Nak Young Chong

2.7k total citations
184 papers, 1.8k citations indexed

About

Nak Young Chong is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Mechanical Engineering. According to data from OpenAlex, Nak Young Chong has authored 184 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Computer Vision and Pattern Recognition, 62 papers in Control and Systems Engineering and 53 papers in Mechanical Engineering. Recurrent topics in Nak Young Chong's work include Robot Manipulation and Learning (45 papers), Robotic Path Planning Algorithms (35 papers) and Modular Robots and Swarm Intelligence (31 papers). Nak Young Chong is often cited by papers focused on Robot Manipulation and Learning (45 papers), Robotic Path Planning Algorithms (35 papers) and Modular Robots and Swarm Intelligence (31 papers). Nak Young Chong collaborates with scholars based in Japan, South Korea and United States. Nak Young Chong's co-authors include Myung-Sik Kim, Geun-Ho Lee, Geun-Ho Lee, Sungmoon Jeong, Armağan Elibol, K. Tanie, Kohtaro Ohba, Woosung Yang, Tetsuo Kotoku and K. Komoriya and has published in prestigious journals such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Robotics and IEEE/ASME Transactions on Mechatronics.

In The Last Decade

Nak Young Chong

173 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nak Young Chong Japan 23 544 521 404 387 364 184 1.8k
Paul E. Rybski United States 29 472 0.9× 878 1.7× 345 0.9× 227 0.6× 315 0.9× 74 2.2k
Karsten Berns Germany 22 685 1.3× 664 1.3× 539 1.3× 842 2.2× 108 0.3× 270 2.3k
Nathan Koenig United States 9 765 1.4× 1.1k 2.0× 374 0.9× 298 0.8× 276 0.8× 13 2.5k
Antonio Sgorbissa Italy 22 351 0.6× 802 1.5× 146 0.4× 167 0.4× 276 0.8× 136 1.5k
Péter Köröndi Hungary 22 844 1.6× 323 0.6× 428 1.1× 206 0.5× 94 0.3× 196 1.8k
Kai‐Tai Song Taiwan 23 824 1.5× 1.4k 2.6× 188 0.5× 358 0.9× 176 0.5× 147 2.1k
Cipriano Galindo Spain 20 420 0.8× 718 1.4× 211 0.5× 129 0.3× 139 0.4× 70 1.7k
Dominic Létourneau Canada 18 408 0.8× 323 0.6× 238 0.6× 202 0.5× 152 0.4× 68 1.1k
Alexander Zelinsky Australia 23 394 0.7× 890 1.7× 207 0.5× 185 0.5× 146 0.4× 69 1.6k
Jennifer Casper United States 9 264 0.5× 341 0.7× 314 0.8× 155 0.4× 144 0.4× 15 961

Countries citing papers authored by Nak Young Chong

Since Specialization
Citations

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

Fields of papers citing papers by Nak Young Chong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nak Young Chong. 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 Nak Young Chong. The network helps show where Nak Young Chong may publish in the future.

Co-authorship network of co-authors of Nak Young Chong

This figure shows the co-authorship network connecting the top 25 collaborators of Nak Young Chong. A scholar is included among the top collaborators of Nak Young Chong 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 Nak Young Chong. Nak Young Chong 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.
Elibol, Armağan, et al.. (2024). Abdominal multi-organ segmentation using multi-scale and context-aware neural networks. 27. 100249–100249. 2 indexed citations
2.
Chong, Nak Young, et al.. (2024). Wall-Sticking Drone for Non-Destructive Inspection of Oblique Planes. 1741–1746. 1 indexed citations
3.
Chong, Nak Young, et al.. (2024). Safety-Optimized Strategy for Grasp Detection in High-Clutter Scenarios. 192–197. 2 indexed citations
4.
Lee, Soon‐Geul, et al.. (2024). Intelligent Autonomous Systems 18. Lecture notes in networks and systems. 4 indexed citations
5.
Elibol, Armağan, et al.. (2023). Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network. IFAC-PapersOnLine. 56(2). 3001–3008. 1 indexed citations
6.
Elibol, Armağan, et al.. (2023). Two-Path Augmented Directional Context Aware Ultrasound Image Segmentation. abs 1804 3999. 1815–1822. 2 indexed citations
7.
Papadopoulos, Chris, Tetiana Hill, Linda Battistuzzi, et al.. (2020). The CARESSES study protocol: testing and evaluating culturally competent socially assistive robots among older adults residing in long term care homes through a controlled experimental trial. Archives of Public Health. 78(1). 26–26. 33 indexed citations
8.
Chong, Nak Young, et al.. (2019). Robot Social Emotional Development through Memory Retrieval. 46–51. 2 indexed citations
9.
Chong, Nak Young, et al.. (2016). Novel Extrapolating Methods Using Exponential Function in Formation Control of Swarm Robots. Transactions of the Society of Instrument and Control Engineers. 52(1). 28–36. 1 indexed citations
10.
Défago, Xavier, et al.. (2014). 2A2-J01 Degree of Formation Shape Convergence in Swarm Robots : Let Equilateral Triangle Formation Be an Example(Swarm Robotics). The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2014(0). _2A2–J01_1.
11.
Lee, Geun-Ho, et al.. (2010). 2A1-G10 Decentralized Self-configuration of Robot Swarms in Three Dimensional Space. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2010(0). _2A1–G10_1. 1 indexed citations
12.
Lee, Geun-Ho, et al.. (2008). Decentralized Flocking Strategy for a Swarm of Robots Adapting to an Unknown Environment. Transactions of the Society of Instrument and Control Engineers. 44(1). 96–105. 2 indexed citations
13.
Lee, Geun-Ho, et al.. (2006). 2P2-C34 Decentralized Formation Control for Small-Scale Mobile Robot Teams. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2006(0). _2P2–C34_1. 2 indexed citations
14.
Kim, Myung-Sik, Takashi Kubo, & Nak Young Chong. (2005). RF Power Detector for Location Sensing. 제어로봇시스템학회 국제학술대회 논문집. 1771–1774. 8 indexed citations
15.
Komatsu, Yuki, Geun-Ho Lee, & Nak Young Chong. (2005). 1P2-S-028 Generation of formation for multiple autonomous mobile robots adapting to an environment(Cooperation Control of Multi Robot,Mega-Integration in Robotics and Mechatronics to Assist Our Daily Lives). The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2005(0). 124–124.
16.
Miyazaki, M., et al.. (2004). Knowledge Structure and Knowldge Management Framework for a Knowkedge Distributed Robot System. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2004(0). 143–143. 1 indexed citations
17.
Takemura, Kazuhisa, et al.. (2004). Knowledge Distributed Tag-Based Vision System. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2004(0). 190–190. 8 indexed citations
18.
Chong, Nak Young, Tetsuo Kotoku, Kohtaro Ohba, & K. Tanie. (2001). 2P2-K4 Remote Multi-telerobot Coordinated Control Using Virtual Repulsive Force Field. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2001(0). 76–76. 2 indexed citations
19.
Choi, Chang-Hwan, Kohtaro Ohba, Kyihwan Park, et al.. (2001). Multi-Camera Vision System for Tele-Robotics. 제어로봇시스템학회 국제학술대회 논문집. 75–78. 2 indexed citations
20.
Chong, Nak Young, et al.. (1989). COLLISION-FREE TRAJECTORY PLANNING FOR DUAL ROBOT ARMS. 2(2). 144–149. 1 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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