Molecular dynamics method is applied to study the machining mechanisms of polishing based on coupling vibrations of liquid. The physical phenomena of abrasive particles bombarding on silicon monocrystal surface are si...Molecular dynamics method is applied to study the machining mechanisms of polishing based on coupling vibrations of liquid. The physical phenomena of abrasive particles bombarding on silicon monocrystal surface are simulated using Tersoff potentials. The effects of vibration parameters, particle size, incident angle and particle material are analyzed and discussed. Material removal mechanisms are studied. Deformation and embedment phenomena are found in the simulations, Bombardment will destroy the crystal structures near the impact point, and adhesion effect is responsible for final removal of material.展开更多
针对室内人员检测环境毫米波雷达点云数据特性,并考虑多目标点云密集复杂情况,提出一种毫米波雷达点云的密度和划分联合聚类方法。毫米波雷达点云数据具有稀疏、均匀性差的特征。首先采用基于DBSCAN(Density-Based Spatial Clustering o...针对室内人员检测环境毫米波雷达点云数据特性,并考虑多目标点云密集复杂情况,提出一种毫米波雷达点云的密度和划分联合聚类方法。毫米波雷达点云数据具有稀疏、均匀性差的特征。首先采用基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)改进的参数自适应算法进行密度聚类,并对其存在的无限制密度扩张问题,通过决策树归类,对异常数据簇进行二次划分,保证了数据簇属性的单一性。试验结果表明,改进的密度聚类算法可自适应地寻找聚类过程中所需要的最佳参数并实现聚类,更适应毫米波雷达点云数据的特性,同时结合划分聚类对异常数据进行二次划分,使得聚类效果更加细腻和准确,实现了多目标密集情况下点云数据精准聚类划分的效果。展开更多
基金This project is supported by National Natural Science Foundation of China (No.50375029)Provincial Natural Science Foundation of Guangdong,China(No.4009486).
文摘Molecular dynamics method is applied to study the machining mechanisms of polishing based on coupling vibrations of liquid. The physical phenomena of abrasive particles bombarding on silicon monocrystal surface are simulated using Tersoff potentials. The effects of vibration parameters, particle size, incident angle and particle material are analyzed and discussed. Material removal mechanisms are studied. Deformation and embedment phenomena are found in the simulations, Bombardment will destroy the crystal structures near the impact point, and adhesion effect is responsible for final removal of material.
文摘针对室内人员检测环境毫米波雷达点云数据特性,并考虑多目标点云密集复杂情况,提出一种毫米波雷达点云的密度和划分联合聚类方法。毫米波雷达点云数据具有稀疏、均匀性差的特征。首先采用基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)改进的参数自适应算法进行密度聚类,并对其存在的无限制密度扩张问题,通过决策树归类,对异常数据簇进行二次划分,保证了数据簇属性的单一性。试验结果表明,改进的密度聚类算法可自适应地寻找聚类过程中所需要的最佳参数并实现聚类,更适应毫米波雷达点云数据的特性,同时结合划分聚类对异常数据进行二次划分,使得聚类效果更加细腻和准确,实现了多目标密集情况下点云数据精准聚类划分的效果。