| Data-fused and concatenated-ensemble learning for in-situ anomaly detection in wire and arc-based direct energy deposition DB Kim, H Chong, MM Mahdi, SJ Shin Journal of Manufacturing Processes 112, 273-289, 2024 | 1 | 2024 |
| Material-adaptive anomaly detection using property-concatenated transfer learning in wire arc additive manufacturing SJ Shin, JH Lee, S Jadhav, DB Kim International Journal of Precision Engineering and Manufacturing 25 (2), 383-408, 2024 | 9 | 2024 |
| Detecting balling defects using multisource transfer learning in wire arc additive manufacturing SJ Shin, SH Hong, S Jadhav, DB Kim Journal of Computational Design and Engineering 10 (4), 1423-1442, 2023 | 3 | 2023 |
| Development of a CNN-based real-time monitoring algorithm for additively manufactured molybdenum ES Kim, DH Lee, GJ Seo, DB Kim, SJ Shin Sensors and Actuators A: Physical 352, 114205, 2023 | 8 | 2023 |
| Integrating Predictive Model Markup Language with Asset Administration Shell SJ Shin, J Um IFAC-PapersOnLine 56 (2), 9823-9830, 2023 | 2 | 2023 |
| Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models YS Choo, SJ Shin Journal of Society of Korea Industrial and Systems Engineering Vol 45 (3), 18-30, 2022 | | 2022 |
| Optimizing the mean and variance of bead geometry in the wire+ arc additive manufacturing using a desirability function method JS Cho, DH Lee, GJ Seo, DB Kim, SJ Shin The International Journal of Advanced Manufacturing Technology 120 (11 …, 2022 | 6 | 2022 |
| Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: molybdenum material HW Cho, SJ Shin, GJ Seo, DB Kim, DH Lee Journal of Materials Processing Technology 302, 117495, 2022 | 60 | 2022 |
| Operation-driven power analysis of discrete process in a cyber-physical system based on a modularized factory J Um, T Park, HW Cho, SJ Shin Sustainability 14 (7), 3816, 2022 | 1 | 2022 |
| Predictive modeling for machining power based on multi-source transfer learning in metal cutting YM Kim, SJ Shin, HW Cho International Journal of Precision Engineering and Manufacturing-Green …, 2022 | 31 | 2022 |
| A review of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems P Meilanitasari, SJ Shin Processes 9 (8), 1391, 2021 | 9 | 2021 |
| Sequence learning-based schedule prediction for flexible manufacturing systems under uncertainties P Meilanitasari, SJ Shin Journal of Physics: Conference Series 1996 (1), 012010, 2021 | | 2021 |
| An energy-related products compliant eco-design method with durability-embedded economic and environmental assessments FS Kiling, SJ Shin, MK Lee, P Meilanitasari International Journal of Precision Engineering and Manufacturing-Green …, 2021 | 5 | 2021 |
| SEQUENCE LEARNING FOR PRODUCT-LEVEL SCHEDULE PREDICTION IN FLEXIBLE MANUFACTURING SYSTEMS UNDER UNCERTAINTIES P Meilanitasari, SJ Shin, W Seo 대한산업공학회 추계학술대회 논문집, 3098-3115, 2020 | | 2020 |
| An OPC UA-compliant interface of data analytics models for interoperable manufacturing intelligence SJ Shin IEEE Transactions on Industrial Informatics 17 (5), 3588-3598, 2020 | 19 | 2020 |
| A holonic-based self-learning mechanism for energy-predictive planning in machining processes SJ Shin, YM Kim, P Meilanitasari Processes 7 (10), 739, 2019 | 22 | 2019 |
| A Data-Driven Predictive Analytics for Job Shop Scheduling in Flexible Manufacturing Systems under High Uncertainty M Prita, SJ Shin 한국정밀공학회 학술발표대회 논문집, 56-56, 2019 | | 2019 |
| A hybrid process planning for energy-efficient machining: Application of predictive analytics SJ Shin IOP Conference Series: Materials Science and Engineering 635 (1), 012032, 2019 | 1 | 2019 |
| An energy-efficient process planning system using machine-monitoring data: A data analytics approach SJ Shin, J Woo, S Rachuri, W Seo Computer-Aided Design 110, 92-109, 2019 | 21 | 2019 |
| A Hybrid Learning-based Predictive Process Planning Mechanism for Cyber-Physical Production Systems 신승준 Journal of the Korean Society for Precision Engineering 36 (4), 391-400, 2019 | | 2019 |
