深空通信天线组阵关键技术及其发展趋势

天线组阵是未来深空通信中的重要发展方向之一。本文介绍了天线组阵的发展历程,重点讨论了组阵中的关键技术,包括阵址选择、阵元口径和数量设计、阵构型设计以及信号处理的方法,分析了上述技术对组阵性能的影响。最后,探讨了天线组阵的发展趋势。
     

深空通信中LDPC码的一种译码算法

LDPC码由于具有逼近仙农极限的性能和硬件可实现的编译码复杂度,在深空通信中具有重要的应用前景。文章在简要介绍CCSDS（空间通信系统咨询委员会）中LDPC码编码规范的基础之上,采用多维EXIT图对其译码门限和收敛速度进行了研究,最后重点研究了译码算法,通过改进归一化最小和算法提出了一种自适应归一化最小和算法,该算法保留了归一化最小和算法的所有优点,改善了不同信噪比条件下的译码性能,并解决了归一化因子的选择难题,适合在资源受限的深空通信中应用。
     

一种基于信息融合的深空巡航段自主导航算法

针对自主光学导航在深空巡航段应用中的问题,提出了一种基于信息融合的自主导航算法。由太阳敏感器测量获得太阳相对探测器的视线矢量,由分光计测量获得探测器相对太阳的径向速度,构建自主导航系统的两种信息观测方程;由基于状态估计误差协方差阵奇异值动态确定信息分配因子,用信息融合技术和扩展卡尔曼滤波算法实时估计探测器的位置与速度。对深度撞击任务的实际飞行数据的数值仿真结果表明:自主导航算法的轨道确定精度满足深空巡航段要求。     

机床组合夹具设计仿真平台在《机械制造技术基础》课程教学中的应用

本文针对传统的机械制造技术基础教学环节中存在的一些问题,提出了用VB（Visual Basic）对Solid-Works开发的机床组合夹具仿真平台进行辅助教学,并介绍了该仿真平台的设计和实现过程及使用和教学效果。     

狠抓基础,突出重点,扎实做好高校统战工作

加强高校统一战线工作,是新时期需要认真探索与实践的一个重要课题。高校统一战线工作的基础一是思想认识的提高,二是组织制度的落实。积极发挥民主党派和无党派人士尽其所能、建言献策的作用,是高校统战工作的重点和切入点。要紧紧围绕学校的根本任务,在实践中积极探索、寻找统战工作与学校中心工作的最佳结合点;要坚持党委对统战工作的领导,牢牢把握走中国特色社会主义道路、构建和谐社会这一统一战线的政治方向。     

一种星-箭连接动态界面力的深度学习反演方法

针对于星-箭连接动态界面力无法通过力传感器直接测量,且典型时域动载反演方法难以准确计算界面力的时域变化等难点,提出了基于长短时记忆(LSTM)神经网络的星-箭界面力深度学习反演方法。首先通过卫星地面测试试验得到数据依据,以卫星主体结构的加速度测量数据为输入层,以星-箭界面力测量数据为输出层,利用LSTM神经网络建立输入和输出间的反演映射关系模型,实现卫星在发射过程中较高精度的界面力反演。进而,设计并开展了某典型卫星结构的正弦扫频和随机振动实验,测试LSTM界面力反演方法的可行性。结果分析可知,所提出的基于LSTM深度学习反演方法能够精确地获得动态界面力时程数据,两项性能指标均优于目前典型的载荷反演方法。     

改进PointNetLK的点云智能配准与位姿图优化方法

针对空间在轨服务任务中的非合作目标相对位姿测量问题,提出一种目标可测部位点云的智能配准方法。首先,通过Straight Through滤波算法对半物理仿真平台采集得到的点云进行目标提取,以消除背景数据等杂乱信息;其次,改进PointNetLK神经网络点云配准算法,将提取后的点云数据作为输入,从而获得初步配准结果,解决非合作目标先验信息缺失导致的无法配准问题;最后,建立基于位姿图的优化模型,以降低配准误差,提高配准精度。实验结果表明,与传统迭代最近点(ICP)算法相比,配准综合误差从6.3598降低到1.7291,精度提高约 72.81% 单次耗时从33.16 s降低到4.2 s,效率提升约87.33%,与当前SM ICP等其他算法相比,也具有一定的优势。     

Atmospheric response to a Hot SST Event in November 2006 as observed by the AIRS instrument

Using Atmospheric Infrared Sounder (AIRS) products of atmospheric temperature and geopotential height, we investigate the atmospheric response to HE0611, which was found and investigated by [Qin, H., Kawamura, H., Sakaida, F., Ando, K. A case study of the tropical Hot Event in November 2006 (HE0611) using a geostationary meteorological satellite and the TAO/TRITON mooring array. J. Geophys. Res. 113, C08045, doi: 10.1029/2007JC004640, 2008]. HE0611 was formed by connecting two very high SST areas, HE0611-East and HE0611-West. The period-mean atmosphere temperatures at levels of 925 and 850 hPa in HE0611-West are higher, by about 0.5 K, than those in WE0611-East while the atmospheric temperatures at middle to high levels (700–300 hPa) are higher in HE0611-East. The period-mean geopotential heights HE0611-East are much lower than those in HE0611-West for the levels from the surface to 400 hPa. The mean geopotential heights from 400 hPa to 200 hPa are higher in HE0611-East. In the middle and high layers over HE0611-West, the atmosphere temperatures gradually decrease from 7th to 17th, and then increase significantly. The increase in HE0611-East starts from 15th November, which is earlier than that of HE0611-West. The geopotential heights in the high layer of both the areas also show corresponding behaviors. The lagged atmospheric response in the western part is confirmed by the correlation analysis. It emerges that the atmospheric response to HE0611 is well organized and associated with deep convention in HE0611-East and subsidence in HE0611-West. These are also consistent with the HE0611 features and evolution revealed by earlier HE studies.     

月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。     

Stall flutter prediction based on multi-layer GRU neural network

The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model (ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit (GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation (LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.