Jaeseok Yun
- Electrical and Electronic Engineering top 5%
- Computer Networks and Communications top 2%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering top 10%
- Information Systems top 5%
- Co-authors
- Jaeho KimSang‐Shin LeeJaiyong LeeJae-Woo KimMin-Woo RyuSung-Chan ChoiJiyoung WooDong Min Kim
- Topics
- Context-Aware Activity Recognition Systems (18 papers)IoT and Edge/Fog Computing (14 papers)IoT-based Smart Home Systems (8 papers)
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionElectrical and Electronic Engineering
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Jaeseok Yun
39 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 112
- Electrical and Electronic Engineering 698
- Computer Networks and Communications 605
- Computer Vision and Pattern Recognition 331
- Biomedical Engineering 274
- Information Systems 179
Countries citing papers authored by Jaeseok Yun
This map shows the geographic impact of Jaeseok Yun'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 Jaeseok Yun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaeseok Yun more than expected).
Fields of papers citing papers by Jaeseok Yun
This network shows the impact of papers produced by Jaeseok Yun. 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 Jaeseok Yun. The network helps show where Jaeseok Yun may publish in the future.
Co-authorship network of co-authors of Jaeseok Yun
This figure shows the co-authorship network connecting the top 25 collaborators of Jaeseok Yun. A scholar is included among the top collaborators of Jaeseok Yun 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 Jaeseok Yun. Jaeseok Yun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 18 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 39 | |
| 10 | Medication Reminder System for Smart Aging Services Using IoT Platforms and Products | 3 |
| 11 | Dynamic Service Composition and Development Using Heterogeneous IoT Systems | 1 |
| 12 | 21 | |
| 13 | 67 | |
| 14 | 16 | |
| 15 | 60 | |
| 16 | 108 | |
| 17 | 2 | |
| 18 | 143 | |
| 19 | 37 | |
| 20 | The User Identification System Using Walking Pattern over the ubiFloor | 30 |
About Jaeseok Yun
Jaeseok Yun is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Human-Computer Interaction, having authored 46 papers that have together received 1.5k indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (18 papers), IoT and Edge/Fog Computing (14 papers) and IoT-based Smart Home Systems (8 papers). The work is most often cited by research in Computer Networks and Communications (605 citations), Computer Vision and Pattern Recognition (331 citations) and Electrical and Electronic Engineering (698 citations). Jaeseok Yun has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Jaeho Kim, Sang‐Shin Lee, Jaiyong Lee, Jae-Woo Kim, Min-Woo Ryu, Sung-Chan Choi, Jiyoung Woo, Dong Min Kim, Daehee Kim and Martin Bauer. Their work appears in journals such as IEEE Communications Surveys & Tutorials, IEEE Access and IEEE Communications Magazine.
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.