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Due to the complex underground environment of coal mines, the unsafe behaviors of miners are likely to lead safety accidents. Therefore, research on underground abnormal behavior recognition methods based on video images is gradually gaining attention. This paper proposes an underground abnormal behavior recognition method based on an optimized Alphapose-ST-GCN. First, an image set captured in underground monitoring video is defogged and enhanced by the CycleGAN. Second, the Alphapose target detection is optimized using the LTWOA-Tiny-YOLOv3 model. Third, the ST-GCN is used for abnormal behavior recognition. The image quality of the dataset before and after a CycleGAN enhancement is compared, the convergence curves of LTWOA under four test functions are compared, and the mean average accuracy mAP of the LTWOA-Tiny-YOLOv3 model is evaluated. Finally, the performance of the proposed method is compared with other detection algorithms. The results show that CycleGAN significantly improves the quality of the dataset images. The whale optimization algorithm improved by the logistic-tent chaos mapping has a more significant convergence effect than the other optimization algorithms, and the LTWOA-Tiny-YOLOv3 model has a better recognition accuracy of 9.1% in mAP compared with the unoptimized model. The underground abnormal detection model proposed in this paper achieves an 82.3% accuracy on the coal mine underground behavior dataset.

This paper focuses on the study of low density parity check (LDPC) coded Multiple-Input Multiple-Output (MIMO) systems for high throughput wireless communication. The evaluation and implementation of quasi-cyclic LDPC (QC-LDPC) code in MIMO-OFDM systems are presented. QC-LDPC has comparative error correcting performance with random LDPC and considerably reduces complexity. We evaluate the communication performance of the QC-LDPC coded system based on the IEEE802.11n standard using a 2x2 MIMO scheme.