Young-Jae Ryoo

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Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 September 2018 in International Journal of Fuzzy Logic and Intelligent Systems

Journal Article

Design of Fuzzy-PID Controller for Path Tracking of Mobile Robot with Differential Drive

Do Khac Tiep, Kinam Lee, Dae-Yeong Im, Bongwoo Kwak, and Young-Jae Ryoo. IJFIS 2018;18:220-8. https://doi.org/10.5391/IJFIS.2018.18.3.220

4 June 2020 in International Journal of Fuzzy Systems

Journal Article

Design of Kinematic Controller Based on Parameter Tuning by Fuzzy Inference System for Trajectory Tracking of Differential-Drive Mobile Robot

1 July 2016 in Sensors

Journal Article

Development of Torque Sensor with High Sensitivity for Joint of Robot Manipulator Using 4-Bar Linkage Shape

Hong-Xia Zhang

Young-Jae Ryoo

Kyung Seok Byun

The torque sensor is used to measure the joint torque of a robot manipulator. Previous research showed that the sensitivity and the stiffness of torque sensors have trade-off characteristics. Stiffness has to be sacrificed to increase the sensitivity of the sensor. In this research, a new torque sensor with high sensitivity (TSHS) is proposed in order to resolve this problem. The key idea of the TSHS comes from its 4-bar linkage shape in which the angular displacement of a short link is larger than that of a long link. The sensitivity of the torque sensor with a 4-bar link shape is improved without decreasing stiffness. Optimization techniques are applied to maximize the sensitivity of the sensor. An actual TSHS is constructed to verify the validity of the proposed mechanism. Experimental results show that the sensitivity of TSHS can be increased 3.5 times without sacrificing stiffness.

30 June 2017 in International Journal of Fuzzy Logic and Intelligent Systems

Journal Article

An Autonomous Control of Fuzzy-PD Controller for Quadcopter

Do Khac Tiep

Young-Jae Ryoo

Do Khac Tiep, and Young-Jae Ryoo. IJFIS 2017;17:107-13. https://doi.org/10.5391/IJFIS.2017.17.2.107

21 July 2025 in Biomimetics

Journal Article

Multi-Sensor Fusion Framework for Reliable Localization and Trajectory Tracking of Mobile Robot by Integrating UWB, Odometry, and AHRS

Quoc-Khai Tran

Young-Jae Ryoo

This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system’s effectiveness and suitability for real-time mobile robot navigation in indoor environments.

8 July 2022 in Sensors

Journal Article

Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images

Giang Thi Huong Truong

Khai Quoc Tran

Dae-Young Im

Young-Jae Ryoo

Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.

About Me

Young-Jae Ryoo received his Ph.D., MS, and BS degree in the Department of Electrical Engineering, Chonnam National University, South Korea in 1998, 1993, and 1991, respectively. He was a visiting professor in the Department of Mechanical Engineering, Virginia Tech, US from 2010 to 2012. He is currently a professor in the Department of Electrical and Control Engineering, Mokpo National University, South Korea since 2000. He also serves as a director of the intelligent space laboratory at Mokpo National University, where he is responsible for research projects in the areas of intelligence, robotics, and vehicles. He served as the president of the Korean Institute of Intelligent Systems in 2021. He is currently a board member of KIIS, an editor for the Journal of Korean Institute of Electrical Engineering since 2010, an editor for the Journal of Fuzzy Logic and Intelligent Systems since 2009, and a committee member of the International Symposium on Advanced Intelligent Systems since 2005. He served as a general chair of the International Symposium on Advanced Intelligent Systems in 2014 and 2015. He won outstanding paper awards, the best presentation awards, and the recognition awards in International Symposiums on Advanced Intelligent Systems. He is the author of over 200 technical publications. His research interests include artificial intelligence, humanoid robotics, legged robotics, wheeled robotics, autonomous vehicles, and futural vehicles.

Young-Jae Ryoo

Young-Jae Ryoo

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About Me