基于增程雷达一维像特征的高轨卫星识别方法

高轨卫星距离地球数万公里,雷达为了实现有效跟踪,通常使用增程技术。远距离使位置测量误差大到千米量级,增程导致数据率低到每帧以分钟计,雷达散射截面积(RCS)细节信息丢失,这给利用目标轨道和RCS进行目标分辨、识别带来困难。幸运的是高轨卫星目标相对观测视线位置和姿态稳定,天然消除了宽带一维像使用中方位敏感性的问题。本文通过一维像的尺寸、波形熵等特征,通过最小二乘支撑向量机,设计了高轨卫星目标的模板匹配算法,利用实测数据进行了识别验证,结果表明了方法的有效性。     

基于AMESim和MATLAB的燃油调节器可视化联合仿真

针对某型弹用发动机燃油液压机械系统搭建的高保真模型存在的结构复杂,并导致其交互性、可扩展性差且难以演示的问题,采用活性能量指数的方法分析简化其 AMESim 模型,同时基于 MATLAB/GUI 设计了燃油调节器仿真界面,实现了燃油调节器可视化虚拟仿真平台的构建。结果表明:该可视化界面友好、操作简便,能够直观反映燃油调节器的调节功能,为辅助用户设计和分析提供了良好的交互性和可扩展性。     

基于深度卷积网络的SAR图像目标检测识别

在SAR图像解译应用领域,目标的自动检测与识别一直是该领域的研究重点和热点,也是该领域的研究难点。针对SAR图像的目标检测与识别方法一般由滤波、分割、特征提取和目标识别等多个相互独立的步骤组成。复杂的流程不仅限制了SAR图像目标检测识别的效率,多步骤处理也使模型的整体优化难以进行,进而制约了目标检测识别的精度。采用近几年在计算机视觉领域表现突出的深度学习方法来处理SAR图像的目标检测识别问题,通过使用CNN、Fast RCNN以及Faster RCNN等模型对MSTAR SAR公开数据集进行目标识别及目标检测实验,验证了卷积神经网络在SAR图像目标识别领域的有效性及高效性,为后续该领域的进一步研究应用奠定了基础。     

基于深度学习的太阳10.7 cm射电流量日值的中期预报

针对太阳10.7cm射电流量中期日值预报问题,采用深度学习方法,建立了一个典型的基于多层感知器模型的神经网络。该网络采用1个包含90个神经元的隐含层,实现了一种非参数的时间序列自回归模型。预报中不仅考虑历史日值,还考虑了历史预报误差。模型根据前27d的历史数据实现了未来27d的日值预报。通过对50多年数据的训练和试验分析,该方法在短期和中期预报上较传统方法的相对误差明显降低。特别是模型经一次训练后,参数可以完全固定,不同于以往研究参数需要每天滚动更新,大大简化了日常预报,同时极为有利于模型在其他相关应用中的推广。     

基于深度学习的先进陶瓷零件实时缺陷检测系统

传统先进陶瓷零件检测与分类的主流方法为纯机械尺寸过滤和人工判断,为解决其成本高、失误率高和损坏率高等问题,提出了基于深度学习的多目标实时检测分类模型（Multi-object real-time detection and classification model, MRDC）。该模型以YOLOv3为基础,使用SKNet作为注意力机制进行特征重构提高精确度,配合灰度图快速转化算法与跳帧检测方法提高检测速度,可实现实时缺陷检测。对实际生产中的先进陶瓷零件进行采集训练,多批次采集图像数据,每批数据含多个陶瓷零件的1 000张图像,平均精确率均值达到99.19％,用先进陶瓷零件生产线视频检验,识别分类的正确率达到100％,可以保证每分钟检测450~550个零件。多目标实时检测分类模型拥有识别速度更快、识别准确率更高和零件不易损坏等优点,可极大地节约生产原料与人力成本,减少废品产出。     

Analysis of ionospheric TEC response to solar and geomagnetic activities at different solar activity stages

