2019/10/21 by Danny Smyl, Dong Liu, Smyl, Danny +1
Earth and Planetary Sciences · Engineering · #Electrical and Bioimpedance Tomography #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Flow Measurement and Analysis #Geophysical and Geoelectrical Methods #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.1910.10077
openalex publication_date 2019/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Electrical Impedance Tomography (EIT) is a powerful tool for non-destructive evaluation, state estimation, and process tomography - among numerous other use cases. For these applications, and in order to reliably reconstruct images of a given process using EIT, we must obtain high-quality voltage measurements from the target of interest. As such, it is obvious that the locations of electrodes used for measuring plays a key role in this task. Yet, to date, methods for optimally placing electrodes either require knowledge on the EIT target (which is, in practice, never fully known) or are computationally difficult to implement numerically. In this paper, we circumvent these challenges and present a straightforward deep learning based approach for optimizing electrodes positions. It is found that the optimized electrode positions outperformed "standard" uniformly-distributed electrode layouts in all test cases. Further, it is found that the use of optimized electrode positions computed using the approach derived herein can reduce errors in EIT reconstructions as well as improve the distinguishability of EIT measurements.