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Recently, as interest in IoT technology has been growing, research on big data processing and analysis, and analysis and design using it have been actively carried out. Particularly in the mechanical field, there is growing interest in data-based analysis, fault diagnosis, and data driven design techniques, but it is not easy for industries to find engineers with integrated knowledge about statistics, mechanical engineering, and design optimization.

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Our laboratory conducts extensive research on data analysis and design, including data preprocessing, big data analysis, statistical modeling and analysis, and design optimization under uncertainties. Currently, our laboratory is carrying out the projects with Korea Agency for Defense Development and Korea Research Foundation, and we are also continuing  research forums with Hyundai Heavy Industries and LG Electronics.

We have the advantage of being able to develop the base technology that can be applied to the real industry by utilizing the research results. Therefore, if you are interested in studying data driven analysis and design among students majoring in mechanical engineering, industrial engineering, or statistics, please apply for our lab and have an opportunity to develop your research capability.

Design under Uncertainties with Lack of Information

Reliability analysis

Reliability-based design optimization

Uncertainty Quantification

Parametric & Nonparametric statistical modeling

Statistical modeling for correlated variables

Big Data Analysis & Data driven Design

Big Data Collection, Processing, and Analysis

Affective Design based on Big Data

Fault diagnosis

Computational Mechanics

Fluid structural interaction (FSI) analysis

Design Optimization using CFD analysis & surrogate models

Jeon, M. Noh, Y.*, Jeon, K., Lee, S., Lee, I.
Data Gap Analysis of Ship and Maritime Data Using Meta-learning
Applied Soft Computing, 2020 , ,Vol. 0No. 0 ,pp. 0 ~ 0(SCIE, 0:0.0

Chae, J., Kang, Y.-J., Noh, Y.
A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data
Sensors, 2020 , ,Vol. 0No. 0 ,pp. 0 ~ 0(SCIE, 0:0.0

Abebe, M., Shin, Y., Noh, Y.*, Lee, S., Lee, I
Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
Applied Science-Basel, 2020 , ,Vol. 0No. 0 ,pp. 0 ~ 0(SCIE, 0:0.0