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Retrieval algorithm of quantitative analysis of passive Fourier transform infrared (FTRD) remote sensing measurements of chemical gas cloud from measuring the transmissivity by passive remote Fourier transform infrared 被引量:3
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作者 刘志明 刘文清 +4 位作者 高闽光 童晶晶 张天舒 徐亮 魏秀丽 《Chinese Physics B》 SCIE EI CAS CSCD 2008年第11期4184-4192,共9页
Passive Fourier transform infrared (FTIR) remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of conce... Passive Fourier transform infrared (FTIR) remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds. 展开更多
关键词 passive remote measurement Fourier transform infrared ftir gas cloud sensing concentration retrieval
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区间极限学习机结合遗传算法用于红外光谱气体浓度反演的研究 被引量:6
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作者 陈媛媛 王志斌 +1 位作者 王召巴 李晓 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2014年第5期1244-1248,共5页
提出一种新的有效的FT IR光谱气体浓度反演的方法。该方法将区间划分的思想用于红外光谱波长优化筛选,即将红外光谱在给定波长范围内划分为若干个子区间,在每个子区间中利用遗传算法(genetic algorithm ,GA)优化后的极限学习机(ex... 提出一种新的有效的FT IR光谱气体浓度反演的方法。该方法将区间划分的思想用于红外光谱波长优化筛选,即将红外光谱在给定波长范围内划分为若干个子区间,在每个子区间中利用遗传算法(genetic algorithm ,GA)优化后的极限学习机(extreme learning machine ,ELM)建立浓度预测模型,根据每个子区间测试集均方根误差RM S E和相关系数 R2的大小评价模型的泛化性能,筛选出最优子区间组合建立预测模型。通过含干扰组分(CO2,N2 O)的CO气体的 FTIR光谱对提出的算法进行了验证,在波段为2140~2220 cm -1范围内利用区间法筛选出的最优组合作为变量,应用GA-ELM 建立的浓度反演模型,其决定系数 R2为0.9874,均方根误差RMSE为154.9963,建模时间仅为0.8 s ,表明该算法(Interval-GA-ELM ,iGELM )的应用不仅缩短了建模时间,而且在干扰组分存在的情况下,依然可以准确筛选出特征波长,从而提高了模型稳定性和预测精度,为大气污染气体遥测分析提供了行之有效的方法。 展开更多
关键词 区间划分 极限学习机 遗传算法 气体浓度反演
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