Adaptive Clustering SOFC Image Segmentation Based on Particle Swarm Optimization

被引：3
|
作者：
Yang, Xuefei [1 ]
Fu, Xiaowei [1 ,2 ]
Li, Xi [3 ]
机构：
[1] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Key Lab Intelligent Informat Proc & Real Time Ind, Wuhan 430065, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Coll Automat, Wuhan 430074, Hubei, Peoples R China
来源：
2019 3RD INTERNATIONAL CONFERENCE ON MACHINE VISION AND INFORMATION TECHNOLOGY (CMVIT 2019) | 2019年 / 1229卷
基金：
中国国家自然科学基金;
关键词：
FUZZY C-MEANS; OXIDE FUEL-CELLS; LOCAL INFORMATION; MICROSTRUCTURE; ANODE; RECONSTRUCTION;
D O I：
10.1088/1742-6596/1229/1/012020
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells (SOFCs). Aiming at the YSZ / Ni anode optical microscopy (OM) image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.
引用
收藏
页数：7
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