Image Reconstruction of Electrical Capacitance Tomography Based on Convolutional Neural Network and Finite Element Simulation

Authors

Lifeng Zhang, Department of Automation, North China Electric Power University, Baoding 071003, China;Follow
Huiren Wang, Department of Automation, North China Electric Power University, Baoding 071003, China;

Abstract

Abstract: To address the issues related to the mixed hybrid telemetry, track and command(TT&C) resource, including multiple attributes, large differences in preferences and scheduling conflicts when resources are limited in joint scheduling, on the basis of the description method in the single system TT&C network scheduling research, the characteristics of the hybrid TT&C task requirements are analyzed and the standardized description of the hybrid TT&C resource scheduling task requirements is provided. With the goal of maximizing the return value of TT&C scheduling, a hybrid resource joint scheduling model is established. A solution strategy based on an improved genetic algorithm is proposed by introducing the idea of infinitesimal method and applying an extended multiplexing method for the available arc segments. The simulation results show that the proposed model and the corresponding conflict resolution algorithm can effectively improve the mission satisfaction rate and scheduling revenue.

Recommended Citation

Zhang, Lifeng and Wang, Huiren (2022) "Image Reconstruction of Electrical Capacitance Tomography Based on Convolutional Neural Network and Finite Element Simulation," Journal of System Simulation: Vol. 34: Iss. 4, Article 6.
DOI: 10.16182/j.issn1004731x.joss.20-0849
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss4/6

First Page

712

Revised Date

2021-01-22

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0849

Last Page

718

CLC

TP391

Recommended Citation

Lifeng Zhang, Huiren Wang. Image Reconstruction of Electrical Capacitance Tomography Based on Convolutional Neural Network and Finite Element Simulation[J]. Journal of System Simulation, 2022, 34(4): 712-718.

DOI

10.16182/j.issn1004731x.joss.20-0849