Optimal Design of Spectroscopic Structure Based on Prism-Grating Combination

Chaobin, 曾朝斌 Zeng; Bin, 刘宾 Liu; Zhengde, 夏正德 Xia; Yuzhang, 师钰璋 Shi; Yan, 韩焱 Han

doi:10.3788/aos202040.2105001

Cited by 1 publication

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“…Therefore, in order to achieve high-speed dynamic temperature measurement, this paper, based on the previously designed 400-1000 nm spectral range, 16 uniform spectral channels and 20μs data acquisition speed system [9], acquired data for high dynamic temperature and used PNN neural network to train the data, a model between multispectral radiance and temperature was build. In the whole learning process, the increase or decrease of category patterns does not require a long-term training and learning of the neural network.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Neural Network Measurement Method for Multispectral High Dynamic Temperature

Zeng

Liu²,

Zhang³

et al. 2023

J. Phys.: Conf. Ser.

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Among various temperature measurement technologies, Multispectral thermometry is a better method to measure temperature under complex conditions. To solve the problems in the multi-spectral temperature measurement technology, which included the detector’s non-linear sensitivity characteristics, consistency calibration, data processing, etc., this paper, based on the previous research and development of the multi-spectral dynamic temperature measurement system, realized data acquisition of the dynamic temperature in 20us sample rates within 1550~2000 Celsius, and then the dynamic measurement data is processed by the probabilistic neural network (PNN). Finally, temperature measurement system realized the accuracy of the measurement error less than 1.1%. The results show that the PNN neural network can fit the temperature distribution curve better and faster, and can be better applied to the miniaturization of the temperature measurement system.

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Section: Introductionmentioning

confidence: 99%