scholarly journals USING ELECTRICAL IMPEDANCE TOMOGRAPHY IN LINEAR ARRAYS OF MEASUREMENT

2017 ◽  
Vol 7 (1) ◽  
pp. 76-79
Author(s):  
Tomasz Rymarczyk
Keyword(s):  
Topology Optimization ◽  
Electrical Impedance Tomography ◽  
Level Set Method ◽  
Level Set ◽  
Electrical Impedance ◽  
Optimization Problems ◽  
Measurement Model ◽  
Impedance Tomography ◽  
Material Derivative ◽  
Level Set Function

The article presents an application to the topology optimization in electrical impedance tomography using the level set method. The level set function is based on shape and topology optimization for areas with partly continuous conductivities. The finite element method has been used to solve the forward problem. The proposed algorithm is initialized using topological sensitivity analysis. Shape derivative and material derivative have been incorporated with the level set method to investigate shape optimization problems. The coupled algorithm is a relatively new procedure to overcome this problem. Using the line measurement model is very useful to solve the inverse problem in the copper-mine ceiling and the flood embankment.

scholarly journals B-Spline Level Set Method for Shape Reconstruction in Electrical Impedance Tomography

2020 ◽  
Vol 39 (6) ◽  
pp. 1917-1929 ◽  
Author(s):  
Dong Liu ◽  
Danping Gu ◽  
Danny Smyl ◽  
Jiansong Deng ◽  
Jiangfeng Du
Keyword(s):  
Electrical Impedance Tomography ◽  
Level Set Method ◽  
Level Set ◽  
Electrical Impedance ◽  
Shape Reconstruction ◽  
Impedance Tomography ◽  
B Spline

Three-Dimensional Electrical Impedance Tomography: A Topology Optimization Approach

2008 ◽  
Vol 55 (2) ◽  
pp. 531-540 ◽  
Author(s):  
LuÍs Augusto Motta Mello ◽  
CÍcero Ribeiro de Lima ◽  
Marcelo Britto Passos Amato ◽  
Raul Gonzalez Lima ◽  
EmÍlio Carlos Nelli Silva
Keyword(s):  
Topology Optimization ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Three Dimensional ◽  
Optimization Approach ◽  
Impedance Tomography

Parametric Shape and Topology Optimization: A New Level Set Approach Based on Cardinal Kernel Functions

2017 ◽  
Author(s):  
Long Jiang ◽  
Shikui Chen ◽  
Xiangmin Jiao
Keyword(s):  
Topology Optimization ◽  
Level Set Method ◽  
Level Set ◽  
Material Property ◽  
Kernel Function ◽  
Level Set Methods ◽  
Level Set Function ◽  
Set Function ◽  
Distance Regularization ◽  
Parametric Level Set Method

The parametric level set method is an extension of the conventional level set methods for topology optimization. By parameterizing the level set function, conventional levels let methods can be easily coupled with mathematical programming to achieve better numerical robustness and computational efficiency. Furthermore, the parametric level set scheme not only can inherit the original advantages of the conventional level set methods, such as clear boundary representation and high topological changes handling flexibility but also can alleviate some un-preferred features from the conventional level set methods, such as needing re-initialization. However, in the RBF-based parametric level set method, it was difficult to determine the range of the design variables. Moreover, with the mathematically driven optimization process, the level set function often results in significant fluctuations during the optimization process. This brings difficulties in both numerical stability control and material property interpolation. In this paper, an RBF partition of unity collocation method is implemented to create a new type of kernel function termed as the Cardinal Basis Function (CBF), which employed as the kernel function to parameterize the level set function. The advantage of using the CBF is that the range of the design variable, which was the weight factor in conventional RBF, can be explicitly specified. Additionally, a distance regularization energy functional is introduced to maintain a desired distance regularized level set function evolution. With this desired distance regularization feature, the level set evolution is stabilized against significant fluctuations. Besides, the material property interpolation from the level set function to the finite element model can be more accurate.

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