Studying the relationship of total electron content (TEC) to solar or geomagnetic activities at different solar activity stages can provide a reference for ionospheric modeling and prediction. On the basis of solar activity indices, geomagnetic activity parameters, and ionospheric TEC data at different solar activity stages, this study analyzes the overall variation relationships of solar and geomagnetic activities with ionospheric TEC, the characteristics of the quasi-27-day periodic oscillations of the three variables at different stages, and the delayed TEC response of solar activity by conducting correlation analysis, Butterworth band-pass filtering, Fourier transform, and time lag analysis. The following results are obtained. (1) TEC exhibits a significant linear relationship with solar activity at different solar activity stages. The correlation coefficients |R| are arranged as follows: |R|_EUV > |R|_F10.7 > |R|sunspot number. No significant linear relationship exists between TEC and geomagnetic activity parameters (|R| < 0.35). (2) TEC, solar activity indices, and geomagnetic activity parameters have a period of 10.5 years. The maximum amplitudes of the Fourier spectrum for TEC and solar activity indices are nearly 27 days and those of geomagnetic activity parameters are nearly 27 and 13.5 days. (3) The deviations of the quasi-27-day significant periodic oscillation of TEC and solar activity indices are consistent. (4) No evident relationship exists between the quasi-27-day periodic oscillation of TEC and geomagnetic activity parameters. (5) The delay time of TEC for the 10.7 cm solar radio flux and extreme ultraviolet is always consistent, whereas that for sunspot number varies at each stage.     

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

载人月球探测月面活动发展设想

载人月球探测是浩大的系统工程，组成复杂，关键技术多，研制周期长，经费投入高，任务风险大。月面活动是载人月球探测任务体现能力、保障安全、提升效益的重要组成部分，必须回答“为什么”“去哪里”“怎么干”等问题。本文从国内外月球探测历程出发，结合当今世界月球探测领域趋势与局势，从月球战略布局、月球科学内涵开展分析。针对月面驻留与活动工程实施，对比Apollo与Artemis计划方案，提出兼顾可行性和工程效益的月面实验室方案。结合未来发展方向，提出载人月球科研站建设发展思路和月球熔洞探测设想，探索具有中国特色可持续发展的载人月面活动发展道路。     

猪IFN-α在巴斯德毕赤酵母中的分泌表达

为了获得高效抗病毒活性的猪α-干扰素蛋白,采用PCR法扩增获得猪α-干扰素基因完整的开放阅读框(ORF),构建重组表达质粒pPICZαC-IFN,电击转化毕赤酵母感受态细胞X33.挑阳性菌落诱导表达,离心取上清,SDS-PAGE和 Western-blotting分析,可见1条相对分子质量约19 400的清晰蛋白条带,与理论值相符,表明表达产物为猪α-干扰素.在Vero细胞上检测该干扰素抗VSV的活性为4.04×10~6 IU/L.     

A model of foreground emission in UV using GALEX deep observations

Foreground emission, mainly airglow and zodiacal light, is a significant contributor in an ultraviolet observation especially from low earth orbit. Its careful estimation and removal are tedious yet unavoidable processes in the study of diffuse UV radiation and by extension interstellar dust studies. Our analysis of deep GALEX observations show that airglow is not only a function of Sun angle but also a strong function of Solar activity at the time of observation. We present here an empirical model of airglow emission, derived from GALEX deep observations, as a function of 10.7 cm Solar flux and Sun angle. We obtained the model by training machine learning models on the data using a variant of the regression algorithm that is both resilient toward outlier data and sensitive to the complexities of the provided data. Our model predictions across various observations show no loss in generalization as well as good agreement with the observed values. We find that the total airglow in an observation is the sum of a baseline part (AG_c) that depends on the Solar flux and Sun angle, and a variable part (AG_v) that depends on the Sun angle and the time of observation with respect to local midnight. We also find that the total airglow can vary between 85 – 390 photon units in FUV and 80 – 465 photon units in NUV.