Iniciar sesión
Registrarse
Restablecer contraseña
Publicar y Distribuir
Soluciones de Publicación
Soluciones de Distribución
Temas
Arquitectura y diseño
Artes
Ciencias Sociales
Ciencias de la Información y Bibliotecas, Estudios del Libro
Ciencias de la vida
Ciencias de los materiales
Deporte y tiempo libre
Estudios clásicos y del Cercano Oriente antiguo
Estudios culturales
Estudios judíos
Farmacia
Filosofía
Física
Geociencias
Historia
Informática
Ingeniería
Interés general
Ley
Lingüística y semiótica
Literatura
Matemáticas
Medicina
Música
Negocios y Economía
Química
Química industrial
Teología y religión
Publicaciones
Revistas
Libros
Actas
Editoriales
Blog
Contacto
Buscar
EUR
USD
GBP
Español
English
Deutsch
Polski
Español
Français
Italiano
Carrito
Home
Revistas
Journal of Electrical Bioimpedance
Volumen 13 (2022): Edición 1 (January 2022)
Acceso abierto
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
y
Masahiro Takei
Masahiro Takei
| 08 ene 2023
Journal of Electrical Bioimpedance
Volumen 13 (2022): Edición 1 (January 2022)
Acerca de este artículo
Artículo anterior
Artículo siguiente
Resumen
Artículo
Figuras y tablas
Referencias
Autores
Artículos en este número
Vista previa
PDF
Cite
Compartir
Publicado en línea:
08 ene 2023
Páginas:
106 - 115
Recibido:
22 nov 2022
DOI:
https://doi.org/10.2478/joeb-2022-0015
Palabras clave
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.