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Undergraduate Internship
We are offering internships for undergraduates. The research will focus on developing Physics-Informed Neural Network (PINN) models, including fine-tuning, time series forecasting, and time series identification.
Payment information:
First 12 months: Limited stipend available for training period
Undergraduate course: Payment up to £550 (1,000,000) per month
Graduate course: Payment up to £1,000 (￦1,800,000) per month
Supporters: HD Hyundai Heavy Industries, Korean Register, Lloyd's Register Foundation
Yangjun Ahn
Assistant Professor
School of AI Convergence, Sungshin Women's University
Email: yangjunahn@sungshin.ac.kr
Awards
2021 Songam Awards by the Society of Naval Architects of Korea (SNAK)
2019 Outstanding Student Scholarship by International Ocean and Polar Engineering Conference (ISOPE)
2013 Best Master's Thesis Award by Nippon Kaiji Kyokai (ClassNK)
Recent Publications - SCI(E)
Jaehak Lee, Jae-Hoon Lee, Yonghwan Kim*, Yangjun Ahn
Prediction of Wave-Induced Nonlinear Ship Motions Based on IRF-LSTM Hybrid Approach
IJOPE, June 2024, 34(2), pp.164-173
Jae-Hoon Lee, Jaehak Lee, Yonghwan Kim*, Yangjun Ahn
Prediction of Wave-Induced Ship Motions Based on Integrated Neural Network System and Spatiotemporal Wave-Field Data
Pysics of Fluids, Sep 19 2023
Yangjun Ahn, Jeoungkhyu Lee, Taehyun Park, Yonghwan Kim*
Long-term approach for assessment of sloshing loads in LNG carrier, part II: Grouping method, Marine Structures
Marine Structures, 89, May 2023, 103398
Yangjun Ahn, Jeoungkyu Lee, Taehyun Park, Yonghwan Kim*, Chang Seop Kwon, Jun Hyung Jung, Mun-gwan Choi, Sang-Yeob Kim
Long-Term Approach for Assessment of Sloshing Loads in LNG Carrier, Part I: Comparison of Short- and Long-term Approaches
Marine Structures, 89, May 2023, 103381
Jae-Hoon Lee, Jaehak Lee, Yonghwan Kim, Yangjun Ahn*
Application of Machine Learning for Prediction of Wave-induced Ship Motion
IJOPE,33(2), June 2023, pp. 164-173
JeoungKyu Lee, Yangjun Ahn, Yonghwan Kim*, Junhyung Jung
Sloshing Experiment for Foremost Two Tanks of LNG Carrier
IJOPE, 32(4), December 2022, pp. 434-439
Sang-Yeob Kim, Yonghwan Kim*, Yangjun Ahn
Outlier Analysis of Sloshing Impact Loads on Liquid Ship Cargo
JEME, 236(3), pp. 630-643, 9 January 2022
Jeoungkyu Lee, Yangjun Ahn, and Yonghwan Kim*
Experimental Study on Effect of Density Ratio and Phase Transition during Sloshing Impact in Rectangular Tank
OE, 242, 15 December 2021, 110105
Yangjun Ahn, Yonghwan Kim*
Data Mining in Sloshing Experiment Database and Application of Neural Network for Extreme Load Prediction
MS, 80, November 2021, 103074
Jeoungkyu Lee, Yangjun Ahn, Jieung Kim, Yonghwan Kim*, Kyung-Kyu Yang, Si-Ik Yui, Myung-Hyun Noh
Observation on Sloshing Flow and Hydrodynamic Pressures on Cylindrical Liquid Natural Gas Tank with Swash Bulkhead
JEME, 235(1), 1 February 2021, pp. 30-40
Yangjun Ahn, Yonghwan Kim*, Sang-Yeob Kim
Experimental Study and Method of Sloshing Model Test Considering Gas-Liquid Density Ratio
IJOPE, September 2019, 29(3), pp. 257-268
Yangjun Ahn, Sang-Yeob Kim, Jieung Kim, Jeoungkyu Lee, Yonghwan Kim*
Experimental Comparison of Sloshing Loads on Weather Side and Lee Side for LNG Carrier Cargo Tanks
IJOPE, September 2019, 29(3), pp. 249-256
Yangjun Ahn, Yonghwan Kim*, Sang-Yeob Kim
Database of Model-scale Sloshing Experiment for LNG Tank and Application of Artificial Neural Network for Sloshing Load Prediction
MS, 66, July 2019, pp. 66-82
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