基于禁忌搜索算法的指挥控制系统网络组织优化方法

基于复杂网络的拓扑结构优化理论与禁忌搜索算法,结合指挥控制系统的特点,建立了优化最优指挥与控制系统网络结构（Command and Control Architecture Network,C2AN）模型,并以复杂网络的网络效率和网络成本分别描述C2AN中的信息传播效率及构建C2AN的资源消耗,建立优化的目标函数,通过禁忌搜索算法获得了最优的C2AN。最后通过仿真实例,分析了最优C2AN生成过程及其基本统计特征。     

无人飞行器Ad Hoc网络中容错节点移动控制算法

针对无人飞行器Ad Hoc网络的容错设计需求,基于UAV节点的可控移动特性,提出了一种基于强化边启发的节点移动控制算法.首先采用文化基因算法对给定通信网络对应的拓扑图进行搜索,求解使图获取顶点2 -连通属性所需新增的最小成本强化边组合.以强化边为启发,将连接的节点移动到彼此通信范围内来实现强化边,同时以这些节点为leader,采用基于一致性算法的leader-follower控制算法移动其他关联节点,使变化后的网络为顶点2-连通,从而实现网络容错.仿真实验结果表明算法的可行性与有效性,节点总的移动距离少于用于对比的块移动算法和紧缩算法.     

一种基于改进Bounding Box的移动信标辅助节点定位算法

针对无线传感器网络的节点自定位问题,提出一种利用单个移动信标的定位算法。该方法在传统Bounding Box定位方法的基础上,结合信标的实际通信区域,提出“O-区域”和“S-区域”的概念,通过对新接收到的信标位置分类讨论,进一步缩小未知节点的可能区域。通过仿真比较,验证了该方法的性能优于质心定位法和Bounding Box定位法,同时定位精度与移动信标的通信半径大小有关。     

基于虚拟力驱动的传感器网络节点定位

为了解决传感器网络节点三边测量定位的全局优化问题,提出了一种基于虚拟力驱动的节点最优相对定位方法。该方法建立了满足节点位置最优最小二乘估计需要的虚拟势能场模型,给出了一种基于虚拟势能场梯度的虚拟力定义新方法,证明了利用该虚拟力驱动节点定位的收敛性,提供了保证节点定位结果全局最优的方法。仿真分析表明,基于虚拟力驱动的节点相对定位方法能够解决节点相对位置的最优估计问题。     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.     

战术移动Ad Hoc网络下的分布式位置辅助功率控制算法

针对战术移动自组(AdHoc)网络提出一种分布式位置辅助的功率控制算法(LAPCA),它通过位置预测来推算节点的邻节点数目,进而调整信号发射功率,以保持最佳的网络连通性,由此提高整个网络的有效流量。该算法作为一个独立模块可以方便地与已有的移动自组网络路由协议相结合。采用较为适合战术环境的参考点群组移动(RPGM)模型来产生网络仿真场景,从仿真实验结果来看,几乎在所有的RPGM场景下采用该算法后的网络有效流量相对于纯粹的AODV路由协议都得到了提高。     

基于LabVIEW的齿轮箱故障诊断

着重介绍了齿轮箱故障诊断系统的实现过程和设计思想,及通过虚拟仪器开发软件LabVIEW、小波变换和神经网络技术,来实现齿轮箱故障诊断系统的核心内容,最后通过实例验证了此设计的可行性。     

改进的BP算法在柴油机故障诊断中的应用研究

利用改进BP算法的神经网络对柴油机进行故障诊断.首先讨论了其训练算法,然后确定了柴油机故障诊断所用特征参数及故障种类,并提出特征参数数据归一化公式,最后以6-135ZC柴油机为例,将实验数据输入网络验证.结果表明,神经网络对柴油机故障识别率很高,应用于柴油机故障诊断领域是切实可行的.     

前馈网络在状态识别中的应用

介绍了前馈网络的结构,BP算法以及算法的推导过程,并对算法做了一些改进.然后将该算法应用于抽油机减速箱的状态识别,给出了识别结果.实验表明,基于BP算法的前馈网络在识别的精度和自学习方面都优于传统方法.