涡流
跟踪(教育)
匹配(统计)
物理
聚类分析
相似性(几何)
基质(化学分析)
维数之咒
比例(比率)
可视化
保险丝(电气)
领域(数学)
人工智能
特征(语言学)
数据挖掘
模式识别(心理学)
计算机科学
机械
数学
图像(数学)
统计
心理学
教育学
语言学
材料科学
哲学
复合材料
量子力学
纯数学
作者
Lin Zhu,Ningbo Guo,Yadong Wu,Liang Deng,Zhouqiao He,Cheng Chen,De Xing
摘要
Tracking time-variant vortex features in unsteady flow fields represents a significant challenge in scientific visualization. In this paper, we propose an innovative new method based on attribute fusion that leverages unsupervised clustering techniques to track time-variant vortex features. This method has the potential for use in large-scale flow field analysis. We apply an attribute matrix integrating the spatial and physical variables of vortex feature points. We then fuse and reduce the dimensionality of this original matrix to create an attribute fusion matrix. Subsequently, we apply coarse clustering to the fusion matrices across time steps. By defining a distribution curve, we derive a similarity measurement matrix among different vortices. Vortex matching is performed based on this similarity measurement to enable the tracking of time-variant vortices. Multiple experimental datasets demonstrate the effectiveness and matching efficiency of the proposed method.
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