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A hybrid feature selection combining wavelet transform for quantitative analysis of heat value of coal using laser-induced breakdown spectroscopy
Applied Physics B ( IF 2.1 ) Pub Date : 2021-01-15 , DOI: 10.1007/s00340-020-07556-8
Peng Lu , Zhuang Zhuo , Wenhao Zhang , Jing Tang , Yan Wang , Hongli Zhou , Xiaole Huang , Tengfei Sun , Jingqi Lu

A hybrid feature selection method combining with wavelet transform (WT) was proposed to analyze the heat value of coal using laser-induced breakdown spectroscopy (LIBS). The hybrid feature selection method consisted of distance correlation (DC) method and recursive feature elimination with cross-validation (RFECV) method, which combined the advantages of DC-based filter method and RFECV-based wrapper method. First, WT method was used to filter noise signal from LIBS spectra of coal samples, and the de-noised wavelet coefficients were obtained. Second, the de-noised wavelet coefficients were further eliminated by the hybrid feature selection method. Finally, the retained wavelet coefficients were used directly as input variables to establish a prediction model for heat value determination of coal. 28 powdery coal samples were used in this experiment, of which 21 were calibration set and 7 were validation set. The effectiveness of the hybrid model was studied. Compared with several other models, the proposed hybrid model showed the greatest improvement in predictive accuracy and precision, and the computing time has been greatly reduced. The experimental results demonstrated that the hybrid model can effectively reduce the calculation time and improve the performance of the model.

中文翻译:

结合小波变换的混合特征选择用于基于激光诱导击穿光谱的煤热值定量分析

提出了一种结合小波变换(WT)的混合特征选择方法,利用激光诱导击穿光谱(LIBS)分析煤的热值。混合特征选择方法由距离相关(DC)方法和带有交叉验证的递归特征消除(RFECV)方法组成,结合了基于DC的过滤方法和基于RFECV的包装方法的优点。首先,利用WT方法对煤样LIBS光谱中的噪声信号进行滤波,得到去噪后的小波系数。其次,通过混合特征选择方法进一步消除了去噪小波系数。最后,将保留的小波系数直接用作输入变量,建立煤热值确定的预测模型。本实验使用28个粉煤样品,其中21个为校准集,7个为验证集。研究了混合模型的有效性。与其他几种模型相比,所提出的混合模型在预测准确度和精度方面表现出最大的提高,并且计算时间大大减少。实验结果表明,混合模型可以有效减少计算时间,提高模型性能。
更新日期:2021-01-15
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