Young-Guk Ha
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Co-authors
- Joo-Chan SohnSung-Min KimYoung-Jo ChoSeong-Hun ParkHyunsoo YoonMyoung‐jae LeeYung-Cheol ByunHeemin Kim
- Topics
- Advanced Neural Network Applications (12 papers)Robotics and Automated Systems (11 papers)Context-Aware Activity Recognition Systems (10 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Networks and CommunicationsAutomotive Engineering
- Partner nations
- South KoreaThailandUnited States
In The Last Decade
Young-Guk Ha
56 papers receiving 475 citations
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 182
- Computer Networks and Communications 127
- Artificial Intelligence 117
- Electrical and Electronic Engineering 103
- Control and Systems Engineering 97
Countries citing papers authored by Young-Guk Ha
This map shows the geographic impact of Young-Guk Ha'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 Young-Guk Ha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young-Guk Ha more than expected).
Fields of papers citing papers by Young-Guk Ha
This network shows the impact of papers produced by Young-Guk Ha. 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 Young-Guk Ha. The network helps show where Young-Guk Ha may publish in the future.
Co-authorship network of co-authors of Young-Guk Ha
This figure shows the co-authorship network connecting the top 25 collaborators of Young-Guk Ha. A scholar is included among the top collaborators of Young-Guk Ha 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 Young-Guk Ha. Young-Guk Ha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 18 | |
| 3 | 16 | |
| 4 | 25 | |
| 5 | 70 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 8 | |
| 9 | 3 | |
| 10 | 9 | |
| 11 | 2 | |
| 12 | 5 | |
| 13 | Indoor location recognition system using environmental sensors | 1 |
| 14 | 1 | |
| 15 | 5 | |
| 16 | 1 | |
| 17 | 18 | |
| 18 | 0 | |
| 19 | 1 | |
| 20 | 25 |
About Young-Guk Ha
Young-Guk Ha is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Health Information Management, having authored 60 papers that have together received 519 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (12 papers), Robotics and Automated Systems (11 papers) and Context-Aware Activity Recognition Systems (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (182 citations), Computer Networks and Communications (127 citations) and Automotive Engineering (53 citations). Young-Guk Ha has collaborated with scholars based in South Korea, Thailand and United States. Frequent co-authors include Joo-Chan Sohn, Sung-Min Kim, Young-Jo Cho, Seong-Hun Park, Hyunsoo Yoon, Myoung‐jae Lee, Yung-Cheol Byun, Heemin Kim, Sung‐Hoon Kim and Myung-Jae Lee. Their work appears in journals such as Information Sciences, IEEE Transactions on Automation Science and Engineering and Soft Computing.
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.