Updated: Apr 1, 2025
Artificial Intelligence Major
Prof. Yonggyun Yu 유용균
Email : ygyu {at} kaeri.re.kr yoyogo {at} gmail.com, ygyu {at} ust.ac.kr
Tel : +82-42-868-8160
2012 ~ present Senior/Principal Researcher, KAERI
2020 ~ present Research Leader, Applied AI Lab., KAERI
2021 ~ 2022 Adjunct Professor, Nuclear and Radiation Safety, UST
2023 ~ present Full-time Professor, Artificial Intelligence, UST
Industrial AI(Simulation AI, AI based topology optimization, Anomaly detection)
2001 B.S. Mechanical Engineering & Computer Science(sub) , KAIST
2003 M.S. Mechanical Engineering, KAIST
2010 Ph.D. Mechanical Engineering, KAIST
2010 ~ 2012 Director, Research Professor, Mobile Harbor Center, KAIST
2005 ~ 2005 Visiting Researcher, AIST, Japan
(On-going)
원자력 인공지능 신뢰성 향상을 위한 기반기술 연구, ‘24~26, 30 억 (PI)
(Completed)
Strengthening the stability of Hanaro and research facilities through intelligent research facility operation, ‘21~23, 30 억 (PI)
Google Scholar : https://scholar.google.co.kr/citations?user=tXElczcAAAAJ&hl=ko
(International Journal)
Y Joo, H Choi, G-E Jeong, Y Yu*, Dynamic graph-based convergence acceleration for topology optimization in unstructured meshes, Engineering Application of Artificial Intelligence, 2024 (IF: 8)
S Ryu, Y Yu, H Seo, Can Untrained Neural Network Detect Anomalies?, IEEE Transactions on Industrial Informatics, 2023 (IF:12.3)
G Lee, Y Joo, Y Yu, HG Kim, Dual-fluid topology optimization of printed-circuit heat exchanger with low-pumping-power design, Case Studies in Thermal Engineering 49, 2023
S Ryu, Y Yu*, Quantile-Mixer: A Novel Deep Learning Approach for Probabilistic Short-term Load Forecasting, IEEE Transactions on Smart Grid, 2023
S Ryu, B Jeon, H Seo, M Lee, JW Shin, Y Yu*, Development of deep autoencoder-based anomaly detection system for HANARO, Nuclear Engineering and Technology 55 (2), 2023
S Ryu, J Yim, J Seo, Y Yu, H Seo, Quantile autoencoder with abnormality accumulation for anomaly detection of multivariate sensor data, IEEE Access 10, 2022
S Moon, K Kim, GG Lee, Y Yu, DJ Kim, Pipeline wall thinning rate prediction model based on machine learning, Nuclear Engineering and Technology, 2021
B Jeon, J Kim, Y Yu, M Moon, Comparison of machine learning-based radioisotope identifiers for plastic scintillation detector, Journal of Radiation Protection and Research, 2021
Y Joo, Y Yu, IG Jang, Unit module-based convergence acceleration for topology optimization using the spatiotemporal deep neural network, IEEE Access, 2021
Y Yu, T Hur, J Jung, IG Jang, Deep learning for determining a near-optimal topological design without any iteration, Structural and Multidisciplinary Optimization, 2018
EH Kim, YS Jung, Y Yu, S Kwon, H Ju, S Kim, BM Kwak, IG Jang, KS Kim, An advanced cargo handling system operating at sea, International Journal of Control, Automation and Systems, 2014
Y Yu, IG Jang, BM Kwak, Topology optimization for a frequency response and its application to a violin bridge, Structural and Multidisciplinary Optimization, 2013
Y Yu, BM Kwak, Design sensitivity analysis of acoustical damping and its application to design of musical bells, Structural and Multidisciplinary Optimization, 2011
Y Yu, IG Jang, IK Kim, BM Kwak, Nodal line optimization and its application to violin top plate design, Journal of sound and vibration, 2010
(2018~present) (사)AI프렌즈 대표(학회장)
(2021~present) Nvidia ambassador / DLI Instructor
(2023~present) 전산유체공학회 이사
(2020~present) 대한기계학회 인공지능머신연구회/IT융합부문 이사
(2020~2021) 대한기계학회 본부이사
Tech-Bridge (AI) (UST, 2023)
Industrial AI (CNU, 2022-2023)
AI Grand Challenge(인공지능그랜드챌린지) Winner(2021), 2nd Place(2022), Ministry of Science and Technology
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