Youngjun Yoo

469 citations
22 papers · 339 · h-index 10

Impact in

Papers in

Youngjun Yoo

19 papers receiving 331 citations

Peers

Youngjun Yoo
Comparison fields: 5 of 102
  • Industrial and Manufacturing Engineering 34
  • Control and Systems Engineering 77
  • Cell Biology 34
  • Mechanical Engineering 65
  • Artificial Intelligence 54
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Citations per field
00.5×3.9×
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Citations per year

Countries citing papers authored by Youngjun Yoo

Since Specialization
Citations

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

Fields of papers citing papers by Youngjun Yoo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Youngjun Yoo. 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 Youngjun Yoo. The network helps show where Youngjun Yoo may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201990
2 200950
3 202328
4 201928
5 201927
6 202126
7 202224
8 202323
9 20249
10 20249
11 20246
12 20225
13 20234
14 20133
15 20142
16 20112
17 20251
18 20131
19
Output feedback controller design of networked control systems with nonlinearity: An LMI approach
20101
20
Congruence between Actual/Social Self-Images, and Brand Images of Preferred Hotels -The Case of Top-Class Hotels in Seoul, Korea-
20100

About Youngjun Yoo

Youngjun Yoo is a scholar working on Control and Systems Engineering, Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence and Mechanical Engineering, having authored 22 papers that have together received 339 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (7 papers), Fault Detection and Control Systems (5 papers), Anomaly Detection Techniques and Applications (3 papers), Machine Fault Diagnosis Techniques (3 papers), Iterative Learning Control Systems (3 papers), Robotic Mechanisms and Dynamics (3 papers), Mineral Processing and Grinding (2 papers) and Time Series Analysis and Forecasting (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (34 citations), Control and Systems Engineering (77 citations), Cell Biology (34 citations), Mechanical Engineering (65 citations) and Artificial Intelligence (54 citations). Youngjun Yoo has collaborated with scholars based in South Korea, United States and Finland. Frequent co-authors include Seongcheol Jeong, Taeheon Lee, Yoon-Seok Kim, Huikyong Lee, Wooseok Im, Robert J. Ferrante, Manho Kim, Hoon Ryu, Yong‐Keun Jung and Nam Soon Kim. Their work appears in journals such as International Journal of Precision Engineering and Manufacturing, Engineering Applications of Artificial Intelligence, Applied Sciences, Robotica and Materials Today Energy.

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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