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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: email

Computer Modeling in Engineering & Sciences 2021, 129(1), 1-30. https://doi.org/10.32604/cmes.2021.016603

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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APA Style
Saito, Y., Yamada, K., Kanda, N., Nakai, K., Nagata, T. et al. (2021). Data-driven determinant-based greedy under/oversampling vector sensor placement. Computer Modeling in Engineering & Sciences, 129(1), 1-30. https://doi.org/10.32604/cmes.2021.016603
Vancouver Style
Saito Y, Yamada K, Kanda N, Nakai K, Nagata T, Nonomura T, et al. Data-driven determinant-based greedy under/oversampling vector sensor placement. Comput Model Eng Sci. 2021;129(1):1-30 https://doi.org/10.32604/cmes.2021.016603
IEEE Style
Y. Saito et al., "Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement," Comput. Model. Eng. Sci., vol. 129, no. 1, pp. 1-30. 2021. https://doi.org/10.32604/cmes.2021.016603



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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