Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation

Autor:	Kaneko, Yuhei, Muramatsu, Shogo, Yasuda, Hiroyasu, Hayasaka, Kiyoshi, Otake, Yu, Ono, Shunsuke, Yukawa, Masahiro
Rok vydání:	2018
Předmět:	Electrical Engineering and Systems Science - Signal Processing Mathematics - Dynamical Systems
Druh dokumentu:	Working Paper
Popis:	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 analysis method for describing a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has an advantage of reflecting spatial structure of the target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. The estimation problem is reduced to sparsity-aware restoration with a hard constraint, which is 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 significance of the proposed method is verified. Comment: To appear at IEEE ICASSP 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.07281 Zobrazit plný text záznamu View this record from Arxiv