Yun Wu

423 total citations
53 papers, 262 citations indexed

About

Yun Wu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Yun Wu has authored 53 papers receiving a total of 262 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 12 papers in Media Technology and 9 papers in Artificial Intelligence. Recurrent topics in Yun Wu's work include Advanced Neural Network Applications (7 papers), Advanced Image Fusion Techniques (5 papers) and Advanced Vision and Imaging (5 papers). Yun Wu is often cited by papers focused on Advanced Neural Network Applications (7 papers), Advanced Image Fusion Techniques (5 papers) and Advanced Vision and Imaging (5 papers). Yun Wu collaborates with scholars based in China, Nepal and United States. Yun Wu's co-authors include Youliang Tian, Shunzhi Zhu, Qisong Chen, Xiaowei Chen, Wusheng Chou, Yong Zhao, Song Xu, Guang Yang, Yan Wang and Yucheng Shi and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Access.

In The Last Decade

Yun Wu

45 papers receiving 252 citations

Peers

Yun Wu
Comparison fields: 5 of 76
  • Computer Vision and Pattern Recognition 126
  • Artificial Intelligence 77
  • Media Technology 35
  • Aerospace Engineering 30
  • Radiology, Nuclear Medicine and Imaging 29
Replace Rafikha Aliana A. Raof with:
Rafikha Aliana A. Raof Malaysia
Shumin Han China
Jiongcheng Li China
Qing Song China
Amin Ullah Pakistan
Junran Peng China
Yongxi Lu United States
Kan Chen United States
Benteng Ma China
Chuming Li China
Rafikha Aliana A. Raof Malaysia View profile →
Citations per field, relative to Yun Wu
Yun Wu · 1×
Citations per year, relative to Yun Wu
Yun Wu · 1×

Countries citing papers authored by Yun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Yun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yun Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Yun Wu. A scholar is included among the top collaborators of Yun Wu 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 Wu. Yun Wu 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 0
2 0
3 3
4 20
5 1
6 0
7 3
8 2
9 11
10 10
11 52
12 3
13 0
14 6
15 2
16 2
17
Control on Steering Wheel Shimmy of the Car at High Speed
1
18 12
19
X-Crossing Members Arrangement for Upper Plane of Duck Bill Cross Arm
1
20 9

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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