Data-driven determinant-based greedy under/oversampling vector sensor placement

Yuji Saito; Keigo Yamada; Naoki Kanda; Kumi Nakai; Takayuki Nagata; Taku Nonomura; Keisuke Asai

doi:10.32604/cmes.2021.016603

Data-driven determinant-based greedy under/oversampling vector sensor placement

Yuji Saito, Keigo Yamada, Naoki Kanda, Kumi Nakai, Takayuki Nagata, Taku Nonomura, Keisuke Asai

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1-30
Number of pages	30
Journal	CMES - Computer Modeling in Engineering and Sciences
Volume	129
Issue number	1
DOIs	https://doi.org/10.32604/cmes.2021.016603
Publication status	Published - 2021

Keywords

Sparse sensor selection
Vector-sensor measurement

ASJC Scopus subject areas

Software
Modelling and Simulation
Computer Science Applications

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10.32604/cmes.2021.016603

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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.",

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author = "Yuji Saito and Keigo Yamada and Naoki Kanda and Kumi Nakai and Takayuki Nagata and Taku Nonomura and Keisuke Asai",

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AU - Saito, Yuji

AU - Yamada, Keigo

AU - Kanda, Naoki

AU - Nakai, Kumi

AU - Nagata, Takayuki

AU - Nonomura, Taku

AU - Asai, Keisuke

N1 - Funding Information: This work was supported by JST ACT-X (JPMJAX20AD), JST CREST (JPMJCR1763) and JST FOREST (JPMJFR202C), Japan. Funding Information: Funding Statement: This work was supported by JST ACT-X (JPMJCR1763) and JST FOREST (JPMJFR202C), Japan. Publisher Copyright: © 2021 Tech Science Press. All rights reserved.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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Data-driven determinant-based greedy under/oversampling vector sensor placement

Abstract

Keywords

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