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Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement
Tohoku University, Sendai, Miyagi, 980-8579, Japan
* Corresponding Author: Yuji Saito. Email:
Computer Modeling in Engineering & Sciences 2021, 129(1), 1-30. https://doi.org/10.32604/cmes.2021.016603
Received 11 March 2021; Accepted 23 June 2021; Issue published 24 August 2021
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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
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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
Copyright © 2021 The Author(s). Published by Tech Science Press.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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