|
|
|
|
|
|
Multispectral True Temperature Inversion Based on Multi-Objective Minimum Optimization Principle of Reference Temperature |
ZHANG Fu-cai1, 2, TANG Wei1, SUN Xiao-gang2* |
1. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
2. School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China |
|
|
Abstract Multispectral thermometry is a process of retrieving the true temperature of radiators by measuring the information of multispectral radiations and using related theories and algorithms. The solution of spectral emissivity is still the key and difficulty in multispectral thermometry. Theoretically, it is necessary to know enough spectral information to obtain the true temperature of the radiator. Considering that the spectral emissivity of actual radiators at different spectrum and temperatures are usually inconsistent, and the solution of spectral emissivity is an unavoidable problem in non-contact radiation temperature measurement, it is of great scientific significance and application value to carry out the research on the solution of multispectral emissivity and the inversion methods of true temperature. After decades of development, the solution spectral emissivity can be generalized into four types of models. One is the grey body hypothesis model, which considers that spectral emissivity is a constant or its change can be neglected in the process of temperature inversion; the other is the wavelength hypothesis model, which considers that there is a certain relationship between spectral emissivity and wavelength in the process of temperature inversion. Thirdly, the true temperature hypothesis model, which considers that there is a certain relationship between spectral emissivity and true temperature in the inversion process of the true temperature, and establishes a model between spectral emissivity and true temperature and realizes the inversion of true temperature with iteration method; Fourthly, the establishment of a neural network model, which achieve true temperature inversion by the neural learning network. Based on the uniqueness of true temperature and the analysis of different hypothetical models, thethesis tries to find a generaltrue temperature inversion method without the hypothesis of spectral emissivity model and carries out the research work with multispectral true temperature inversion method as the core. The paper summarizes the characteristics of traditional multispectral true temperature inversion theories and methods. In view of the complexity of selecting the spectral emissivity model in the existing multispectral true temperature inversion process, a true temperature inversion method based on the constrained optimization principle of single objective function minimization is proposed. This method does not need to assume the spectral emissivity model and convert the true temperature solution problem into an optimization problem to solve the minimum of the objective function. By using a blackbody furnace and adding a filter with known spectral emissivity at the output port of the blackbody furnace light source to simulate the radiation source, the true temperature inversion of multispectral pyrometer based on minimum optimization method is realized. Compared with the traditional second measurement method, under the same initial conditions and compared with the original second measurement method proposed by the research group, the new method has the same inversion accuracy as the second measurement method, but the inversion speed has been greatly improved.
|
Received: 2019-06-20
Accepted: 2019-11-15
|
|
Corresponding Authors:
SUN Xiao-gang
E-mail: qingtengzfc@yeah.net
|
|
[1] Araújo A. Infrared Physics & Technology, 2016, 76: 365.
[2] YANG Yong-jun, WANG Zhong-yu, ZHANG Shu-kun, et al(杨永军, 王中宇, 张术坤, 等). Journal of Beijing University of Aeronautics and Astronautics(北京航空航天大学学报), 2014, 40(8): 1022.
[3] Zhang L, Dai J M, Yin Z. Chinese Optics Letters, 2015, 13(6): 83.
[4] Liu H, Zheng S, Zhou H, et al. Measurement Science & Technology, 2016, 27(2): 025201.
[5] ZHANG Lei, CHEN Shao-wu, ZHAO Hai-chuan, et al(张 磊, 陈绍武, 赵海川, 等). Chinese Optics(中国光学), 2019, 12(2): 289.
[6] SUN Xiao-gang, HE Jin, DAI Jing-min, et al(孙晓刚, 何 瑾, 戴景民, 等). Journal of Harbin Institute of Technology(哈尔滨工业大学学报), 1998, 30(6): 1.
[7] Khatami R, Levendis Y A. Combustion and Flame, 2011, 158(9): 1822.
[8] Vandersteegen M, Beeck K V, Goedemé T. Real-Time Multispectral Pedestrian Detection with a Single-Pass Deep Neural Network. International Conference Image Analysis and Recognition. Springer, Cham, 2018: 419.
[9] CONG Da-cheng, DAI Jing-min, SUN Xiao-gang, et al(丛大成, 戴景民, 孙晓刚, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2001, 20(2): 97.
[10] Song Y, Sun X, Tang H. Chinese Optics Letters, 2007, 5(8): 457.
[11] Liang Mei, Sun Bojun, Sun Xiaogang, et al. International Journal of Thermophysics, 2017, 38(3): 35.
[12] DAI Jingmin. Theory and Practice of Multi-spectral Thermometry. Beijing: Higher Education Press, 2002.
[13] SUN Xiao-gang, DAI Jing-min, WANG Xue-feng, et al(孙晓刚, 戴景民, 王雪峰, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2003, 22(2): 141. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
ZHU Wen-jing1, 2,FENG Zhan-kang1, 2,DAI Shi-yuan1, 2,ZHANG Ping-ping3,JI Wen4,WANG Ai-chen1, 2,WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
[5] |
ZHANG Nan-nan1, 3, CHEN Xi-ya1,CHANG Xin-fang1, XING Jian1, GUO Jia-bo1, CUI Shuang-long1*, LIU Yi-tong2*, LIU Zhi-jun1. Distributed Design of Optical System for Multi-Spectral Temperature
Pyrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 230-233. |
[6] |
GAO Wei-ling, ZHANG Kai-hua*, XU Yan-fen, LIU Yu-fang*. Data Processing Method for Multi-Spectral Radiometric Thermometry Based on the Improved HPSOGA[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3659-3665. |
[7] |
ZHANG Ning-chao1, YE Xin1, LI Duo1, XIE Meng-qi1, WANG Peng1, LIU Fu-sheng2, CHAO Hong-xiao3*. Application of Combinatorial Optimization in Shock Temperature
Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3666-3673. |
[8] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[9] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
[10] |
WANG Zhen-tao1, DAI Jing-min1*, YANG Sen2. Research on Multi-Spectral Thermal Imager Explosion Flame True
Temperature Field Measurment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3885-3890. |
[11] |
CHENG Gang1, CAO Ya-nan1, TIAN Xing1, CAO Yuan2, LIU Kun2. Simulation of Airflow Performance and Parameter Optimization of
Photoacoustic Cell Based on Orthogonal Test[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3899-3905. |
[12] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
[13] |
WANG Wen-song1, PEI Chen-xi2, YANG Bin1*, WANG Zhi-xin2, QIANG Ke-jie2, WANG Ying1. Flame Temperature and Emissivity Distribution Measurement MethodBased on Multispectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3644-3652. |
[14] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
[15] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
|
|
|
|