Sungho Suh

1.6k total citations · 1 hit paper
75 papers, 978 citations indexed

About

Sungho Suh is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Sungho Suh has authored 75 papers receiving a total of 978 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 20 papers in Electrical and Electronic Engineering and 17 papers in Artificial Intelligence. Recurrent topics in Sungho Suh's work include Context-Aware Activity Recognition Systems (17 papers), CCD and CMOS Imaging Sensors (12 papers) and Human Pose and Action Recognition (11 papers). Sungho Suh is often cited by papers focused on Context-Aware Activity Recognition Systems (17 papers), CCD and CMOS Imaging Sensors (12 papers) and Human Pose and Action Recognition (11 papers). Sungho Suh collaborates with scholars based in Germany, South Korea and United States. Sungho Suh's co-authors include Paul Lukowicz, Yong Oh Lee, Vítor Fortes Rey, Shinya Itoh, Shoji Kawahito, Bo Zhou, Seul Kee Kim, I W Choo, Satoshi Aoyama and Takashi Watanabe and has published in prestigious journals such as Radiology, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Sungho Suh

63 papers receiving 949 citations

Hit Papers

Human-centric artificial intelligence architecture for in... 2022 2026 2023 2024 2022 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Human-centric artificial intelligence architecture for industry 5.0 applications
    2022 137 citations Jože M. Rožanec, Inna Novalija et al. International Journal of Production Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sungho Suh Germany 16 234 174 155 150 136 75 978
Ying Bai United States 16 275 1.2× 181 1.0× 68 0.4× 221 1.5× 220 1.6× 92 1.1k
Montserrat Ros Australia 17 467 2.0× 144 0.8× 197 1.3× 91 0.6× 120 0.9× 86 1.3k
Ming‐Fong Tsai Taiwan 14 213 0.9× 146 0.8× 121 0.8× 70 0.5× 48 0.4× 75 799
Rajesh Kumar India 21 110 0.5× 291 1.7× 278 1.8× 502 3.3× 478 3.5× 74 1.5k
Friso De Boer Australia 20 100 0.4× 174 1.0× 464 3.0× 83 0.6× 243 1.8× 69 1.5k
José Manuel Menéndez Spain 18 248 1.1× 390 2.2× 56 0.4× 87 0.6× 33 0.2× 126 1.2k
Edmond Q. Wu China 20 97 0.4× 209 1.2× 200 1.3× 165 1.1× 105 0.8× 84 1.1k
Susana K. Lai-Yuen United States 13 98 0.4× 214 1.2× 337 2.2× 26 0.2× 88 0.6× 43 759

Countries citing papers authored by Sungho Suh

Since Specialization
Citations

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

Fields of papers citing papers by Sungho Suh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungho Suh

This figure shows the co-authorship network connecting the top 25 collaborators of Sungho Suh. A scholar is included among the top collaborators of Sungho Suh 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 Sungho Suh. Sungho Suh 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.
Suh, Sungho, et al.. (2025). Exploration and exploitation in continual learning. Neural Networks. 188. 107444–107444.
2.
Kim, Jeongdong, et al.. (2025). Assessing Grid Penalized Reinforcement Learning for Renewable Energy Management of Power-to-X Integrated With Intermediate Storage. IEEE Transactions on Sustainable Energy. 17(1). 407–420. 1 indexed citations
3.
Suh, Sungho, Artur Widera, Herwig Ott, et al.. (2025). LLM-generated tips rival expert-created tips in helping students answer quantum-computing questions. EPJ Quantum Technology. 12(1). 2 indexed citations
4.
Suh, Sungho, et al.. (2025). CoSS: Co-optimizing sensor and sampling rate for data-efficient human activity recognition. Expert Systems with Applications. 299. 129959–129959.
5.
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7.
Suh, Sungho, et al.. (2024). AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation. Knowledge-Based Systems. 293. 111692–111692. 3 indexed citations
8.
9.
Kim, Donghun, et al.. (2024). ContextMix: A context-aware data augmentation method for industrial visual inspection systems. Engineering Applications of Artificial Intelligence. 131. 107842–107842. 1 indexed citations
10.
Rey, Vítor Fortes, et al.. (2024). Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition. 1–8. 2 indexed citations
11.
Suh, Sungho, et al.. (2024). Fault Diagnosis of Indium Tin Oxide Electrodes by Multi-channel S-parameter Patterns. 1–4. 1 indexed citations
12.
Suh, Sungho, et al.. (2024). An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-Parameter Patterns. IEEE Transactions on Device and Materials Reliability. 24(3). 380–389.
13.
Suh, Sungho, et al.. (2023). InMyFace: Inertial and mechanomyography-based sensor fusion for wearable facial activity recognition. Information Fusion. 99. 101886–101886. 15 indexed citations
14.
Suh, Sungho, Vítor Fortes Rey, Sizhen Bian, et al.. (2023). Worker Activity Recognition in Manufacturing Line Using Near-Body Electric Field. IEEE Internet of Things Journal. 11(7). 11554–11565. 8 indexed citations
15.
Rožanec, Jože M., Inna Novalija, Patrik Zajec, et al.. (2022). Human-centric artificial intelligence architecture for industry 5.0 applications. International Journal of Production Research. 61(20). 6847–6872. 137 indexed citations breakdown →
16.
Suh, Sungho, et al.. (2020). CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems. Neural Networks. 133. 69–86. 65 indexed citations
17.
Suh, Sungho, Minsik Lee, & Chong‐Ho Choi. (2012). Robust Albedo Estimation From a Facial Image With Cast Shadow Under General Unknown Lighting. IEEE Transactions on Image Processing. 22(1). 391–401. 2 indexed citations
18.
Suh, Sungho, et al.. (2008). Immuno-regulatory Effect of Dryopteriscrassirhizoma Extract on Chicken Splenocyte. 68–71. 1 indexed citations
19.
Kim, Sang‐Heon, et al.. (2005). CMOS imager with 128×128 pixels for edge detection using a simplified CDS circuit. 대한전자공학회 ISOCC. 528–529.
20.
Suh, Sungho, et al.. (1996). Nonsurgical placement of a nasolacrimal polyurethane stent: long-term effectiveness.. Radiology. 200(3). 759–763. 53 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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