Na Dong
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- Robotic Path Planning Algorithms 8
- Artificial Intelligence top 5%
- AI in cancer detection 8
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- Advanced Control Systems Optimization 11
- Adaptive Control of Nonlinear Systems 10
- Iterative Learning Control Systems 7
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- EEG and Brain-Computer Interfaces 10
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- Robotics and Sensor-Based Localization 8
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- Adaptive Dynamic Programming Control 7
Na Dong
115 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 160
- Statistics, Probability and Uncertainty 117
- Computer Vision and Pattern Recognition 314
- Artificial Intelligence 454
- Management Science and Operations Research 170
- Control and Systems Engineering 309
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 12 | |
| 8 | 2024 | 7 | |
| 9 | 2024 | 1 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 8 | |
| 12 | 2021 | 37 | |
| 13 | 2021 | 10 | |
| 14 | 2019 | 48 | |
| 15 | 2019 | 2 | |
| 16 | 2019 | 22 | |
| 17 | 2018 | 50 | |
| 18 | 2015 | 0 | |
| 19 | Improved adaptive data-driven control for discrete nonlinear systems | 2013 | 1 |
| 20 | An Effective Combination of Different Order N-grams | 2003 | 3 |
About Na Dong
Na Dong is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Structural Biology and Computational Theory and Mathematics, having authored 128 papers that have together received 1.9k 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), AI in cancer detection (8 papers), Iterative Learning Control Systems (7 papers) and Adaptive Dynamic Programming Control (7 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (117 citations), Computer Vision and Pattern Recognition (314 citations), Artificial Intelligence (454 citations), Management Science and Operations Research (170 citations) and Control and Systems Engineering (309 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, Yuxuan Yang, J. F. Chang, Liang Zhao, Hu‐Chen Liu, Pengcheng Xu, Long Liu and W.H. Ip. Their work appears in journals such as Nonlinear Dynamics, Expert Systems with Applications, Organic Chemistry Frontiers, Neurocomputing and IET Generation Transmission & Distribution.
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.