Gyeonghoon Kim

445 total citations
42 papers, 348 citations indexed

About

Gyeonghoon Kim is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Gyeonghoon Kim has authored 42 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Electrical and Electronic Engineering, 20 papers in Computer Vision and Pattern Recognition and 7 papers in Aerospace Engineering. Recurrent topics in Gyeonghoon Kim's work include CCD and CMOS Imaging Sensors (17 papers), Advanced Image and Video Retrieval Techniques (12 papers) and Advanced Memory and Neural Computing (9 papers). Gyeonghoon Kim is often cited by papers focused on CCD and CMOS Imaging Sensors (17 papers), Advanced Image and Video Retrieval Techniques (12 papers) and Advanced Memory and Neural Computing (9 papers). Gyeonghoon Kim collaborates with scholars based in South Korea, United States and Singapore. Gyeonghoon Kim's co-authors include Hoi‐Jun Yoo, Injoon Hong, Kyuho Lee, Jinwook Oh, Kyeongryeol Bong, Seungjin Lee, Youchang Kim, Byeong‐Gyu Nam, Jun-Young Park and Changhyeon Kim and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Electronics Letters and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Gyeonghoon Kim

40 papers receiving 341 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gyeonghoon Kim South Korea 12 190 173 55 39 37 42 348
Injoon Hong South Korea 11 195 1.0× 163 0.9× 55 1.0× 26 0.7× 48 1.3× 37 323
Shyh-Yih Ma Taiwan 10 157 0.8× 589 3.4× 39 0.7× 17 0.4× 16 0.4× 17 727
Fengwei An China 11 128 0.7× 198 1.1× 26 0.5× 73 1.9× 11 0.3× 71 330
Nabeel Khan United Kingdom 13 196 1.0× 211 1.2× 63 1.1× 46 1.2× 114 3.1× 36 448
Chunyan Wang Canada 10 98 0.5× 159 0.9× 27 0.5× 27 0.7× 10 0.3× 56 394
Stephan Hengstler United States 9 192 1.0× 157 0.9× 44 0.8× 13 0.3× 192 5.2× 10 406
Yi‐Min Tsai Taiwan 11 117 0.6× 346 2.0× 33 0.6× 29 0.7× 13 0.4× 31 436
Zhiyuan Gao China 10 264 1.4× 76 0.4× 79 1.4× 19 0.5× 6 0.2× 60 371
Rengarajan Pelapur United States 10 87 0.5× 175 1.0× 44 0.8× 30 0.8× 40 1.1× 19 329

Countries citing papers authored by Gyeonghoon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Gyeonghoon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gyeonghoon Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Gyeonghoon Kim. A scholar is included among the top collaborators of Gyeonghoon Kim 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 Gyeonghoon Kim. Gyeonghoon Kim 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.
Jung, Seulgi, Byong Duk Ye, Ho‐Su Lee, et al.. (2021). Identification of Three Novel Susceptibility Loci for Inflammatory Bowel Disease in Koreans in an Extended Genome-Wide Association Study. Journal of Crohn s and Colitis. 15(11). 1898–1907. 14 indexed citations
2.
Kim, Gyeonghoon, et al.. (2021). A Study on the Water Quality Improvement of Major Tributaries in Seoul, Applying Watershed E valuation T ec hniques. 37(1). 32–46. 1 indexed citations
3.
Shin, Sangmin, et al.. (2020). Priority Selection of Water Quality Improvement Through Water Quality Data of Tributaries of Nakdong River. 36(5). 364–372. 1 indexed citations
4.
Kim, Gyeonghoon, et al.. (2019). Comparative Study on Evaluating Standard Flow in Partially Gauged and Ungauged Watershed. 35(6). 481–496. 2 indexed citations
5.
6.
Yoo, Hoi Jun, Kyuho Lee, Kyeongryeol Bong, et al.. (2016). A 502GOPS and 0.984mW Dual-Mode ADAS SoC with RNN-FIS Engine for Intention Prediction in Automotive Black-Box System. 13 indexed citations
7.
Hong, Injoon, Gyeonghoon Kim, Youchang Kim, et al.. (2015). A 27 mW Reconfigurable Marker-Less Logarithmic Camera Pose Estimation Engine for Mobile Augmented Reality Processor. IEEE Journal of Solid-State Circuits. 50(11). 2513–2523. 10 indexed citations
8.
Kim, Gyeonghoon, Sungpill Choi, & Hoi‐Jun Yoo. (2015). K-glass: Real-time markerless augmented reality smart glasses platform. 1712–1717. 1 indexed citations
9.
Kim, Gyeonghoon, Youchang Kim, Kyuho Lee, et al.. (2014). 10.4 A 1.22TOPS and 1.52mW/MHz augmented reality multi-core processor with neural network NoC for HMD applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 182–183. 10 indexed citations
10.
Hong, Injoon, Gyeonghoon Kim, Youchang Kim, et al.. (2014). A 27mW reconfigurable marker-less logarithmic camera pose estimation engine for mobile augmented reality processor. 57. 209–212. 2 indexed citations
11.
Kim, Gyeonghoon, Kyuho Lee, Youchang Kim, et al.. (2014). A 1.22 TOPS and 1.52 mW/MHz Augmented Reality Multicore Processor With Neural Network NoC for HMD Applications. IEEE Journal of Solid-State Circuits. 50(1). 113–124. 28 indexed citations
12.
Bong, Kyeongryeol, Gyeonghoon Kim, & Hoi‐Jun Yoo. (2014). Energy-efficient Mixed-mode support vector machine processor with analog Gaussian kernel. 1–4. 1 indexed citations
13.
Lee, Kyuho, Gyeonghoon Kim, Jun-Young Park, & Hoi‐Jun Yoo. (2014). A Vocabulary Forest-based object matching processor with 2.07M-vec/s throughput and 13.3nJ/vector energy in full-HD resolution. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–2. 1 indexed citations
14.
Hong, Injoon, et al.. (2013). A 125,582 vector/s throughput and 95.1% accuracy ANN searching processor with Neuro-Fuzzy Vision Cache for real-time object recognition. 6 indexed citations
15.
Hong, Injoon, et al.. (2013). A 646GOPS/W multi-classifier many-core processor with cortex-like architecture for super-resolution recognition. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 168–169. 35 indexed citations
16.
Oh, Jinwook, Gyeonghoon Kim, Byeong‐Gyu Nam, & Hoi‐Jun Yoo. (2013). A 57 mW 12.5 µJ/Epoch Embedded Mixed-Mode Neuro-Fuzzy Processor for Mobile Real-Time Object Recognition. IEEE Journal of Solid-State Circuits. 48(11). 2894–2907. 13 indexed citations
17.
Hong, Injoon, et al.. (2013). Intelligent Network-on-Chip With Online Reinforcement Learning for Portable HD Object Recognition Processor. IEEE Transactions on Circuits and Systems I Regular Papers. 61(2). 476–484. 8 indexed citations
18.
Oh, Jinwook, Gyeonghoon Kim, Junyoung Park, et al.. (2012). A 320 mW 342 GOPS Real-Time Dynamic Object Recognition Processor for HD 720p Video Streams. IEEE Journal of Solid-State Circuits. 48(1). 33–45. 23 indexed citations
19.
Oh, Jinwook, Gyeonghoon Kim, Injoon Hong, et al.. (2012). Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems. IEEE Micro. 32(6). 38–50. 8 indexed citations
20.
Oh, Jinwook, et al.. (2012). A 320mW 342GOPS real-time moving object recognition processor for HD 720p video streams. 220–222. 27 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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