Multispectral true temperature inversion algorithm based on QR decomposition
Multispectral radiation temperature measurement technology is extensively applied across military, industrial, and metallurgical sectors. Such as solid rocket engine tail flame and metal surface temperature measurement. To achieve these target material surface temperature measurements. This paper pr...
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| Veröffentlicht in: | Optics and laser technology Jg. 187; S. 112885 |
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01.09.2025
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| Abstract | Multispectral radiation temperature measurement technology is extensively applied across military, industrial, and metallurgical sectors. Such as solid rocket engine tail flame and metal surface temperature measurement. To achieve these target material surface temperature measurements. This paper presents a multi-spectral true temperature inversion algorithm that leverages QR decomposition of multi-channel spectral information, designed to enhance the correlation between spectral information extraction and multi-channel data. The algorithm employs matrix QR decomposition to construct a multi-channel spectral information dataset, addressing the issue of inaccurate target temperature measurements attributed to unknown material emissivity. In addition, in order to improve the accuracy of multispectral inversion algorithm, this paper developed a CNN-LSTM-ATTENTION neural network, the convolutional layer and attention mechanism are added to the LSTM neural network, which enhances the correlation of spectral information and is conducive to more in-depth information mining, so as to measure the target real temperature. The algorithm is called QR decomposition and CNN-LSTM-ATTENTION combined true temperature inversion algorithm, abbreviation QRD-CLA, In the simulation experiment, the QRD-CLA algorithm demonstrates a 0.29% increase in accuracy compared to the GIM-LSTM algorithm. Validation through measured data confirms that the QRD-CLA outperforms the GIM-LSTM by 0.4% in terms of accuracy. |
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| AbstractList | Multispectral radiation temperature measurement technology is extensively applied across military, industrial, and metallurgical sectors. Such as solid rocket engine tail flame and metal surface temperature measurement. To achieve these target material surface temperature measurements. This paper presents a multi-spectral true temperature inversion algorithm that leverages QR decomposition of multi-channel spectral information, designed to enhance the correlation between spectral information extraction and multi-channel data. The algorithm employs matrix QR decomposition to construct a multi-channel spectral information dataset, addressing the issue of inaccurate target temperature measurements attributed to unknown material emissivity. In addition, in order to improve the accuracy of multispectral inversion algorithm, this paper developed a CNN-LSTM-ATTENTION neural network, the convolutional layer and attention mechanism are added to the LSTM neural network, which enhances the correlation of spectral information and is conducive to more in-depth information mining, so as to measure the target real temperature. The algorithm is called QR decomposition and CNN-LSTM-ATTENTION combined true temperature inversion algorithm, abbreviation QRD-CLA, In the simulation experiment, the QRD-CLA algorithm demonstrates a 0.29% increase in accuracy compared to the GIM-LSTM algorithm. Validation through measured data confirms that the QRD-CLA outperforms the GIM-LSTM by 0.4% in terms of accuracy. |
| ArticleNumber | 112885 |
| Author | Peng, Ruifei Yu, Kun Liu, Yufang Hu, Zhijian Yang, Zongju wang, Peimin |
| Author_xml | – sequence: 1 givenname: Zongju surname: Yang fullname: Yang, Zongju organization: Henan Key Laboratory of Infrared Materials & Spectrum Measures and Applications, School of Physics, Henan Normal University, Xinxiang, Henan 453007, China – sequence: 2 givenname: Peimin surname: wang fullname: wang, Peimin organization: Henan Key Laboratory of Infrared Materials & Spectrum Measures and Applications, School of Physics, Henan Normal University, Xinxiang, Henan 453007, China – sequence: 3 givenname: Zhijian surname: Hu fullname: Hu, Zhijian email: huzhijian1991@gmail.com organization: LAAS-CNRS, University of Toulouse, CNRS, Toulouse 31400, France – sequence: 4 givenname: Ruifei surname: Peng fullname: Peng, Ruifei organization: Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China – sequence: 5 givenname: Kun surname: Yu fullname: Yu, Kun organization: Henan Key Laboratory of Infrared Materials & Spectrum Measures and Applications, School of Physics, Henan Normal University, Xinxiang, Henan 453007, China – sequence: 6 givenname: Yufang surname: Liu fullname: Liu, Yufang email: yf-liu@htu.edu.cn organization: Henan Key Laboratory of Infrared Materials & Spectrum Measures and Applications, School of Physics, Henan Normal University, Xinxiang, Henan 453007, China |
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| Cites_doi | 10.1016/j.rinp.2020.103388 10.1088/1361-6501/acc047 10.1016/j.infrared.2022.104408 10.1007/s10765-005-6724-6 10.1063/5.0016747 10.1016/j.measurement.2024.114346 10.1016/j.rinp.2023.107014 10.1364/OE.25.030560 10.1364/OE.475680 10.1007/s10765-008-0448-3 10.1364/OE.24.019185 10.1109/CCDC.2017.7978572 10.1016/S1350-4495(02)00182-2 10.1016/j.infrared.2020.103523 10.1364/OE.26.025706 10.1364/OE.414844 10.1016/j.yofte.2024.103986 10.1016/j.measurement.2020.108725 10.1117/1.OE.61.12.124109 |
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| Keywords | True temperature Emissivity Self-attention mechanism Neural network algorithm QR decomposition |
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| Title | Multispectral true temperature inversion algorithm based on QR decomposition |
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