文章先是阐述了能见度值在飞行指挥工作中的重要性,从能见度设备的测量原理上描述了飞虫类等对能见度测量值的影响。结合TR30LED大气透射仪的组成结构,深入分析飞虫、飞鸟活动如何导致其测量值异常变化,列举了对此类影响的各项常规防控...文章先是阐述了能见度值在飞行指挥工作中的重要性,从能见度设备的测量原理上描述了飞虫类等对能见度测量值的影响。结合TR30LED大气透射仪的组成结构,深入分析飞虫、飞鸟活动如何导致其测量值异常变化,列举了对此类影响的各项常规防控手段和措施,并通过设计开发技防措施和装置来进行防控,对TR30LED大气透射仪的飞虫飞鸟影响起到了积极的防护作用。This article first elaborates on the importance of visibility values in flight command work, and describes the impact of flying insects and other factors on visibility measurement values from the measurement principle of visibility equipment. Based on the composition and structure of the TR30LED atmospheric transmittance meter, this article deeply analyzes how the activities of flying insects and birds cause abnormal changes in their measurement values. Various conventional prevention and control measures for such effects are listed, and technical prevention measures and devices are designed and developed to prevent and control the impact of flying insects and birds on the TR30LED atmospheric transmittance meter, playing a positive protective role.展开更多
文摘文章先是阐述了能见度值在飞行指挥工作中的重要性,从能见度设备的测量原理上描述了飞虫类等对能见度测量值的影响。结合TR30LED大气透射仪的组成结构,深入分析飞虫、飞鸟活动如何导致其测量值异常变化,列举了对此类影响的各项常规防控手段和措施,并通过设计开发技防措施和装置来进行防控,对TR30LED大气透射仪的飞虫飞鸟影响起到了积极的防护作用。This article first elaborates on the importance of visibility values in flight command work, and describes the impact of flying insects and other factors on visibility measurement values from the measurement principle of visibility equipment. Based on the composition and structure of the TR30LED atmospheric transmittance meter, this article deeply analyzes how the activities of flying insects and birds cause abnormal changes in their measurement values. Various conventional prevention and control measures for such effects are listed, and technical prevention measures and devices are designed and developed to prevent and control the impact of flying insects and birds on the TR30LED atmospheric transmittance meter, playing a positive protective role.
文摘针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像目标识别算法识别准确率极易受到斑点噪声影响,且模型容易出现过拟合的问题,提出了一种基于Frost滤波和改进的卷积神经网络的目标识别方法。利用Frost滤波算法对SAR图像进行了滤波处理,减少了相干斑噪声;对CNN网络进行改进,建立了一个SAR图像目标识别模型,在网络中引入L2正则化和Dropout结构,抑制过拟合现象的发生;采用Adam优化算法,提高模型的收敛效率;最后采用组合的数据增强方法,扩充SAR图像数据集,进一步提高识别的准确率。利用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition,MSTAR)SAR图像数据进行实验,综合识别准确率可以达到98.06%,结果表明本文所提的方法具有更好的识别效果。
