6LoWPAN接入互联网的自适应联合网关的设计与实现

无线传感器网络与互联网结构的差异使得两者在接入与数据包处理等方面都面临着一定的技术难点。针对基于IPv6协议的低速无线个域网6LoWPAN( IPv6 over Low Power Wireless Personal Area Network)与互联网接入方式的问题,用无线射频接入方式代替串口通信方式,提高通信过程中通信速率。同时结合无线传感器网络技术、嵌入式技术、轻量级网络地址转换/协议转换技术uNAT-PT(Micro Network Address Translation-Protocol Translation)、站间自动隧道寻址协议技术ISATAP(In-tra-Site Automatic Tunnel Addressing Protocol)和进程间通信IPC( Inter-Process Communication)机制,在网关处实现了两者之间的自适应适配互通机制,优化数据包流向问题。实验结果表明,无线射频接入方式与协议适配的自适应数据包调度机制能够提高通信过程中的通信速率,减少网络时延,优化网关通信性能。     

电磁超声检测钢板厚度实验的参数优化

为提高电磁超声检测信号的信噪比,研究激励信号频率、脉宽及换能器提离距离对厚度测量结果的影响,搭建了电磁超声实验系统。该系统利用RITEC-SNAP 5000产生高能正弦波激励电磁超声换能器EMAT ( Electromagnetic Acoustic Trans-ducer),通过调节激励信号的频率、脉宽,改变提离距离来观测超声回波幅度的变化。实验结果表明：当激励频率和EMAT固有频率相同时其换能效率最高,采用6个周期的脉冲串时前后波包之间分界明显,为获得较大的回波幅度应尽可能减小提离距离。     

基于拓扑结构变化趋势的MCL优化算法

该文提出一种高精度的移动传感器网络中实现定位跟踪的方法,该方法利用未知节点在运动过程中网络拓扑结构变化的信息提高锚节点利用率,并改善样本采集效率。在无拓扑结构变化的情况下采用牛顿插值方法对节点当前位置进行预测,当拓扑结构有变化时,采用拓扑结构变化构建适应值函数,并用粒子群算法优化样本点的质量。仿真实验结果表明,该文算法与传统算法相比加快了收敛速度,提高了定位精度,改善了在低锚节点密度时的性能。     

一种高方向灵敏度光纤F-P超声传感系统设计研究

针对光纤F-P超声传感器工作点易偏离问题,设计了基于双波长稳定技术的低细度光纤F-P传感系统,建立了双波长光纤F-P传感系统的DE算法数学模型,优化设计了一高正交精度光纤F-P传感系统,建立基于该传感器的激光超声检测系统,实验研究了该传感器探测超声信号的有效性和方向灵敏度。结果表明,该传感器可以有效检测试样中激发出的超声表面波信号。激发源与传感器轴向夹角为0°时,表面波幅值最大。随着激发源与传感器轴向夹角增大,表面波幅值降低。激发源与传感器轴线垂直时,幅值下降达80%,说明该传感器有很强的方向性。     

一种改进小波阈值函数的光纤光栅传感信号去噪方法

光纤布拉格光栅( FBG)传感信号中存在的噪声严重影响了其中心波长的检测精度,因此必须进行去噪处理。本文针对传统的小波软、硬阈值函数去噪的不足,提出了一种改进的对数阈值函数,并确定了最优的小波基和分解尺度。仿真表明,本文提出的改进阈值函数去噪后信噪比( SNR)较大,均方根误差( RMSE)较小,其中信噪比比传统的软、硬阈值方法提高了1.5 dB~4 dB。最后通过实验验证了本文方法的去噪效果。     

The governance strategies for public emergencies on social media and their effects: a case study based on the microblog data

Since the 1980s, the development of information and communication technologies (ICTs) has greatly changed people’s modes of production and lifestyle, and it has also had a significant influence on traditional social structures. Microblogs – a type of social media application such as Twitter or Weibo – have served as an important platform for network governance in some local governments in China. This study makes an attempt to answer the following questions: What types of strategies should governments implement on social media platforms during public emergencies? What are the effects of these strategies? Based on the case of the Shifang Incident, which was a large-scale environmental protest that occurred in Shifang, China in 2012, we analyze all the messages posted during the incident on the official microblog of the Shifang government and examine the public feedback by using an online big data analysis tool. In line with the time sequence and the extent of the conflict, we divide the Shifang Incident into three phases: the fermentation period, the confrontation period, and the digestion period. In addition, we classify government strategies on social media into five categories: introducing, appealing, explaining, rumor-refuting, and decision-making. The analysis results show that different government strategies are applied to different phases of the incident and that the responses of the public also vary during different periods.     

Determining Geographic Vulnerabilities Using a Novel Impact Based Resilience Metric

Various natural and man-made disasters as well as major political events (like riots) have increased the importance of understanding geographic failures and how correlated failures impact networks. Since mission critical networks are overlaid as virtual networks over a physical network infrastructure forming multilayer networks, there is an increasing need for methods to analyze multilayer networks for geographic vulnerabilities. In this paper, we present a novel impact-based resilience metric. Our new metric uses ideas borrowed from performability to combine network impact with state probability to calculate a new metric called Network Impact Resilience. The idea is that the highest impact to the mission of a network should drive its resilience metric. Furthermore, we present a state space analysis method that analyzes multilayer networks for geographic vulnerabilities. To demonstrate the methods, the inability to provision a given number of upper layer services is used as the criteria for network failure. Mapping techniques for multilayer network states are presented. Simplifying geographic state mapping techniques to reduce enumeration costs are also presented and tested. Finally, these techniques are tested on networks of varying sizes.     

Exact and approximate flexible aggregate similarity search

Aggregate similarity search, also known as aggregate nearest-neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves from a database the objects most similar to Q, where the similarity is an aggregation (e.g., \({{\mathrm{sum}}}\), \(\max \)) of the distances between each retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of \(\phi M\) objects in Q for some support \(0< \phi \le 1\). We call this new definition flexible aggregate similarity search and accordingly refer to a query as a flexible aggregate nearest-neighbor ( Fann ) query. We present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.     

Hopf Bifurcation Analysis of a Reaction-Diffusion Neural Network with Time Delay in Leakage Terms and Distributed Delays

In this paper, a reaction-diffusion neural network with time delay in leakage terms and distributed synaptic transmission delays under homogeneous Neumann boundary conditions is investigated. By analyzing the corresponding characteristic equation, the local stability of the trivial uniform steady state and the existence of Hopf bifurcation are established. By using the normal form theory and the center manifold reduction of partial functional differential equations, explicit formulae are obtained to determine the direction of bifurcations and the stability of bifurcating periodic solutions. Numerical simulations are carried out to illustrate the main results.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.