Won-Mook Kang

509 citations
23 papers · 399 · h-index 12

Impact in

Papers in

Won-Mook Kang

23 papers receiving 390 citations

Peers

Won-Mook Kang
Comparison fields: 5 of 24
  • Electrical and Electronic Engineering 355
  • Cellular and Molecular Neuroscience 103
  • Cognitive Neuroscience 86
  • Artificial Intelligence 81
  • Materials Chemistry 104
Replace Keji Zhou with:
Keji Zhou China
Sarbashis Das United States
Harikrishnan Ravichandran United States
Shuo Ke China
Kyung Kyu Min South Korea
Jee Young Kwak South Korea
Chuanyu Fu China
Mengge Yan China
Colin O’Callaghan Ireland
Won-Mook Kang relative to Keji Zhou China Keji Zhou's profile →
Citations per field
00.5×10×14.3×
Keji Zhou · 1×
Citations per year

Countries citing papers authored by Won-Mook Kang

Since Specialization
Citations

This map shows the geographic impact of Won-Mook 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 Won-Mook Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Won-Mook Kang more than expected).

Fields of papers citing papers by Won-Mook Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Won-Mook 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 Won-Mook Kang. The network helps show where Won-Mook Kang may publish in the future.

Co-authors

The 25 scholars most cited alongside Won-Mook Kang, 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 Won-Mook Kang Line = papers co-authored together Won-Mook Kang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201870
2 201852
3 201743
4 201740
5 201831
6 202219
7 202018
8 201918
9 201917
10 202016
11 201614
12 202111
13 201610
14 20198
15 20218
16 20226
17 20195
18 20224
19 20193
20 20192

About Won-Mook Kang

Won-Mook Kang is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience, Artificial Intelligence and Materials Chemistry, having authored 23 papers that have together received 399 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (17 papers), Neuroscience and Neural Engineering (8 papers), Ferroelectric and Negative Capacitance Devices (8 papers), Neural dynamics and brain function (8 papers), Neural Networks and Reservoir Computing (6 papers), 2D Materials and Applications (5 papers), CCD and CMOS Imaging Sensors (4 papers) and Gas Sensing Nanomaterials and Sensors (2 papers). The work is most often cited by research in Electrical and Electronic Engineering (355 citations), Cellular and Molecular Neuroscience (103 citations), Cognitive Neuroscience (86 citations), Artificial Intelligence (81 citations) and Materials Chemistry (104 citations). Won-Mook Kang has collaborated with scholars based in South Korea and United States. Frequent co-authors include Jong‐Ho Lee, Sung Yun Woo, Jong‐Ho Bae, Soochang Lee, Chul-Heung Kim, Byung‐Gook Park, In-Tak Cho, Suhwan Lim, Dongseok Kwon and Jangsaeng Kim. Their work appears in journals such as Solid-State Electronics, IEEE Access, IEEE Transactions on Electron Devices, Journal of Nanoscience and Nanotechnology and Neurocomputing.

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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