Density-based spatial clustering of applications with noise (DBSCAN) is a typical kind of algorithm based on density clustering in unsupervised learning. It can cluster data of arbitrary shape and also identify noise samples in the dataset. However, an unavoidable defect of the DBSCAN algorithm exists since the clustering performance is quite sensitive to the parameter settings of MinPts and Eps, and there is no theory to guide the setting of its parameters. Therefore, a new method is proposed to optimize the DBSCAN parameters in this paper. Multi-verse optimizer algorithm, a special variable updating method with excellent optimization performance, is selected and improved for optimizing the parameters of DBSCAN, which not only can quickly find out the highest clustering accuracy of DBSCAN, but also find the interval of Eps corresponding to the highest accuracy. In order to search the range of Eps more quickly and efficiently, we design a new mechanism for the variable update of MVO. The experimental results show that the improved MVO is used to optimize DBSCAN, which not only can quickly find out its highest clustering accuracy but also can search the parameters of MinPts and Eps corresponding to the highest clustering accuracy efficiently.

Accurate identification of coal and gangue is an important prerequisite for the effective separation of coal and gangue. The application of imaging technology combined with image processing steps (like enhancement, feature extraction, etc.) and classifier is used to identify coal and gangue, which effectively avoids the shortcomings of traditional methods (radiation, pollution, etc.). However, ordinary image detection is greatly influenced by environmental factors such as light, dust and so on. Multispectral imaging technology, as a new generation of optical non-destructive testing technology, is less affected by illumination, so we propose a new solution for the recognition of coal and gangue by using multispectral imaging. Firstly, we respectively tested the classification performance of different image feature extraction methods under GS-SVM, GA-SVM, and PSO-SVM classifiers, and selected the best feature extraction method is LBP. And then, we compared the classification effects under different wavelengths and found that the ninth wavelength works best. That is, the difference in imaging between coal and gangue at 773.776 nm is greatest. Finally, the performance of the proposed model for the identification of coal and gangue was carried out. And the highest classification accuracy can be obtained by using GS-SVM as the classifier, at which point, c=8, g=0.17678. The results show that multispectral imaging technology can be used for the identification of coal and gangue, and the prediction accuracy of the model combined with LBP feature extraction and GS-SVM can reach 96.25% (77/80). The conclusions could provide reference evidence for the intelligent dry selection in coal preparation plants and underground coal mine.

LIF spectroscopy combined with 1D CNN can identify mine water inrush quickly and accurately without complicated pretreatment.

Coal is one of the main sources of human energy. In the process of coal mining, separating gangue from coal has great significance for environmental protection and energy conservation. The core problem in the separation of gangue is the recognition of coal and gangue. In this study, multispectral technology was used to identify gangue. First, set up a data acquisition system in the laboratory, and then collect spectral data. Coal and gangue spectral data were collected in 202 and 201 groups, respectively. Secondly, design a spectral data dimension reduction model called two-dimension autoencoder(2D-AE). Finally, Random Forest was used to recognize coal and gangue. Meanwhile, CART, KNN, SVM, and AdaBoost were also used for gangue identification. The experimental results show that the maximum average accuracy by 2D-AE combined with RF was the largest, which was 98.89%. Also, the accuracy of gangue recognition is different for spectral images of different wavelengths. This paper mainly studies the recognition of coal and gangue based on multispectral technology, which is of great significance for the next step of detecting gangue based on the technology.