基于经验模态分解的点云数据平滑与增强

在计算机辅助设计与逆向工程应用中,针对缺乏拓扑连接关系的点云数据,提出了基于经验模态分解(EMD)的点云数据平滑与增强算法。首先,以点云模型的拉普拉斯矩阵坐标与法向的内积作为EMD输入信号,提取点云模型输入信号的极值点作为插值节点计算信号的上下包络;然后,为实现特征保持的EMD信号分解,通过检测点云数据上特征点,并在计算信号上下包络的过程中作为约束,克服传统EMD算法无法保持特征的局限;最后,迭代地从输入信号中减去上下包络的均值得到内蕴模态函数(IMF)和余量,并通过设计滤波器实现了点云数据平滑和增强。实验结果表明,本文算法有效地将EMD推广到三维散乱点云数据中,扩大EMD在三维几何中的应用范围,并在点云数据平滑和增强方面取得了很好的效果。     

雷暴天气的分析识别及对飞行的影响

积雨云产生的雷暴作为航空安全最重要的天气之一,对它及时的从航空气象资料中获取,识别和分析,对保障飞行安全具有重要意义.从雷暴天气产生的机理讨论雷暴在卫星云图上的识别与分析,阐述雷暴天气对航班飞行过程中的影响及处置办法.     

Deuterium Fractionation in Interstellar Clouds

This paper reviews the chemical processes responsible for fractionating deuterium in interstellar molecules. I show that this process is intrinsically a low temperature phenomenon and discuss how the degree of enhancement of the deuterium content of molecules is related to the physical conditions, particularly abundances, in molecular clouds. If significant amounts of abundant species, such as CO, are frozen out on to interstellar dust grains, the resulting enhancement in H₂D⁺ can result in its abundance being greater than that of H ₃ ⁺ at 10K. Transfer of the deuteron from H₂D⁺ can then lead to the efficient formation of multiply deuterated species, such as NHD₂ and ND₃. Fractionation can also occur in grain surface reactions and some simple models are discussed. This revised version was published online in August 2006 with corrections to the Cover Date.     

A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.