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Intelligent sorting of coal and gangue is of great significance to the intelligent construction of coal mines as well as green development. In this study, we propose a coal and gangue segmentation method with an improved classical segmentation network Mask R‐CNN, denoted as Multichannel Forward‐Linked Confusion Convolution Module (MFCCM)‐Mask R‐CNN. First, we design a MFCCM to construct the feature extraction network by stacking, second, we design a multiscale high‐resolution feature pyramid network structure to realize multipath fusion of feature information to enhance the position and contour information of the target, and finally, we propose a multiscale Mask head to enhance the diversity of information, and capture the more representative and unique features. Training and testing models using self‐built RGB coal and gangue data sets, the accuracy of the improved algorithm reaches 97.38%, which is an improvement of 1.66% compared to the original model. Compared with other segmentation models Unet, Deeplab V3+, Yoloact, Yolov7, and the model after replacing the backbone network, the MFCCM‐Mask R‐CNN has higher precision and recall, and can more accurately realize the efficient segmentation of coal and gangue. [ABSTRACT FROM AUTHOR]

Aiming at the problems of insufficient attention to spatial information, long classification training time, and high model complexity in current gangue image classification algorithms, a lightweight fast recognition model for gangue based on improved MobileNetV3-small is proposed. First, we optimize the feature extraction part by introducing the CeLU activation function to alleviate the neuron death and gradient vanishing problems, thus improving the performance of the model. Second, Bneck is further optimized using an improved Triplet Attention Module to achieve almost parameter-free spatial and channel dimension interactions, which drastically reduces the complexity of the model. Finally, we construct a new Efficient Last Stage structure, which improves the model performance while successfully reducing the computation and model size by replacing the traditional convolution with Ghost Module and introducing Mix-pool as a pooling layer. In addition, the effectiveness of each component was fully demonstrated through ablation experiments, visualization analysis, and comparative experiments. The experimental results show that training the homemade gangue dataset using the improved MobilenetV3-small model improves Accuracy and F1-Score on the validation set by 0.48% and 0.42%, respectively, compared to the original network, and in terms of model complexity, we can see that FLOPs, number of parameters, and Weight size are reduced by 2.8%, 33.0%, and 32.3%, respectively, and FPS reaches 28 frames per second. The improved algorithm greatly reduces the model complexity and decreases the dependence on hardware devices while improving the classification accuracy and anti-interference capability. This provides an important reference basis for deploying automated underground coal gangue sorting, which helps to realize intelligent integrated mining. [ABSTRACT FROM AUTHOR]

In the field of optical measurement, the light source has higher requirements to the constant current source, and the existing constant current source system has the system noise and measurement noise, which affects the output precision of the current. This paper combines Kalman filter algorithm with FOPID control strategy, constructs Kalman-FOPID controller, based on the mathematical model of flow control constant current source system based on BUCK circuit, generates random noise by MATLAB, and carries out current control research. Simulation and physical verification show that the Kalman-FOPID controller can effectively filter the noise interference of the constant current source system and improve the control precision of the output current.

It is vital to differentiate between coal and gangue in coal mining effectively. In recent years, classification methods for images of coal‐ and gangue‐based convolutional neural networks have emerged, which makes the analysis process simple and fast. However, these methods still have the limitation of balancing recognition performance and computational efficiency due to the limitation of device hardware. This research proposes a group convolution and channel shuffle augmentation Ghost network (GSAGNet) for the classification task of coal and gangue images. First, we design a new Ghost module with group convolution and channel shuffle to improve the stability of the Ghost module for better extraction of features of coal and gangue. Second, we redesign GhostNet to improve the model's classification accuracy while guaranteeing similar floating‐point operations. Finally, an efficient channel attention module is embedded in the prediction output of the model to improve the model's prediction results further. Extensive experiments on actual image datasets of coal and gangue show the feasibility and superiority of our proposed GSAG module. The GSAG module is applied to models such as AlexNet, VGG13, and ResNet50, and the accuracy of the models is improved by 0.27%, 1.24%, and 1.25%, respectively, compared to the Ghost module. The classification accuracy and F1‐score of the proposed GSAGNet model reach 97.50% and 97.50%, respectively, which are 1.01% and 1.04% higher than that of GhostNet. The GSAGNet model in this study can classify coal and gangue efficiently, which can effectively enable the separation of underground coal and gangue and has specific practical application significance and value. [ABSTRACT FROM AUTHOR]

In view of problems that traditional risk assessment and reliability analysis methods of power grid focused on relay protection reliability indicators and did not involve change of reliability and actual risk in operation of relay protection device, the paper put forward a method that used three-parameter Weibull distribution to estimate time-varying failure rate, and established Markov model with 10 state based on multiple factors. It obtained time-varying failure rate function model through analyzing and processing data of power grid operation and management by Matlab simulation software; in combination with software and hardware and management factors, it also built process hidden risk assessment system of relay protection device based on Markov model by PSS/E software. The experimental results show that the system can early find and rectify hidden danger in power grid, which improves management level of the power grid, and realizes safe and stable operation of the power grid.

In the dense medium separation process of the coal preparation plant, the fluctuation of raw coal quality and the lag of heavy media separation density adjustment often lead to the instability of clean coal quality. To solve this problem, phase space reconstruction with online data was carried out to derive a mathematical formula of working dense medium density with raw coal ash and clean coal ash produced. Then the data was trained by two ways, namely Least Square Support Vector Machine (LS-SVM) and LS-SVM based on time series. Simulation results show that the latter way can achieve a more accurate prediction of heavy medium suspension density. [ABSTRACT FROM AUTHOR]

Meter reading recognition is an important link for robots to complete inspection tasks. To solve the problems of low detection accuracy and inaccurate localization of current meter reading recognition algorithms, the YOLOV7-SSWD (YOLOV7-SiLU-SimAM-Wise-IoU-DyHeads) model is proposed, a novel detection model based on the multi-head attention mechanism, which is improved on the YOLOV7-Tiny model. First, the Wise-IoU loss function is used to solve the problem of sample quality imbalance and improve the model's detection accuracy. Second, a new convolutional block is constructed using the SiLU activation function and applied to the YOLOV7-Tiny model to enhance the model's generalization ability. The dynamic detection header is then built as the header of YOLOV7-Tiny, which realizes the fusion of multi-scale feature information and improves the target recognition performance. Finally, we introduce SimAM to improve the feature extraction capability of the network. In this paper, the importance of each component is fully verified by ablation experiments and comparative analysis. The experiments showed that the mAP and F1-scores of the YOLOV7-SSWD model reached 89.8% and 0.84. Compared with the original network, the mAP increased by 8.1% and the F1-scores increased by 0.1. The YOLOV7-SSWD algorithm has better localization and recognition accuracy and provides a reference for deploying inspection robots to perform automatic inspections., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)