Myo Taeg Lim

2.5k total citations
121 papers, 1.9k citations indexed

About

Myo Taeg Lim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Myo Taeg Lim has authored 121 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computer Vision and Pattern Recognition, 34 papers in Artificial Intelligence and 26 papers in Control and Systems Engineering. Recurrent topics in Myo Taeg Lim's work include Target Tracking and Data Fusion in Sensor Networks (22 papers), Video Surveillance and Tracking Methods (17 papers) and Autonomous Vehicle Technology and Safety (12 papers). Myo Taeg Lim is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (22 papers), Video Surveillance and Tracking Methods (17 papers) and Autonomous Vehicle Technology and Safety (12 papers). Myo Taeg Lim collaborates with scholars based in South Korea, Australia and United States. Myo Taeg Lim's co-authors include Choon Ki Ahn, Jung Min Pak, Peng Shi, Hyun Duck Choi, Yuriy S. Shmaliy, Tae‐Koo Kang, Moon Kyou Song, Dong-Sung Pae, Ligang Wu and Zoran Gajić and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Geoscience and Remote Sensing and Optics Express.

In The Last Decade

Myo Taeg Lim

111 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Myo Taeg Lim South Korea 22 709 494 457 427 288 121 1.9k
Hongbin Ma China 19 810 1.1× 367 0.7× 226 0.5× 355 0.8× 295 1.0× 166 1.7k
Haiquan Zhao China 36 623 0.9× 612 1.2× 404 0.9× 282 0.7× 519 1.8× 246 4.0k
Zhuping Wang China 25 1.4k 2.0× 311 0.6× 340 0.7× 1.1k 2.6× 487 1.7× 167 2.5k
Sidney Givigi Canada 19 464 0.7× 293 0.6× 197 0.4× 531 1.2× 472 1.6× 172 1.6k
William Melek Canada 25 866 1.2× 893 1.8× 246 0.5× 152 0.4× 234 0.8× 114 2.3k
Yixin Yin China 28 842 1.2× 580 1.2× 299 0.7× 386 0.9× 403 1.4× 194 2.3k
Jian Wu China 23 552 0.8× 239 0.5× 695 1.5× 474 1.1× 281 1.0× 135 2.3k
Hongfeng Tao China 22 1.2k 1.7× 523 1.1× 226 0.5× 173 0.4× 413 1.4× 82 2.4k
Tankut Acarman Türkiye 24 605 0.9× 229 0.5× 257 0.6× 513 1.2× 391 1.4× 115 1.8k
Tamás Keviczky Netherlands 26 2.0k 2.8× 268 0.5× 398 0.9× 1.1k 2.5× 376 1.3× 125 3.1k

Countries citing papers authored by Myo Taeg Lim

Since Specialization
Citations

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

Fields of papers citing papers by Myo Taeg Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Myo Taeg 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 Myo Taeg Lim. The network helps show where Myo Taeg Lim may publish in the future.

Co-authorship network of co-authors of Myo Taeg Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Myo Taeg Lim. A scholar is included among the top collaborators of Myo Taeg 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 Myo Taeg Lim. Myo Taeg 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.
Kim, Jun Young, et al.. (2025). High-Speed and Enhanced Motion Control for a Wheeled-Legged Humanoid Robot Using a Two-Wheeled Inverted Pendulum With Roll Joint. IEEE Access. 13. 33330–33340. 2 indexed citations
2.
Ahn, Woo Jin, et al.. (2025). OCNav: Object-centric Navigation via Parallel Language Grounding on Semantic Topological Graphs. International Journal of Control Automation and Systems. 23(12). 3536–3546.
3.
Pae, Dong-Sung, et al.. (2024). Fusion-attention network using dense scale-invariant feature transform flow image and point cloud for 3D pedestrian detection. Multimedia Tools and Applications. 84(14). 12813–12833. 1 indexed citations
4.
5.
Kang, Tae‐Koo, et al.. (2023). Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features. Electronics. 12(13). 2933–2933. 7 indexed citations
6.
Ahn, Woo Jin, Dong-Won Kim, Tae‐Koo Kang, Dong-Sung Pae, & Myo Taeg Lim. (2023). Unsupervised Semantic Segmentation Inpainting Network Using a Generative Adversarial Network with Preprocessing. Applied Sciences. 13(2). 781–781. 1 indexed citations
7.
Ahn, Woo Jin, et al.. (2023). Multiple Object Tracking Using Re-Identification Model with Attention Module. Applied Sciences. 13(7). 4298–4298. 6 indexed citations
8.
Pae, Dong-Sung, et al.. (2021). Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving. Applied Sciences. 11(8). 3703–3703. 16 indexed citations
9.
Kim, Dong Hwan, Woo Jin Ahn, Myo Taeg Lim, Tae‐Koo Kang, & Dong-Won Kim. (2021). Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder. Applied Sciences. 11(6). 2873–2873. 5 indexed citations
10.
Lee, Yun Kyu, et al.. (2020). Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features. Applied Sciences. 10(10). 3501–3501. 49 indexed citations
11.
Kim, Jun-Sik, et al.. (2020). Tendon-Inspired Piezoelectric Sensor for Biometric Application. IEEE/ASME Transactions on Mechatronics. 26(5). 2538–2547. 18 indexed citations
12.
Kim, Dong-Won, et al.. (2019). MIFT: A Moment-Based Local Feature Extraction Algorithm. Applied Sciences. 9(7). 1503–1503. 4 indexed citations
13.
Lee, Yun Kyu, et al.. (2019). Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network. Applied Sciences. 9(16). 3355–3355. 70 indexed citations
14.
Pae, Dong-Sung, et al.. (2018). A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field. Journal of Advanced Transportation. 2018. 1–15. 38 indexed citations
15.
Lee, Won‐Jae, Dong-Won Kim, Tae‐Koo Kang, & Myo Taeg Lim. (2018). Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection. Applied Sciences. 8(12). 2468–2468. 4 indexed citations
16.
Pak, Jung Min, Choon Ki Ahn, Myo Taeg Lim, & Yuriy S. Shmaliy. (2014). Switching extensible FIR filter bank for adaptive horizon size in FIR filtering. European Signal Processing Conference. 711–715. 2 indexed citations
17.
Kim, Young Joong & Myo Taeg Lim. (2006). Parallel Robust H ∞ Control for Weakly Coupled Bilinear Systems with Parameter Uncertainties Using Successive Galerkin Approximation. International Journal of Control Automation and Systems. 4(6). 689–696. 4 indexed citations
18.
Lim, Myo Taeg, et al.. (2004). Prediction of Etch Profile Uniformity Using Wavelet and Neural Network. International Journal of Control Automation and Systems. 2(2). 256–262. 5 indexed citations
19.
Kim, Beom Soo & Myo Taeg Lim. (2003). Robust H ∞ Control Method for Bilinear Systems. International Journal of Control Automation and Systems. 1(2). 171–177. 14 indexed citations
20.
Kang, Chung G., et al.. (2000). Frequency Reuse Efficiency under Reverse-Link Closed-Loop Power Control with Unequal Cell Loading in a CDMA Cellular System. IEICE Transactions on Communications. 83(6). 1366–1369.

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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