Research on optimization method of capacitance tomography based on contribution degree analysis

Abstract

Landweber algorithm is limited to further applications due to its problems of semi-convergence and slow reconstruction speed. To solve the above issues, this paper firstly analyzes the causes of semi-convergence characteristic of Landweber algorithm from the perspective of the negative sensitivity field. Secondly, a method of data screening based on contribution degree analysis is proposed to weaken the influence of negative sensitivity fields on the semi-convergence characteristic of the algorithm. Then the valid capacitance data are selected from the original capacitance data based on the method. Finally, the reconstructed results of Landweber algorithm with the valid capacitance data and original capacitance data are evaluated by taking correlation coefficient and computation time as evaluation criteria. The results indicate that the new method effectively suppresses the semi-convergence characteristic of the algorithm, improves the convergence effect of the algorithm, and increases the image reconstruction quality and speed.

Void fraction measurement of gas–liquid two-phase flow based on compressed sensing theory and capacitively coupled electrical resistance tomography system

“…The rows of the projection data (resistance measurements) are one-to-one correspondence with the rows of the sensitivity matrix, so when the rows of the projection data are randomly nulled, the rows of the corresponding sensitivity field should also be nulled. Additionally, [21,22] have explored a method for extracting effective data (sensitive area) from ECT measurements and reconstructing images solely from this data. Inspired by this approach, we extract the invalid data from the normalized resistance value vector R and set these values to zero.…”

“…Currently, CS theory has been studied in many fields such as wireless sensing, medical imaging, communication, and radar [14]. In recent years, CS theory has been developed in electrical capacitance tomography (ECT) and ERT image reconstruction [15][16][17][18][19][20][21][22][23][24]. The fundamental challenge in applying CS theory to ECT/ERT image reconstruction is that its sensitivity field is a non-linear 'soft field', as is the case with CCERT.…”