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基于SPA的矿井突水激光诱导荧光光谱特征波长选择

Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush.

作者信息

Hu Feng, Zhou Mengran, Yan Pengcheng, Li Datong, Lai Wenhao, Zhu Song, Wang Yu

机构信息

School of Electrical and Information Engineering, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province 232001, PR China.

School of Electrical and Information Engineering, Anhui University of Science and Technology, No. 168 Taifeng Road, Huainan, Anhui Province 232001, PR China.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2019 Aug 5;219:367-374. doi: 10.1016/j.saa.2019.04.045. Epub 2019 Apr 25.

Abstract

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And it also provided a theoretical basis for the development of a special online identification instrument for mine water inrush.

摘要

在煤矿突水灾害防治过程中,准确快速地识别突水水源类型对煤矿安全生产具有重要意义。激光诱导荧光(LIF)技术在煤矿突水识别中的应用打破了传统水化学方法的不足,能够实现突水类型的准确快速识别。首先,为避免光谱数据随机变化的影响,分析比较了四种常见的预处理方法,选择移动平均平滑法对原始荧光光谱数据进行预处理。然后,为选择合适的样本划分方法以提高模型的预测性能,比较了四种常见的样本划分方法,采用基于联合x - y距离的样本集划分(SPXY)方法将样本划分为训练集和测试集。进一步,通过连续投影算法(SPA)选择10个特征波长以减少数据量。最后,将所选数据作为输入,选择sigmoid函数作为极限学习机(ELM)的激活函数,将隐藏层神经元数量设置为34,实现了水源识别模型的构建。ELM模型对训练集和测试集的预测准确率分别为99.0%和94.0%。此外,将不同时间采集的水样以相同方式混合形成独立验证集,ELM水源识别模型对独立验证集的预测准确率为91.5%。结果表明利用SPA选择荧光光谱特征波长是可行的,10个特征波长的数据能够充分代表全波段光谱的有效信息,也为开发煤矿突水专用在线识别仪器提供了理论依据。

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