Sugil Lee

496 total citations
25 papers, 351 citations indexed

About

Sugil Lee is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Sugil Lee has authored 25 papers receiving a total of 351 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Sugil Lee's work include Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Advanced Neural Network Applications (6 papers). Sugil Lee is often cited by papers focused on Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Advanced Neural Network Applications (6 papers). Sugil Lee collaborates with scholars based in South Korea, United States and Saudi Arabia. Sugil Lee's co-authors include Jongeun Lee, Mohammed E. Fouda, Fadi Kurdahi, Hyeonuk Sim, Ahmed M. Eltawil, Chulung Lee, Sung Won Cho, Dong Thanh Nguyen, Dae Woo Kim and Gildong Kim and has published in prestigious journals such as IEEE Access, Sustainability and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Sugil Lee

22 papers receiving 344 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sugil Lee South Korea 12 213 80 74 44 43 25 351
Runsheng Liu China 10 75 0.4× 198 2.5× 54 0.7× 10 0.2× 31 0.7× 50 519
Morshed Chowdhury Australia 10 46 0.2× 55 0.7× 29 0.4× 91 2.1× 4 0.1× 24 273
Sergey Serebryakov United States 7 96 0.5× 80 1.0× 25 0.3× 35 0.8× 2 0.0× 31 215
Preeti Sharma India 9 82 0.4× 51 0.6× 151 2.0× 37 0.8× 4 0.1× 69 368
Dominik Baumann Germany 9 72 0.3× 30 0.4× 9 0.1× 85 1.9× 12 0.3× 19 235
Jianfeng Zhao China 9 182 0.9× 16 0.2× 29 0.4× 246 5.6× 3 0.1× 25 455
Nan Xiao China 9 154 0.7× 135 1.7× 33 0.4× 87 2.0× 23 457
Fangyuan Li China 12 148 0.7× 48 0.6× 13 0.2× 52 1.2× 6 0.1× 26 320
Zhewei Zhang China 9 91 0.4× 45 0.6× 48 0.6× 35 0.8× 1 0.0× 37 332

Countries citing papers authored by Sugil Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sugil Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sugil Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Sugil Lee. A scholar is included among the top collaborators of Sugil Lee 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 Sugil Lee. Sugil Lee 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.
Lee, Sugil, et al.. (2024). Mitigating the Impact of ReRAM I-V Nonlinearity and IR Drop via Fast Offline Network Training. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(3). 951–960.
2.
Kim, Gildong, et al.. (2024). Development of cold energy utilization of liquid hydrogen for a locomotive by pre-experimental approach. Journal of Mechanical Science and Technology. 39(1). 409–416. 1 indexed citations
3.
Fouda, Mohammed E., et al.. (2022). Training-Free Stuck-At Fault Mitigation for ReRAM-Based Deep Learning Accelerators. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42(7). 2174–2186. 7 indexed citations
4.
Lee, Sugil, Mohammed E. Fouda, Jongeun Lee, Ahmed M. Eltawil, & Fadi Kurdahi. (2022). Offline Training-Based Mitigation of IR Drop for ReRAM-Based Deep Neural Network Accelerators. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42(2). 521–532. 9 indexed citations
5.
Lee, Sugil, et al.. (2022). Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation. Sustainability. 14(2). 917–917. 23 indexed citations
6.
7.
Lee, Sugil, et al.. (2022). Promising Technology Analysis and Patent Roadmap Development in the Hydrogen Supply Chain. Sustainability. 14(21). 14210–14210. 7 indexed citations
8.
Sim, Hyeonuk, et al.. (2022). MLogNet: A Logarithmic Quantization-Based Accelerator for Depthwise Separable Convolution. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(12). 5220–5231. 5 indexed citations
9.
Lee, Sugil, Mohammed E. Fouda, Jongeun Lee, Ahmed M. Eltawil, & Fadi Kurdahi. (2022). Accurate Prediction of ReRAM Crossbar Performance Under I-V Nonlinearity and IR Drop. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 9–16. 3 indexed citations
10.
Sim, Hyeonuk, et al.. (2021). Automated Log-Scale Quantization for Low-Cost Deep Neural Networks. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 742–751. 16 indexed citations
11.
Lee, Sugil, Mohammed E. Fouda, Jongeun Lee, Ahmed M. Eltawil, & Fadi Kurdahi. (2021). Fast and Low-Cost Mitigation of ReRAM Variability for Deep Learning Applications. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 269–276. 5 indexed citations
12.
Fouda, Mohammed E., et al.. (2021). Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 1733–1738. 17 indexed citations
13.
Fouda, Mohammed E., et al.. (2020). IR-QNN Framework: An IR Drop-Aware Offline Training of Quantized Crossbar Arrays. IEEE Access. 8. 228392–228408. 24 indexed citations
14.
Lee, Sugil, et al.. (2020). Learning to Predict IR Drop with Effective Training for ReRAM-based Neural Network Hardware. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–6. 35 indexed citations
15.
Fouda, Mohammed E., Sugil Lee, Jongeun Lee, Ahmed M. Eltawil, & Fadi Kurdahi. (2019). Mask Technique for Fast and Efficient Training of Binary Resistive Crossbar Arrays. IEEE Transactions on Nanotechnology. 18. 704–716. 34 indexed citations
16.
Lee, Sugil, et al.. (2019). Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer. Sustainability. 11(22). 6240–6240. 45 indexed citations
17.
Lee, Sugil, et al.. (2019). Analysis of Research Trends of Wireless Power Transfer System for Locomotives Using Topic Modeling Based on LDA Algorithm. Journal of Korean Institute of Industrial Engineers. 45(4). 284–301. 3 indexed citations
18.
19.
Lee, Sugil, et al.. (2018). Sign-magnitude SC. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–6. 18 indexed citations
20.
Lee, Sugil, Dae Woo Kim, Dong Thanh Nguyen, & Jongeun Lee. (2018). Double MAC on a DSP: Boosting the Performance of Convolutional Neural Networks on FPGAs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 38(5). 888–897. 25 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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