Vibration discrimination based upon multifractal spectrum and improved probabilistic neural network in the dual Mach–Zehnder interferometric perimeter system

Li, Meng; Li, Jiaxin; Xiong, Xiaozhen; Zhang, Xiaochen; Ma, Yiwei

doi:10.1007/s10043-021-00719-8

Cited by 1 publication

(1 citation statement)

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“…Li et al [21] proposed a new method based on multifractal theory, sequence feature fusion, and an improved probabilistic neural network, which improved the vibration identification performance of the perimeter system of the dual Mach Zehnder interferometer. The feature of the original signal is extracted in the form of multifractal spectra and associated with a probabilistic neural network, which realizes the beneficial fusion of multifractal theory and the probabilistic neural network.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Ceramic Thermal Shock Cracks Based on the Multifractal Spectrum

et al. 2022

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Ceramics are commonly used as high-temperature structural materials which are easy to fracture because of the propagation of thermal shock cracks. Characterizing and controlling crack propagation are significant for the improvement of the thermal shock resistance of ceramics. However, observing crack morphology, based on macro and SEM images, costs much time and potentially includes subjective factors. In addition, complex cracks cannot be counted and will be simplified or omitted. Fractals are suitable to describe complex and inhomogeneous structures, and the multifractal spectrum describes this complexity and heterogeneity in more detail. This paper proposes a crack characterization method based on the multifractal spectrum. After thermal shocks, the multifractal spectrum of alumina ceramics was obtained, and the crack fractal features were extracted. Then, a deep learning method was employed to extract features and automatically classify ceramic crack materials with different strengths, with a recognition accuracy of 87.5%.

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Section: Introductionmentioning

confidence: 99%