Research Director

Motoki Shiga

Department of Electrical, Gifu University
Associate Professor

Outline

Advances in microstructure measurement technologies have enabled us to automatically obtain microstructural information of a specified region of interest in a material in a short period of time. Along with these technological developments, the data volume of measurements to be analyzed has been drastically increasing and then the data analysis cost has become a serious problem. This project aims to resolve this problem by developing efficient data analysis methods based on statistical machine learning for microscopic measurements such as STEM-EELS/EDX. These developed methods will be applied to identify unknown microstructures of materials.