基于聚类融合的三维流线可视化方法

doi:10.16182/j.issn1004731x.joss.22-1257

摘要/Abstract

摘要：

为解决使用聚类方法实现三维流线可视化时，存在特征提取不全面、可视结果破坏流场连续性、聚类簇划分不稳定导致流线代表性差等问题，提出了基于聚类融合的三维流线可视化方法。该方法由特征间距离度量方法和聚类融合方法两部分组成，将特征间距离和空间距离分别作为流线间的相似度进行聚类，对得到的聚类结果进行加权合并后再划分。将该方法在具有多个不同特征的数据集上进行了实验，并与现有方法进行了定性、定量比较。结果表明，与现有方法相比，该方法能够较好地平衡特征提取和流线分布之间的关系，聚类簇划分的稳定性提高了2%~5%，矢量场重构的精度提高了3%~5%。

关键词: 流场可视化, 流线可视化, 聚类融合, 特征提取, 流线选择

Abstract:

In order to solve the problems of incomplete feature extraction, continuity destruction of flow field by visual results, and poor representation of streamline caused by unstable clustering division when the clustering method is used to realize 3D streamline visualization. A 3D streamline visualization method based on clustering fusion is proposed. It consists ofadistance measurement method between features and a clustering fusion method, which takes the inter-feature distance and spatial distance as the similarity between streamlines for clustering and then performs weighted merging and subdivision of the obtained clustering result. The method has been tested on data sets with different features and compared qualitatively and quantitatively with the existing methods. The results show that compared with the existing methods, the proposed method can better balance the relationship between feature extraction and streamline distribution, and the stability of clustering division is improved by 2%~5%. The accuracy of vector filed reconstruction is improved by 3%~5%.

Key words: flow filed visualization, streamline visualization, clustering fusion, feature extraction, streamline selection

中图分类号:

TP391.41

邵绪强,程雅,金佚钟 . 基于聚类融合的三维流线可视化方法[J]. 系统仿真学报, 2024, 36(3): 625-635.

Shao Xuqiang,Cheng Ya,Jin Yizhong . 3D Streamline Visualization Method Based on Clustering Fusion[J]. Journal of System Simulation, 2024, 36(3): 625-635.

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数据集	图5(a)	图5(b)	图5(c)	图5(d)	图5(e)
1	0.004 7	0.004 8	0.005 2	0.005 0	0.014 1
2	0.004 4	0.004 6	0.005 2	0.005 1	0.014 5
3	0.003 4	0.003 6	0.004 5	0.004 3	0.013 7
4	0.004 5	0.0048	0.005 3	0.004 6	0.012 9

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