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arXiv 2209.06155math.ATcs.LGcs.MS
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关于SHM的拓扑数据分析

On topological data analysis for SHM; an introduction to persistent homology

University of Sheffield(谢菲尔德大学)

Tristan Gowdridge, Nikolaos Devilis, Keith Worden

摘要:

本文旨在讨论一种方法,量化的“形状”的数据,通过一种方法称为拓扑数据分析。拓扑数据分析中的主要工具是持久同源性;这是一种从单纯复形的同源性测量数据形状的方法,在值域上计算。所需的背景理论和计算持久同源性的方法在这里,具体的结构健康监测的应用。这些结果允许拓扑推理和推断高维数据中的特征的能力,否则可能会被忽视。一个单纯复形的数据构造一个给定的距离参数。该复合体编码关于数据点的局部接近度的信息。奇异同调值可以从这个单纯复形中计算出来。扩展这一思想,距离参数的值的值域,并在此范围内计算的同源性。持续同源性是数据的同源特征如何在此区间内持续的表示。结果与数据相符。还讨论了一种方法,允许比较不同的数据集的持久性同源性。

英文摘要:

This paper aims to discuss a method of quantifying the 'shape' of data, via a methodology called topological data analysis. The main tool within topological data analysis is persistent homology; this is a means of measuring the shape of data, from the homology of a simplicial complex, calculated over a range of values. The required background theory and a method of computing persistent homology is presented here, with applications specific to structural health monitoring. These results allow for topological inference and the ability to deduce features in higher-dimensional data, that might otherwise be overlooked. A simplicial complex is constructed for data for a given distance parameter. This complex encodes information about the local proximity of data points. A singular homology value can be calculated from this simplicial complex. Extending this idea, the distance parameter is given for a range of values, and the homology is calculated over this range. The persistent homology is a representation of how the homological features of the data persist over this interval. The result is characteristic to the data. A method that allows for the comparison of the persistent homology for different data sets is also discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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