Young-Dae Hong

640 total citations
40 papers, 487 citations indexed

About

Young-Dae Hong is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Young-Dae Hong has authored 40 papers receiving a total of 487 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Biomedical Engineering, 15 papers in Computer Vision and Pattern Recognition and 12 papers in Aerospace Engineering. Recurrent topics in Young-Dae Hong's work include Robotic Locomotion and Control (27 papers), Prosthetics and Rehabilitation Robotics (23 papers) and Robotic Path Planning Algorithms (14 papers). Young-Dae Hong is often cited by papers focused on Robotic Locomotion and Control (27 papers), Prosthetics and Rehabilitation Robotics (23 papers) and Robotic Path Planning Algorithms (14 papers). Young-Dae Hong collaborates with scholars based in South Korea and United States. Young-Dae Hong's co-authors include Jong-Hwan Kim, Bum‐Joo Lee, Inwon Park, Donghan Kim, Jong-Hwan Kim, Chang‐Soo Park, Ye-Hoon Kim, Si‐Hyun Kim, Ji-Hyeong Han and Young-Joo Kim and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Sensors and Applied Sciences.

In The Last Decade

Young-Dae Hong

37 papers receiving 481 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Young-Dae Hong South Korea	14	342	169	109	97	67	40	487
Takahide Yoshiike Japan	14	668 2.0×	307 1.8×	72 0.7×	57 0.6×	64 1.0×	34	728
X Xinjilefu United States	10	500 1.5×	254 1.5×	108 1.0×	68 0.7×	74 1.1×	13	605
Ryan W. Sinnet United States	9	408 1.2×	216 1.3×	41 0.4×	44 0.5×	49 0.7×	15	485
Alexander Winkler Germany	12	524 1.5×	419 2.5×	214 2.0×	83 0.9×	118 1.8×	43	707
Kaveh Akbari Hamed United States	16	664 1.9×	339 2.0×	70 0.6×	97 1.0×	125 1.9×	50	805
Ross Hartley United States	6	328 1.0×	132 0.8×	69 0.6×	187 1.9×	43 0.6×	8	481
Rafael Cisneros Japan	11	316 0.9×	214 1.3×	58 0.5×	43 0.4×	74 1.1×	46	433
Mehdi Benallegue Japan	13	301 0.9×	189 1.1×	81 0.7×	37 0.4×	35 0.5×	38	414
Arturo Laurenzi Italy	14	398 1.2×	390 2.3×	185 1.7×	70 0.7×	187 2.8×	44	663
Enrico Mingo Hoffman Italy	13	461 1.3×	453 2.7×	180 1.7×	68 0.7×	185 2.8×	47	695

Countries citing papers authored by Young-Dae Hong

Since Specialization

Citations

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

Fields of papers citing papers by Young-Dae Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young-Dae Hong. 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 Young-Dae Hong. The network helps show where Young-Dae Hong may publish in the future.

Co-authorship network of co-authors of Young-Dae Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Young-Dae Hong. A scholar is included among the top collaborators of Young-Dae Hong 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 Young-Dae Hong. Young-Dae Hong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Oh, Hye-Won, et al.. (2025). Predicting elbow motion intention based on different electromyography and inertial measurement unit sensor fusion structure. Robotics and Autonomous Systems. 191. 105029–105029.

Oh, Hye-Won & Young-Dae Hong. (2023). Divergent Component of Motion-Based Gait Intention Detection Method Using Motion Information From Single Leg. Journal of Intelligent & Robotic Systems. 107(4). 3 indexed citations

Hong, Young-Dae. (2021). Real-time footstep planning including capture point trajectory optimization for stable biped navigation. Transactions of the Institute of Measurement and Control. 43(9). 2058–2068. 4 indexed citations

Kim, Si‐Hyun, et al.. (2019). Stability Control and Turning Algorithm of an Alpine Skiing Robot. Sensors. 19(17). 3664–3664. 7 indexed citations

