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.

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.

To effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared images without considering the selection of feature extraction and classifier. Firstly, the specific architecture and detailed parameters of the model are optimized and the CNN model based on only one Inception Block contains three different convolution kernels are considered to be the most appropriate model. Next, performance of the proposed identification model is analyzed and evaluated by the infrared image dataset, and we discovered that the CNN model is capable of correctly identifying 192 training samples and 48 test samples. Finally, compared with the traditional recognition model and other CNN recognition model, it is proved that the proposed CNN model has superior recognition performance. The results state clearly that the combination of infrared image and CNN can quickly and accurately identify coal and gangue without complex image processing steps. At the same time, the model has a certain anti-interference ability for different noises. And it has a certain reference value for the research and development of intelligent coal preparation equipment.

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

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.