A quantum-inspired fuzzy clustering for solid oxide fuel cell anode optical microscope images segmentation

Abstract

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.