Ning Yan
Impact in
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- Industrial Vision Systems and Defect Detection
- Manufacturing Process and Optimization
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- Optical measurement and interference techniques
- Image and Object Detection Techniques
- Advanced Neural Network Applications
Papers in
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- Optical measurement and interference techniques 4
- Advanced Vision and Imaging 3
- Image and Object Detection Techniques 2
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- Industrial Vision Systems and Defect Detection 5
- Co-authors
- Zhonghe Ren (1 shared paper)You Wu (1 shared paper)Fengzhou Fang (1 shared paper)Xiaodong Zhang (4 shared papers)Zexiao Li (3 shared papers)Xiaodong Zhang (2 shared papers)Nana Li (1 shared paper)Linlin Zhu (1 shared paper)
In The Last Decade
Ning Yan
9 papers receiving 461 citations
Ning Yan's Hit Papers
Peers
Comparison fields: 5 of 72
- Industrial and Manufacturing Engineering 278
- Computer Vision and Pattern Recognition 181
- Media Technology 41
- Geology 25
- Computational Mechanics 68
Countries citing papers authored by Ning Yan
This map shows the geographic impact of Ning Yan'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 Ning Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ning Yan more than expected).
Fields of papers citing papers by Ning Yan
This network shows the impact of papers produced by Ning Yan. 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 Ning Yan. The network helps show where Ning Yan may publish in the future.
Co-authors
The 11 scholars most cited alongside Ning Yan, 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 | State of the Art in Defect Detection Based on Machine Vision Hit paper breakdown → | 2021 | 411 |
| 2 | 2021 | 24 | |
| 3 | 2022 | 15 | |
| 4 | 2021 | 12 | |
| 5 | 2020 | 6 | |
| 6 | 2022 | 5 | |
| 7 | 2019 | 2 | |
| 8 | 2022 | 1 | |
| 9 | 2024 | 1 |
About Ning Yan
Ning Yan is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Computational Mechanics, Mechanics of Materials and Artificial Intelligence, having authored 9 papers that have together received 477 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (5 papers), Optical measurement and interference techniques (4 papers), Advanced Vision and Imaging (3 papers), Surface Roughness and Optical Measurements (3 papers), Image and Object Detection Techniques (2 papers), Textile materials and evaluations (1 paper), Second Language Acquisition and Learning (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (278 citations), Computer Vision and Pattern Recognition (181 citations), Media Technology (41 citations), Geology (25 citations) and Computational Mechanics (68 citations). Ning Yan has collaborated with scholars based in China and Ireland. Frequent co-authors include Zhonghe Ren, You Wu, Fengzhou Fang, Xiaodong Zhang, Zexiao Li, Xiaodong Zhang, Nana Li, Linlin Zhu, Xudong Yang and Liangliang Chen. Their work appears in journals such as Applied Mathematics and Nonlinear Sciences, IEEE Transactions on Instrumentation and Measurement, Applied Sciences, International Journal of Precision Engineering and Manufacturing-Green Technology and Applied Optics.
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.