Multi-Resolution Convolutional Dictionary Learning for Riverbed Dynamics Modeling
E. Kobayashi, Hiroyasu Yasuda, Kiyoshi Hayasaka, Yu Otake, Shunsuke Ono, Shogo Muramatsu
Abstract
This work proposes a novel formulation of convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) incorporating a deep learning framework. CSC-DMD is a high-dimensional data analysis method with a convolutional synthesis dictionary and applicable to analyze dynamics such as seismic motions and river flows. An authors’ previous work has shown the effectiveness of CSC-DMD for riverbed state estimation. However, there still remains a room to improve the performance in expressing evolution of temporal and spatial changes in riverbed shape. Hence, this work proposes to adopt multi-resolution convolutional dictionary by introducing a deep learning framework so that the capability of simultaneously capturing local and global features is added to CSC-DMD. The significance of the proposed method is verified by evaluation of riverbed state estimation for time-series data of water surface and riverbed shape obtained through an experimental setup of river model.
BibTeX
@inproceedings{icassp2023_multiresolutionc,
title = {Multi-Resolution Convolutional Dictionary Learning for Riverbed Dynamics Modeling},
author = {E. Kobayashi and Hiroyasu Yasuda and Kiyoshi Hayasaka and Yu Otake and Shunsuke Ono and Shogo Muramatsu},
booktitle = {ICASSP 2023},
year = {2023}
}