Changhyeon Kim

1.5k total citations
36 papers, 1.2k citations indexed

About

Changhyeon Kim is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Changhyeon Kim has authored 36 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Electrical and Electronic Engineering, 12 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Changhyeon Kim's work include Advanced Memory and Neural Computing (10 papers), CCD and CMOS Imaging Sensors (9 papers) and Face and Expression Recognition (7 papers). Changhyeon Kim is often cited by papers focused on Advanced Memory and Neural Computing (10 papers), CCD and CMOS Imaging Sensors (9 papers) and Face and Expression Recognition (7 papers). Changhyeon Kim collaborates with scholars based in South Korea and Belgium. Changhyeon Kim's co-authors include Hoi‐Jun Yoo, Sanghoon Kang, Dongjoo Shin, Jinmook Lee, Sangyeob Kim, Kyeongryeol Bong, Sungpill Choi, Kuk Young Cho, Jung‐Ki Park and Donghyeon Han and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Journal of Applied Polymer Science and Polymer Engineering and Science.

In The Last Decade

Changhyeon Kim

34 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Changhyeon Kim South Korea 17 674 485 225 125 119 36 1.2k
Parviz Keshavarzi Iran 17 428 0.6× 146 0.3× 231 1.0× 120 1.0× 157 1.3× 68 1.0k
Guangjun Xie China 16 739 1.1× 66 0.1× 113 0.5× 15 0.1× 89 0.7× 141 1.1k
Xing Hu China 19 851 1.3× 327 0.7× 618 2.7× 29 0.2× 27 0.2× 64 1.4k
Tony F. Wu United States 21 1.2k 1.7× 69 0.1× 186 0.8× 11 0.1× 155 1.3× 38 1.5k
Wen Zhou China 15 597 0.9× 292 0.6× 235 1.0× 3 0.0× 96 0.8× 49 1.1k
Tianhang Zheng China 14 283 0.4× 174 0.4× 544 2.4× 9 0.1× 53 0.4× 36 994
Bashir I. Morshed United States 16 218 0.3× 88 0.2× 74 0.3× 51 0.4× 512 4.3× 116 1.4k
Chang Gao China 19 381 0.6× 230 0.5× 214 1.0× 7 0.1× 221 1.9× 70 1.1k
Jiadong Wang China 15 286 0.4× 94 0.2× 124 0.6× 32 0.3× 34 0.3× 44 731
Hazem M. Abbas Egypt 18 162 0.2× 393 0.8× 286 1.3× 4 0.0× 40 0.3× 109 1.0k

Countries citing papers authored by Changhyeon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Changhyeon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changhyeon Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Changhyeon Kim. A scholar is included among the top collaborators of Changhyeon 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 Changhyeon Kim. Changhyeon 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.
Kim, Changhyeon, et al.. (2024). Hyper-CL: Conditioning Sentence Representations with Hypernetworks. 700–711.
2.
Lee, Juhyoung, Changhyeon Kim, Donghyeon Han, et al.. (2021). Energy-Efficient Deep Reinforcement Learning Accelerator Designs for Mobile Autonomous Systems. 1–4. 1 indexed citations
3.
Lee, Kyoung-Rog, Ji-Hoon Kim, Changhyeon Kim, et al.. (2020). A 1.02-μW STT-MRAM-Based DNN ECG Arrhythmia Monitoring SoC With Leakage-Based Delay MAC Unit. IEEE Solid-State Circuits Letters. 3. 390–393. 16 indexed citations
4.
Han, Donghyeon, et al.. (2020). A 0.22–0.89 mW Low-Power and Highly-Secure Always-On Face Recognition Processor With Adversarial Attack Prevention. IEEE Transactions on Circuits & Systems II Express Briefs. 67(5). 846–850. 11 indexed citations
5.
Kim, Ji-Hoon, Changhyeon Kim, Kwantae Kim, & Hoi‐Jun Yoo. (2019). An Ultra-Low-Power Analog-Digital Hybrid CNN Face Recognition Processor Integrated with a CIS for Always-on Mobile Devices. 24 indexed citations
6.
Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision. 218–220. 232 indexed citations
7.
Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision. IEEE Journal of Solid-State Circuits. 54(1). 173–185. 236 indexed citations
8.
Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: A 50.6TOPS/W Energy-Efficient Unified Deep Neural-Network Accelerator with 1-to-16b Fully Variable Bit Precision UNPU: A 50.6TOPS/W Energy-Efficient Unified Deep Neural-Network Accelerator with 1-to-16b Fully Variable Bit Precision. 1 indexed citations
9.
Kang, Sanghoon, Jinmook Lee, Kyeongryeol Bong, et al.. (2018). Low-Power Scalable 3-D Face Frontalization Processor for CNN-Based Face Recognition in Mobile Devices. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 8(4). 873–883. 11 indexed citations
10.
Kang, Sanghoon, Jinmook Lee, Changhyeon Kim, & Hoi‐Jun Yoo. (2018). B-Face: 0.2 MW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification. 137–138. 20 indexed citations
11.
Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, et al.. (2017). 14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector. 248–249. 106 indexed citations
12.
Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, & Hoi‐Jun Yoo. (2017). Low-Power Convolutional Neural Network Processor for a Face-Recognition System. IEEE Micro. 37(6). 30–38. 24 indexed citations
13.
Kim, Changhyeon, Kyeongryeol Bong, Injoon Hong, et al.. (2017). An ultra-low-power and mixed-mode event-driven face detection SoC for always-on mobile applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 255–258. 23 indexed citations
14.
Kim, Changhyeon, et al.. (2016). Construction of a Generalized DFT Codebook Using Channel-Adaptive Parameters. IEEE Communications Letters. 21(1). 196–199. 22 indexed citations
15.
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
16.
Lee, Kyuho, Kyeongryeol Bong, Changhyeon Kim, Jun-Young Park, & Hoi‐Jun Yoo. (2016). An energy-efficient parallel multi-core ADAS processor with robust visual attention and workload-prediction DVFS for real-time HD stereo stream. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3. 2 indexed citations
17.
Yoo, Hoi‐Jun, Unsoo Ha, Yongsu Lee, et al.. (2015). A Wearable EEG-HEG-HRV Multimodal System with Real-time Transcranial Electrical Stimulation Monitoring for Mental Health Management. 396–397. 3 indexed citations
18.
Ha, Unsoo, Yongsu Lee, Hyunki Kim, et al.. (2015). 21.9 A wearable EEG-HEG-HRV multimodal system with real-time tES monitoring for mental health management. 1–3. 4 indexed citations
19.
Kim, Changhyeon, et al.. (2015). A parallel migration scheme for fast virtual machine relocation on a cloud cluster. The Journal of Supercomputing. 71(12). 4623–4645. 11 indexed citations
20.
Ha, Unsoo, Changhyeon Kim, Yongsu Lee, et al.. (2015). A multimodal stress monitoring system with canonical correlation analysis. PubMed. 2015. 1263–1266. 8 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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