航空复杂壳体零件深孔加工技术研究

深孔加工在航空制造业中具有广泛需求,是加工难度最大的工序之一。复杂壳体零件是航空发动机的关键部件,其深孔加工质量直接影响航空发动机的服役性能和使用寿命。以航空复杂壳体零件为对象,针对航空复杂壳体零件深孔加工的工艺特点及难点,就目前现有深孔加工方法、深孔钻削力学、深孔钻削切屑形态与排屑方法、深孔加工在线监控及深孔加工设备等方面关键技术进行综述,并探讨了深孔加工未来的发展趋势。     

深空辐射环境对高吸收率消杂光涂层的性能影响

为了研究深空辐射环境对SCB-1型空间高吸收率消杂光涂层的太阳光谱吸收性能影响。采用5 000 ESH剂量的真空-紫外、2.5×10¹⁵ p/cm²注量的真空-质子和2.5×10¹⁶ e/cm²注量的真空-电子依次对SCB-1消杂光涂层进行串联辐照试验,分析各项辐照试验前后消杂光涂层外观、太阳吸收比α_s及半球发射率ε_H的变化情况。并采用热失重分析（Thermal Gravity Analysis）判断消杂光涂层受辐照试验后的分解情况与热稳定性变化。经串联辐照试验后,SCB-1消杂光涂层全波段太阳吸收比总变化值Δα_s下降了0.011~0.012,400~1 100 nm波段太阳吸收比总变化值Δα_s下降了0.011~0.013,以上三项串联辐照过程中SCB-1消杂光涂层的半球发射率总变化值Δε_H无明显变化。SCB-1消杂光涂层呈现了极佳的深空辐射环境下的消杂光持久性,可对未来深空探测领域的光学技术发展提供有力支持。     

深空探测用柔性太阳毯的制备及性能

面向我国深空探测任务用新型柔性太阳毯,开展了柔性复合材料制备工艺技术、性能评价研究。结果表明,采用模压聚酰亚胺纤维增强有机硅橡胶柔性复合材料,能够满足柔性太阳毯需求。自动涂胶工艺能够实现高黏度硅橡胶均匀薄涂在聚酰亚胺薄膜表面。对比测试表明,硅烷偶联剂能够提高柔性太阳毯的T剥离强度,但使其低温柔韧性下降。     

基于单光子探测器恢复时间的误码率分析

在深空激光通信中,为满足通信距离远、传输速率高的要求,常采用脉冲位置调制（Pulse Position Modula-tion, PPM）与单光子探测器（Single-Photon Detector, SPD）相结合的探测体制。本文从单光子探测器的恢复时间特性出发,推导了探测器单元探测概率和虚警概率模型,建立了基于信号或硬判决和最大计数硬判决的多元单光子探测器阵列误码率模型。仿真分析结果表明：在16-PPM调制和RS（15,8）编码（Reed-Solomon Codes,里德-所罗门编码）体制下,为实现1.22 Gbps的通信速率、误码率优于10^-7,当探测器单元数为4时,探测器恢复时间应≤3.2 ns;当探测器单元数为25时,探测器恢复时间应≤10 ns。     

