Jingjun Gu
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
Papers in
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- Gear and Bearing Dynamics Analysis 5
- Mechanical Engineering and Vibrations Research 4
- Hydraulic and Pneumatic Systems 3
- Co-authors
- Jiajun Bu (7 shared papers)Sheng Zhou (3 shared papers)Lei Wu (1 shared paper)Xin Shen (1 shared paper)Zhe Liu (1 shared paper)Frédéric Sansoz (3 shared papers)Song Wang (2 shared papers)Jin Zhang (1 shared paper)
- Journals
- Carbon (2 papers)Neural Networks (2 papers)Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (1 paper)International Journal of Neural Systems (1 paper)IEEE Transactions on Medical Imaging (1 paper)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Jingjun Gu
16 papers receiving 249 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 88
- Health Informatics 5
- Radiology, Nuclear Medicine and Imaging 55
- Neurology 19
- Artificial Intelligence 77
Countries citing papers authored by Jingjun Gu
This map shows the geographic impact of Jingjun Gu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jingjun Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingjun Gu more than expected).
Fields of papers citing papers by Jingjun Gu
This network shows the impact of papers produced by Jingjun Gu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jingjun Gu. The network helps show where Jingjun Gu may publish in the future.
Co-authors
The 25 scholars most cited alongside Jingjun Gu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 129 | |
| 2 | 2020 | 21 | |
| 3 | 2022 | 18 | |
| 4 | 2013 | 14 | |
| 5 | 2024 | 12 | |
| 6 | 2019 | 10 | |
| 7 | 2021 | 10 | |
| 8 | 2018 | 7 | |
| 9 | 2021 | 6 | |
| 10 | 2015 | 6 | |
| 11 | 2013 | 6 | |
| 12 | 2024 | 4 | |
| 13 | 2019 | 3 | |
| 14 | 2017 | 2 | |
| 15 | Holospectrum analysis for bearing cage behaviour | 2015 | 1 |
| 16 | 2023 | 1 | |
| 17 | 2025 | 0 | |
| 18 | 2024 | 0 | |
| 19 | 2024 | 0 | |
| 20 | 2024 | 0 |
About Jingjun Gu
Jingjun Gu is a scholar working on Mechanical Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications and Control and Systems Engineering, having authored 20 papers that have together received 250 indexed citations. Recurring topics across this work include Gear and Bearing Dynamics Analysis (5 papers), Mechanical Engineering and Vibrations Research (4 papers), COVID-19 diagnosis using AI (3 papers), Hydraulic and Pneumatic Systems (3 papers), Carbon Nanotubes in Composites (3 papers), Graphene research and applications (2 papers), Advanced Neural Network Applications (2 papers) and Artificial Intelligence in Healthcare (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (88 citations), Health Informatics (5 citations), Radiology, Nuclear Medicine and Imaging (55 citations), Neurology (19 citations) and Artificial Intelligence (77 citations). Jingjun Gu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Jiajun Bu, Sheng Zhou, Lei Wu, Xin Shen, Zhe Liu, Frédéric Sansoz, Song Wang, Jin Zhang, Haishuai Wang and Ming Li. Their work appears in journals such as Carbon, Neural Networks, Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology, International Journal of Neural Systems and IEEE Transactions on Medical Imaging.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.