International Journal
Kim, I., Kim, S. W., Kim, J., Huh, H., Jeong, I., Choi, T., ... & Lee, S. (2024). Single domain generalizable and physically interpretable bearing fault diagnosis for unseen working conditions. Expert Systems with Applications, 241, 122455.
Kim, T., Hong, I., Roh, Y., Kim, D., Kim, S., Im, S., Kim, C., Jang, K., Kim, S., Kim, M. and Park, J., 2023. Spider-inspired tunable mechanosensor for biomedical applications. npj Flexible Electronics, 7(1), p.12.
S. W. Kim, E. J. Kwak, J. H. Kim, K. Y. Oh, and S. Lee, 2023, "Modeling and Prediction of Lithium-Ion Battery Thermal Runaway via a Multiphysics-Informed Neural Network," Journal of Energy Storage https://doi.org/10.1016/j.est.2023.106654
S. W. Kim, K. Y. Oh, and S. Lee, 2022, "Novel Informed Deep Learning-Based Prognostics Framework for On-Board Health Monitoring of Lithium-Ion Batteries," Applied Energy https://doi.org/10.1016/j.apenergy.2022.119011
S. W. Kim, J. Gong, S. W. Lee, and S. Lee, 2021, "Recent Advances of AI in Manufacturing Industrial Sectors: A Review," International Journal of Precision Engineering and Manufacturing https://doi.org/10.1007/s12541-021-00600-3
S. W. Kim, S-H. Kang, S-J. Kim, and S. Lee, 2021, "Estimating the Phase Volume Fraction of Multi-Phase Steel via Unsupervised Deep Learning," Scientific Reports, 11, 5902, https://doi.org/10.1038/s41598-021-85407-y
S. W. Kim, I. Kim, J. Lee, and S. Lee, 2021, "Knowledge Integration into Deep Learning in Dynamical Systems: An Overview and Taxonomy," Journal of Mechanical Science and Technology, 35, 1331-1342, https://doi.org/10.1007/s12206-021-0342-5
S. W. Kim, Y. G. Lee, B. A. Tama, and S. Lee, 2020, "Reliability-enhanced Camera Lens Module Classication using Semi-supervised Regression Method," Applied Sciences, 10(11), 3832, https://doi.org/10.3390/app10113832
H. J. Kwon, S. H. Kim, S. W. Kim, J. H. Kim, and G. B. Lim, 2017, "Controlled production of monodisperse polycaprolactone microspheres using flow-focusing microfluidic device", Biochip Journal, 11, 214-218, https://doi.org/10.1007/s13206-017-1306-9, cover paper
Domestic Journal
S. W. Kim, K-Y. Oh, and S. Lee, 2021, "Physics-Informed Neural Network For Estimation Of Lithium-Ion Battery State-of-Health," The Korean Society for Noise and Vibration Engineering, 31, 177-184, https://doi.org/10.5050/KSNVE.2021.31.2.177 [In Korean]
International & Domestic Conference
KSME 22 Conference, Busan, South Korea 2022.05.19
Oral presentation: Multiphysics-Informed Neural Network for the Simulation of Lithium-Ion Battery Thermal Runaway
PHMAP 21 Conference, Jeju, South Korea 2021.09.08
Oral presentation: Novel Physics-Infused Recurrent Neural Network for Health Monitoring of Lithium-Ion Batteries
KSPHM 21 Conference, Jeju, South Korea 2021.09.08
Oral presentation: Novel Physics-Infused Recurrent Neural Network for Health Monitoring of Lithium-Ion Batteries
KSME 21 Conference, Jeju, South Korea 2021.04.28
Oral presentation: SOH Monitoring and RUL Estimation of Lithium-Ion Batteries Using Physics-Based Deep Learning
KSNVE 20 Conference, Jeju, South Korea 2020.11.18
Oral presentation: Physics-informed neural network for the estimation of Li-ion battery state-of-health (Best paper award)
KSME 19 Conference, Jeju, South Korea 2019.11.13
Poster presentation: Phase analysis of multi-phase steel using unsupervised deep learning
QR2MSE 19 Conference, Zhangjiajie, China 2019.08.19
Oral presentation: Camera lens module classification and recommendation model based on deep neural network
PHM Korea 19 Conference, Seoul, South Korea 2019.04.19
Poster presentation: Deep learning-based diagnostics and prediction for camera lens module assembly