H. Kang
Impact in
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- Carbon Nanotubes in Composites
- Graphene research and applications
- Diamond and Carbon-based Materials Research
- Boron and Carbon Nanomaterials Research
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- Supercapacitor Materials and Fabrication
Papers in
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- Domain Adaptation and Few-Shot Learning 2
- Topic Modeling 1
- Text and Document Classification Technologies 1
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- Cosmology and Gravitation Theories 1
- Co-authors
- S. C. Lyu (1 shared paper)Cheol Jin Lee (1 shared paper)Seng Hua Lee (1 shared paper)Chae Yeon Park (1 shared paper)Cheol‐Woong Yang (1 shared paper)Dongsu Ryu (1 shared paper)Youngjune Gwon (2 shared papers)Kyoungwon Park (2 shared papers)
- Journals
- The Journal of Physical Chemistry B (1 paper)2022 26th International Conference on Pattern Recognition (ICPR) (2 papers)Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (1 paper)
- Partner nations
- South Korea
In The Last Decade
H. Kang
3 papers receiving 58 citations
Peers
Comparison fields: 5 of 21
- Materials Chemistry 54
- Electronic, Optical and Magnetic Materials 7
- Mechanical Engineering 9
- Inorganic Chemistry 3
- Organic Chemistry 6
Countries citing papers authored by H. Kang
This map shows the geographic impact of H. Kang'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 H. Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites H. Kang more than expected).
Fields of papers citing papers by H. Kang
This network shows the impact of papers produced by H. Kang. 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 H. Kang. The network helps show where H. Kang may publish in the future.
Co-authors
The 9 scholars most cited alongside H. Kang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 57 | |
| 2 | Vorticity and Turbulence in the Large Scale Structure of the Universe | 2008 | 1 |
| 3 | 2022 | 1 | |
| 4 | 2022 | 0 |
About H. Kang
H. Kang is a scholar working on Artificial Intelligence, Astronomy and Astrophysics, Oceanography, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 4 papers that have together received 59 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Advanced Neural Network Applications (1 paper), Cosmology and Gravitation Theories (1 paper), Multimodal Machine Learning Applications (1 paper), Graphene research and applications (1 paper), Topic Modeling (1 paper), Text and Document Classification Technologies (1 paper) and Geophysics and Gravity Measurements (1 paper). The work is most often cited by research in Materials Chemistry (54 citations), Electronic, Optical and Magnetic Materials (7 citations), Mechanical Engineering (9 citations), Inorganic Chemistry (3 citations) and Organic Chemistry (6 citations). H. Kang has collaborated with scholars based in South Korea. Frequent co-authors include S. C. Lyu, Cheol Jin Lee, Seng Hua Lee, Chae Yeon Park, Cheol‐Woong Yang, Dongsu Ryu, Youngjune Gwon, Kyoungwon Park and Joonseok Lee. Their work appears in journals such as The Journal of Physical Chemistry B, 2022 26th International Conference on Pattern Recognition (ICPR) and Scholarworks@UNIST (Ulsan National Institute of Science and Technology).
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.