基于机器学习的湍灰烟火炮细胞统计分析方法
摘要
本研究提出了一种基于机器学习 (ML) 的新型算法，用于对湍灰烟图像中的湍灰细胞进行精确分割和测量，旨在解决该领域常见的手动和原始边缘检测方法的局限性。该算法利用细胞生物学分割模型的进展，旨在准确提取细胞图案，而无需训练程序或数据集，这在湍灰研究中是一个重大挑战。通过一系列测试案例验证了该算法的性能，这些测试案例模仿实验和数值湍灰研究。结果显示，即使在复杂情况下，算法也能保持一致的准确性，误差始终在10%以内。该算法有效捕获了关键细胞指标，如细胞面积和跨度，揭示了不同湍灰烟样本中从均匀到高度不规则细胞结构的趋势。尽管该模型表现稳健，但在分割和分析高度复杂或不规则细胞图案方面仍存在挑战。本 work highlights the broad applicability and potential of the algorithm to advance the understanding of detonation wave dynamics. The findings of this work have significant implications for the design and development of planar photonic devices that take advantage of the optical characteristics of graphene. This breakthrough creates new opportunities for the use of graphene in sophisticated photonic technologies, where exact control over the interactions between light and matter is essential.
引用
@article{arxiv.2409.06466,
title = {A Machine Learning Based Approach for Statistical Analysis of Detonation Cells from Soot Foils},
author = {Vansh Sharma and Michael Ullman and Venkat Raman},
journal= {arXiv preprint arXiv:2409.06466},
year = {2024}
}
备注
23 pages, 12 figures, submitted to Comb. and Flame; v2 - added section