When a medical infusion pump is used with a disposable pump infusion set for infusion, the change in infusion precision will affect the treatment effect, which will have a greater impact on burn patients and critically ill patients. Failure to infuse fluid in time will increase the mortality rate. Therefore, the factors influencing the infusion accuracy of disposable pump infusion sets under medium and high flow rate conditions are analyzed by conducting infusion experiments with flow rates of 100 ml/h, 200 ml/h, 500 ml/h and 1000 ml/h at temperatures of 10°C, 20°C and 30°C. The degree of correlation and influence between the infusion accuracy and the influencing factors are studied, and suggestions are provided. The results show that the grey correlation degrees between temperature, time and flow rate and infusion accuracy are all greater than 0.6, with obvious correlation. The correlation between temperature and infusion accuracy is the strongest. Temperature, time and flow rate all have significant effects on infusion accuracy and temperature has the greatest impact on infusion accuracy. When infusion is carried out under the condition of medium and high flow rate, the influence of temperature on the infusion accuracy should be considered first.
The lack of lighting in the space environment results in low segmentation accuracy and target lost. To solve this problem, a satellite component tracking method based on Few-Shot learning is proposed in this paper. First, we design a convolutional neural network, which inputs the first frame of mask information, and outputs the true label and important weight parameters. The Few-Shot learning incorporates the real labels, important weight parameters and the first frame feature information to generate target model parameters. Subsequent frames combine target model parameters with feature extraction, and finally output target mask after encoding and decoding. Our algorithm is evaluated on a new satellite partial component data set, and the simulation results show that the proposed method improves the segmentation accuracy and reduces the target loss rate compared to SiamMask under low-light environment.
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