一种改进的超轻量级RFID认证协议

针对当前超轻量级RFID认证协议中存在的安全缺陷,提出一种改进的超轻量级RFID双向认证协议。通过引入新的函数,使消息之间的数学关联性更加复杂,避免了泄露标签标识符,提高了阅读器和标签在开放环境中通信的保密性。对该协议的安全性和效率进行分析,并利用OPNET软件对该协议仿真实验,结果表明,该协议能抵抗代数攻击,重传攻击,假冒攻击等多种常见恶意攻击,在同等计算量下具有更高的安全性,可用于实际的移动身份认证环境。     

一种多目标资源受限项目调度问题的教学算法

针对多目标资源受限项目调度的特性, 基于结合活动列表和资源列表的编码设计了合理的交叉操作, 提出一种多目标教学算法. 为了在个体间有效交互信息, 在教师阶段非支配个体作为教师与学生执行交叉, 而在学生阶段学生间执行交叉, 同时在每个阶段通过前向-反向改进增强局部搜索能力, 并用Pareto 档案集存储和更新非支配个体.基于标准测试集的数值仿真及与现有最好算法的比较, 验证了所提出算法的有效性.

     

基于特征偏好的聚类研究

传统的聚类方法,如k均值和模糊c均值,通常并不区分数据特征对聚类的不同贡献或重要度,因此在面对高维数据聚类时,常会导致偏低的聚类性能,这归咎于聚类时未考虑高维数据特征间所存在的高度相关性或冗余.而通过在聚类时为每一特征引入权重并通过聚类目标的优化,不仅能自动获得对应的权重,而且也获得了聚类性能的提升.尽管如此,但无监督获取的特征权重未必吻合用户所期望的特征间的相对重要性(或偏好).因此尝试利用用户给定的实际偏好设计出能反映特征偏好的聚类方法,其将现有独立于个体聚类的全局加权型偏好聚类方法拓展至聚类依赖的局部特征加权型方法,由此弥补了前者的不足,提升了偏好聚类算法的性能.     

基于云模型的灾难恢复能力评价方法研究

鉴于现有灾难恢复能力评价体系对动态指标的考虑不足,不能全面地反映现实系统,评价方法未对系统的整体稳定性做分析且权重计算偏于主观,易受人为因素影响等情况,构建了一套新的灾难恢复能力评估体系和评价方法。提炼归纳了体现灾难恢复能力的综合性动态指标,丰富了指标体系。引入云理论,提出了基于正态分布区间数的指标评价值规范化方法和改进的G1-熵权法的综合赋权模型,最终得出了系统灾难恢复能力的云模型表示方法,完成了定性到定量的转换。通过实例仿真验证了该方法的可行性。     

步进电机控制系统的设计

系统由单片机产生数字脉冲信号,通过ULN2003驱动芯片控制电机的运行。由于脉冲信号的频率决定电机的转动速度,所以要改变转速就需要改变脉冲频率。系统采用定时中断的方法改变脉冲频率,而按键次数主要控制外部中断用以改变存储区中的速度值,步进电机的输出脉冲频率就发生相应的改变,最终达到改变转速的效果。     

基于模糊控制的低能耗分簇协议

能耗是影响无线传感器网络生命周期的关键,具有低能耗的无线传感器网络是业界所期望的。现有的几个网络分簇协议能在一定程度上解决和改善网络能耗问题。如LEACH协议能在一定程度上延长网络生命周期,但网络环境的可变性以及实际环境中有诸多不可估计的因素,LEACH协议就没考虑当前节点剩余能量和节点分布情况等问题。提出的基于模糊控制的低能耗分簇协议LECP-FC能较好地解决环境可变等不可估计的问题,在簇头选取的过程中着重考虑了能量、节点度、节点中心度、距离、侦听密度等因子。经过网络结构配置和仿真,其结果表明网络工作效率得到很大程度上的提高。     

Re-ranking for microblog retrieval via multiple graph model

As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.     

Adaptive niche quantum-inspired immune clonal algorithm

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.     

Editorial

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems. Although high model complexity and spatial complexity can be physically handled by current powerful controllers/agents, the lack of efficient analysis and synthesis methods prevents the performance improvement of control systems with high model or/and spatial complexity. As we know, conventional control methods are mainly developed under the assumptions of linear, time-invariant and centralized models. In order to resolve the issues due to the aforementioned two types of complexities, new methods are expected, which is exactly the major aim of the present special issue.