Hybrid Compression Optimization Based Rapid Detection Method for Non-Coal Conveying Foreign Objects.

The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6~0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development.

Construction of SIRT1 gene shRNA lentivirus vector and its effect on the proliferation of breast cancer cells.

Current experiment aimed to investigate the construction of the SIRT1 gene shRNA lentivirus vector and its effect on proliferation of breast cancer cells. Altogether 80 cases of breast cancer tissues and 80 cases of normal adjacent tissues were collected. qPCR was used for detecting SIRT1 expression. Western blot was used to detect the expression of EMT marker protein. The effect of lentivirus infected sh-SIRT1 on the cell biological function of SK-BR-3 and MDA-MB-231 cells was detected. MTT assay was used to detect cell activity, Transwell cell was used to detect cell invasion and migration, and cell apoptosis detected by flow cytometry. Compared with normal tissues adjacent to cancer, the expression of SIRT1 in cancer tissues increased significantly. Compared with human breast epithelial cells (MCF 10A), SIRT1 expression in breast cancer cells (MDA-MB-231, SK-BR-3) increased significantly. The above results showed that SIRT1 was significant greatly expressed in breast cancer. Compared with the sh-Control group, the cell activity, invasion and migration of the sh-SIRT1 group were enhanced, while cell apoptosis was weakened. In the sh-SIRT1 group infected by lentivirus, cell activity, cell invasion and migration decreased, while cell apoptosis increased. Compared with sh-Control, the expression of α-catenin, PTEN and E-cadherin in the sh-SIRT1 group in SK-BR-3 and MDA-MB-231 cells was down-regulated, while the expression of N- cadherin, ß-catenin and Vimentin was up-regulated. Compared with sh-Control, the expression of α-catenin, PTEN and E-cadherin in the sh-SIRT1 group infected by lentivirus was up-regulated, while the expression of N- cadherin, ß-catenin and Vimentin was down-regulated. To sum up, SIRT1 is highly expressed in breast cancer cells. The proliferation of breast cancer cells was inhibited after lentivirus infection with sh-SIRT1.