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199 results on '"Zhong Lijun"'

8. Keypoints Filtrating Nonlinear Refinement in Spatial Target Pose Estimation with Deep Learning

Author

Zhong, Lijun, Chen, Shengpeng, Jin, Zhi, Guo, Pengyu, and Yang, Xia

Abstract

Spatial target pose estimation with deep learning has garnered increasing attention in recent years. However, the existing methods in this field suffer from poor generalization. In this study, we propose a robust and reliable pose estimation method for spatial targets. The method aims to achieve keypoints filtrating. It involves a detection network tasked with identifying the target area, while the subsequent stage employs a classification network to regress keypoints from the detected target area. To improve the accuracy of pose estimation, we leverage spatial target geometric constraints to formulate 2-D–3-D keypoints equations for an initial pose. Then, we create a nonlinear optimization equation based on the confidence of 2-D keypoints and accomplish nonlinear refinement. We conduct extensive experiments on commonly used datasets and demonstrate the effectiveness of the proposed method. Furthermore, thanks to the effectiveness of keypoints filtrating and nonlinear refinement, the proposed method is robust with challenging scenarios and domain bias.

Published
2024
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9. A Hybrid Deep Learning-Based Framework for Chip Packaging Fault Diagnostics in X-Ray Images

Author

Wang, Jie, Li, Gaomin, Bai, Haoyu, Yuan, Guixin, Li, Xuan, Lin, Bin, Zhong, Lijun, and Zhang, Xiaohu

Abstract

In the testing of chips, defect diagnostics in X-ray images of packaging chips is mainly performed by humans, which is time-consuming and inefficient. To overcome the abovementioned problems, a novel intelligent defect diagnostics system based on hybrid deep learning for chip X-ray images was proposed. The system consists of four successive stages: image segmentation and normalization, image reconstruction and defect detection, contour matching, and qualification diagnosis. The first stage is used to localize the external contours of the target chip and remove extraneous backgrounds through the improved UNet. Then, considering the variety of defects and the complexity of labeling, an unsupervised learning model is designed to reconstruct defect-free images to detect defects, which requires only normal samples for training. Third, the multicomponent template matching based on structural prior is used to localize the internal contours of the chip. In the final stage, the qualification is diagnosed based on the previous results through the Floyd–Warshall algorithm. The effectiveness and robustness of the proposed methods are verified by experiments on real-world inspection lines. The experimental results demonstrate that the developed system can successfully perform fault diagnostics tasks, achieving a judgment accuracy of 92.5%.

Published
2024
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11. Real-Time Interpretation Method for Shooting-Range Image Based on Position Prediction

Author

Zhong, Lijun, Yu, Qifeng, Zhou, Jiexin, Zhang, Xiaohu, Lu, Yani, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhao, Yao, editor, Barnes, Nick, editor, Chen, Baoquan, editor, Westermann, Rüdiger, editor, Kong, Xiangwei, editor, and Lin, Chunyu, editor

Published
2019
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27. Creative Arts Therapy on the Mental Health of Students in Smoke-free Universities

Author

Chang Wen-Lung, Zhong Lijun, Ma Liwen, Chen Yi-Mei, Chang We-Hsuan, and Wu Peng

Subjects
Health (social science), Health Policy, Public Health, Environmental and Occupational Health
Abstract

