Infusion pumps are commonly used clinical medical equipment which can precisely control the speed of infusion and deliver nutrients or medications to the patients. The infusion accuracy of infusion pumps seriously affects the life and health safety of patients, and infusion accuracy is the most important factor to assess the reliability of infusion pumps. Representative researches on the factors influencing the infusion accuracy of medical infusion pumps are summarized systematically. Influencing factors are divided into four aspects: external environmental factors, internal structure of pump body (mechanical accuracy), auxiliary consumables, and parameter settings. This paper details the current status of research in these four areas and provides an outlook on future research directions for quality control of infusion pumps.
Acquisition of target information in satellite interactive missions plays a key role in aerospace field. Tracking and Segmentation of Satellite Components are faced with some problems, such as insufficient illumination in space environment and occlusion of satellite components. This paper presents an effective approach to achieve video object segmentation under low light and occlusion of satellite components. Our approach is based on Rethinking Space-Time Networks with Improved Memory Coverage(STCN), and it can track and segment satellite components in video sequences. To solve the problems of target loss and low light in the space environment during the overturning of satellite components, we propose a position information encoding strategy. We improve the generalization ability of the model for image position information by embedding the position information matrix. Finally, we trained the model using the DAVIS dataset and the satellite dataset we built. Experiment results verify that our model improves 3.9% of J&F compared to STCN and its speed can reach 20+ frames per second(FPS).
This paper presents safe zone detection and tracking methods only based on vision for landing spacecraft on celestial bodies. Digital elevation model is used to generate a lunar surface image dataset. A modified residual-convolutional neural network is trained to extract craters from the trained binary images. For image sequences, safe landing areas without craters are recognized using Hough detection. Furthermore, when the camera loses the detected safe zone because of a violent shake, and then the safe zone moves back to the camera, our method can recognize it as beginning. The experimental proofs that our method improves the accuracy of crater identification and it can detect safe landing areas in the image sequence, In the case of large camera movement, the proposed method provides robust tracking results.
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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