Dong‐Soo Kwon

288 papers receiving 3.5k citations

Peers

Dong‐Soo Kwon
Comparison fields: 5 of 133
  • Human-Computer Interaction 661
  • Cognitive Neuroscience 1.0k
  • Control and Systems Engineering 1.1k
  • Biomedical Engineering 1.4k
  • Computer Vision and Pattern Recognition 590
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Çağatay Başdoğan Türkiye
Shigeki Sugano Japan
Zhaojie Ju United Kingdom
Jeha Ryu South Korea
Abderrahmane Kheddar France
Matteo Bianchi Italy
Antonio Frisoli Italy
Marcia K. O’Malley United States
Yoji Yamada Japan
Katherine J. Kuchenbecker United States
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Citations per field
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Citations per year

Countries citing papers authored by Dong‐Soo Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Dong‐Soo Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong‐Soo Kwon. 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 Dong‐Soo Kwon. The network helps show where Dong‐Soo Kwon may publish in the future.

Co-authors

The 25 scholars most cited alongside Dong‐Soo Kwon, 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 Dong‐Soo Kwon Line = papers co-authored together Dong‐Soo Kwon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 297 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1994128
2 200391
3 202083
4 200478
5 200475
6 201766
7 201965
8 199063
9 202260
10 200459
11 200152
12 200747
13 200947
14 201046
15 202045
16 202044
17 200842
18 200641
19 200241
20 200540

About Dong‐Soo Kwon

Dong‐Soo Kwon is a scholar working on Control and Systems Engineering, Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction and Mechanical Engineering, having authored 297 papers that have together received 3.6k indexed citations. Recurring topics across this work include Tactile and Sensory Interactions (72 papers), Soft Robotics and Applications (57 papers), Teleoperation and Haptic Systems (45 papers), Social Robot Interaction and HRI (35 papers), Robot Manipulation and Learning (33 papers), Interactive and Immersive Displays (28 papers), Surgical Simulation and Training (26 papers) and Robotic Mechanisms and Dynamics (23 papers). The work is most often cited by research in Human-Computer Interaction (661 citations), Cognitive Neuroscience (1.0k citations), Control and Systems Engineering (1.1k citations), Biomedical Engineering (1.4k citations) and Computer Vision and Pattern Recognition (590 citations). Dong‐Soo Kwon has collaborated with scholars based in South Korea, United States and Germany. Frequent co-authors include Ki‐Uk Kyung, Wayne J. Book, Minho Hwang, Jinung An, Gi‐Hun Yang, Seung-Chan Kim, Tae‐Heon Yang, Joonhwan Kim, Sang‐Youn Kim and Seong-Young Ko. Their work appears in journals such as Advanced Robotics, IEEE Robotics and Automation Letters, Journal of Intelligent Material Systems and Structures, IEEE Access and Journal of Intelligent & Robotic Systems.

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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