Soo‐Chul Lim

1.0k total citations
39 papers, 733 citations indexed

About

Soo‐Chul Lim is a scholar working on Cognitive Neuroscience, Human-Computer Interaction and Control and Systems Engineering. According to data from OpenAlex, Soo‐Chul Lim has authored 39 papers receiving a total of 733 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cognitive Neuroscience, 15 papers in Human-Computer Interaction and 11 papers in Control and Systems Engineering. Recurrent topics in Soo‐Chul Lim's work include Tactile and Sensory Interactions (23 papers), Robot Manipulation and Learning (9 papers) and Teleoperation and Haptic Systems (7 papers). Soo‐Chul Lim is often cited by papers focused on Tactile and Sensory Interactions (23 papers), Robot Manipulation and Learning (9 papers) and Teleoperation and Haptic Systems (7 papers). Soo‐Chul Lim collaborates with scholars based in South Korea, United States and China. Soo‐Chul Lim's co-authors include Joonah Park, Wonjun Hwang, Dong‐Soo Kwon, Minhyun Jung, Sanghun Jeon, Hyung-Kew Lee, Tran Quang Trung, Nae‐Eung Lee, Kyoobin Lee and Soonjae Pyo and has published in prestigious journals such as Proceedings of the IEEE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Soo‐Chul Lim

37 papers receiving 699 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Soo‐Chul Lim South Korea 16 414 295 169 130 111 39 733
Stephen A. Mascaro United States 15 429 1.0× 294 1.0× 300 1.8× 189 1.5× 73 0.7× 70 778
Frank L. Hammond United States 17 671 1.6× 181 0.6× 80 0.5× 171 1.3× 173 1.6× 64 859
Gaoyang Pang China 17 583 1.4× 264 0.9× 90 0.5× 152 1.2× 106 1.0× 35 933
Ali Alazmani United Kingdom 15 830 2.0× 323 1.1× 58 0.3× 61 0.5× 178 1.6× 43 1.2k
Luca Ascari Italy 13 345 0.8× 247 0.8× 42 0.2× 79 0.6× 56 0.5× 33 592
Tae‐Heon Yang South Korea 16 474 1.1× 544 1.8× 222 1.3× 72 0.6× 241 2.2× 71 1.0k
Dana D. Damian United Kingdom 14 499 1.2× 240 0.8× 64 0.4× 54 0.4× 226 2.0× 40 775
Tapomayukh Bhattacharjee United States 19 311 0.8× 297 1.0× 136 0.8× 363 2.8× 205 1.8× 61 889
João Bimbo Italy 17 666 1.6× 551 1.9× 158 0.9× 511 3.9× 266 2.4× 36 1.1k

Countries citing papers authored by Soo‐Chul Lim

Since Specialization
Citations

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

Fields of papers citing papers by Soo‐Chul Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Soo‐Chul Lim. 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 Soo‐Chul Lim. The network helps show where Soo‐Chul Lim may publish in the future.

Co-authorship network of co-authors of Soo‐Chul Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Soo‐Chul Lim. A scholar is included among the top collaborators of Soo‐Chul Lim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Soo‐Chul Lim. Soo‐Chul Lim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jeong, Eui-Young, et al.. (2025). Deep Neural Network for Valve Fault Diagnosis Integrating Multivariate Time-Series Sensor Data. Actuators. 14(2). 70–70. 1 indexed citations
2.
Ryu, Jee-Hwan, et al.. (2024). Latency-Free Driving Scene Prediction for On-Road Teledriving With Future-Image-Generation. IEEE Transactions on Intelligent Transportation Systems. 25(11). 16676–16686. 2 indexed citations
3.
Qin, Yuzhe, et al.. (2024). DexTouch: Learning to Seek and Manipulate Objects With Tactile Dexterity. IEEE Robotics and Automation Letters. 9(12). 10772–10779. 5 indexed citations
4.
Yuan, Ying, et al.. (2024). Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing. 6558–6565. 21 indexed citations
5.
Lim, Soo‐Chul, et al.. (2023). A Neural Network-based Suture-tension Estimation Method Using Spatio-temporal Features of Visual Information and Robot-state Information for Robot-assisted Surgery. International Journal of Control Automation and Systems. 21(12). 4032–4040. 4 indexed citations
6.
Lim, Soo‐Chul, et al.. (2023). Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition. Sensors. 23(6). 3201–3201. 8 indexed citations
7.
Lim, Soo‐Chul, et al.. (2023). Real-time Video Prediction Using GANs With Guidance Information for Time-delayed Robot Teleoperation. International Journal of Control Automation and Systems. 21(7). 2387–2397. 4 indexed citations
8.
Kim, Seung-Chan, et al.. (2023). Anonymizing at-home fitness: enhancing privacy and motivation with virtual reality and try-on. Frontiers in Public Health. 11. 1333776–1333776.
9.
Kim, Seung-Chan, et al.. (2022). DeepTouch: Enabling Touch Interaction in Underwater Environments by Learning Touch-Induced Inertial Motions. IEEE Sensors Journal. 22(9). 8924–8932. 9 indexed citations
10.
Lee, Dong Han, et al.. (2022). Vision-based interaction force estimation for robot grip motion without tactile/force sensor. Expert Systems with Applications. 211. 118441–118441. 17 indexed citations
11.
Lim, Soo‐Chul, et al.. (2021). Toward Vision-Based High Sampling Interaction Force Estimation With Master Position and Orientation for Teleoperation. IEEE Robotics and Automation Letters. 6(4). 6640–6646. 10 indexed citations
12.
Lee, D., et al.. (2020). Continuous Image Generation From Low-Update-Rate Images and Physical Sensors Through a Conditional GAN for Robot Teleoperation. IEEE Transactions on Industrial Informatics. 17(3). 1978–1986. 8 indexed citations
13.
Ryu, Semin, et al.. (2020). Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback. Applied Sciences. 10(14). 4990–4990. 1 indexed citations
14.
Jeon, Sanghun, Soo‐Chul Lim, Tran Quang Trung, Minhyun Jung, & Nae‐Eung Lee. (2019). Flexible Multimodal Sensors for Electronic Skin: Principle, Materials, Device, Array Architecture, and Data Acquisition Method. Proceedings of the IEEE. 107(10). 2065–2083. 85 indexed citations
15.
Lim, Soo‐Chul, et al.. (2019). An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video. Sensors. 19(16). 3579–3579. 22 indexed citations
16.
Lim, Soo‐Chul, et al.. (2019). Sequential Image-Based Attention Network for Inferring Force Estimation Without Haptic Sensor. IEEE Access. 7. 150237–150246. 13 indexed citations
17.
Jung, Minhyun, Sujaya Kumar Vishwanath, Jihoon Kim, et al.. (2019). Transparent and Flexible Mayan-Pyramid-based Pressure Sensor using Facile-Transferred Indium tin Oxide for Bimodal Sensor Applications. Scientific Reports. 9(1). 14040–14040. 32 indexed citations
18.
Lee, D., Wonjun Hwang, & Soo‐Chul Lim. (2018). Interaction Force Estimation Using Camera and Electrical Current Without Force/Torque Sensor. IEEE Sensors Journal. 18(21). 8863–8872. 20 indexed citations
19.
Ryu, Semin, et al.. (2018). Mechanical Vibration Influences the Perception of Electrovibration. Scientific Reports. 8(1). 4555–4555. 21 indexed citations
20.
Lim, Soo‐Chul, Ki‐Uk Kyung, & Dong‐Soo Kwon. (2011). Effect of frequency difference on sensitivity of beats perception. Experimental Brain Research. 216(1). 11–19. 9 indexed citations

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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