Benchmark computations for the polarization tensor characterization of small conducting objects
Benchmark computations for the polarization tensor characterization of small conducting objects
复制标题
小型导电物体偏振张量表征的基准计算
DOI：
10.1016/j.apm.2022.06.024
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发表时间：
2022
影响因子：
5
通讯作者：
Amad A
中科院分区：
文献类型：
--
作者：
Amad A
通过低频电场测量在均匀背景中表征小型低导电夹杂物在使用电阻抗层析成像的医学成像以及使用电阻率层析成像的地质成像中具有重要应用。已知这样的天体可以用Póyla-Szegö（极化率）张量来表征。这样的表征引起了人们的兴趣，因为它们可以在机器学习分类算法中提供对象特征，并提供替代成像解决方案。然而，为了能够训练机器学习算法，需要大型字典，并且特征必须准确。在这项工作中，我们获得精确的数值逼近张量系数，通过应用自适应边界元法。目标是为张量系数提供一系列基准计算，以允许其他软件开发人员检查其代码的准确性。
The characterisation of small low conducting inclusions in an otherwise uniform background from low-frequency electrical field measurements has important applications in medical imaging using electrical impedance tomography as well as in geological imaging using electrical resistivity tomography. It is known that such objects can be characterised by a Póyla-Szegö (polarizability) tensor. Such characterisations have attracted interest as they can provide object features in a machine learning classification algorithm and provide an alternative imaging solution. However, to be able train machine learning algorithms, large dictionaries are required and it is essential that the characterisations are accurate. In this work, we obtain accurate numerical approximations to the tensor coefficients, by applying an adaptive boundary element method. The goal being to provide a sequence of benchmark computations for the tensor coefficients to allow other software developers check the accuracy of their codes.
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影响因子：
1.2
作者：
P. Ledger;W. Lionheart
通讯作者：
P. Ledger;W. Lionheart
影响因子：
1.2
作者：
P. Ledger;W. Lionheart
通讯作者：
P. Ledger;W. Lionheart
DOI：
10.1016/s0045-7825(97)00236-3
发表时间：
1998-05-11
影响因子：
7.2
作者：
Erichsen, S;Sauter, SA
通讯作者：
Sauter, SA
计算复杂多面体静电容量的自适应边界元方法
影响因子：
4.1
作者：
Betcke T
通讯作者：
Betcke T
影响因子：
3.4
作者：
Khairuddin, Taufiq K. Ahmad;Lionheart, William R. B.
通讯作者：
Lionheart, William R. B.