This study explored the mental health of students in smoke-free universities and the influence of creative arts therapy during COVID-19 pandemic period. 1) The survey was conducted from March to April, 2020, and students in smoke-free universities were invited to fill out and forward the questionnaire through social software such as QQ and WeChat. The subjects of the survey are 800 students studying in smoke-free colleges and universities in South China. 2) The experimental research method was adopted. 40 students in smoke-free universities suffering from psychological stress and depression caused by COVID-19 pandemic were divided into 20 experimental groups and 20 control groups. The control group received no intervention treatment, while the experimental group received creative arts therapy. The survey data of mental health of students in smoke-free universities, mental health assessment standard data and self-esteem scale (SES) were observed. Results: A total of 850 questionnaires were collected, of which 839 were valid, and the effective response rate was 98.7%. As for the scores of the new psychological status of students in smoke-free universities during covid-19 pandemic period and mental health of students in smoke-free universities, the slight degree accounted for the highest proportion (39.7%), followed by the moderate degree of 34.2%. And 47% of the respondents felt lonely, decadent, irritable, emotionally fluctuating and difficult to concentrate. the results of self-esteem scale (SES) showed that there was no significant difference between the two groups before intervention (t=1.782, P=0.08>0.05). After intervention, the score of the study group was 42.74±2.69 points, which was significantly higher than that of the control group (36.11±4.58 points), and the difference was statistically significant (Z=-1.192, P=0.04

Published
2021

29. YOLO-Xray: A Bubble Defect Detection Algorithm for Chip X-ray Images Based on Improved YOLOv5.

Author

Wang, Jie, Lin, Bin, Li, Gaomin, Zhou, Yuezheng, Zhong, Lijun, Li, Xuan, and Zhang, Xiaohu

Subjects
X-ray imaging, OBJECT recognition (Computer vision), ALGORITHMS, X-ray detection, PROBLEM solving
Abstract

In the manufacturing of chips, the accurate and effective detection of internal bubble defects of chips is essential to maintain product reliability. In general, the inspection is performed manually by viewing X-ray images, which is time-consuming and less reliable. To solve the above problems, an improved bubble defect detection model YOLO-Xray based on the YOLOv5 algorithm for chip X-ray images is proposed. First, the chip X-ray images are preprocessed by image segmentation to construct the chip X-ray defect dataset, namely, CXray. Then, in the input stage, the K-means++ algorithm is used to re-cluster the CXray dataset to generate the anchors suitable for our dataset. In the backbone network, a micro-scale detection head is added to improve the capabilities for small defect detection. In the neck network, the bi-direction feature fusion idea of BiFPN is used to construct a new feature fusion network based on the improved backbone to fuse the semantic features of different layers. In addition, the Quality Focal Loss function is used to replace the cross-entropy loss function to solve the imbalance of positive and negative samples. The experimental results show that the mean average precision (mAP) of the YOLO-Xray algorithm on the CXray dataset reaches 93.5%, which is 5.1% higher than the original YOLOv5. Meanwhile, the YOLO-Xray algorithm achieves state-of-the-art detection accuracy and speed compared with other mainstream object detection models. This shows the proposed YOLO-Xray algorithm can provide technical support for bubble defect detection in chip X-ray images. The CXray dataset is also open and available at CXray. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Additional file 2 of Predictive value of proteomic markers for advanced rectal cancer with neoadjuvant chemoradiotherapy

Author

Wang, Hanyang, Ji, Dengbo, Tian, Huifang, Gao, Zhaoya, Song, Can, Jia, Jinying, Cui, Xinxin, Zhong, Lijun, Shen, Jing, and Gu, Jin

Abstract

Additional file 2: Supplementary Figure 1. Sera levels of several proteins in response and resistant to nCRT groups of rectal cancer. (A-B) Scatter plots of baseline sera GELS and HEP2 concentrations obtained from pCR and non-pCR patients using the PRM. (C-D) Scatter plots of baseline sera GELS and HEP2 concentrations obtained from the responding and the non-responding patients using the PRM. (E-F) Scatter plots of post-nCRT sera HEP2 and APOH concentrations obtained from the responding and the non-respondingpatients using the PRM.

