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Issue title: Artificial Intelligence and Advanced Manufacturing (AIAM 2020)
Guest editors: Shengzong Zhou
Article type: Research Article
Authors: Zhang, Xuejuna; c; d | Lao, Huanc | Wu, Dongbob; * | Liang, Chana | Liang, Binmeia | Ma, Liangdid
Affiliations: [a] School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China | [b] Department of No. 1 General Surgery, The forth Affiliated Hospital of Guangxi Medical University, Liuzhou, China | [c] Medical College, Guangxi University, Nanning, China | [d] Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning, Guangxi, China
Correspondence: [*] Corresponding author. Dongbo Wu, Department of No. 1 General Surgery, The forth Affiliated Hospital of Guangxi Medical University, Liuzhou, China. E-mail: wudongbobo@126.com.
Abstract: Mass of an object is an important characteristic for quality assessment. However, in some cases, it is hard to measure the mass of objects with instruments directly. In this paper, we proposed a novel method based on Fuzzy c-means (FCM) to measure the mass of an object by analyzing the deformation degree in a grid pattern. In the spatial field, thin plate spline (TPS) algorithm was adopted to calculate the minimum deformation bending energy in order to give a quantitative analysis of the weight; In frequency domain, the Fast Fourier Transform (FFT) algorithm was used to calculate the spectrum within a deformation frequency area before and after the change of grids, from which the relationship between weight and spectrum was investigated using FCM algorithm. Two different equations evaluated by the above two methods were proposed in order to calculate the mass of an object. Both of them showed a high level of explanatory power of R2 (R2 = 0.9833 and R2 = 0.9698, respectively). The equations were then used to determine the estimated mass. Estimated and measured values were plotted against each other. A high correlation (R2 = 0.9833 and R2 = 0.9698, respectively) was found between actual and calculated mass. Finally, Bland-Altman plot was introduced to access the agreement of the calculated mass and the actual mass. The average bias was –54.408 g and –0.007 g for spatial domain method and frequency domain method, respectively. Theoretical analysis and experiments were performed to verify the effectiveness of our approaches.
Keywords: Thin plate spline (TPS), fast fourier transform, mass, bending energy, image processing
DOI: 10.3233/JIFS-189703
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 3, pp. 4431-4438, 2021
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