国内外深空探测器精密定轨软件研究综述及WUDOGS简介

深空探测器精密定轨软件系统的研制在深空探测活动中是一个非常重要的环节,一直受到各大航天机构的重视。针对国内外深空探测器精密定轨软件平台的研究现状,重点介绍了具有代表性的美国JPL(Jet Propulsion Laboratory,喷气推进实验室)的DPTRAJ/ODP(Double Precision TRAJectory program/Orbit Determination Program,双精度轨道程序/定轨程序)和MONTE(Mission analysis,Operations,and Navigation Toolkit Environment,任务分析、操作和导航工具箱环境),GSFC(Goddard Space Flight Center,戈达德航天飞行中心)的GEODYN-II以及法国CNES(Centre National dEtudes Spatiales,国家空间研究中心)的GINS(Géodésie par Intégrations Numériques Simultanées,同步数值积分大地测量)软件系统,对这些软件的结构与功能进行了总结。之后对武汉大学自主研制的深空探测器精密定轨软件系统WUDOGS(Wuhan University Deep space Orbit determination and Gravity recovery System,武汉大学深空探测器精密定轨与重力场解算软件系统)的主要模块与功能进行了介绍,通过与GEODYN-II的交叉对比验证,表明:对于探测器的轨道预报,WUDOGS与GEODYN-II的1个月位置差异小于0.3mm,2d位置差值小于5×10~(-3) mm;双程测距、双程测速的理论计算值和GEODYN-II的差值RMS(Root Mean Square,均方根)分别在0.06mm,0.002mm/s的水平;WUDOGS目前已初步具备了月球和火星探测器精密定轨能力。最后对WUDOGS的下一步发展方向进行了展望。     

聚酰亚胺薄膜与碳网格共固化基板成型工艺研究

为满足未来深空探测对航天器太阳翼基板更高的耐温性需求,提出一种新型的太阳翼基板成型工艺方法：聚酰亚胺薄膜与碳网格面板共固化成型。重点研究了聚酰亚胺薄膜与网格面板的剥离性能、网格面板的拉伸性能、蜂窝夹层结构的弯曲性能、共固化基板的耐温性能,并与J-133胶粘贴聚酰亚胺薄膜的传统工艺进行对比。结果表明,共固化工艺的聚酰亚胺薄膜剥离强度约为传统工艺的2倍,且在140℃下的各项力学性能保持率均大于87%,具有很好的耐温性;共固化基板在经历-100~140℃热真空试验后,外观质量及各项性能均合格,满足太阳翼基板耐140℃及以下空间环境的使用需求。     

基于MADDPG的多无人机协同任务决策

针对多无人机任务决策方法研究中传统优化算法难以在短时间内得到期望结果的问题,基于深度强化学习提出一种无人机多智能体深度确定性策略梯度(MADDPG)算法,通过允许无人机在学习时使用全局信息,在应用决策的时候只使用局部信息的方法,从网络结构、状态空间、动作空间和奖励函数设计了 MADDPG算法的模型结构.最后通过仿真实验...     

Retrieval and validation of aerosol optical depth using GF-1 WFV cameras data

Conventional AOD (Aerosol Optical Depth) retrieval is restricted to the global and regional scale due to the limited spatial resolution of satellites. This does not allow for aerosol monitoring at the city level. The Chinese GF-1 Wide Field of View (WFV) sensors have sufficiently fine resolution as a data source for AOD retrieval with fine spatial resolution and a 4-day revisit time. In this study, principles similar to those in the Deep Blue (DB) and Dark Target (DT) algorithms were used to retrieve AOD at 100 m spatial resolution from GF-1 WFV images supported by Moderate Resolution Imaging Spectraradiometer (MODIS) surface reflectance (SR) products (MOD09A1). The derived GF-1 WFV AOD were compared with a combination of MOD04_3K DT AOD and MOD04_L2 DB AOD (MODIS AOD) to find that they yield reasonable Spearman correlations (R_S > 0.82) over Taiwan and Beijing. The derived GF-1 WFV AOD were also validated against Aerosol Robotic Network (AERONET) AOD; the Spearman correlation values were R_S = 0.911 in Beijing and R_S = 0.858 in Taiwan.     

基于机器视觉的万用表数字识别技术研究

为提高手持式数字万用表校准系统的自动化水平，本文通过研究图像灰度阈值法、传统方法的目标分类和深度学习三种不同类型的字符识别技术，提出了两种基于机器视觉的数字识别方案。测试结果显示，两种方案的字符识别准确率均可达到99.8%，但其在硬件资源占比、编程难易程度上二者还存在显著差异。该机器视觉字符识别功能的成功开发与应用，可为更多无程控通信接口的计量测试设备，及一些不适于人工作业的危险计量工作环境进行类似的数字识别提供借鉴。     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).