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Classroom behavior is an important criterion for evaluating instructional efficacy. In comparison to other behaviors, the challenge of classroom behavior detection is primarily influenced by ambient light variables and the presence of too many targets to recognize, resulting in missed detection. Recent research has demonstrated that information about the human skeleton can be used to identify classroom conduct. As a result, we present an enhanced yolov5-based skeletal recognition system for detecting classroom behavior in this paper. First, the YOLOv5 detection algorithm is improved to extract target prospects for the problem of missed detection; then, the human skeleton information is obtained using the Alphapose framework; finally, the skeletal data is sent into a two-stream adaptive graph convolution network to allow for the accurate recognition of various classroom behaviors. According to extensive tests, the detection algorithm based on bone recognition improves detection accuracy and lowers the false detection rate.

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Basis weight and moisture content are two important parameters of paper's quality. We can preferably pledge quality of production, increase output, economize material, save energy sources through control the two parameter. Computer control system for controlling the basis weight and moisture content of paper machine has a very complicated configuration, more disturb, and great pure lag. I n this paper, we analyse the arts and crafts flow of the papermaking system, and model using Petri net. We have simulated and prove that the model is advantage. Ktyvordsbasis weight, moisture content, Petri net