Yun Peng

106 total papers · 1.1k total citations
47 papers, 578 citations indexed

About

Yun Peng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Yun Peng has authored 47 papers receiving a total of 578 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 6 papers in Computer Networks and Communications. Recurrent topics in Yun Peng's work include Advanced Neural Network Applications (9 papers), Robotics and Sensor-Based Localization (6 papers) and Rough Sets and Fuzzy Logic (4 papers). Yun Peng is often cited by papers focused on Advanced Neural Network Applications (9 papers), Robotics and Sensor-Based Localization (6 papers) and Rough Sets and Fuzzy Logic (4 papers). Yun Peng collaborates with scholars based in China, Hong Kong and United States. Yun Peng's co-authors include Ming Liu, Hengli Wang, Yuxiang Sun, Weixun Zuo, Lei Tai, Jianhao Jiao, Yuan Wang, Chengju Liu, Suhan Zhang and Yi Zhao and has published in prestigious journals such as Nature Communications, Scientific Reports and Sensors.

In The Last Decade

Yun Peng

40 papers receiving 566 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Yun Peng	292	126	108	49	48	47	578
Van-Dung Hoang	325 1.1×	132 1.0×	91 0.8×	26 0.5×	62 1.3×	64	548
Liang Du	383 1.3×	127 1.0×	172 1.6×	32 0.7×	37 0.8×	40	598
Liang Li	289 1.0×	211 1.7×	242 2.2×	56 1.1×	45 0.9×	46	683
Chunwei Wang	407 1.4×	61 0.5×	173 1.6×	46 0.9×	38 0.8×	37	671
Liwei Deng	153 0.5×	63 0.5×	81 0.8×	49 1.0×	38 0.8×	85	678
Sreenivas R. Sukumar	152 0.5×	135 1.1×	46 0.4×	55 1.1×	13 0.3×	55	556
Rui Chen	487 1.7×	41 0.3×	98 0.9×	32 0.7×	14 0.3×	30	648
Jiahao Fan	181 0.6×	234 1.9×	42 0.4×	67 1.4×	63 1.3×	34	610
Zehao Yu	375 1.3×	73 0.6×	110 1.0×	41 0.8×	92 1.9×	32	654
Tao Wang	360 1.2×	101 0.8×	30 0.3×	42 0.9×	47 1.0×	35	675

Countries citing papers authored by Yun Peng

Since Specialization

Citations

This map shows the geographic impact of Yun Peng'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 Yun Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Peng more than expected).

Fields of papers citing papers by Yun Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yun Peng. 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 Yun Peng. The network helps show where Yun Peng may publish in the future.

Co-authorship network of co-authors of Yun Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Yun Peng. A scholar is included among the top collaborators of Yun Peng 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 Yun Peng. Yun Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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