Junyoung Park

784 total citations · 1 hit paper
23 papers, 451 citations indexed

About

Junyoung Park is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Junyoung Park has authored 23 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Electrical and Electronic Engineering, 8 papers in Computer Vision and Pattern Recognition and 5 papers in Computer Networks and Communications. Recurrent topics in Junyoung Park's work include CCD and CMOS Imaging Sensors (8 papers), Advanced Memory and Neural Computing (6 papers) and Optimization and Search Problems (3 papers). Junyoung Park is often cited by papers focused on CCD and CMOS Imaging Sensors (8 papers), Advanced Memory and Neural Computing (6 papers) and Optimization and Search Problems (3 papers). Junyoung Park collaborates with scholars based in South Korea and United States. Junyoung Park's co-authors include Jinkyoo Park, Sang Hun Kim, Youngkook Kim, Hoi‐Jun Yoo, Seungjin Lee, Jinwook Oh, Lance Manuel, Injoon Hong, Gyeonghoon Kim and Joo-Young Kim and has published in prestigious journals such as Energy, IEEE Journal of Solid-State Circuits and International Journal of Production Research.

In The Last Decade

Junyoung Park

19 papers receiving 438 citations

Hit Papers

Learning to schedule job-shop problems: representation an... 2021 2026 2022 2024 2021 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junyoung Park South Korea 10 186 166 93 61 58 23 451
Kaspars Ozols Latvia 9 63 0.3× 63 0.4× 101 1.1× 75 1.2× 39 0.7× 37 391
Shiyu Li China 11 43 0.2× 63 0.4× 150 1.6× 44 0.7× 57 1.0× 49 406
Jiaxiang Luo China 12 211 1.1× 39 0.2× 70 0.8× 76 1.2× 25 0.4× 57 449
Sanchita Basak United States 7 21 0.1× 82 0.5× 154 1.7× 79 1.3× 50 0.9× 13 431
Muhammad Ilyas Menhas China 12 37 0.2× 125 0.8× 120 1.3× 98 1.6× 48 0.8× 30 391
Bilal Babayiğit Türkiye 15 29 0.2× 244 1.5× 108 1.2× 44 0.7× 91 1.6× 43 614
Simon Wiedemann Germany 7 54 0.3× 39 0.2× 99 1.1× 69 1.1× 17 0.3× 14 333
Ankit Saroj India 10 27 0.1× 105 0.6× 176 1.9× 103 1.7× 24 0.4× 11 531
Zhuangkun Wei United Kingdom 13 18 0.1× 137 0.8× 52 0.6× 21 0.3× 62 1.1× 44 376

Countries citing papers authored by Junyoung Park

Since Specialization
Citations

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

Fields of papers citing papers by Junyoung Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junyoung Park

This figure shows the co-authorship network connecting the top 25 collaborators of Junyoung Park. A scholar is included among the top collaborators of Junyoung Park 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 Junyoung Park. Junyoung Park 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.
Park, Junyoung, et al.. (2025). Retrieval-Augmented Generation with Estimation of Source Reliability. 34267–34291.
2.
Park, Junyoung, Yunho Kim, & Insu Yun. (2025). RGFuzz: Rule-Guided Fuzzer for WebAssembly Runtimes. 920–938.
3.
Lee, Adam J., et al.. (2024). Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks. 15155–15165. 1 indexed citations
4.
Park, Junyoung, et al.. (2024). Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems. Transportation Science. 59(2). 322–339. 6 indexed citations
5.
Kim, Kyung Hwan, et al.. (2023). Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model. Radiotherapy and Oncology. 183. 109617–109617. 14 indexed citations
6.
Kim, Sang Hun, et al.. (2023). Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learning. Journal of Manufacturing Science and Engineering. 146(1). 2 indexed citations
7.
Choi, Hosik, et al.. (2023). Fréchet distance-based cluster analysis for multi-dimensional functional data. Statistics and Computing. 33(4). 1 indexed citations
8.
Park, Junyoung, et al.. (2021). Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research. 59(11). 3360–3377. 196 indexed citations breakdown →
9.
Park, Junyoung & Jinkyoo Park. (2019). Physics-induced graph neural network: An application to wind-farm power estimation. Energy. 187. 115883–115883. 78 indexed citations
10.
Park, Junyoung, et al.. (2018). Predicting Wind Turbine Power and Load Outputs by Multi-task Convolutional LSTM Model. 1–5. 23 indexed citations
11.
Park, Junyoung, et al.. (2015). A Free Market Economy Model for Resource Management in Wireless Sensor Networks. Wireless Sensor Network. 7(6). 76–82. 4 indexed citations
12.
Hong, Injoon, Seong‐Wook Park, Junyoung Park, & Hoi‐Jun Yoo. (2015). A 1.9nJ/pixel embedded deep neural network processor for high speed visual attention in a mobile vision recognition SoC. 22. 1–4. 3 indexed citations
13.
Kim, Gyeonghoon, Seong‐Wook Park, Kyuho Lee, et al.. (2014). A task-level pipelined many-SIMD augmented reality processor with congestion-aware network-on-chip scheduler. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3.
14.
Kim, Hyunju, et al.. (2013). A Study of Fuzzy Inference System Based Task Prioritizations for the Improvement of Tracking Performance in Multi-Function Radar. The Journal of Korean Institute of Electromagnetic Engineering and Science. 24(2). 198–206. 1 indexed citations
15.
Park, Junyoung, Injoon Hong, Gyeonghoon Kim, et al.. (2013). A multi-granularity parallelism object recognition processor with content-aware fine-grained task scheduling. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3.
16.
Park, Junyoung, et al.. (2012). A 92-mW Real-Time Traffic Sign Recognition System With Robust Illumination Adaptation and Support Vector Machine. IEEE Journal of Solid-State Circuits. 47(11). 2711–2723. 12 indexed citations
17.
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
18.
Park, Junyoung, Injoon Hong, Gyeonghoon Kim, et al.. (2012). Online Reinforcement Learning NoC for portable HD object recognition processor. 20. 1–4. 1 indexed citations
19.
Oh, Jinwook, Junyoung Park, Gyeonghoon Kim, Seungjin Lee, & Hoi‐Jun Yoo. (2011). A 57mW embedded mixed-mode neuro-fuzzy accelerator for intelligent multi-core processor. 21. 130–132. 15 indexed citations
20.
Kim, Joo-Young, Junyoung Park, Seungjin Lee, et al.. (2010). A 118.4 GB/s Multi-Casting Network-on-Chip With Hierarchical Star-Ring Combined Topology for Real-Time Object Recognition. IEEE Journal of Solid-State Circuits. 45(7). 1399–1409. 19 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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