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. INDEX TERMS Improved MVO, DBSCAN, parameter optimization, unsupervised learning.
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. INDEX TERMS Coal-gangue identification, multispectral imaging, feature extraction, support vector machine.
This manuscript addresses the finite-time (FT) H ∞ control issue for distributed parameter switched systems (DPSSs). First, finite-time boundedness for DPSSs is studied. By applying piecewise Lyapunov-Krasovskii functional method incorporated with average dwell time approach, sufficient conditions of the FT boundedness for the DPSSs are derived in terms of linear matrix inequalities.Then, an event-triggered scheme is constructed to design output feedback controller, which guarantees the closed-loop system to be FT bounded. Based on the conditions in FT boundedness and event-triggered FT stabilization, FT H ∞ control problem for DPSSs is developed. Finally, two numerical examples are given to verify the effectiveness of the proposed results. K E Y W O R D Sdistributed parameter switched systems, event-triggered scheme, finite-time H ∞ control, finitetime boundedness, Lyapunov functional theory
Short-term electric load forecasting plays a significant role in the safe and stable operation of the LO 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.
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