Na Dong
Impact in
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- Risk and Safety Analysis
Papers in
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- Advanced Control Systems Optimization 11
- Adaptive Control of Nonlinear Systems 10
- Iterative Learning Control Systems 7
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- Robotic Path Planning Algorithms 8
- Co-authors
- Zhongke Gao (11 shared papers)Ai‐Guo Wu (20 shared papers)C.H. Wu (11 shared papers)Jianfang Chang (12 shared papers)Yuxuan Yang (4 shared papers)J. F. Chang (1 shared paper)Liang Zhao (1 shared paper)Pengcheng Xu (1 shared paper)
- Journals
- Nonlinear Dynamics (5 papers)Expert Systems with Applications (4 papers)Neurocomputing (2 papers)Applied Soft Computing (2 papers)Computer Communications (2 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Na Dong
114 papers receiving 1.9k citations
Peers
Comparison fields: 5 of 158
- Statistics, Probability and Uncertainty 118
- Computer Vision and Pattern Recognition 326
- Artificial Intelligence 455
- Management Science and Operations Research 171
- Control and Systems Engineering 316
Countries citing papers authored by Na Dong
This map shows the geographic impact of Na Dong'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 Na Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Na Dong more than expected).
Fields of papers citing papers by Na Dong
This network shows the impact of papers produced by Na Dong. 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 Na Dong. The network helps show where Na Dong may publish in the future.
Co-authors
The 25 scholars most cited alongside Na Dong, 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 127 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 217 | |
| 2 | 2020 | 183 | |
| 3 | 2016 | 156 | |
| 4 | 2019 | 130 | |
| 5 | 2015 | 60 | |
| 6 | 2019 | 60 | |
| 7 | 2012 | 53 | |
| 8 | 2020 | 51 | |
| 9 | 2018 | 50 | |
| 10 | 2019 | 49 | |
| 11 | 2021 | 41 | |
| 12 | 2016 | 40 | |
| 13 | 2020 | 40 | |
| 14 | 2021 | 39 | |
| 15 | 2021 | 38 | |
| 16 | 2022 | 35 | |
| 17 | 2016 | 33 | |
| 18 | 2016 | 33 | |
| 19 | 2016 | 27 | |
| 20 | 2021 | 24 |
About Na Dong
Na Dong is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering and Aerospace Engineering, having authored 127 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (11 papers), Adaptive Control of Nonlinear Systems (10 papers), EEG and Brain-Computer Interfaces (10 papers), Robotic Path Planning Algorithms (8 papers), Robotics and Sensor-Based Localization (8 papers), Adaptive Dynamic Programming Control (7 papers), Iterative Learning Control Systems (7 papers) and AI in cancer detection (7 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (118 citations), Computer Vision and Pattern Recognition (326 citations), Artificial Intelligence (455 citations), Management Science and Operations Research (171 citations) and Control and Systems Engineering (316 citations). Na Dong has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Zhongke Gao, Ai‐Guo Wu, C.H. Wu, Jianfang Chang, Yuxuan Yang, J. F. Chang, Liang Zhao, Pengcheng Xu, Hu‐Chen Liu and Long Liu. Their work appears in journals such as Nonlinear Dynamics, Expert Systems with Applications, Neurocomputing, Applied Soft Computing and Computer Communications.
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.