论文标题
机器学习辅助液滴轨迹在密度乳液中提取及其分析
Machine learning assisted droplet trajectories extraction in dense emulsions and their analysis
论文作者
论文摘要
这项工作分析了通过晶格玻尔兹曼方法模拟的密集乳液系统的Yolo和DeepSort算法获得的轨迹。结果表明，与前面的液滴相比，立即在其后面的液滴影响单个液滴的移动方向。该分析还提供了关于写下狭窄通道中致密乳液的动态模型的约束的提示。
This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of dense emulsion systems simulated by Lattice Boltzmann methods. The results indicate that the individual droplet's moving direction is influenced more by the droplets immediately behind it than the droplets in front of it. The analysis also provides hints on constraints on writing down a dynamical model of droplets for the dense emulsion in narrow channels.
