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- W4291155384 abstract "The key to the interpretation of temperature-indicating paint (TIP) is the accurate measurement of color. In this work, combined with the color information carried by the reflectance spectrum, a fiber-based TIP interpretation method is proposed and evaluated. The sub-band statistical feature extraction algorithm (SFEA) is constructed to deal with the high dimensionality and high redundancy of spectra in data processing. During the research, an online TIP temperature interpretation system based on Y-type fiber is designed and implemented. Three types of TIP samples (KN3, KN6, KN8) are employed for testing. The results indicate that the SFEA reduces the influence of light intensity fluctuations on temperature interpretation. And the ambient light has no obvious influence on the measurement accuracy of this system. When the error tolerance is 10 °C, the interpretation accuracy is 99%, 99%, 95% respectively. The method has been tested on V-notch specimen. • Reflectance spectrum of TIP is utilized to realize temperature interpretation. • A newly developed instrument with Y-type optical fiber is used for the OTI system. • A sub-band statistical feature extraction method is proposed to process the spectrum. • Machine learning algorithms are applied to improve interpretation accuracy. • Tests on probe repeatability, pressing force and fiber bending demonstrate stability." @default.
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- W4291155384 date "2022-09-01" @default.
- W4291155384 modified "2023-09-28" @default.
- W4291155384 title "Surface temperature measurement based on reflectance spectrum of temperature indicating paint and machine learning algorithms" @default.
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- W4291155384 doi "https://doi.org/10.1016/j.measurement.2022.111741" @default.
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