真实地址访问是可信任的基础和前提.自治域级真实地址访问是整个可信任互联网体系结构中最为复杂的一个层次.基于标签的源地址验证不受拓扑结构影响,无需中间节点特殊处理,是实现域间真实地址访问的有效方法.然而,现有方法中信任联盟过于扁平化和单一化的问题导致验证开销随联盟规模增大而急剧增大,影响和制约了机制的可扩展性和过滤能力,难以进行增量部署.对此,文中提出了一种层次化的基于标签替换的域间真实源地址验证方法(Hidasav),该方法通过合理规划联盟层次和聚类整合,构建出一种多级并存的信任联盟体系结构,通过引入实现轻量级标签替换的联盟边界,将每一层级联盟和外界网络隔离,使得下层联盟和更高层联盟内部的网络环境彼此互不可见、互无影响.与现有同类典型方法在CNGI真实环境中的实验结果比较表明,该方法能够在确保域间高速通信的同时有效降低边界路由设备的状态机存储、更新和报文验证开销.     

用户体验质量(QoE)的模型与评价方法综述

随着信息时代的到来,新的多样性的信息服务大量涌现,人们对信息服务的要求也越来越高.为了获得用户对服务的认可,服务提供商必须确保良好的用户体验质量.因此,用户体验质量不仅成为学术界重要的研究课题之一,而且也成为工业界关键的服务评价指标之一.文中综述了用户体验质量的模型与评价方法等方面的工作.介绍了该领域的研究现状与进展,总结归纳了用户体验质量的影响因素、量化方法及三类评价方法学,重点讨论了几种具体的基于不同学科的用户体验质量的模型和评价方法.基于这些讨论,该文以视频流媒体服务为背景,提出了基于隐马尔可夫的用户体验质量模型,建立了基于随机模型的评价方法,对未来的研究方向进行了展望.     

On trajectory tracking control for nonholonomic mobile manipulators with dynamic uncertainties and external torque disturbances

This paper studies the trajectory tracking control problem of mobile manipulators subject to nonholonomic constraints, operating in task space, with the presence of external torque disturbances and dynamic uncertainties. The proposed controls are robust to external torque disturbances and can overcome the effects of the unknown dynamic parameters. The stability of the closed-loop system and the asymptotic convergences of tracking errors are proved using Lyapunov synthesis. The proposed control strategies have been designed to drive the system motion converges to the desired manifold and, at the same time, guarantees the boundedness of all the closed-loop signals. Simulation results validate that the system trajectory converge to the desired one.     

On optimization of expertise matching with various constraints

This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper-reviewer assignment and teacher-course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper-reviewer suggestions, which has been used for paper-reviewer assignment in a top conference and feedbacks from the conference organizers are very positive.     

Efficient Nonnegative Matrix Factorization via projected Newton method

Nonnegative Matrix Factorization (NMF) is a popular decomposition technique in pattern analysis, document clustering, image processing and related fields. In this paper, we propose a fast NMF algorithm via Projected Newton Method (PNM). First, we propose PNM to efficiently solve a nonnegative least squares problem, which achieves a quadratic convergence rate under appropriate assumptions. Second, in the framework of an alternating optimization method, we adopt PNM as an essential subroutine to efficiently solve the NMF problem. Moreover, by exploiting the low rank assumption of NMF, we make PNM very suitable for solving NMF efficiently. Empirical studies on both synthetic and real-world (text and image) data demonstrate that PNM is quite efficient to solve NMF compared with several state of the art algorithms.     

The small sample size problem of ICA: A comparative study and analysis

On the small sample size problems such as appearance-based recognition, empirical studies have shown that ICA projections have trivial effect on improving the recognition performance over whitened PCA. However, what causes the ineffectiveness of ICA is still an open question. In this study, we find out that this small sample size problem of ICA is caused by a special distributional phenomenon of the high-dimensional whitened data: all data points are similarly distant, and nearly perpendicular to each other. In this situation, ICA algorithms tend to extract the independent features simply by the projections that isolate single or very few samples apart and congregate all other samples around the origin, without any concern on the clustering structure. Our comparative study further shows that the ICA projections usually produce misleading features, whose generalization ability is generally worse than those derived by random projections. Thus, further selection of the ICA features is possibly meaningless. To address the difficulty in pursuing low-dimensional features, we introduce a locality pursuit approach which applies the locality preserving projections in the high-dimensional whitened space. Experimental results show that the locality pursuit performs better than ICA and other conventional approaches, such as Eigenfaces, Laplacianfaces, and Fisherfaces.