https://scholar.dgist.ac.kr/handle/20.500.11750/109 2026-10-03T22:30:54Z 2026-10-03T22:30:54Z Lee, Kyungbae Lee, Seungyeop Kim, Seunghyeon Eun, Yongsoon https://scholar.dgist.ac.kr/handle/20.500.11750/60894 2026-09-29T05:10:14Z 2026-08-31T15:00:00Z

Title: Reinforcement Learning-Based Metro Train Tracking Control to Overcome Input Time Delay Author(s): Lee, Kyungbae; Lee, Seungyeop; Kim, Seunghyeon; Eun, Yongsoon Abstract: Speed tracking and precise stop control of metro trains play a key role in train operation. Reinforcement learning (RL) is one of the methods that can solve train control challenges and adapt to various environments. However, when applying RL to train control, the input time delay of the train presents particular difficulties. This paper proposes a RL design method that integrates prediction techniques and the deep deterministic policy gradient algorithm to overcome the input time delay. The training and performance evaluation of the proposed RL is conducted using the validated Automatic Train Operation (ATO) simulator for Seoul Metro Line 5. The results demonstrate that prediction-based RL (PRL) controllers outperform RL without prediction controllers. Additionally, the PRL controller robustness is verified through performance comparisons with a classical model-based approach across various realistic scenarios.

2026-08-31T15:00:00Z Lee, Haeyun Lee, Kyungsu Lee, Moon Hwan Kim, Sewoong Eun, Yongsoon Eun, Lucy Youngmin Hwang, Jae Youn https://scholar.dgist.ac.kr/handle/20.500.11750/58607 2026-02-22T12:40:14Z 2025-08-31T15:00:00Z

Title: Expert-level differentiation of incomplete Kawasaki disease and pneumonia from echocardiography via multiple large receptive attention mechanisms Author(s): Lee, Haeyun; Lee, Kyungsu; Lee, Moon Hwan; Kim, Sewoong; Eun, Yongsoon; Eun, Lucy Youngmin; Hwang, Jae Youn Abstract: Background: Incomplete Kawasaki disease (KD) is challenging to diagnose due to its lack of classic clinical features, yet it has a higher incidence of coronary artery lesions, making early detection crucial. Echocardiography plays a vital role in identifying these lesions, but differentiating incomplete KD from other febrile illnesses, such as COVID-19, is difficult. Algorithms capable of achieving expert-level performance are needed to aid diagnosis, particularly in the absence of pediatric cardiologists. Methods: To address this need, we developed two novel deep learning models: the Multiple Receptive Attention Network (MRANet) and the Multiple Large Receptive Attention Network (MLRANet). These models incorporate multiple receptive attention layers and multiple large receptive attention layers to enhance their ability to identify KD-related coronary artery abnormalities on echocardiography. The models were trained and tested on 203 echocardiographic datasets and compared with advanced deep learning models to assess diagnostic performance. Results: Both MRANet and MLRANet outperformed existing deep learning models, achieving diagnostic accuracy comparable to experienced pediatric cardiologists. Notably, MLRANet demonstrated the highest sensitivity (93.48%) and specificity (66.15%), exceeding expert-level performance in detecting coronary artery abnormalities. Furthermore, MLRANet was able to distinguish incomplete KD from pneumonia effectively, showing diagnostic results aligned with the KD specialists. Conclusions: MLRANet has proven to be a valuable tool for computer-aided diagnosis of incomplete KD, offering accurate and reliable detection of coronary artery abnormalities without requiring specialist input. These findings suggest that MLRANet can facilitate timely and precise incomplete KD diagnosis, improving patient outcomes and addressing the shortage of pediatric cardiologists worldwide. © 2025 Elsevier Ltd

2025-08-31T15:00:00Z An, Youngwoo Eun, Yongsoon https://scholar.dgist.ac.kr/handle/20.500.11750/58488 2025-07-25T02:46:44Z 2025-05-31T15:00:00Z

Title: Online Sensor Fault Detection and Toleration for Four-wheeled Skid-steered UGV Author(s): An, Youngwoo; Eun, Yongsoon Abstract: This paper presents a fault detection and toleration scheme for Unmanned Ground Vehicles (UGVs) with two position sensors and orientation sensors. Four representative types of sensor faults are considered: complete fault, bias fault, drift fault, and precision degradation. The proposed detection method consists of a Long Short-Term Memory (LSTM) Network Module, an Amplitude Difference Thresholding Module, and an Actuation Motion Coherence Module. A Husarion Rosbot 2.0 and VICON motion capture system compose a platform that is used to collect motion data for network training and experimental validation of the proposed scheme. Sensor fault detection performance is experimentally validated using a trajectory that was not included in the training data set. The fault detection accuracy is compared to other learning-based fault detection methods. Based on the fault detection result, we propose the fault toleration method. © ICROS, KIEE and Springer 2025.

2025-05-31T15:00:00Z Kim, Seunghyeon Park, Kyung-Joon Eun, Yongsoon https://scholar.dgist.ac.kr/handle/20.500.11750/57207 2026-01-08T04:10:11Z 2024-10-31T15:00:00Z

Title: Buffer Parameter Optimization for Advanced Automated Material Handling Systems in Serial Production Lines Author(s): Kim, Seunghyeon; Park, Kyung-Joon; Eun, Yongsoon Abstract: An automated material handling system (AMHS) is a production line component responsible for transporting products from one machine to another for manufacturing processes. The AMHS also acts as a buffer that enhances overall productivity by reducing the dependency on individual machine operations. This paper introduces a buffer parameter optimization algorithm designed for advanced AMHS with the capability to control the speed of individual products. The buffer parameters targeted for optimization are buffer length (distance between machines) and transfer speed. The algorithm addresses each parameter separately through two distinct optimization problems. The buffer length optimization problem is formulated with the constraint of limited space assigned to the production system. On the other hand, the transfer speed optimization problem is formulated based on the constraints of network resources and hardware limitations. The proposed algorithm employs an aggregation method to evaluate the performance of the production systems analytically. © ICROS, KIEE and Springer 2024.

2024-10-31T15:00:00Z