Optimization and verification of single pilot operations model for commercial aircraft based on biclustering method

Commercial aircraft crews have experienced a trend from five-person crew to dual-pilot crew. Arised from both technological and market demands, Single Pilot Operations (SPO) is considered an important development direction in modern aviation technology. In this paper, starting from Dual-Pilot Operations (DPO), the piloting process, decision-making process and decision-making mode of DPO for commercial aircraft are studied to obtain the operational requirements of SPO. Then, based on above analysis, the operational mechanism of SPO is studied and the core technology of SPO mode is proposed. Next, a new closed frequent bicluster mining algorithm named FsCluster is proposed for the optimization of the SPO model, and the other efficient bicluster mining algorithm named TsCluster is proposed for the analysis and verification of the SPO model. Finally, a typical flight phase scenario is modelled by Magic System of System, and combined with the proposed algorithms for analysis and verification to determine whether the SPO mode can meet the DPO requirements.     

A Robotic TeleoperationSystem for Precise Robotic Manipulation by Human-Machine Interaction

Teleoperation is of great importance in the area of robotics， especially when people are unavailable in the robot workshop. It provides a way for people to control robots remotely using human intelligence. In this paper， a robotic teleoperation system for precise robotic manipulation is established. The data glove and the 7-degrees of freedom (DOFs) force feedback controller are used for the remote control interaction. The control system and the monitor system are designed for the remote precise manipulation. The monitor system contains an image acquisition system and a human-machine interaction module， and aims to simulate and detect the robot running state. Besides，a visual object tracking algorithm is developed to estimate the states of the dynamic system from noisy observations. The established robotic teleoperation systemis applied to a series of experiments， and high-precision results are obtained， showing the effectiveness of the physical system.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).     

多空间机器人协同工作空间的数值求解方法

针对未来在轨服务过程中，可能遇到单个空间机器人难以独立胜任的应用场景，需要多个空间机器人协同操作，但目前针对多空间机器人协同工作空间的研究很少。考虑到解析法计算工作空间难度很大，提出了一种基于蒙特卡洛法的一般协同工作空间数值求解方法。再进一步针对空间机器人协同的特殊性，提出了广义协同工作空间的概念，并给出了数值求解方法。仿真结果表明，基座姿态约束会引起协同工作空间的缩小，同时广义协同工作空间的范围明显大于一般协同工作空间。所得结论可以为空间机器人任务设计与规划提供参考。     

基于注意力机制特征重建网络的舰船目标检测

深度学习为遥感领域诸多应用提供了重要的技术支撑,光学遥感图像的舰船目标检测对国防侦察和预警具有重要意义。真实场景中的舰船往往呈不同方向任意排列,且小目标的占比大,经典的深度学习目标检测算法在这种复杂条件下精度低、易漏检。为此,本文设计了基于注意力机制特征重建网络的舰船目标检测算法。首先,通过引入注意力机制对多尺度特征融合网络模型进行训练,以高召回率产生水平锚框;然后,旋转锚框以缓解密集排列目标引起的噪声问题,并利用特征重建模块来缓解特征不对齐的问题,实现模型精炼。在HRSC2016和DOTA数据集上的测试结果表明:舰船目标检测平均精度分别达到90.20和87.52,相比经典的深度学习目标检测算法得到了有效提升,并在模拟星载嵌入式智能图像处理平台上验证了算法在轨应用的可行性。