Rockburst hazards pose a severe threat to mine safety. To accurately predict the risk level of rockburst, a LightGBM−TCN−RF prediction model is proposed in this paper. The correlation coefficient heat map combined with the LightGBM feature selection algorithm is used to screen the rockburst characteristic variables and establish rockburst predicted characteristic variables. Then, the TCN prediction model with a better prediction performance is selected to predict the rockburst characteristic variables at time t + 1. The RF classification model of rockburst risk level with a better classification effect is used to classify the risk level of rockburst characteristic variables at time t + 1. The comparison experiments show that the rockburst characteristic variables after screening allow a more accurate prediction. The overall RMSE and MAE of the TCN prediction model are 0.124 and 0.079, which are better than those of RNN, LSTM, and GRU by about 0.1–2.5%. The accuracy of the RF classification model for the rockburst risk level is 96.17%, which is about 20% higher than that of KNN and SVM, and the model accuracy is improved by 1.62% after parameter tuning by the PSO algorithm. The experimental results show that the LightGBM−TCN−RF model can better classify and predict rockburst risk levels at future moments, which has a certain reference value for rockburst monitoring and early warning.

A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster’s combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

Improper wearing of personal protective equipment may lead to safety incidents; this paper proposes a combined detection algorithm for personal protective equipment based on the lightweight YOLOv4 model for mobile terminals. To ensure high detection accuracy, a channel and layer pruning method (CLSlim) to lightweight algorithm is used to reduce computing power consumption and improve the detection speed on the basis of the YOLOv4 network. This method applies L1 regularization and gradient sparse training on the scaling factor of the BN layer in the convolutional module: global pruning threshold and local safety threshold are used to eliminate redundant channels, the layer pruning threshold is used to prune the structure of the shortcuts in the Cross Stage Partial (CSP) module for inference speed improvement, and finally, a lightweight network model is obtained. The experiment improves the YOLOv4 and YOLOv4-Tiny models for CLSlim lightweight separately in GTX2080ti environment. Results show that (1) CLSlim-YOLOv4 compresses the YOLOv4 model parameters by 98.2% and increases the inference speed by 1.8 times with mAP loss of only 2.1% and (2) CLSlim-YOLOv4-Tiny compresses the original model parameters by 74.3% and increases the inference speed by 1.1 times with mAP increase of 0.8%, which certificates that this improved lightweight algorithm serves better for the real-time ability and accuracy of combined detection on PPE with mobile terminals.

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Modality differences and intra-class differences have been hot research problems in the field of cross-modality person re-identification currently. In this paper, we propose a cross-modality person re-identification method based on joint middle modality and representation learning. To reduce the modality differences, a middle modal generator is used to map different modal images to a unified feature space to generate middle modality images. A two-stream network with parameter sharing is used to extract the combined features of the original image and the middle modality image. In addition, a multi-granularity pooling strategy combining global features and local features is used to improve the representation learning capability of the model and further reduce the modality differences. To reduce the intra-class differences, the model is further optimized by combining distribution consistency loss, label smoothing cross-entropy loss, and hetero-center triplet loss to reduce the intra-class distance and accelerate the model convergence. In this paper, we use the publicly available datasets RegDB and SYSU-MM01 for validation. The results show that the proposed approach in this paper reaches 68.11% mAP in All Search mode for the SYSU-MM01 dataset and 86.54% mAP in VtI mode for the RegDB dataset, with a performance improvement of 3.29% and 3.29%, respectively, which demonstrate the effectiveness of the proposed method.