基于强化学习和蚁群算法的WSN节点故障诊断

为了克服现有的WSN节点故障诊断方法所具有的难以实现在线诊断和诊断精度仍然不够高的缺点,设计了一种基于Sarsa算法和改进蚁群算法的WSN节点在线故障诊断方法。首先,建立了监测区域的网络模型和WSN节点故障诊断模型,然后,采用主成分分析法对节点故障样本数据进行降维,从而提高诊断效率,将样本数据作为层次,将故障诊断类作为各层节点建立层次树,采用改进的Sarsa算法求取各层节点的Q值,并将其用于初始化蚁群算法中路径的信息素,最后,提出了一种改进的蚁群算法求取从第一层出发的蚁群到各层节点之间的路径,将各层中信息素最大的节点作为最终的故障诊断类别。在Matlab环境下进行仿真实验,结果证明文中方法能有效实现WSN节点故障诊断,且与其它方法相比,具有故障诊断精确度高且能在线故障的优点,是一种有效的节点故障诊断方法。     

基于自规避随机游走的节点排序算法

评估复杂网络系统的节点重要性有助于提升其系统抗毁性和结构稳定性. 目前, 定量节点重要性的排序算法通常基于网络结构的中心性指标如度数、介数、紧密度、特征向量等. 然而, 这些算法需要以知晓网络结构的全局信息为前提, 很难在大规模网络中实际应用. 基于自规避随机游走的思想, 提出一种结合网络结构局域信息和标签扩散的节点排序算法. 该算法综合考虑了节点的直接邻居数量及与其他节点之间的拓扑关系, 能够表征其在复杂网络系统中的结构影响力和重要性. 基于三个典型的实际网络, 通过对极大连通系数、网络谱距离数、节点连边数和脆弱系数等评估指标的实验对比, 结果表明提出的算法显著优于现有的依据局域信息的节点排序算法.     

基于概率感知和改进量子遗传算法的节点部署策略

为了最大化监测区域的网络覆盖率和实现网络的负载均衡,设计了一种基于改进概率感知模型和量子遗传算法的移动节点部署方法。首先,在传统概率感知模型中加入环境干扰噪声因素和节点剩余能量因素进而获得改进的概率感知模型,然后,对传统的量子遗传算法进行改进,设计了新的量子旋转门和量子比特变异方式,以加快全局收敛速度和防止陷入局部最优。最后,定义了基于改进量子遗传算法获取移动节点最优部署位置的算法。仿真实验表明：文中方法能有效地对网络区域进行部署,实现最大化网络的覆盖率和最小化能量消耗,与其它方法相比,具有较大的优越性,是一种可行的方法。     

C8051F040在基于CAN总线的光传输设备监控系统中的应用

分析了CYGNAL单片机C8051F040集成的CAN控制器结构，并给出在光传输设备监控系统中使用该芯片的智能节点的硬件、软件设计方案。     

竖线型结点组上的插值及向高维情形的推广

本文讨论了Ｒ＾２中竖线型结点组插值的适定性，得到了相应的插值多项式，并将这些结果推广到Ｒ＾ｓ（ｓ〉２）的情形。     

The photonic ring: Algorithms for optimized node arrangements

New applications such as the future university and research environment, medical imaging, and supercomputer interconnects indicate that the future end-user will need to operate at close to peak electronic processing speed, viz. a few Gigabits per second. By exploiting the unique properties of emerging lightwave technology, it is now possible to embed different logical structures over a given physical network topology. Regular multihop structures, for example, bus, ring, toroid, are attractive candidates for logical topologies since they use simple routing schemes, that require less processing time.     

LONWORKS 网络技术在海军装备领域的应用前景

本文从介绍ＬＯＮＷＯＲＫＳ网络技术入手，论述了ＬＯＮＷＯＲＫＳ技术在海军装备上应用的广阔前景，提出了一条针对我国国情、军情，对改造已有装备以及建立新的系统都有着重要意义的新途径     

Exploiting perturbed and coalescent anchor node geometry with semidefinite relaxation for sensor network localization

Localization in wireless sensor networks (WSNs) suffer from performance issues whenever the anchor nodes (which are aware of their location) are subjected to motion from their usual position. Moreover, accurate localization demands more anchor nodes which is a scarce resource and needs to be used judiciously. In the current work, we propose a novel framework that addresses these two prime concerns by harnessing the inter relationship of anchor node geometry. For an unknown source node surrounded by anchor nodes, the anchors lying on the inner boundary of the deployment geometry may be carrying closely related information about source node, leading to redundancy and inefficient utilization. By anticipating the level of correlation between these anchors, localization can be made more frugal. Rigorous mathematical analysis is carried out to derive lower bounds on estimated locations. Based on fisher information from two proposed models, a convex estimation objective function is formulated using semidefinite programming (SDP) approach to validate the theoretical proceedings. Based on the findings, the proposed method is able to successfully extract useful information about the unknown source node location with limited number of anchor nodes, hence achieving superior localization.     

Detecting local community structures in complex networks based on local degree central nodes

Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks.