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4,237 results on '"Zhang, Yonghong"'

4. Automatic laminectomy cutting plane planning based on artificial intelligence in robot assisted laminectomy surgery

Author

Li, Zhuofu, Zhang, Yonghong, Wang, Chengxia, Liu, Shanshan, Song, Xiongkang, Ji, Xuquan, Jiang, Shuai, Zhong, Woquan, Hu, Lei, and Li, Weishi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Objective: This study aims to use artificial intelligence to realize the automatic planning of laminectomy, and verify the method. Methods: We propose a two-stage approach for automatic laminectomy cutting plane planning. The first stage was the identification of key points. 7 key points were manually marked on each CT image. The Spatial Pyramid Upsampling Network (SPU-Net) algorithm developed by us was used to accurately locate the 7 key points. In the second stage, based on the identification of key points, a personalized coordinate system was generated for each vertebra. Finally, the transverse and longitudinal cutting planes of laminectomy were generated under the coordinate system. The overall effect of planning was evaluated. Results: In the first stage, the average localization error of the SPU-Net algorithm for the seven key points was 0.65mm. In the second stage, a total of 320 transverse cutting planes and 640 longitudinal cutting planes were planned by the algorithm. Among them, the number of horizontal plane planning effects of grade A, B, and C were 318(99.38%), 1(0.31%), and 1(0.31%), respectively. The longitudinal planning effects of grade A, B, and C were 622(97.18%), 1(0.16%), and 17(2.66%), respectively. Conclusions: In this study, we propose a method for automatic surgical path planning of laminectomy based on the localization of key points in CT images. The results showed that the method achieved satisfactory results. More studies are needed to confirm the reliability of this approach in the future.

Published
2023

6. Critical Review of Multi-focus Image Fusion Based on Deep Learning Method

Author

LI Ziqi, SU Yuxuan, SUN Jun, ZHANG Yonghong, XIA Qingfeng, YIN Hefeng

Subjects
deep learning, image fusion, multi-focus, Electronic computers. Computer science, QA75.5-76.95
Abstract

Multi-focus image fusion is an effective image fusion technology, which aims to combine source images from different focal planes of the same scene to obtain a good fusion result. This means that the fused image will focus on all focal planes, that is, it contains more abundant scene information. The development of deep learning promotes the great progress of image fusion, and the powerful feature extraction and reconstruction ability of neural network makes the fusion result promising. In recent years, more and more multi-focus image fusion methods based on deep learning have been proposed, such as convolutional neural network (CNN), generative adversarial network (GAN) and automatic encoder, etc. In order to provide effective reference for relevant researchers and technicians, firstly, this paper introduces the concept of multi-focus image fusion and some evaluation indicators. Then, it analyzes more than ten advanced methods of multi-focus image fusion based on deep learning in recent years, discusses the characteristics and innovation of various methods, and summarizes their advantages and disadvantages. In addition, it reviews the application of multi-focus image fusion technology in various scenes, including photographic visualization, medical diagnosis, remote sensing detection and other fields. Finally, it proposes some challenges faced by current multi-focus image fusion related fields and looks forward to future possible research trends.

Published
2024
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16. Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

Author

Liu, Qi, Yang, Zhiyun, Ji, Ru, Zhang, Yonghong, Bilal, Muhammad, Liu, Xiaodong, Vimal, S, and Xu, Xiaolong

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.2, I.4, I.5
Abstract

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed; particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned field., Comment: This article has been accepted for publication in IEEE Systems, Man and Cybernetics Magazine

Published
2023
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19. Design and Experimental Analysis of Propeller Variable-Pitch Mechanism for Near-Space Aircraft

Author

Liu, Bin, Ge, Wenjie, Zhang, Yonghong, Wang, Zhuo, Li, Yuzhu, Chinese Society of Aeronautics and Astronautics, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, and Xu, Jinyang, Editorial Board Member

Published
2024
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31. Modeling and calculation of the three-dimensional reactor core and comparative analysis of various homogenization schemes for asymmetrical fuel assemblies based on BEAVRS 2.02

Author

YUAN Xianbao, LIU Zenghao, ZHANG Binhang, ZHANG Yonghong, TANG Haibo, YANG Senquan, and XIAO Yunlong

Subjects
two-step approach, beavrs, homogenization techniques, dragon/donjon, simulation, Nuclear engineering. Atomic power, TK9001-9401
Abstract

BackgroundThe two-step approach, as the major method in reactor numerical calculation, faces the challenge of accuracy in calculating high-fidelity models because of limitations imposed by simplified assumptions such as assembly homogenization and the core diffusion calculation.PurposeThis study aims to validate the applicability of the two-step method in high-fidelity numerical simulation calculations and homogenization scheme of asymmetrical assemblies.MethodsBased on the Benchmark for Evaluation And Validation of Reactor Simulations Rev.2.0.2 (BEAVRS 2.02), the deterministic codes DRAGON5 and DONION5 were used to calculate the BEAVRS benchmark in the hot zero-power state. Firstly, a transport calculation was performed with DRAGON5 on the fuel assemblies. Then, the complete core calculation was done with DONJON5 using diffusion theory. Finally, the calculation errors of traditional homogenization, one-time multi zone homogenization, and multiple multi zone homogenization schemes were compared.ResultsThe results show that these calculations are comparable to the accuracy of the benchmark, with the deviation of critical boron concentration being less than 5×10-5 and the error of the control rod value calculation being within 5×10-4. Comparison results of the fission rate indicate that the average deviation of the assemblies near the asymmetric fuel assemblies is reduced from 5.62% to 3.345% using one-time multi zone homogenization scheme.ConclusionsThis study verifies the applicability of the two-step approach in high-fidelity numerical simulation calculations.

Published
2024
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36. Transformer-Unet: Raw Image Processing with Unet

Author

Sha, Youyang, Zhang, Yonghong, Ji, Xuquan, and Hu, Lei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical image segmentation have drawn massive attention as it is important in biomedical image analysis. Good segmentation results can assist doctors with their judgement and further improve patients' experience. Among many available pipelines in medical image analysis, Unet is one of the most popular neural networks as it keeps raw features by adding concatenation between encoder and decoder, which makes it still widely used in industrial field. In the mean time, as a popular model which dominates natural language process tasks, transformer is now introduced to computer vision tasks and have seen promising results in object detection, image classification and semantic segmentation tasks. Therefore, the combination of transformer and Unet is supposed to be more efficient than both methods working individually. In this article, we propose Transformer-Unet by adding transformer modules in raw images instead of feature maps in Unet and test our network in CT82 datasets for Pancreas segmentation accordingly. We form an end-to-end network and gain segmentation results better than many previous Unet based algorithms in our experiment. We demonstrate our network and show our experimental results in this paper accordingly.

Published
2021

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