TY - GEN
T1 - Convolutional-sparse-coded Dynamic Mode Decomposition and Its Application to River State Estimation
AU - Kaneko, Y.
AU - Muramatsu, S.
AU - Yasuda, H.
AU - Hayasaka, K.
AU - Otake, Y.
AU - Ono, S.
AU - Yukawa, M.
N1 - Funding Information:
This work was supported by a U-go Grant of Niigata University.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data-driven method of analysis used to describe a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has the advantage of reflecting the spatial structure of a target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. This estimation problem is reduced to sparsityaware signal restoration with a hard constraint given by the CSC-DMD prediction, where the algorithm is derived by the primal-dual splitting method. A time series set of water surface and bed shape measured through an experimental river setup is used to train and test the system. From the result, the efficacy of the proposed method is verified.
AB - This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data-driven method of analysis used to describe a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has the advantage of reflecting the spatial structure of a target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. This estimation problem is reduced to sparsityaware signal restoration with a hard constraint given by the CSC-DMD prediction, where the algorithm is derived by the primal-dual splitting method. A time series set of water surface and bed shape measured through an experimental river setup is used to train and test the system. From the result, the efficacy of the proposed method is verified.
KW - Convolutional sparse coding
KW - Extended dynamic mode decomposition
KW - NSOLT
KW - Primal-dual splitting
UR - http://www.scopus.com/inward/record.url?scp=85069463036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069463036&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8683848
DO - 10.1109/ICASSP.2019.8683848
M3 - Conference contribution
AN - SCOPUS:85069463036
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1872
EP - 1876
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -