Computational inverse imaging method by machine learning-informed physical model for electrical capacitance tomography

2021 ◽  
pp. 101507
Author(s):  
Jing Lei ◽  
Qibin Liu ◽  
Xueyao Wang
Keyword(s):  
Machine Learning ◽  
Physical Model ◽  
Electrical Capacitance Tomography ◽  
Imaging Method ◽  
Electrical Capacitance ◽  
Capacitance Tomography ◽  
Inverse Imaging

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

scholarly journals An Imaging Method for Electrical Capacitance Tomography Based on Projections Multiplication

2011 ◽  
Vol 307 ◽  
pp. 012032 ◽  
Author(s):  
A Martínez Olmos ◽  
E Castillo ◽  
F Martínez-Martí ◽  
D P Morales ◽  
A García ◽  
...  
Keyword(s):  
Electrical Capacitance Tomography ◽  
Imaging Method ◽  
Electrical Capacitance ◽  
Capacitance Tomography

scholarly journals Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yanpeng Zhang ◽  
Deyun Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Reconstruction ◽  
High Performance ◽  
Imaging Techniques ◽  
Electrical Capacitance Tomography ◽  
Imaging Method ◽  
Electrical Capacitance ◽  
Artificial Neural ◽  
Capacitance Tomography

For great achievements in recent decades, image reconstruction for electrical capacitance tomography (ECT) has been considered in this study. ECT has demonstrated impressive potentials in multiprocess measurement, and the obtained images are of high resolution, which are suitable for advanced procedures in industrial and medical applications and across different tasks and domains. But the ECT system still requires improvements in the quality of image reconstruction given its importance of great significance to obtain the reliability and usefulness of measurement results. The deep neural network is used in this study to extract new features and to update the number of nodes and hidden layers in the system. Recently, deep learning exhibits suitable solutions in many flourishing fields based on different series of artificial neural networks for mapping nonlinear functions. To address the obstacles, this paper proposes an imaging method using an optimizer reconstruction model. An optimization model for imaging is generated as a powerful optimizer for building a computational model to ameliorate the reconstruction accuracy. Based on the deep learning methodology, the previous images reconstructed by using one of the imaging techniques to the required images are abstracted and stored in the deep learning machine, resulting in an error rate of 8.9%, and this is considered good on ECT. Therefore, an artificial neural network of the capacitance (ANNoC) system is introduced to estimate capacitance measurements.

DESIGN OF FLEXIBLE ELECTRICAL CAPACITANCE TOMOGRAPHY SENSOR

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
MT Masturah ◽  
MHF Rahiman ◽  
Zulkarnay Zakaria ◽  
AR Rahim ◽  
NM Ayob
Keyword(s):  
Outer Layer ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Capacitance Tomography ◽  
Fluid Imaging

This paper discussed the design–functionality and application of Flexible Electrical Capacitance Tomography sensor (FlexiECT). The sensors consist of 12 electrodes allocated surrounding the outer layer of the pipeline. The sensor is designed in such that the flexibility features suit the applications in the pipeline of multiple size. This paper also discussed the preliminary result of FlexiECT applications in fluid imaging by identifying the percentage of two mixing fluids.

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