卫星介质深层充电中的主要物理问题

卫星介质深层充电效应是诱发地球同步轨道卫星运行故障和异常的重要因素之一,通过 计算机模拟对介质深层充电的时间常数、电场特征、以及屏蔽、介质厚度和接地方式对充电 所致最大电场的影响等主要物理问题进行了详细的分析,并给出了初步的防护措施.     

深空探测用低温推进剂贮箱热力排气系统研究

针对低温推进剂长期在轨贮存所面临的贮箱压力升高这一实际问题,热力排气系统(TVS)已被国内外研究学者认为是最具应用前景的贮箱控压方式.通过对国内外不同机构所开展的有关TVS方面的研究进行文献分析,重点梳理了低温贮箱TVS控压实验方面的发展脉络;对比分析了不同研究机构所开展的TVS工况设置与运行性能差异;提炼了TVS运行...     

基于长短期记忆神经网络的太阳耀斑短期预报

提出了一个基于长短期记忆神经网络的耀斑预报模型,利用过去24 h太阳活动区的磁场变化时序构建样本,通过长短期记忆神经网络对磁场特征时序演化进行分析,预报未来48 h内是否发生≥M级别耀斑事件。使用的数据集为2010年5月到2017年5月所有活动区样本,选取了SDO/HMI SHARP的10个磁场特征参量。在建模过程中通过XGBoost方法选取权重、增益率和覆盖率均较高的6个特征参量作为输入参数。通过测试对比,模型的虚报率和准确率与传统机器学习模型相近,报准率和临界成功指数分别为0.7483和0.7402,优于传统机器学习模型。模型总体效果优于传统机器学习模型。     

用于深空探测的400 MHz带宽Chirp变换谱分析仪的设计与实现

Chirp变换频谱仪（CTS）具有低功耗、高稳定性等优点,在深空探测领域中具有独特的优势。罗塞塔（Rosetta）彗星探测器搭载的180 MHz带宽Chirp变换频谱分析仪,是迄今为止唯一成功完成空间任务的后端外差式实时频谱分析仪。基于Chirp变换谱分析的原理,设计构建了数字展宽线技术与声表面波压缩线技术相结合的400 MHz带宽Chirp变换谱分析仪。完成了数字展宽线与模拟声表面波压缩线的优化匹配设计,使系统的分辨率达到了理论值100 kHz。进一步采用调频信号和多频率点信号对CTS系统进行了测试验证。     

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.     

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).     

教育之本师德之魂--浅谈对师爱的认识

从师爱是教育之本、师德之魂、教师之责、施教之法等四个方面阐述师爱是教师发自内心的对学生关心、爱护、尊重、信任、期待的美好情感活动的体现,揭示作为一名合格的人民教师必须具备师爱素质的道理。     

基于单应视差的月面着陆区离面测量及平坦性分析

文章针对月球探测器的软着陆,提出了基于由平面诱导的单应视差的月面着陆区离面距离测量及平坦性分析方法.对于特征点分布不均匀情况,采用邻近特征点内插方法在全图强制性近似均匀选择点云作为兴趣点,用基于极线约束和单应约束融合的方法匹配纹理不丰富的兴趣点.使用由平面诱导的单应视差计算兴趣点的离面距离,以离面距离的最大值、绝对值的...     

桩筏基础沉降试验研究

本文对唐山市信息港工程进行沉降观测试验研究。沉降观测布设高程控制网点共5个,共观测6次,另外监测7次,实际观测13次。通过对沉降观测进行分析,从群桩沉降时间关系曲线可以看出,沉降的总趋势是沉降速率随时间的增长而衰减,最后趋于基本稳定,图中出现的沉降曲线上下波动,作者认为是由于群桩中各部分荷载的重新分配造成的。     

月球采样机械臂系统设计及试验验证

在对月球采样返回任务需求及探测器系统任务剖面进行分析的基础上，设计并研制了一种轻量化、大负载、高精度、宽采样范围月球采样机械臂系统。该系统主要由4自由度机械臂及两种不同采样形式的末端采样器组成，可对不同指定区域浅层月壤进行铲、挖、浅钻等多形式采集。在综合考虑机械臂系统器上布局的基础上，基于运动学模型对采样机械臂的可达采样区域进行仿真分析，提出采样机械臂月面采样策略。基于等尺寸着陆器模拟平台，开展了针对3种不同密实度模拟月壤的采样试验，采样试验结果表明在可达采样空间内，采样机械臂对不同密实度月壤均具有较好的适应性，最大采样深度可达30 mm，最大单次采样量可达270 g，单次采样时间小于2 min。