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.

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.

Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R² is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

The identification of coal gangue is of great significance for its intelligent separation. To overcome the interference of visible light, we propose coal gangue recognition based on multispectral imaging and Extreme Gradient Boosting (XGBoost). The data acquisition system is built in the laboratory, and 280 groups of spectral data of coal and coal gangue are collected respectively through the imager. The spectral intensities of all channels of each group of spectral data are averaged, and then the dimensionality is reduced by principal component analysis. XGBoost is used to identify coal and coal gangue based on the reduced dimension spectral data. The results show that PCA combined with XGBoost has the relatively best classification performance, and its recognition accuracy of coal and coal gangue is 98.33%. In this paper, the ensemble-learning algorithm XGBoost is combined with spectral imaging technology to realize the rapid and accurate identification of coal and coal gangue, which is of great significance to the intelligent separation of coal gangue and the intelligent construction of coal mines.

Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a $v$ Support Vector Regression (vSVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the vSVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of vSVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with vSVR predictions. Experimental results show that the prediction results from SOS-vSVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience.

The rapid and accurate identification of water source types in mine water inrush has been achieved by combining laser-induced fluorescence technology (LIF) with artificial intelligence algorithms. However, these algorithms solely rely on data and image processing analysis to identify different kinds of water samples. To address this issue, we analyzed the fluorescence spectrum and the types of mine water inrush sources from the signal point of view. Firstly, a LIF water inrush spectral analysis system was built to collect spectral data and exhibit fluorescence spectra. Different methods of spectral signal decomposition and reconstruction were compared. The complementary ensemble empirical mode decomposition (CEEMD) algorithm with a better signal evaluation index was selected to preprocess raw spectral signals. Then, the multi-class support vector machine of the cuckoo search optimization (CS-MSVM) model was implemented to the reconstructed spectral signals in different stages. The classification accuracy of the reconstructed signals in the fifth stage was 100%. Compared with raw spectra, other signal processing methods, and other different classifiers, the proposed method has the highest classification accuracy. Finally, the reliability of the algorithm was validated by using the LIF spectral signals of different edible oils and the classification accuracy was 100%. The experimental results show that the CEEMD signal processing method combined with LIF spectroscopy is effective for the accurate identification of mine water inrush source, and it also provides a theoretical basis for the spectral analysis technology that can be used for the identification of other substances.