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Planar array capacitive imaging method based on data optimization
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DOI：10.1016/j.sna.2022.113941.png)
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Abstract
En 中文
This paper studies the problem of unstable capacitance data collected by the planar array capacitance imaging system, and proposes a method of capacitance data optimization based on improved fuzzy c-means clustering (FCM) algorithm. For the sensor with 3 x 4 array electrodes, considering that the coplanar arrangement of electrodes produces a weak fringe electric field, the measured capacitance data will be unstable. This method first uses the optimal solution of the particle swarm optimization algorithm as the initial clustering center of the FCM algorithm. Then the improved FCM algorithm is used to optimize the unstable capacitance data. This improved method can avoid falling into local optimization and makes the capacitance data closer to the real values. The optimized capacitance data are used to reconstruction image by Linear Back Projection (LBP) algorithm and Landweber algorithm, respectively. The experimental results show that the stability of the processed capacitance data is enhanced, the relative image error Ie is reduced, and the image correlation coefficient Ic is improved. It can be seen that the effectiveness of the proposed method for defects detection has been validated.
Keywords:
Planar array capacitance imaging
Improved fuzzy c-means clustering algorithm
Image reconstruction
Quantitative evaluation
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