Kim G#, Yang SM, Jeon S, Park S, Choi JG, Park HW, Lim S*. Development of a robust tool wear prediction method under novel operating conditions using deep unsupervised domain adaptation and physics-guided adjustment. INFORMS Annual Meeting, Atlanta, Georgia, USA, 2025.

Park S, Yang SM#, Kim G, Kim DM, Lee HH, Choi JG, Jeon S, Lim S*, Park HW*. Accurate monitoring of machining process of Ti-6Al-4V using deep multi-task learning. International Conference on Advanced Mechatronic Systems (ICAMechS), Shiga, Japan, 2024.

Kim G#. Result summary presentation for INFORMS QSR Manufacturing AI Competition. INFORMS Annual Meeting, Seattle, Washington, USA, 2024.

Kim G#, Kang YS, Yang SM, Choi JG, Hwang G, Park HW, Lim S*. Continual learning-based remaining useful life prediction of machining tools under varying operating conditions. INFORMS Annual Meeting, Seattle, Washington, USA, 2024.

Kim G#, Lim S*. Development of a deep learning-based uncertainty-aware predictive maintenance method. IISE Annual Conference & Expo, New Orleans, Louisiana, USA, 2023.

Kim G#, Yang SM, Kim S, Kim DM, Lim S*, Park HW*. Tool wear prediction in the end milling process of Ti-6Al-4V using Bayesian learning. International Conference on Advanced Mechatronic Systems (ICAMechS). Toyama, Japan, 2022.

Ku M#, Kim G, and Lim S*. Developing a quality level prediction framework with semi-supervised learning and ordinal classification for UV lamps. IISE Annual Conference & Expo 2022, Seattle, Washington, USA, 2022.

Kim G#, Park S, Yang SM, Kim DM, Kim DC, Lee HH, Choi JG, Jeon S, Park HW, and Lim S*. 티타늄 합금의 전체적인 가공성 예측 방법론 개발: 향상된 특성 추출 기법 및 물리 기반 심층 다중 작업 학습을 기반으로. KSMTE Fall Conference, Busan, Republic of Korea, 2025.

Kim G#*. Data-driven prognostics of machining tools under various operating conditions. KORAS Fall Conference, Yeosu, Republic of Korea, 2025.

Kim G#, Park S, Yang SM, Kim DM, Kim DC, Lee HH, Choi JG, Jeon S, Lim S*, and Park HW. Machinability estimation of titanium alloy: An integrated approach with enhanced feature extraction and physics-guided deep multi-task learning. KORAS Fall Conference, Yeosu, Republic of Korea, 2025.

Kim G#, Jeon S, Park S, and Lim S*. Hybrid data-driven approach for manufacturability prediction of 3D microbial fuel cell anode. KIIE Fall Conference, Daejeon, Republic of Korea, 2025.

Kim G#, Yang SM, Choi JG, Jeon S, Park S, Park HW, and Lim S*. 새로운 가공 조건에서의 공구 마모 예측을 위한 비지도 도메인 적응 기반 인공지능 방법론 개발. KSMTE Spring Conference, Gangneung, Republic of Korea, 2025.

Kim G#, Yang SM, Jeon S, Park S, Choi JG, Park HW, and Lim S*. Development of a robust tool wear prediction method under novel operating conditions using deep unsupervised domain adaptation and physics-guided adjustment. KORAS Spring Conference, Jeju, Republic of Korea, 2025.

Kim G#, Yang SM, Choi JG, Jeon S, Park S, Park HW, and Lim S*. Development of a physics-guided deep domain adaptive regression method for robust tool wear prediction under novel operating conditions. KIIE/KORMS Spring Conference, Jeju, Republic of Korea, 2025.

Park S#, Kim G, Yang SM, Park HW, and Lim S*. Predicting machinability using cross-task attention-based multi-task learning. KIIE/KORMS Spring Conference, Jeju, Republic of Korea, 2025.

Kim G#, Kang YS, Yang SM, Choi JG, Hwang G, Kim J, Lim C, Park HW, and Lim S*. Continual learning-based remaining useful life prediction of machining tools under varying operating conditions. KORAS Fall Conference, Gyeongju, Republic of Korea, 2024.

Kim G#, Choi JG, Jeon S, Park S, and Lim S*. Hybrid deep active learning under low-budget scenarios for efficient fault detection. KIIE Fall Conference, Seoul, Republic of Korea, 2024.

Park S#, Yang SM, Kim G, Cheon J, and Lim S*. Uncertainty-aware machining process monitoring using multi-task learning. KIIE Fall Conference, Seoul, Republic of Korea, 2024.

Kim G#, Kang YS, Yang SM, Choi JG, Hwang G, Kim J, Cheon J, Lim C, Park HW, and Lim S*. Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions. KSMTE Spring Conference, Gangneung, Republic of Korea, 2024.

Yang SM#, Kim G, Choi J, Lim S, and Park HW*. Ti-6Al-4V End-milling 가공 시 불확실성을 고려 한 공구 마모 모델 개발. KSMTE Fall Conference, Jeju, Republic of Korea, 2023.

Kim G# and Lim S*. Development of a deep learning-based uncertainty-aware predictive maintenance method for machining tools. K-DS Conference, Siheung, Republic of Korea, 2023.

Kim G#, Yang SM, Kim SW, Kim DY, Choi JG, Park HW, and Lim S*. Deep learning-based tool wear prediction under multiple machining conditions. PHM Korea, Seoul, Republic of Korea, 2023.

Kim G#, Park S, Choi JG, Choi H, and Lim S*. Grinding process parameter optimization using machine learning techniques. KDMS Summer Conference, Gangneung, Republic of Korea, 2023.

Kim G#, Yang SM, and Lim S*. Development of a Bayesian-based uncertainty-aware tool wear prediction model in the end milling process. KIIE Fall Conference, Incheon, Republic of Korea, 2022.

Kim G# and Lim S*. Development of a remaining useful life estimation method using transformer and a reweighting technique. KDMS Summer Conference, Busan, Republic of Korea, 2022.

Choi JG#, Kong CW, Kim G, and Lim S*. Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Korea Safety Management & Science Fall Conference, Ulsan, Republic of Korea, 2021.

Kim G, Choi JG, Ku M, Cho H, and Lim S#*. Developing a deep learning-based fault detection model for plastic injection molding for car parts companies. KSQM Spring Conference, Seoul, Republic of Korea, 2021.

Kim G. Data-driven prognostics of machining tools under various operating conditions. Special Seminar, Kyungpool National University, Republic of Korea, 2025.

Kim G. Deep learning-based prognostics of machining tools. Making Decisions with AI Seminar, Pusan National University, Republic of Korea, 2025.