A Quantum-Inspired Fuzzy Clustering for Solid Oxide Fuel Cell Anode Optical Microscope Images Segmentation
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.
Solid Oxide Fuel Cell (SOFC) Microstructure Fuzzy clustering Image segmentation Quantum Signal Processing (QSP)
Yuhan Xiang Xiaowei Fu Li Chen Xin Xu Xi Li
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
国际会议
第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)
成都
英文
55-64
2016-11-03(万方平台首次上网日期,不代表论文的发表时间)