China's Lunar and Deep Space Exploration Program for the Next Decade (2020-2030)

China has carried out four unmanned missions to the Moon since it launched Chang'E-1, the first lunar orbiter in 2007. With the implementation of the Chang'E-5 mission this year, the three phases of the lunar exploration program, namely orbiting, landing and returning, have been completed. In the plan of follow-up unmanned lunar exploration missions, it is planned to establish an experimental lunar research station at the lunar south pole by 2030 through the implementation of several missions, laying a foundation for the establishment of practical lunar research station in the future. China successfully launched its first Mars probe on 23 July 2020, followed in future by an asteroid mission, second Mars mission, and a mission to explore Jupiter and its moons.     

China's Future Missions for Deep Space Exploration and Exoplanet Space Survey by 2030

Four future missions for deep space exploration and future space-based exoplanet surveys on habitable planets by 2030 are scheduled to be launched. Two Mars exploration missions are designed to investigate geological structure, the material on Martian surface, and retrieve returned samples. The asteroids and main belt comet exploration is expected to explore two objects within 10 years. The small-body mission will aim to land on the asteroid and get samples return to Earth. The basic physical characteristics of the two objects will be obtained through the mission. The exploration of Jupiter system will characterize the environment of Jupiter and the four largest Moons and understand the atmosphere of Jupiter. In addition, we further introduce two space-based exoplanet survey by 2030, Miyin Program and Closeby Habitable Exoplanet Survey (CHES Mission). Miyin program aims to detect habitable exoplanets using interferometry, while CHES mission expects to discover habitable exoplanets orbiting FGK stars within 10 pc through astrometry. The above-mentioned missions are positively to achieve breakthroughs in the field of planetary science.     

基于目标特性的航天器太阳光压面积计算方法

太阳光压是影响深空探测航天器轨道确定与预报精度最主要的摄动力.针对实际任务需求,采用了一种基于目标特性的光压面积建模与计算方法,根据航天器形状、尺寸、表面材料以及材料光学特性等信息,实现了分析型光压模型的建立与求解,提高了计算效率和精度,可快速计算目标在光照方向上的光压面积、投影面积以及光压比例因子等参数.通过长方体光压面积理论值与仿真值的对比,验证了该方法的准确性和有效性.针对复杂结构探测器开展了光压面积计算,可为深空探测航天器精密定轨中的光压模型解算、定轨及预报提供参考.     

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration.In this paper,a novel method named augmented deep sparse autoencoder(ADSAE) is proposed.The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data.T...     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aero...     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles(UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive des...     

HINet： 一种面向冰雹识别的多源数据融合网络

冰雹天气具有突发性和局地性强,以及破坏力大的特点。尽管地面自动站、雷达和卫星等获取的观测资料在冰雹识别中发挥了一定的作用,但单一观测资料的局限性导致冰雹识别虚警率较高和准确率较低。因此,亟需构建基于多源高分辨率观测的冰雹识别技术。本文提出了一种面向冰雹识别的多源数据融合网络,该深度学习方法利用时空特征提取模块、多源数据特征融合模块和UCUNet（U Connection Unet,U形连接卷积神经网络）识别模块,充分挖掘冰雹发生时FY4B（风云四号B星）、天气雷达和数值模式等多源数据的时空特征,并创新地加入地形高度、坡度、坡向等作为冰雹识别因子。为评估所提网络方法的性能,本文进行了系列实验,并将实验结果与真实标签数据进行对比。结果显示,HINet（Hail Identification Net,冰雹识别网络）能够充分利用多源数据,在复杂地形条件下有效改善冰雹识别结果,在冰雹研究和识别中具有较高的准确性和实用性。     

期望状态序列导向的深空探测器规划修复方法

针对探测器动作持续、并行、消耗能源所导致的规划修复难求解问题,提出一种基于期望状态序列的深空探测器规划修复方法.该方法根据动作的执行完成情况,从既定规划中提取未完成执行动作的期望效果以及待执行动作的期望前提,构成一组有序的、逻辑与能源混合的期望状态集合,为规划修复提供子目标,从而将规划修复问题转化为状态转移路径搜索问题...     

一种用于深空探测的Chirp变换频谱分析仪设计与实现

根据深空探测对频谱分析仪抗辐射、高分辨率、低功耗的需求,研究一种基于Chirp变换的高分辨率频谱分析仪设计及其实现.利用中心频率为1GHz、带宽为400MHz、色散时间为10μs的声表面波滤波器,提出了一种模拟和数字相结合的高分辨率频谱分析仪设计方案,并搭建原理实验系统完成了实测验证,其分析带宽为400MHz,频率分辨率达到152kHz,功耗约为3.6W.分析了压缩线器件非理想特性对频率分辨率的影响,利用实测压缩线频率响应特性获得幅度和相位补偿曲线,对展宽线信号进行幅相失配补偿.仿真结果表明,该补偿方法可将频率分辨率提高至108kHz.     

Gravity waves generated by thunderstorms: South-East Asia tropical region study

Gravity waves are recognized as an integral part of earth’s atmosphere which are mainly responsible for energy and momentum distribution among different layers and regions in the atmosphere. Various sources present in land, ocean, and atmosphere such as mountains, convection, jets and fronts etc. are responsible for gravity waves generation. Thunderstorms (deep convection) are one of the major sources of gravity waves in the tropical region, capable of generating waves with a wide range of frequencies and scales and significantly affecting the existing waves. Previous numerical studies have characterised the wave properties that are generated from thunderstorms, but there are no statistically quantified studies. In this paper, we have modelled the relationship between the latent heat generated inside a thunderstorm and the gravity wave properties at the geo-collocated points. Gravity waves are identified over Singapore radiosonde station (with data available until 30?km altitude with 12?h temporal resolution) in the stratosphere using wavelet studies. Based on the GROGRAT ray tracing methods to identify the thunderstorm locations, and RAMS cloud-resolving models simulations to obtain the latent heating of the thunderstorm, a regression analysis is performed using 200 cases of gravity waves. Furthermore, cloud-top momentum flux analysis is performed for various cases latent heat. This study is expected to provide more quantified and concrete information on the coupling between the thunderstorm and gravity wave which includes the variance in these relationships due to wave frequency spectrum and generation mechanisms.