Shao‐Wen Yang

713 citations
26 papers · 478 · h-index 11

Impact in

Papers in

Shao‐Wen Yang

26 papers receiving 462 citations

Peers

Shao‐Wen Yang
Comparison fields: 5 of 55
  • Computer Vision and Pattern Recognition 268
  • Geology 58
  • Aerospace Engineering 166
  • Instrumentation 22
  • Computer Networks and Communications 113
Replace Manato Hirabayashi with:
Manato Hirabayashi Japan
Yuki Kitsukawa Japan
Yiqi Zhong China
Abraham Monrroy Japan
Shaowu Yang China
Scott Thayer United States
Sherif A. S. Mohamed Finland
Seiya Maeda Japan
Jirui Yuan China
Shao‐Wen Yang relative to Manato Hirabayashi Japan Manato Hirabayashi's profile →
Citations per field
00.5×5.3×
Manato Hirabayashi · 1×
Citations per year

Countries citing papers authored by Shao‐Wen Yang

Since Specialization
Citations

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

Fields of papers citing papers by Shao‐Wen Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shao‐Wen Yang. 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 Shao‐Wen Yang. The network helps show where Shao‐Wen Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Shao‐Wen Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shao‐Wen Yang Line = papers co-authored together Shao‐Wen Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018143
2 201041
3 200841
4 201529
5 201028
6 201119
7 201119
8 201919
9 200916
10
Cost-aware pre-training for multiclass cost-sensitive deep learning
201614
11 201714
12 201110
13 201910
14 201810
15 20179
16 20078
17 20158
18 20147
19 20146
20 20166

About Shao‐Wen Yang

Shao‐Wen Yang is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering, having authored 26 papers that have together received 478 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (12 papers), Robotics and Sensor-Based Localization (9 papers), Autonomous Vehicle Technology and Safety (4 papers), Anomaly Detection Techniques and Applications (4 papers), Advanced Neural Network Applications (4 papers), IoT and Edge/Fog Computing (4 papers), Context-Aware Activity Recognition Systems (2 papers) and Robotic Path Planning Algorithms (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (268 citations), Geology (58 citations), Aerospace Engineering (166 citations), Instrumentation (22 citations) and Computer Networks and Communications (113 citations). Shao‐Wen Yang has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Chieh‐Chih Wang, Padmanabhan Pillai, Junjue Wang, Shilpa George, Ziqiang Feng, Mahadev Satyanarayanan, Zhuo Chen, Yen-Kuang Chen, Hsin‐Mu Tsai and Zhiyong Cui. Their work appears in journals such as IEEE Design and Test, The International Journal of Robotics Research, IEEE Internet Computing, Journal of Field Robotics and IEEE/ASME Transactions on Mechatronics.

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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