An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization

 

P. Wang^a*, J.S. Lin^b, M. Wang^a

 

^a School of Electric and Control of Xi'an University of Science and Technology, Xi'an, China. *Correponding author. E-mail address: wangpai2013@xust.edu.cn.

^b Department of Computer Science and Information Engineering at National Chin-Yi University of Technology, Taichung, Taiwan.

 

Abstract

In this paper, we introduce a novel image reconstruction algorithm with Least Squares Support Vector Machines (LS-SVM) and Simulated Annealing Particle Swarm Optimization (APSO), named SAP. This algorithm introduces simulated annealing ideas into Particle Swarm Optimization (PSO), which adopts cooling process functions to replace the inertia weight function and constructs the time variant inertia weight function featured in annealing mechanism. Meanwhile, it employs the APSO procedure to search for the optimized resolution of Electrical Capacitance Tomography (ECT) for image reconstruction. In order to overcome the soft field characteristics of ECT sensitivity field, some image samples with typical flow patterns are chosen for training with LS-SVM. Under the training procedure, the capacitance error caused by the soft field characteristics is predicted, and then is used to construct the fitness function of the particle swarm optimization on basis of the capacitance error. Experimental results demonstrated that the proposed SAP algorithm has a quick convergence rate. Moreover, the proposed SAP outperforms the classic Landweber algorithm and Newton-Raphson algorithm on image reconstruction.

Keywords: Electrical capacitance tomography; Simulated annealing algorithm; Least squares support vector machines; Particle swarm optimization.

 

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Acknowledgements

This work is financially supported by Projects 51405381 and 51475013 from the National Natural Science Foundation of China, Project 201314 supported by Engagement Foundation of Xi'an University of Science and Technology, and a Project supported by Scientific Research Foundation for Returned Scholars, Ministry of Education of China 2011508.

 

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