Earticle

초록

영어

In order to solve the problem of image reconstruction in electrical capacitance tomography (ECT) technology, the feasibility of applying convolutional neural network (CNN) to ECT image reconstruction is studied. The convolution layer and the training of the structure of the sub sampling method is improved based on deep research for convolution neural network for the more time-consuming process of deep structure and training issues, and a fast convergence convolution neural network (FCCNN) image reconstruction method is proposed. Matlab was used to build a ECT simulation system. For each algorithm, the simulation results were compared and analyzed. The experimental results show that our algorithm improved the image reconstruction efficiency and quality of the common flow pattern.

목차

Abstract
 1. Introduction
 2. ECT Introduction
 3. Improved Convolutional Neural Network Algorithm
 4. Simulation and Analysis of ECT Image Reconstruction
 5. Conclusion
 References

키워드

저자

• Lanying Li [ The College of Computer Science and Technology ,Harbin University of Science and Technology, Harbin, China ] Corresponding Author
• Yin Kong [ The College of Computer Science and Technology ,Harbin University of Science and Technology, Harbin, China ]
• Jianda Sun [ The College of Computer Science and Technology ,Harbin University of Science and Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605