Un-Chul Moon

925 citations
35 papers · 745 · h-index 12

Impact in

Papers in

Un-Chul Moon

33 papers receiving 714 citations

Peers

Un-Chul Moon
Comparison fields: 5 of 54
  • Control and Systems Engineering 530
  • Electrical and Electronic Engineering 295
  • Artificial Intelligence 107
  • Energy Engineering and Power Technology 7
  • Automotive Engineering 18
Replace Emmanuel A. Gonzalez with:
Emmanuel A. Gonzalez Slovakia
Yassine Koubaa Tunisia
Saeed Jafarzadeh United States
Lunhaojie Liu China
Lal Bahadur Prasad India
Po-Huang Shieh Taiwan
Li-Tao Teng Taiwan
Shi Kai Jing China
Ryan Shun-Cheung Yeung Hong Kong
Wenyan Bai China
Un-Chul Moon relative to Emmanuel A. Gonzalez Slovakia Emmanuel A. Gonzalez's profile →
Citations per field
00.5×1.5×2.2×
Emmanuel A. Gonzalez · 1×
Citations per year

Countries citing papers authored by Un-Chul Moon

Since Specialization
Citations

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

Fields of papers citing papers by Un-Chul Moon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Un-Chul Moon. 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 Un-Chul Moon. The network helps show where Un-Chul Moon may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014164
2 199596
3 200990
4 200386
5 201149
6 200339
7 199632
8 201728
9 200623
10 201115
11 200514
12 200012
13 202212
14 202011
15 201710
16 20178
17 20077
18 20107
19 20147
20 20116

About Un-Chul Moon

Un-Chul Moon is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Computational Mechanics and Aerospace Engineering, having authored 35 papers that have together received 745 indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (14 papers), Fault Detection and Control Systems (9 papers), Optimal Power Flow Distribution (6 papers), Power System Optimization and Stability (5 papers), Iterative Learning Control Systems (4 papers), Fuzzy Logic and Control Systems (4 papers), Control Systems and Identification (4 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Control and Systems Engineering (530 citations), Electrical and Electronic Engineering (295 citations), Artificial Intelligence (107 citations), Energy Engineering and Power Technology (7 citations) and Automotive Engineering (18 citations). Un-Chul Moon has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include K.Y. Lee, Kwang Y. Lee, Sangshin Kwak, K.Y. Lee, Seung‐Chul Lee, Youngjun Lee, Jung‐Hyuk Koh, Hyuck‐In Kwon, Jae‐Hoon Ji and Ho‐Hyun Park. Their work appears in journals such as IEEE Transactions on Energy Conversion, IEEE Transactions on Control Systems Technology, IEEE Transactions on Magnetics, IEEE Transactions on Power Electronics and Ceramics International.

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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