Some authors consider the ψ（4415） to be the 4S or 5S excited state of a cc pair. Starting from this assumption, we study the decays of the ψ（4415） to DD, D^＊D^＊, DsDs, Ds^＊Ds^＊, and get the corresponding br...Some authors consider the ψ（4415） to be the 4S or 5S excited state of a cc pair. Starting from this assumption, we study the decays of the ψ（4415） to DD, D^＊D^＊, DsDs, Ds^＊Ds^＊, and get the corresponding branching ratios in terms of the Quark-Pair-Creation （QPC） model. Compared with the experimental data, we find that the results of 4S state agree much better than those of the 5S state. Therefore, it is more reasonable to assume the ψ（4415） to be a 4S state.展开更多
针对无人驾驶车辆路径规划问题,基于快速扩展随机树(rapidly-exploring random tree,RRT)算法,提出了1种5次多项式曲线(quintic polynomial curve)与MT-RRT(multi-targeting rapidly-exploring random tree)的融合算法,即QPC-MT-RRT算...针对无人驾驶车辆路径规划问题,基于快速扩展随机树(rapidly-exploring random tree,RRT)算法,提出了1种5次多项式曲线(quintic polynomial curve)与MT-RRT(multi-targeting rapidly-exploring random tree)的融合算法,即QPC-MT-RRT算法。该算法根据无人驾驶车辆路径规划的相关理论,建立无人驾驶车辆路径规划问题的车辆运动学模型,为规划无人驾驶车辆最优、最高效、最安全路径提供理论依据。将上述算法在MATLAB上仿真,并在平均路径长度、平均路径规划时间、平均采样节点个数及节点利用率4个方面与基本RRT算法及MT-RRT算法进行了对比。仿真结果表明:5次多项式曲线与MT-RRT算法的融合算法具有最高的性能,可以规划出最优路径。展开更多
基金Natural Science Fund of Hebei Province (A2005000090,E2005000129)Fund of Education Department of Hebei Province (2007409)Research Fund for Doctoral Programs of Hebei University (Y2006081)
文摘Some authors consider the ψ（4415） to be the 4S or 5S excited state of a cc pair. Starting from this assumption, we study the decays of the ψ（4415） to DD, D^＊D^＊, DsDs, Ds^＊Ds^＊, and get the corresponding branching ratios in terms of the Quark-Pair-Creation （QPC） model. Compared with the experimental data, we find that the results of 4S state agree much better than those of the 5S state. Therefore, it is more reasonable to assume the ψ（4415） to be a 4S state.
文摘针对无人驾驶车辆路径规划问题,基于快速扩展随机树(rapidly-exploring random tree,RRT)算法,提出了1种5次多项式曲线(quintic polynomial curve)与MT-RRT(multi-targeting rapidly-exploring random tree)的融合算法,即QPC-MT-RRT算法。该算法根据无人驾驶车辆路径规划的相关理论,建立无人驾驶车辆路径规划问题的车辆运动学模型,为规划无人驾驶车辆最优、最高效、最安全路径提供理论依据。将上述算法在MATLAB上仿真,并在平均路径长度、平均路径规划时间、平均采样节点个数及节点利用率4个方面与基本RRT算法及MT-RRT算法进行了对比。仿真结果表明:5次多项式曲线与MT-RRT算法的融合算法具有最高的性能,可以规划出最优路径。
