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Wang, P.; Lin, J.S.; Wang, M.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization
Fecha
2015-04-01
Resumen
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. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.
Tema
Electrical Capacitance Tomography; Simulated Annealing Algorithm; Least Squares Support Vector Machines; Particle Swarm Optimization
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423
DOI
https://doi.org/10.1016/j.jart.2015.06.018
