Lingdong Kong
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 5%
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Environmental Engineering top 10%
- Topics
- Advanced Neural Network Applications (7 papers)Robotics and Sensor-Based Localization (6 papers)Remote Sensing and LiDAR Applications (5 papers)
In The Last Decade
Lingdong Kong
29 papers receiving 625 citations
Peers
Comparison fields: 5 of 59
- Computer Vision and Pattern Recognition 257
- Control and Systems Engineering 222
- Artificial Intelligence 177
- Computational Mechanics 129
- Environmental Engineering 104
Countries citing papers authored by Lingdong Kong
This map shows the geographic impact of Lingdong Kong'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 Lingdong Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lingdong Kong more than expected).
Fields of papers citing papers by Lingdong Kong
This network shows the impact of papers produced by Lingdong Kong. 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 Lingdong Kong. The network helps show where Lingdong Kong may publish in the future.
Co-authorship network of co-authors of Lingdong Kong
This figure shows the co-authorship network connecting the top 25 collaborators of Lingdong Kong. A scholar is included among the top collaborators of Lingdong Kong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lingdong Kong. Lingdong Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 47 | |
| 12 | 30 | |
| 13 | 6 | |
| 14 | 2 | |
| 15 | 1 | |
| 16 | Apple grading detection based on fusion of shape and color features | 1 |
| 17 | Multi-dimension and Multi-level Association Rule Mining Based on Immune Genetic Algorithm | 1 |
| 18 | A New Spatial Association Rules Mining Method Based on Immune Algorithms | 1 |
| 19 | 1 | |
| 20 | Simulation Framework for Mobile Ad Hoc Network | 0 |
About Lingdong Kong
Lingdong Kong is a scholar working on Energy Engineering and Power Technology, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 40 papers that have together received 641 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (7 papers), Robotics and Sensor-Based Localization (6 papers) and Remote Sensing and LiDAR Applications (5 papers). The work is most often cited by research in Geology (87 citations), Computer Vision and Pattern Recognition (257 citations) and Control and Systems Engineering (222 citations). Lingdong Kong has collaborated with scholars based in China, Singapore and Hong Kong. Frequent co-authors include Zhijun Zhang, Lunan Zheng, Ziwei Liu, Youquan Liu, Xinge Zhu, Yuenan Hou, Liang Pan, Yuexin Ma, Jiawei Ren and Bolin Liao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.
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.