On May 19^th, PhD student Dong-Hyu Lee and master's student Hye-Jun Oh of the System Integrity and Risk Management Lab (Advisor Byung-Dong Yoon) of the Department of Mechanical Engineering, Seoul National University won the Excellence Award by an original research result in the Student Contest held at the 2023 Spring Conference of the Korean Society of Mechanical Engineers for CAE and Applied Mechanics.

Doctoral student Dong-Hyu's Lee paper titled "Deep Learning-based Reverse Engineering of Faulty Acoustic Quantum Crystals for Target Frequency Matching and Maximization of Transmittance" introduces a deep learning-based reverse engineering model for utilizing acoustic quantum determination as a narrowband filter. In this study, a framework was developed to create and filter designs that meet design requirements using deep learning-based reverse design models and analysis models, and to solve multiple design mapping problems, stochastic deep learning models such as CGAN were used as reverse design models. Accordingly, the proposed reverse design method has attracted attention by providing an optimal design plan in a short time for arbitrary target frequency conditions.

Hyejun Oh's master's paper title is "Cross-modal knowledge distillation method for improving current-based motor failure diagnosis performance". In the case of vibration-based motor fault diagnosis method, it is difficult to use in the field because it is vulnerable to external vibration and difficult to generalize, and vibration data is used in the learning phase to solve the problem that current-based motor fault diagnosis method has low performance. It received great attention in that it proposed a new deep learning model learning method that can achieve improved performancely with current data in the test phase.