Vol.129, No.1, 2021, pp.1-30, doi:10.32604/cmes.2021.016603
OPEN ACCESS
ARTICLE
Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement
  • Yuji Saito*, Keigo Yamada, Naoki Kanda, Kumi Nakai, Takayuki Nagata, Taku Nonomura, Keisuke Asai
Tohoku University, Sendai, Miyagi, 980-8579, Japan
* Corresponding Author: Yuji Saito. Email:
Received 11 March 2021; Accepted 23 June 2021; Issue published 24 August 2021
Abstract
A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches: a greedy method based on D-optimality and a noise-robust greedy method in this paper. Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement.
Keywords
Sparse sensor selection; vector-sensor measurement
Cite This Article
Saito, Y., Yamada, K., Kanda, N., Nakai, K., Nagata, T. et al. (2021). Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement. CMES-Computer Modeling in Engineering & Sciences, 129(1), 1–30.
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