Yun Peng

1.1k total citations
48 papers, 589 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 48 papers receiving a total of 589 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, Weixun Zuo, Hengli Wang, Yuxiang Sun, Lei Tai, Jianhao Jiao, Yuan Wang, Chengju Liu, Yi Liu and Bing Yan and has published in prestigious journals such as Nature Communications, Scientific Reports and Sensors.

In The Last Decade

Yun Peng

41 papers receiving 576 citations

Peers

Yun Peng
Comparison fields: 5 of 98
  • Computer Vision and Pattern Recognition 294
  • Artificial Intelligence 128
  • Aerospace Engineering 110
  • Automotive Engineering 49
  • Environmental Engineering 49
Replace Van-Dung Hoang with:
Van-Dung Hoang South Korea
Yong Ding China
François Rameau South Korea
Erkan Bostancı Türkiye
Kang Li China
Peng Qiao China
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Van-Dung Hoang South Korea View profile →
Citations per field, relative to Yun Peng
Yun Peng · 1×
Citations per year, relative to Yun Peng
Yun Peng · 1×

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

20 of 20 papers shown
# Work Indexed citations
1 1
2 0
3 0
4 0
5 6
6 3
7 24
8 6
9 1
10 8
11 16
12 16
13 174
14 3
15 39
16 49
17 57
18 43
19
A Clustering Multi-Hop Routing Protocol EB-LEACH Based on Energy Balance
0
20
Grouping and Hierarchical Trust Model in Wireless Sensor Networks
3

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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