A Quantum-Inspired Fuzzy Clustering for Solid Oxide Fuel Cell Anode Optical Microscope Images Segmentation

摘要：

　　For better three-phase identification of Solid Oxide Fuel Cell(SOFC)microstructure,this paper presents a novel quantum-inspired clustering method for YSZ/Ni anode Optical Microscopic(OM)images.Motivated by Quantum Signal Processing(QSP),a quantum-inspired adaptive fuzziness factor is introduced to adaptively estimate the parameters of the spatial constraint term in the fuzzy clustering based on Markov Random Filed(MRF).Experimental results show that the proposed method is effective to identify the three phases.The combination of image processing and micro-investigation provides an innovative way to analyze the performance of SOFC.

关键词： Solid Oxide Fuel Cell (SOFC) Microstructure Fuzzy clustering Image segmentation Quantum Signal Processing (QSP)

作者: Yuhan Xiang Xiaowei Fu Li Chen Xin Xu Xi Li

作者单位: College of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,C College of Automation,Huazhong University of Science and Technology,Wuhan 430074,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 55-64

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）