TITLE:
Classifying Vibration Modes Generated by The Michelson Interferometer Using Machine Learning Methods
AUTHORS:
Xin-Han Tsai, Anthony An-Chih Yeh, Chen-Hsin Lu, Shang-Yu Chou, Shih-Wei Wang, Chi-Wei Lee, Po-Han Lee
KEYWORDS:
Michelson Interferometer, Machine Learning, Vibration Modes, Long Short-Term Memory (LSTM)
JOURNAL NAME:
Journal of Modern Physics,
Vol.15 No.12,
November
11,
2024
ABSTRACT: In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with attention mechanism, Video Vision Transformer (ViViT), and Long-term Recurrent Convolutional Network (LRCN)—were evaluated to determine the most effective method for analyzing time series patterns generated by a Michelson interferometer. The interferometer was used to detect vibration modes created by handwriting, capturing time-series data from the diffraction patterns. Among these models, the LSTM-Attention network achieved the highest validation accuracy, reaching up to 92%, outperforming both ViViT and LRCN. These findings highlight the potential of deep learning in material science for detecting and classifying vibration patterns. The powerful performance of the LSTM-Attention model suggests that it could be applied to similar classification tasks in related fields.