Seong-Ik Han

1.8k citations
75 papers · 1.5k indexed · h-index 20

Seong-Ik Han

71 papers receiving 1.5k citations

Peers

Seong-Ik Han
Comparison fields: 5 of 58
  • Control and Systems Engineering 1.2k
  • Computational Theory and Mathematics 235
  • Computer Networks and Communications 256
  • Mechanical Engineering 325
  • Artificial Intelligence 203
Replace Linghuan Kong with:
Linghuan Kong China
Aydın Yeşildirek United States
Eng Hock Tay Singapore
R. Garrido Mexico
Mohammad Mehdi Fateh Iran
Xuhui Bu China
Zhi‐Liang Zhao China
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B. M. Patre India
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Citations per field
00.5×10×15×19.3×
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Citations per year

Countries citing papers authored by Seong-Ik Han

Since Specialization
Citations

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

Fields of papers citing papers by Seong-Ik Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seong-Ik Han. 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 Seong-Ik Han. The network helps show where Seong-Ik Han may publish in the future.

Co-authorship network

The 23 scholars most cited alongside Seong-Ik Han, 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 Seong-Ik Han Line = papers co-authored together Seong-Ik Han links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20235
2 202011
3 202031
4 201816
5 20172
6 201660
7 201612
8 201513
9 20151
10 201382
11 20127
12 201036
13
Adaptive friction observer and sliding mode controller development with RFNN for nonlinear friction compensation
20095
14
Adaptive Tire-Road Dynamic Friction Estimation and Emergency Braking Control Using Sliding Mode and Recurrent Fuzzy Neural Network
20091
15 200910
16 20092
17 200641
18
Disturbance Observer- Based Sliding Mode Control for the Precise Mechanical System with the Bristle Friction Model
20031
19
Precise Control for Servo Systems Using Sliding Mode Observer and Controller
20021
20
Quasi-LQG/H ∞ /LTR Control for a Nonlinear Servo System with Coulomb Friction and Dead-zone
20001

About Seong-Ik Han

Seong-Ik Han is a scholar working on Control and Systems Engineering, Mechanical Engineering and Computer Networks and Communications, having authored 75 papers that have together received 1.5k indexed citations. Recurring topics across this work include Adaptive Control of Nonlinear Systems (49 papers), Iterative Learning Control Systems (27 papers), Control and Dynamics of Mobile Robots (16 papers), Hydraulic and Pneumatic Systems (15 papers), Dynamics and Control of Mechanical Systems (8 papers), Control Systems in Engineering (8 papers), Advanced Control Systems Design (7 papers) and Distributed Control Multi-Agent Systems (7 papers). The work is most often cited by research in Control and Systems Engineering (1.2k citations), Computational Theory and Mathematics (235 citations) and Computer Networks and Communications (256 citations). Seong-Ik Han has collaborated with scholars based in South Korea. Frequent co-authors include Jang M. Lee, Jang-Myung Lee, Jang Myung Lee, Kwon Soon Lee, Jang-Myung Lee, Jong‐Shik Kim, Sung Bum Park, Jae‐Myung Lee, Min Gyu Park and Hyun Lee. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Industrial Electronics and IEEE Access.

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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