https://scholar.dgist.ac.kr/handle/20.500.11750/110 2026-04-05T14:16:05Z 2026-04-05T14:16:05Z Lee, Haeyun Lee, Moon Hwan Youngmin, Lucy Eun, Yongsoon Hwang, Jae Youn https://scholar.dgist.ac.kr/handle/20.500.11750/58937 2025-08-22T07:40:10Z 2022-10-26T15:00:00Z

Title: Diagnosis of Incomplete Kawasaki Disease using Deep learning Techniques with Ultrasound Images of Coronary Artery Lesions Author(s): Lee, Haeyun; Lee, Moon Hwan; Youngmin, Lucy; Eun, Yongsoon; Hwang, Jae Youn Abstract: Kawasaki disease (KD) is the most common cause of acquired heart disease in young children and can lead to sudden death. Incomplete KD lacks clinical characteristics of KD and is thus difficult to distinguish from other diseases presenting similar symptoms. Although ultrasound imaging is useful to identify one of the most fatal complications, coronary aneurysms, the diagnosis of incomplete KD is still difficult due to its similar symptoms to other diseases. We here demonstrated the feasibility of the deep learning algorithms for the diagnosis of incomplete KD. Various deep learning networks were trained, and their accuracy was compared. Although the accuracy is lower than the experienced specialist, the experimental results suggest that deep learning algorithms may assist clinicians to diagnose KD. © ICA 2022.All rights reserved

2022-10-26T15:00:00Z Pedari, Yasaman Lee, Jaeho Eun, Yongsoon Ossareh, Hamid https://scholar.dgist.ac.kr/handle/20.500.11750/57855 2025-07-25T04:09:09Z 2024-07-11T15:00:00Z

Title: Data-Driven System Interconnections and a Novel Data-Enabled Internal Model Control Author(s): Pedari, Yasaman; Lee, Jaeho; Eun, Yongsoon; Ossareh, Hamid Abstract: Over the past two decades, there has been a growing interest in control systems research to transition from model-based methods to data-driven approaches. In this study, we aim to bridge a divide between conventional model-based control and emerging data-driven paradigms grounded in Willems' 'fundamental lemma'. Specifically, we study how input/output data from two separate systems can be manip-ulated to represent the behavior of interconnected systems, either connected in series or through feedback. Using these results, this paper introduces the Internal Behavior Control (IBC), a new control strategy based on the well-known Internal Model Control (IMC) but viewed under the lens of Behavioral System Theory. Similar to IMC, the IBC is easy to tune and results in perfect tracking and disturbance rejection but, unlike IMC, does not require a parametric model of the dynamics. We present two approaches for IBC implementation: a component-by-component one and a unified one. We compare the two approaches in terms of filter design, computations, and memory requirements. © 2024 AACC.

2024-07-11T15:00:00Z Lee, Kyungsu Lee, Haeyun Lee, Moon Hwan Yang, Jaeseung Kim, Sewoong Eun, Youngsoon Eun, Lucy Youngmin Hwang, Jae Youn https://scholar.dgist.ac.kr/handle/20.500.11750/57833 2025-07-25T03:38:14Z 2024-02-04T15:00:00Z

Title: Explainable Multiple Receptive Attention Network for Expert Cardiologist Compatible Incomplete Kawasaki Disease Diagnosis on Echocardiography Author(s): Lee, Kyungsu; Lee, Haeyun; Lee, Moon Hwan; Yang, Jaeseung; Kim, Sewoong; Eun, Youngsoon; Eun, Lucy Youngmin; Hwang, Jae Youn Abstract: Identifying incomplete Kawasaki disease (KD) has been challenging due to its atypical clinical manifestations. Differentiating it from other febrile illnesses, including COVID-19, is crucial for pediatric patients. Early detection of coronary artery abnormalities through echocardiographic examination is vital for accurate diagnosis and favorable outcomes. With the increased prevalence of KD among pediatric populations, there is a need for continued research and innovative diagnostic tools to improve early detection and management. To address this, we introduce a Multiple Receptive Attention Network (MRANet) incorporating a multi-receptive attention layer, designed to enhance the discrimination of incomplete KD from echocardiographic images, achieving better sensitivity and specificity. A total of 147 echocardiographic imaging datasets were utilized for training MRANet and other state-of-the-art deep learning models. The performance of MRANet was compared with compatible deep learning networks for evaluation. The results demonstrate that MRANet outperforms other advanced deep learning methodologies. MRANet’s performance is comparable to that of an experienced pediatric cardiologist in detecting coronary artery abnormalities for accurate KD diagnosis. This study highlights the potential of MRANet as a valuable tool for aiding early detection and management of complex conditions in medical imaging and computer vision. Further research and validation are warranted to establish MRANet as a reliable tool in pediatric cardiology practice. ©2024 IEEE.

2024-02-04T15:00:00Z An, Youngwoo Eun, Yongsoon https://scholar.dgist.ac.kr/handle/20.500.11750/57808 2025-07-25T03:35:27Z 2024-10-30T15:00:00Z

Title: Multi-UGV Task Reallocation for Sensor and Actuator Faults Author(s): An, Youngwoo; Eun, Yongsoon Abstract: This paper proposes a task reallocation method for multi Unmanned Grounded Vehicle (UGV) systems in sensor and actuator fault situations. The proposed method formulates the task reallocation problem as a Mixed-Integer Linear Programming (MILP) using the distance between UGVs and task priority. When a UGV sensor or actuator fault occurs, the proposed method solves the MILP and finds the optimal task reallocation solution. The solution ensures continuing high-priority tasks for the multi-UGV systems. The proposed method is validated in simulation and experiment. © 2024 ICROS.

2024-10-30T15:00:00Z