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High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively...
For the three-phase identification of Solid Oxide Fuel Cell (SOFC) electrode, this paper presents a novel segmentation method based on Gaussian Mixture Model (GMM). A coarseness-entropy adaptive factor is defined to incorporate the spatial information based on Markov Random Filed (MRF) into GMM. Furthermore the proposed method defines can control the trade-off between robustness to noise and effectiveness...
In order to accurately evaluate the microstructure parameters of Solid Oxide Fuel Cell (SOFC) electrode, this paper presents a novel image segmentation method based on Gaussian Mixture Model (GMM) to identify the three phases of electrode optical microscope image. Firstly, the spatial neighbor information is introduced into EM optimization algorithm to constrain the weighted probability distribution...
In this paper, an effective multi-threshold image segmentation method is proposed based on the measure of an adaptive fuzzy maximum entropy. In the traditional image segmentation algorithms with fuzzy entropy, C-threshold is usually determined by 2*C parameters at least, which are generally searched by a conventional genetic algorithm (GA) or simulated anneal algorithm (SA). Adaptive fuzzy entropy...
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