Qipeng Chen
Impact in
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- Industrial Vision Systems and Defect Detection
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- Advanced Neural Network Applications
Papers in
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- Advanced Neural Network Applications 6
- Co-authors
- Haisong Huang (8 shared papers)Yiting Li (6 shared papers)Qingsheng Xie (3 shared papers)Liguo Yao (3 shared papers)Qingsong Fan (3 shared papers)Qingni Yuan (1 shared paper)Guohua Liu (7 shared papers)Dong Huang (1 shared paper)
In The Last Decade
Qipeng Chen
33 papers receiving 555 citations
Peers
Comparison fields: 5 of 110
- Industrial and Manufacturing Engineering 168
- Computer Vision and Pattern Recognition 170
- Media Technology 36
- Artificial Intelligence 109
- Computational Mathematics 2
Countries citing papers authored by Qipeng Chen
This map shows the geographic impact of Qipeng Chen'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 Qipeng Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qipeng Chen more than expected).
Fields of papers citing papers by Qipeng Chen
This network shows the impact of papers produced by Qipeng Chen. 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 Qipeng Chen. The network helps show where Qipeng Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Qipeng Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 239 | |
| 2 | 2019 | 58 | |
| 3 | 2021 | 54 | |
| 4 | 2022 | 30 | |
| 5 | 2021 | 28 | |
| 6 | 2023 | 19 | |
| 7 | 2021 | 15 | |
| 8 | 2012 | 14 | |
| 9 | 2019 | 14 | |
| 10 | 2023 | 11 | |
| 11 | 2021 | 9 | |
| 12 | 2015 | 9 | |
| 13 | 2019 | 7 | |
| 14 | 2014 | 7 | |
| 15 | 2021 | 6 | |
| 16 | 2023 | 5 | |
| 17 | 2023 | 5 | |
| 18 | 2021 | 5 | |
| 19 | 2021 | 4 | |
| 20 | 2021 | 4 |
About Qipeng Chen
Qipeng Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Industrial and Manufacturing Engineering, Organic Chemistry and Experimental and Cognitive Psychology, having authored 39 papers that have together received 573 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (9 papers), Advanced Neural Network Applications (6 papers), Asymmetric Hydrogenation and Catalysis (5 papers), Cognitive Abilities and Testing (3 papers), Advanced machining processes and optimization (2 papers), Behavioral Health and Interventions (2 papers), Tensor decomposition and applications (2 papers) and Psychometric Methodologies and Testing (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (168 citations), Computer Vision and Pattern Recognition (170 citations), Media Technology (36 citations), Artificial Intelligence (109 citations) and Computational Mathematics (2 citations). Qipeng Chen has collaborated with scholars based in China, Malaysia and Japan. Frequent co-authors include Haisong Huang, Yiting Li, Qingsheng Xie, Liguo Yao, Qingsong Fan, Qingni Yuan, Guohua Liu, Dong Huang, Chengyi Wang and Tao Hu. Their work appears in journals such as Symmetry, Scientific Reports, Electronics, IEEE Access and Behavior Research Methods.
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.