Published
2022
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41. Curcumin Alleviates the Senescence of Canine Bone Marrow Mesenchymal Stem Cells during In Vitro Expansion by Activating the Autophagy Pathway

Author

Deng, Jiaqiang, primary, Ouyang, Ping, additional, Li, Weiyao, additional, Zhong, Lijun, additional, Gu, Congwei, additional, Shen, Liuhong, additional, Cao, Suizhong, additional, Yin, Lizi, additional, Ren, Zhihua, additional, Zuo, Zhicai, additional, Deng, Junliang, additional, Yan, Qigui, additional, and Yu, Shumin, additional

Published
2021
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43. The business model of 5G base station energy storage participating in demand response

Author

Zhong Lijun, Ling Zhi, Shen Haocong, Ren Baoping, Shi Minda, and Huang Zhenyu

Abstract

To achieve the goal of “carbon peak, carbon neutralization”, the proportion of renewable energy access will continue to increase, which will bring a severe test to the balance adjustment ability of the new power system, and the demand for flexible adjustment and real-time balance of the power system will continue to increase. However, pumped storage power stations and grid-side energy storage facilities, which are flexible peak-shaving resources, have relatively high investment and operation costs. 5G base station energy storage to participate in demand response can share the cost of energy storage system construction by power companies and communication operators to achieve a win-win situation between the communication system and the power system. Based on the analysis of the feasibility and incremental cost of 5G communication base station energy storage participating in demand response projects, combined with the interest interaction mechanism of all parties in the project, this paper proposes a business model for 5G energy storage to participate in the grid collaboration and interaction to improve the profit model of various market players, thereby promoting the penetration rate of the project.

Published
2022

46. Circulating lead modifies hexavalent chromium-induced genetic damage in a chromate-exposed population: An epidemiological study

Author

Hu, Guiping, primary, Long, Changmao, additional, Hu, Lihua, additional, Xu, Benjamin Ping, additional, Chen, Tian, additional, Gao, Xiaoyin, additional, Zhang, Yali, additional, Zheng, Pai, additional, Wang, Li, additional, Wang, Tiancheng, additional, Yan, Lailai, additional, Yu, Shanfa, additional, Zhong, Lijun, additional, Chen, Wei, additional, and Jia, Guang, additional

Published
2021
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48. Controlling the thermally activated delayed fluorescence of axially chiral organic emitters and their racemate for information encryption

Author

Xu, Bingjia, primary, Song, Zicun, additional, Zhang, Minmin, additional, Zhang, Qingqing, additional, Jiang, Long, additional, Xu, Cao, additional, Zhong, Lijun, additional, Su, Changlin, additional, Ban, Qiqi, additional, Liu, Cong, additional, Sun, Fengqiang, additional, Zhang, Yi, additional, Chi, Zhenguo, additional, Zhao, Zujin, additional, and Shi, Guang, additional

Published
2021
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50. Noncontact deflection measurement for bridge through a multi‐UAVs system.

Author

Zhuge, Sheng, Xu, Xiangpeng, Zhong, Lijun, Gan, Shuwei, Lin, Bin, Yang, Xia, and Zhang, Xiaohu

Subjects
BRIDGES, STANDARD deviations, BRIDGE foundations & piers, DRONE aircraft
Abstract

Deflection measurement is the research focus of health monitoring for bridges during the operation period. This study develops a contactless measurement technique to monitor the bridge deflection, leveraging visual information from a team of unmanned aerial vehicles (UAVs). On the basis of the collinearity of the laser spots projected on the plane by the coplanar laser indicator, we can eliminate the motion of UAV, and calculate the vertical displacement of the position to be measured relative to the bridge pier. In the proposed method, the center of the laser spot is extracted through a method based on deep learning, and an algorithm based on scale‐invariant features registration was developed to track the feature points of the bridge in the image sequence. According to the algorithm, we demonstrate the accuracy and feasibility of our approach through simulation and simulated bridge experiments. The result shows that the root mean squared error (RMSE) of measurement through our technique is less than 0.5 mm in the laboratory conditions. In addition, the limits and scalability of the presented method have been explored through a field experiment. [ABSTRACT FROM AUTHOR]

Published
2022
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