Hong, Young-Dae, et al.. (2019). Logarithmic Strain Model for Nonlinear Load Cell. Sensors. 19(16). 3486–3486. 2 indexed citations

Hong, Young-Dae. (2019). Capture Point-Based Controller Using Real-Time Zero Moment Point Manipulation for Stable Bipedal Walking in Human Environment. Sensors. 19(15). 3407–3407. 13 indexed citations

Hong, Young-Dae, et al.. (2018). Approach toward footstep planning considering the walking period: Optimization-based fast footstep planning for humanoid robots. ETRI Journal. 40(4). 471–482. 1 indexed citations

Hong, Young-Dae, et al.. (2018). Optimization-based humanoid robot navigation using monocular camera within indoor environment. ETRI Journal. 40(4). 446–457. 7 indexed citations

Hong, Young-Dae, et al.. (2016). Adaptive Robust Swing-up and Balancing Control of Acrobot using a Fuzzy Disturbance Observer. Journal of Institute of Control Robotics and Systems. 22(5). 346–352. 1 indexed citations

10.

Hong, Young-Dae, et al.. (2016). Dynamic Simulation of Modifiable Bipedal Walking on Uneven Terrain with Unknown Height. Journal of Electrical Engineering and Technology. 11(3). 733–740. 2 indexed citations

11.

Hong, Young-Dae, et al.. (2016). Evolutionary Optimization for Optimal Hopping of Humanoid Robots. IEEE Transactions on Industrial Electronics. 64(2). 1279–1283. 16 indexed citations

12.

Hong, Young-Dae, et al.. (2016). Stable Walking of Humanoid Robots Using Vertical Center of Mass and Foot Motions by an Evolutionary Optimized Central Pattern Generator. International Journal of Advanced Robotic Systems. 13(1). 4 indexed citations

13.

Hong, Young-Dae. (2015). Real-time Footstep Planning and Following for Navigation of Humanoid Robots. Journal of Electrical Engineering and Technology. 10(5). 2142–2148. 5 indexed citations

14.

Hong, Young-Dae. (2015). An Evolutionary Optimization Approach for Optimal Hopping of Humanoid Robots. Journal of Electrical Engineering and Technology. 10(6). 2420–2426. 4 indexed citations

15.

Park, Chang‐Soo, Young-Dae Hong, & Jong-Hwan Kim. (2013). Evolutionary-Optimized Central Pattern Generator for Stable Modifiable Bipedal Walking. IEEE/ASME Transactions on Mechatronics. 19(4). 1374–1383. 22 indexed citations

16.

Hong, Young-Dae, et al.. (2013). Stable Bipedal Walking With a Vertical Center-of-Mass Motion by an Evolutionary Optimized Central Pattern Generator. IEEE Transactions on Industrial Electronics. 61(5). 2346–2355. 42 indexed citations

17.

Hong, Young-Dae & Jong-Hwan Kim. (2012). AN EVOLUTIONARY OPTIMIZED FOOTSTEP PLANNER FOR THE NAVIGATION OF HUMANOID ROBOTS. International Journal of Humanoid Robotics. 9(1). 1250005–1250005. 12 indexed citations

18.

Hong, Young-Dae & Jong-Hwan Kim. (2012). 3-D Command State-Based Modifiable Bipedal Walking on Uneven Terrain. IEEE/ASME Transactions on Mechatronics. 18(2). 657–663. 37 indexed citations

19.

Hong, Young-Dae, et al.. (2011). Human-like stable bipedal walking with a large stride by the height variation of the center of mass using an evolutionary optimized central pattern generator. 177–182. 4 indexed citations

20.

Hong, Young-Dae, et al.. (2010). Evolutionary Multiobjective Footstep Planning for Humanoid Robots. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 41(4). 520–532. 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.

Explore authors with similar magnitude of impact