Liangqi Yuan
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition
- Biomedical Engineering
- Co-authors
- Ziran WangChristopher G. BrintonStanisław H. ŻakJia LiLichao SunPhilip S. YuLü SuHongwei Qu
- Topics
- Privacy-Preserving Technologies in Data (6 papers)Privacy, Security, and Data Protection (4 papers)Vehicular Ad Hoc Networks (VANETs) (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceSensors
- Partner nations
- United StatesSwitzerlandItaly
In The Last Decade
Liangqi Yuan
17 papers receiving 363 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 176
- Electrical and Electronic Engineering 95
- Computer Networks and Communications 62
- Computer Vision and Pattern Recognition 57
- Biomedical Engineering 48
Countries citing papers authored by Liangqi Yuan
This map shows the geographic impact of Liangqi Yuan'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 Liangqi Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liangqi Yuan more than expected).
Fields of papers citing papers by Liangqi Yuan
This network shows the impact of papers produced by Liangqi Yuan. 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 Liangqi Yuan. The network helps show where Liangqi Yuan may publish in the future.
Co-authorship network of co-authors of Liangqi Yuan
This figure shows the co-authorship network connecting the top 25 collaborators of Liangqi Yuan. A scholar is included among the top collaborators of Liangqi Yuan 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 Liangqi Yuan. Liangqi Yuan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 3 | |
| 6 | 5 | |
| 7 | Decentralized Federated Learning: A Survey and Perspectivebreakdown → | 88 |
| 8 | Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengesbreakdown → | 89 |
| 9 | 5 | |
| 10 | 23 | |
| 11 | 6 | |
| 12 | 23 | |
| 13 | 9 | |
| 14 | 8 | |
| 15 | 32 | |
| 16 | 31 | |
| 17 | 13 | |
| 18 | 28 |
About Liangqi Yuan
Liangqi Yuan is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 18 papers that have together received 372 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (6 papers), Privacy, Security, and Data Protection (4 papers) and Vehicular Ad Hoc Networks (VANETs) (3 papers). The work is most often cited by research in Artificial Intelligence (176 citations), Automotive Engineering (35 citations) and Computer Vision and Pattern Recognition (57 citations). Liangqi Yuan has collaborated with scholars based in United States, Switzerland and Italy. Frequent co-authors include Ziran Wang, Christopher G. Brinton, Stanisław H. Żak, Jia Li, Lichao Sun, Philip S. Yu, Lü Su, Hongwei Qu, Yunsheng Ma and Erik Blasch. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Sensors.
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.