Cell aspiration is a common micro-manipulation technique for cell transfer, particularly in in vitro fertilization (IVF) procedures. The minuscule volume of a cell (pL) and limited damping provided by the medium make it challenging to accurately and quickly aspirate a cell to the desired position inside the micropipette. Experienced clinicians intentionally insert an air segment inside the micropipette in advance to make the aspiration easier. Nevertheless, the unclear damping effects and the varying initial length of the air segment in each aspiration pose difficulties for most operators. Inadequate judgment and response may lead to overshoot or even loss of the cell. This paper constructs a nonlinear dynamics model to elucidate the cell motion inside a micropipette containing an inserted air segment. The model reveals the impact of the air segment. A model-based controller is designed to facilitate the accurate aspiration of human sperm to a desired position, incorporating an estimated initial length of the air segment. Experiments were conducted to quantitatively evaluate the performance of both the model and the controller involving various initial air segment lengths. The results demonstrated a 100 % success rate in 50 sperm aspiration experiments, achieving an average positional accuracy within$\pm 2$pixels and an average settling time of 5.89 seconds.

Abstract Aiming at a series of problems such as detection accuracy, calculation blocking, display delay, and so on in the ship detection of surveillance video, an improved YOLOv5 algorithm is proposed in this paper. First, to improve the detection performance, it is proposed to optimize the anchor box algorithm in the YOLOv5 network according to the ship target characteristics. Then, the t‐SNE algorithm is used to reduce and visualize the data set label information and perform weighted analysis on the processed features for low‐dimensional data. The mapped kernel k‐means clustering algorithm adaptively selects a more appropriate anchor box and considers the detection performance of large and small ship targets. Secondly, to improve the problem of computational blocking and delay, the BN scaling factor γ is used to compress the YOLOv5 network, so that the model can be reduced without reducing the detection performance. The optimized YOLOv5 framework is trained on the self‐integrated data set. The accuracy of the algorithm is increased by 2.34%, and the ship detection speed reaches 98 fps and 20 fps in the server environment and the low computing power version (Jetson nano), respectively.