Login
Register
Reset Password
Publish & Distribute
Publishing Solutions
Distribution Solutions
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Blog
Contact
Search
EUR
USD
GBP
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Journal of Electrical Bioimpedance
Volume 13 (2022): Issue 1 (January 2022)
Open Access
A high accuracy voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) in electrical impedance tomography
Zengfeng Gao
Zengfeng Gao
,
Panji Nursetia Darma
Panji Nursetia Darma
,
Daisuke Kawashima
Daisuke Kawashima
and
Masahiro Takei
Masahiro Takei
| Jan 08, 2023
Journal of Electrical Bioimpedance
Volume 13 (2022): Issue 1 (January 2022)
About this article
Previous Article
Next Article
Abstract
Article
Figures & Tables
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Published Online:
Jan 08, 2023
Page range:
106 - 115
Received:
Nov 22, 2022
DOI:
https://doi.org/10.2478/joeb-2022-0015
Keywords
Electrical impedance tomography
,
object-oriented sensitivity matrix estimation
,
high reconstruction accuracy
© 2022 Zengfeng Gao, Panji Nursetia Darma, Daisuke Kawashima, and Masahiro Takei, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Fig. 1
Flowchart of conductivity reconstruction with the OO-SME model.
Fig. 2
Mesh, conductivity of background- and object-fields
Fig. 3
Voltage changes of different objects in the simulation.
Fig. 4
Reconstructed conductivity based on different conductivity reconstruction models in the simulation. (a) Object-fields; (b) Linear model; (c) Sensitivity updating model; (d) Second-order sensitivity model; (e) OO-SME model.
Fig. 5
Comparison of RA of reconstructed conductivity based on the linear model, sensitivity updating model, second-order sensitivity model, and OO-SME model in the simulation.
Fig. 6
Experimental setup of EIT system
Fig. 7
Voltage changes of different objects in the experiment.
Fig. 8
Reconstructed conductivity based on different conductivity reconstruction models in the experiment. (a) Object-fields; (b) Linear model; (c) Sensitivity updating model; (d) Second-order sensitivity model; (e) OO-SME model.
Fig. 9
Comparison of RA of reconstructed conductivity based on the linear model, sensitivity updating model, second-order sensitivity model, and OO-SME model in the experiment.
Fig. 10
Comparison between ΔU* and u(Δσ) based on different conductivity reconstruction models in the simulation.
Fig. 11
Comparison of components of u(Δσ) with different objects in the simulation.
Fig. 12
Comparison of sensitivity based on different conductivity reconstruction models in the simulation. (a) Object-field; (b) Sb in linear model; (c) Sb* in sensitivity updating model; (d) Sb + Sb† in second-order sensitivity model; (e) So* in OO-SME model.
Fig. 13
Comparison of sensitivity based on different conductivity reconstruction models in the experiment. (a) Object-field; (b) Sb in linear model; (c) Sb* in sensitivity updating model; (d) Sb + Sb† in second-order sensitivity model; (e) So* in OO-SME model.