谱聚类结合LIF在矿井突水水源类型识别中的应用

doi:10.3964/j.issn.1000-0593(2021)02-0435-06

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Abstract Water inrush accidents threaten the lives of people and cause property damage. Therefore, it has great significance in accurately detecting the type of water inrush. Hydrochemical analysis method takes a long time and has a complicated process to detect the type of water inrush. Laser-induced fluorescence (LIF) technique has the advantages of fastness, high sensitivity, and low interference. Water inrush source recognition model building with LIF technique and intelligent algorithms can accurately detect the type of water inrush. At present, such models generally require de-noising, dimension reduction, and band selection on the fluorescence spectra, and this process is complicated. The models are built on the fluorescence spectra of the water inrush source which is evenly grouped. The influence of the uneven grouping on the model is not discussed, and the model is not built for the uneven grouping. In practical engineering applications, the number of samples collected is highly likely to be uneven, so Moth-flame optimization (MFO) algorithm combined with spectral clustering (SC) is proposed to realize the uneven grouping of water inrush fluorescence spectrain this paper. In the experiment, firstly, five kinds of experimental water samples were obtained from Huainan coal mine. Laser-induced fluorescence experimental equipment was used to collect fluorescence spectra of all water samples. The number of groups of five water samples is 75, 80, 80, 30 and 135. Secondly, build MFO-SC water sample recognition model. After comparison, K-Means is selected for the label mapping method, the Gaussian kernel function is selected for the calculation method of the similarity matrix, and the ncut is selected for the partition criterion. The parameters of the Gaussian kernel function were optimized by using MFO to obtain the parameter value of 1.745, and the initial clustering center of the model was fixed. Subsequently, build three water sample recognition models of K-Means, SVM and MFO-SVM, respectively. Comparing the MFO-SC model with the K-Means model, the optimal accuracy of the MFO-SC model is 100%, and the average accuracy is 100%. The optimal accuracy of the K-Means model is 99.75%, and the average accuracy is 79.57%. Then calculate the training set accuracy and test set accuracy of the SVM model and MFO-SVM model respectively. The accuracy of the training set of the SVM model is 80%, and the accuracy of the test set is 80%; the accuracy of the training set of the MFO-SVM model is 100%, and the accuracy of the test set is 95.625%. Finally, four models were used to identify water inrush fluorescence spectra of the other three uneven groups. The research results show that the MFO-SC algorithm is effective in identifying the type of water inrush, and can accurately detect the type of water inrush, which has great significance on the safety of coal mine production.

Key words：Laser induced fluorescence; Mine water inrush source; Spectral clustering; MFO

Received: 2020-01-05 Accepted: 2020-04-27

ZTFLH:

O657.3

Corresponding Authors: SONG Hong-ping E-mail: shp7420@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)02-0435-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I02/435

[1] FENG Zhi-dong, WANG Tao，GU Qing-hua，et al（冯治东, 王桃, 顾清华, 等）. System Engineering Theory and Practice（系统工程理论与实践）, 2017, 37(12): 3289.
[2] XUE Liang, YAO Duo-xi, LU Hai-feng, et al（薛凉, 姚多喜, 鲁海峰, 等）. Coal Technology（煤炭技术）, 2017, 36(11): 120.
[3] LIU Jian-min, WANG Ji-ren, LIU Yin-peng, et al（刘剑民, 王继仁, 刘银朋, 等）. Journal of Safety and Environment（安全与环境学报）, 2015, 15(1): 31.
[4] XU Xing, TIAN Kun-yun, LI Feng-qin, et al（徐星, 田坤云, 李凤琴, 等）. Journal of Southwest University·Natural Science Edition（西南大学学报·自然科学版）, 2018, 40(4): 170.
[5] WANG Yan-bin, SUN Shao-guang, ZHANG Zi-jian（王彦彬, 孙韶光, 张子建）. Mathematics in Practice and Theory（数学的实践与认识）, 2017, 47(21): 172.
[6] WU Wen-fei, LIAO Qing-ling（吴文飞, 廖庆玲）. Laser Journal（激光杂志）, 2019, 40(10): 36.
[7] ZHANG Ling-ling, WEN Long, CHEN Yuan, et al（张玲玲, 文龙, 陈媛, 等）. Applied Laser（应用激光）, 2019, 39(4): 703.
[8] ZHANG Wen-xue, ZHANG Xiao-rong, QIN Cheng-bing, et al（张文学, 张晓荣, 秦成兵, 等）. Laser & Optoelectronics Progress（激光与光电子学进展）, 2019, 56(20): 147.
[9] ZHOU Meng-ran, BIAN Kai, HU Feng，et al（周孟然, 卞凯, 胡锋, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(7): 2196.
[10] JU Qi-ding, HU You-biao, ZHANG Shu-ying（琚棋定, 胡友彪, 张淑莹）. Coal Engineering（煤炭工程）, 2018, 50(12): 90.
[11] Mashaan Alshammari, Masahiro Takatsuka. Pattern Recognition Letters, 2019, 128: 155.
[12] Thenmozhi K, Karthikeyani Visalakshi N, Shanthi S. Asian Pacific Journal of Cancer Prevention: APJCP, 2018, 19(7): 1935.
[13] Seyedali Mirjalili. Knowledge-Based Systems, 2015, 89: 228.