基于FCM优化的平面阵列电容成像算法

温银堂,曹鹏鹏,田洪刚,张玉燕,罗小元

计量学报 ›› 2020, Vol. 41 ›› Issue (2) : 231-237.

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PDF(1971 KB)
计量学报 ›› 2020, Vol. 41 ›› Issue (2) : 231-237. DOI: 10.3969/j.issn.1000-1158.2020.02.18
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基于FCM优化的平面阵列电容成像算法

  • 温银堂,曹鹏鹏,田洪刚,张玉燕,罗小元
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An Optimized Planar Array Capacitance Imaging Algorithm Based on FCM

  • WEN Yin-tang,CAO Peng-peng,TIAN Hong-gang,ZHANG Yu-yan,LUO Xiao-yuan
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摘要

为提高平面阵列电容成像系统的成像精度,提出了一种基于FCM数据优化的成像算法。根据平面阵列电极电容数据的特点,为减小电容测量误差对介电常数的影响,利用FCM算法对测量电容值的不断收敛以实现数据优化的作用,在减弱噪声的同时提高电容值数据的稳定性。在此基础上,对一种隔热材料胶层进行缺陷检测实验,使重建图像的相关系数得到了提高,减小了图像重建误差。实验结果表明:图像重建结果的优化算法可获得更加稳定、有效的电容数据,胶层缺陷图像重建精度具有较大提升。

Abstract

In order to improve the accuracy of planar array capacitance imaging, an imaging algorithm based on fuzzy C-means algorithm(FCM) data optimization is proposed. According to the characteristics of capacitance data of planar array electrodes, in order to reduce the influence of capacitance measurement error on dielectric constant, application of FCM algorithm to the continuous convergence of measured capacitance to achieve data optimization. On this basis, a defect detection experiment was carried out on a kind of insulating material adhesive layer, and the image correlation coefficient of the reconstructed image was improved, and the image reconstruction error was reduced. The experimental results show that the proposed optimization algorithm can obtain more stable and effective capacitance data, and the reconstruction accuracy of glue layer defect image has been greatly improved.

关键词

计量学 / 胶层缺陷图像重建 / 隔热复合材料 / FCM算法 / 平面阵列传感器 / 电容层析成像

Key words

metrology / insulation composite / reconstruction of glue layer defect image / FCM / planar array sensor / electrical capacitance tomography

引用本文

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温银堂,曹鹏鹏,田洪刚,张玉燕,罗小元. 基于FCM优化的平面阵列电容成像算法[J]. 计量学报. 2020, 41(2): 231-237 https://doi.org/10.3969/j.issn.1000-1158.2020.02.18
WEN Yin-tang,CAO Peng-peng,TIAN Hong-gang,ZHANG Yu-yan,LUO Xiao-yuan. An Optimized Planar Array Capacitance Imaging Algorithm Based on FCM[J]. Acta Metrologica Sinica. 2020, 41(2): 231-237 https://doi.org/10.3969/j.issn.1000-1158.2020.02.18
中图分类号: TB971    TP212.1   

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基金

国家自然科学基金(61573302);河北省自然科学基金(E2017203240)

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