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- W2364400613 abstract "This paper presents a multi-specteral data processing method based on BP neural networks that gives more accurate temperature measurements for most engineering materials by using the neural network to learn the emissivity-wavelength relationship from a training data set. The model also includes the effects of random noise on the temperature measurements. Experimental results show that the recognition error is ±30 K for trained emissivity samples which is better than the recognition error of (±50 K) for untrained samples. Therefore, the temperature recognition accuracy can be improved by enlarging the number of emissivity samples." @default.
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- W2364400613 date "2005-01-01" @default.
- W2364400613 modified "2023-09-23" @default.
- W2364400613 title "Data processing method for multi-spectral thermometry based on neural networks" @default.
- W2364400613 hasPublicationYear "2005" @default.
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