https://scholar.dgist.ac.kr/handle/20.500.11750/107 2026-04-08T13:21:11Z https://scholar.dgist.ac.kr/handle/20.500.11750/58937 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 https://scholar.dgist.ac.kr/handle/20.500.11750/58704 Title: Blends of alkylene glycols and relatively high equivalent weight active hydrogen compounds containing additives Author(s): 유부연; 은용순 Abstract: An active hydrogen compound-alkylene glycol composition comprises components (A) a relatively high equivalent weight material having an average of at least about 1.8 active hydrogen containing groups per molecule and a weight from about 500 to about 5000 per active hydrogen containing group; (B) an alkylene glycol; and (C) a catalytic amount of at least one tin mercaptide; said composition containing at least one compound having at least one N--H containing group. Such compositions are useful in producing polyurethanes. The compositions can generally be stored without substantial loss of activity. https://scholar.dgist.ac.kr/handle/20.500.11750/58607 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 https://scholar.dgist.ac.kr/handle/20.500.11750/58488 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