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Donahue, J., Hendricks, L.A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., et al. (2017) Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 677-691.
https://doi.org/10.1109/tpami.2016.2599174

has been cited by the following article:

• 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.