Kang‐Il Song
- Biomedical Engineering top 5%
- Electrical and Electronic Engineering
- Cellular and Molecular Neuroscience top 5%
- Electronic, Optical and Magnetic Materials top 10%
- Cognitive Neuroscience top 5%
- Co-authors
- Inchan YounHaochen ZhangHaiding SunDonghee SonHuabin YuDanhao WangChong XingChen Huang
- Topics
- Neuroscience and Neural Engineering (15 papers)Advanced Sensor and Energy Harvesting Materials (9 papers)Muscle activation and electromyography studies (6 papers)
- Partner nations
- South KoreaUnited StatesChina
In The Last Decade
Kang‐Il Song
35 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Biomedical Engineering 694
- Electrical and Electronic Engineering 307
- Cellular and Molecular Neuroscience 301
- Electronic, Optical and Magnetic Materials 276
- Cognitive Neuroscience 273
Countries citing papers authored by Kang‐Il Song
This map shows the geographic impact of Kang‐Il Song'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 Kang‐Il Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kang‐Il Song more than expected).
Fields of papers citing papers by Kang‐Il Song
This network shows the impact of papers produced by Kang‐Il Song. 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 Kang‐Il Song. The network helps show where Kang‐Il Song may publish in the future.
Co-authorship network of co-authors of Kang‐Il Song
This figure shows the co-authorship network connecting the top 25 collaborators of Kang‐Il Song. A scholar is included among the top collaborators of Kang‐Il Song 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 Kang‐Il Song. Kang‐Il Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 16 | |
| 4 | 130 | |
| 5 | 137 | |
| 6 | An artificial neural tactile sensing systembreakdown → | 312 |
| 7 | 149 | |
| 8 | 19 | |
| 9 | 35 | |
| 10 | 89 | |
| 11 | 4 | |
| 12 | 18 | |
| 13 | 11 | |
| 14 | 13 | |
| 15 | 24 | |
| 16 | 5 | |
| 17 | 34 | |
| 18 | 10 | |
| 19 | 6 | |
| 20 | 3 |
About Kang‐Il Song
Kang‐Il Song is a scholar working on Cellular and Molecular Neuroscience, Surfaces, Coatings and Films and Cognitive Neuroscience, having authored 40 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neuroscience and Neural Engineering (15 papers), Advanced Sensor and Energy Harvesting Materials (9 papers) and Muscle activation and electromyography studies (6 papers). The work is most often cited by research in Condensed Matter Physics (238 citations), Polymers and Plastics (239 citations) and Cellular and Molecular Neuroscience (301 citations). Kang‐Il Song has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Inchan Youn, Haochen Zhang, Haiding Sun, Donghee Son, Huabin Yu, Danhao Wang, Chong Xing, Chen Huang, Changhyun Pang and Sunghee Estelle Park. Their work appears in journals such as Advanced Materials, Nature Communications and Applied Physics Letters.
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.