By Topic

A 3D Image Reconstruction Algorithm of Electrical Capacitance Tomography Based on Support Vector Machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
He Shijun ; Coll. of Inf. Technol., Shanghai Ocean Univ., Shanghai, China ; Wang Huaxiang ; Zhou Ruyan

Electrical capacitance tomography technique (ECT) developed during the mid-late 1980's is a process tomography based on capacitance sensing principle. The forward problem is solved using the three-dimension finite element method which fitted the ECT physical model in this paper, and the inverse problem is analyzed using a new kind of machine learning method, support vector machine (SVM), based on statistical learning theory. The author designs a three-dimension (3D) image reconstruction algorithm for two-phase flow. The simulation results indicate that the algorithm has the high space resolution and generalization ability based on three-dimension finite element and combining SVM.

Published in:

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Date of Conference:

17-19 Oct. 2009