适用于长时间烧蚀防热的蜂窝增强低密度材料

针对长时间烧蚀防热对防热材料的需求,研制了两种蜂窝增强低密度烧蚀防热材料,采用电弧风
洞加热设备对其防热性能进行了考核,并对其力学及热物理性能进行了研究。结果表明,在两种长时间(1 000
s 左右)热环境的考核下,蜂窝增强低密度材料烧蚀后背面温度均低于200℃,表面碳层均匀、完整,无明显烧蚀
量,显示了优异的隔热性能和良好的抗气流冲刷能力。     

The potential of Meudon spectroheliograph for investigating long term solar activity and variability

Observations of the chromosphere with Deslandres’s spectroheliograph started at Paris Observatory in 1893 and were followed by systematic observations at Meudon since 1908. The solar collection of H$\alpha$ and CaII K images is probably the longest available worldwide, with associated products such as synoptic maps and tables. Since 2018, Meudon spectroheliograph is the only instrument producing data-cubes of full line profiles of CaII H, CaII K and H$\alpha$, for each pixel of the solar disk. Slices of the cubes provide monochromatic images. We summarize in this paper the capabilities of the successive generations of the instrument, and explore the potential of the collection and products for analysis of rare events, investigations of past solar activity and studies of long term variability.     

基于DKF的IMU误差预测算法

作为导航领域常用的组合导航方式,全球导航卫星系统(GNSS)/惯性导航系统(INS)组合导航在GNSS信号失锁后,由于惯性测量单元(IMU)误差随时间迅速积累,其定位结果会偏离载体真实位置,导航精度下降.针对此问题,提出了一种长短期记忆网络(LSTM)辅助的算法,称之为深度卡尔曼滤波(DKF)算法.DKF算法的核心思想是使用LSTM训练IMU误差模型,然后通过训练出的模型预测IMU误差,最后将预测的IMU误差代入IMU数据以校正导航结果.仿真结果表明:在200s测试数据上,DKF算法将误差从1.1537m/s降低到0.3746m/s.与平均预测、卡尔曼预测和最小二乘估计等方法相比,DKF算法的误差最小,具有更优越的导航性能.     

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M...