Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments
From Cloud to Edge: A Knowledge Distillation Approach for Battery SOH Prediction
Explainable pruning-informed deep reinforcement learning for CBM optimization
Dissecting in-FAB failure prediction via XAI
False alarm detection in semiconductor manufacturing via explainable artificial intelligence
Analyzing FAB dispatching rules via Explainable Reinforcement Learning
16. Kim H. & Barde, S.*, A Meta-Learning Enhanced Deep Reinforcement Learning Approach for Generalizing Across Orienteering Problem with Time Windows, Major revision @ Transportation Research Part C: Emerging Technologies.
15. Barde, S. & Kim H.*, Novel explainable reinforcement learning approach to dissect optimal condition-based maintenance policies, Major revision @ European Journal of Operational Research.
14. Seo Y., Kim T. & Barde, S.* (2025), Enhancing Battery SOH Prediction with Butler-Volmer Informed Neural Networks in Data-Scarce Environments, Energy, 138316 [DOI: https://doi.org/10.1016/j.energy.2025.138316 ].
13. Kim T., Seo Y. & Barde, S.* (2025), Edge-Compatible SOH Estimation for Li-Ion Batteries via Hybrid Knowledge Distillation and Model Compression, Journal of Energy Storage, 135, 118275. [DOI: https://doi.org/10.1016/j.est.2025.118275 ]
12. Seo Y., Kim T. & Barde, S.* (2025), Robust SOH Prediction for Lithium-Ion Batteries via ProbSparse Informer Architecture, Accepted @ 2025 IEEE Global Reliability & PHM Conference (sponsored by IEEE Reliability Society).
11. Kim T., Seo Y. & Barde, S.* (2025), Hybrid Compression for Accurate End of Life Prediction on Edge Battery Management System, Accepted @ 2025 IEEE Global Reliability & PHM Conference (sponsored by IEEE Reliability Society).
10. Barde, S., Ko, Y.M., & Shin, H.* (2025) Overcoming the curse of dimension of the optimal group maintenance policy of a heterogeneous multi-component series system, OR Spectrum, 1-30. [DOI: https://doi.org/10.1007/s00291-025-00831-0 ]
9. Kim, M. G., Kim, H., Barde, S., & Lee, C. H. (2025). Balancing yield and makespan in wafer fabrication: A two-stage data-driven scheduling approach. Journal of Manufacturing Systems, 82, 874-904. [DOI: https://doi.org/10.1016/j.jmsy.2025.07.009 ]
8. Yun H. & Barde, S.* (2025), Leveraging Digital Dashboards for Automotive Reliability: A Case Study on Noise and Vibration Reduction in Front-Wheel Driveshaft Joints, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X251362910. [DOI: https://doi.org/10.1177/1748006X251362910 ]
7. Barde, S. & Ko, Y.M.* (2025), Analysis on non-monotone control-limit condition-based maintenance policies, IEEE Transactions on Reliability. [DOI: https://doi.org/10.1109/TR.2025.3582813 ]
6. Barde, S.* (2024) Efficient opportunistic maintenance strategies via pruning in parallel-series systems with economic dependence, Computers & Industrial Engineering, 110451. [DOI: https://doi.org/10.1016/j.cie.2024.110451 ]
5. Kim, N., Barde, S., Bae, K., & Shin, H.* (2023). Learning per-machine linear dispatching rule for heterogeneous multi-machines control. International Journal of Production Research, 61(1), 162-182. [DOI: https://doi.org/10.1080/00207543.2021.1942283 ]
4. Barde, S., Ko, Y.M., & Shin, H.* (2022). General EM algorithm for fitting non-monotone hazard functions from truncated and censored observations. Operations Research Letters, 50(5), 476-483. [DOI: https://doi.org/10.1016/j.orl.2022.07.001 ]
3. Barde, S., Ko, Y.M., & Shin, H.*(2020). Fitting Discrete Phase-type Distribution from Censored and Truncated Observations with Pre-specified Hazard Sequence. Operations Research Letters, 48 (3), 233-239. [DOI: https://doi.org/10.1016/j.orl.2020.02.009 ]
2. Barde, S., Yacout, S., & Shin, H.* (2019). Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks. Journal of Intelligent Manufacturing, 30 (1), 147-161. [DOI: https://doi.org/10.1007/s10845-016-1237-7 ]
1. Barde, S., Shin, H., & Yacout, S.* (2016, September). Opportunistic preventive maintenance strategy of a multi-component system with hierarchical structure by simulation and evaluation. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-8). [DOI: https://doi.org/10.1109/ETFA.2016.7733708 ]
Authors in bold are our lab members. * denote the corresponding author.
1. Barde S. (2024), The Art of Digitalization: A Dive into e-Estonia, Productivity Insights, Asian Productivity Organization (APO), DOI: https://doi.org/10.61145/IINI4552.
3. 윤혁춘, 스테판 바르드. (2025). "자동차 보증 신뢰성 정보를 종합 디지털 대시보드를 통해 통합하고 제공하는 새로운 개념." 신뢰성응용연구 25.1: 11-25.
2. 성영조, 김성하, 송정은, 김선영, 허현아, 스테판 바르드. (2024). 연구역량 강화를 위한 생성형 인공지능 활용 방안. 정책연구, 1-120.
1. 최재훈, Barde S., 김주현. (2014). 헬스케어와 사물인터넷 융합기술 동향. 한국통신학회지(정보와통신), 31(12), 10-16.
7. 김태이, 바르드 스테판 (Barde S.) (2025), 리튬이온 배터리 SOH 예측 모델 경량화를 위한 응답 기반 지식 증류·프루닝·양자화 접근, 대한신뢰성학회
본 논문이 우수발표 논문상을 수상하였음.
6. 서영건, 바르드 스테판 (Barde S.) (2025), Physics-Informed Neural Network를 이용한 리튬 이온 배터리 건강 상태 예측 프레임워크, 대한신뢰성학회
5. 김민걸, 이창호, 바르드 스테판 (Barde S.), 김현준 (2025), Balancing Yield and Makespan in Wafer Fabrication: A Two-Stage Data-Driven Scheduling Approach, 대한산업공학회
4. 윤혁춘, 바르드 스테판 (Barde S.) (2024), 자동차 보증 신뢰성 정보를 종합 디지털 대시보드를 통해 통합하고 제공하는 새로운 개념, 신뢰성/품질경영학회 추계 학술대회
3. 김민걸, 이창호, 바르드 스테판 (Barde S.), 김현준 (2024), 품질과 생산성을 모두 고려한 웨이퍼 제조 데이터 기반 하이브리드 플로우샵 스케줄링, 대한산업공학회 추계학술대회
2. 오요셉, 바르드 스테판 (Barde S.) (2024), 전자부품분야의 주문량 결정 문제를 위한 강화학습 기반 설명가능한 인공지능, 한국경영과학회 학술대회논문집, 1794-1801.
1. 배기욱, Barde S., 신하용 (2016). 시뮬레이션을 활용한 공중전에서 NCW 효과와 전투 결과의 상관 관계 분석 연구, 한국시뮬레이션학회 학술대회 논문집, 4-15.