Optimal Design of Electrical Capacitance Tomography Sensor and Improved ART Image Reconstruction Algorithm Based On the Internet of Things

Authors

  • Feng Chen School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
  • Deyun Chen School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
  • Lili Wang School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
  • Yang Botao School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2046

Keywords:

Electrical Capacitance Tomography, Optimal design of sensor, image reconstruction, modified ART iterative algorithm, convergence

Abstract

For the problems of low sensitivity, weak signal of high and low frequency and low signal-to-noise ratio in ECT, the mathematical model of the sensor is established. From the aspects of electrostatic field distribution and soft field effect, the influence of the structural parameters of the sensor on the sensor performance is analyzed. According to the influence of the components of the sensor on the sensitivity, the principle of optimal design is put forward. Based on the optimized Landweber image reconstruction algorithm, an ART image reconstruction algorithm with iterative correction is proposed, and the mathematical model of the algorithm is designed. According to constructing the target functional regularization term in the negative problems of electrical capacitance tomography, the iterative process of the modified art algorithm is deduced, and with adaptive step size, the convergence is speeded and accuracy of image reconstruction is improved. The experimental results show that the semi-convergence in the improved algorithm is obviously weakened, and the reconstructed image quality is better than that of the traditional art algorithm.

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Author Biographies

Feng Chen, School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

Chen Feng, male, 1982.08, bachelor degree/Harbin University of Science and Technology/computer science and technology major/2005, master degree/Harbin University of Science and Technology/computer application technology major/2009, doctoral degree/Harbin University of Science and Technology/computer application technology major/Studying in 2015-present, he is currently a lecturer at Rongcheng College of Harbin University of Science and Technology, and deputy teaching director of the Department of Software Engineering. His main research directions are multiphase flow detection, pattern recognition and computer vision research. Published more than 10 papers, participated in Heilongjiang Province Philosophy and Social Science Research Planning Project, etc. E-mail: chenfeng@hrbust.edu.cn

Deyun Chen, School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

Chen Deyun, male, 1962.06, bachelor degree/Harbin University of Science and Technology/electronic engineering major/1983, master degree/Harbin University of Science and Technology/electrical engineering major/1988, doctor degree/Harbin University of Science and Technology/measurement and control technology and instrument major/2006, He is currently a professor and doctoral supervisor at Harbin University of Science and Technology, and the dean of the School of Computer Science and Technology. His main research direction is detection and imaging technology and image processing. Published more than 200 papers, presided over and participated in the National Natural Science Foundation of China, Heilongjiang Provincial Science Foundation, Doctoral Program Special Scientific Research Fund Project, Heilongjiang Provincial Department of Education Science and Technology Project, Heilongjiang Provincial University Key Teacher Program, Harbin High-tech Fund, etc. E-mail: chendeyun@hrbust.edu.cn

Lili Wang, School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

Wang Lili, female, 1980.02, bachelor degree/Harbin University of Science and Technology/computer science and technology/2003, master degree/Harbin University of Science and Technology/software and theory major/2006, doctor degree/Harbin University of Science and Technology/ computer application major/2011 Graduated from the postdoctoral mobile station of instrument science and technology of Harbin University of Science and Technology in 2016. Now he is a professor and master tutor of Harbin University of Science and Technology. His main research direction is detection and imaging technology. Published more than 40 papers, presided over and participated in Heilongjiang Provincial Natural Science Youth Fund, Heilongjiang Provincial Department of Education Science and Technology Program Project, Heilongjiang Province Postdoctoral Funding Project, University Doctoral Program Special Scientific Research Fund Project, Heilongjiang Provincial Science Foundation Project, etc. E-mail: wanglili@hrbust.edu.cn

Yang Botao, School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China

Yang Botao, male, 1993.10, Bachelor/Harbin University of Science and Technology/Software Engineering/2017, Master/Harbin University of Science and Technology/Computer Technology/2021, now a teacher and teaching assistant in Rongcheng College of Harbin University of Science and Technology. The main research directions are detection and imaging technology, pattern recognition, etc. Published more than 10 papers, patents and soft works, and participated in 2 projects including the Natural Science Foundation of Heilongjiang Province, etc. E-mail: yangbotao@hrbust.edu.cn

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Published

2021-07-08

How to Cite

Chen, F., Chen, D., Wang, L., & Botao, Y. (2021). Optimal Design of Electrical Capacitance Tomography Sensor and Improved ART Image Reconstruction Algorithm Based On the Internet of Things. Journal of Web Engineering, 20(4), 1027–1052. https://doi.org/10.13052/jwe1540-9589.2046

Issue

2021: Vol 20 Iss 4

Section

Articles