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Titlebook: Intelligent Robotics and Applications; 16th International C Huayong Yang,Honghai Liu,Zhiyong Wang Conference proceedings 2023 The Editor(s)

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樓主: Prehypertension
41#
發(fā)表于 2025-3-28 16:22:39 | 只看該作者
Design and Control of a Two-Segment Rotatable Wire-Driven Flexible Arm paper, we performed kinematic modeling and control of the manipulator, achieved different trajectory movements, and obtained excellent experimental results of the manipulator‘s performance. This demonstrates the rationality of the manipulator‘s design, and enhances its control performance, providin
42#
發(fā)表于 2025-3-28 21:52:50 | 只看該作者
43#
發(fā)表于 2025-3-29 01:44:02 | 只看該作者
44#
發(fā)表于 2025-3-29 03:13:42 | 只看該作者
45#
發(fā)表于 2025-3-29 07:18:49 | 只看該作者
46#
發(fā)表于 2025-3-29 13:42:47 | 只看該作者
Prior Region Mask R-CNN for Thyroid Nodule Segmentation in Ultrasound Imageso the FPN and based on this performed spatial attention. In addition, a channel attention mechanism has been applied to screen for important features. Furthermore, RPN based on the prior region has also been proposed to reduce interference from surrounding tissues. In this paper, our data set has 15
47#
發(fā)表于 2025-3-29 17:07:22 | 只看該作者
48#
發(fā)表于 2025-3-29 21:10:54 | 只看該作者
A Modified BiSeNet for Spinal Segmentationc residual structure is introduced to enhance the context extractor’s effectiveness. For the spatial path, we introduce a novel multi-scale convolution attention module based on the SegNext structure. These main contributions in our framework improve the segmentation effectiveness by capturing long-
49#
發(fā)表于 2025-3-30 03:58:31 | 只看該作者
Retinal Vascular Segmentation Based on Depth-Separable Convolution and Attention Mechanismsel for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract mo
50#
發(fā)表于 2025-3-30 07:02:51 | 只看該作者
SW-YOLO: Improved YOLOv5s Algorithm for Blood Cell Detection detection algorithms such as Faster-RCNN, YOLOv4 and YOLOv5s, SW-YOLO improves to 99.5%, 95.3% and 93.3% mAP on the blood cell dataset BCCD for white blood cells, red blood cells and platelets respectively. The experimental results are eximious and the algorithm is highly practical for blood cell d
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