Chien Chern Cheah

5.7k citations
182 papers · 4.2k indexed · h-index 35

Chien Chern Cheah

170 papers receiving 4.1k citations

Peers

Chien Chern Cheah
Comparison fields: 5 of 98
  • Control and Systems Engineering 2.6k
  • Computer Vision and Pattern Recognition 736
  • Computer Networks and Communications 762
  • Media Technology 270
  • Biomedical Engineering 1.3k
Replace P.K. Khosla with:
P.K. Khosla United States
Paolo Robuffo Giordano France
Marcio de Queiroz United States
J.-J.E. Slotine United States
James K. Mills Canada
Hong Liu China
L. Sciavicco Italy
Fumitoshi Matsuno Japan
Rafael Kelly Mexico
Vincenzo Lippiello Italy
Chien Chern Cheah relative to P.K. Khosla United States P.K. Khosla's profile →
Citations per field
00.5×3.8×
P.K. Khosla · 1×
Citations per year

Countries citing papers authored by Chien Chern Cheah

Since Specialization
Citations

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

Fields of papers citing papers by Chien Chern Cheah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chien Chern Cheah. 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 Chien Chern Cheah. The network helps show where Chien Chern Cheah may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20251
4 20240
5 202312
6 20233
7 20230
8 202117
9 20200
10 201910
11 20162
12 20163
13 20164
14 201631
15 201583
16 201524
17 20053
18 20026
19 199612
20 199414

About Chien Chern Cheah

Chien Chern Cheah is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Media Technology, having authored 182 papers that have together received 4.2k indexed citations. Recurring topics across this work include Adaptive Control of Nonlinear Systems (52 papers), Robot Manipulation and Learning (41 papers), Robotic Mechanisms and Dynamics (39 papers), Orbital Angular Momentum in Optics (35 papers), Microfluidic and Bio-sensing Technologies (31 papers), Iterative Learning Control Systems (30 papers), Teleoperation and Haptic Systems (28 papers) and Advanced Vision and Imaging (20 papers). The work is most often cited by research in Control and Systems Engineering (2.6k citations), Computer Vision and Pattern Recognition (736 citations) and Computer Networks and Communications (762 citations). Chien Chern Cheah has collaborated with scholars based in Singapore, United States and Japan. Frequent co-authors include J.-J.E. Slotine, Saing Paul Hou, S. Arimoto, S. Kawamura, C. Liu, Chao Liu, Juanjuan Zhang, Reza Haghighi, Danwei Wang and Danwei Wang. Their work appears in journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Industrial Electronics and Automatica.

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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