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Efficient Sensor Node Selection for Observability Gramian Optimization

Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approx...

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Detalles Bibliográficos
Autores principales: Yamada, Keigo, Sasaki, Yasuo, Nagata, Takayuki, Nakai, Kumi, Tsubakino, Daisuke, Nonomura, Taku
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347126/
https://www.ncbi.nlm.nih.gov/pubmed/37447809
http://dx.doi.org/10.3390/s23135961
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author Yamada, Keigo
Sasaki, Yasuo
Nagata, Takayuki
Nakai, Kumi
Tsubakino, Daisuke
Nonomura, Taku
author_facet Yamada, Keigo
Sasaki, Yasuo
Nagata, Takayuki
Nakai, Kumi
Tsubakino, Daisuke
Nonomura, Taku
author_sort Yamada, Keigo
collection PubMed
description Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.
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spelling pubmed-103471262023-07-15 Efficient Sensor Node Selection for Observability Gramian Optimization Yamada, Keigo Sasaki, Yasuo Nagata, Takayuki Nakai, Kumi Tsubakino, Daisuke Nonomura, Taku Sensors (Basel) Article Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results. MDPI 2023-06-27 /pmc/articles/PMC10347126/ /pubmed/37447809 http://dx.doi.org/10.3390/s23135961 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yamada, Keigo
Sasaki, Yasuo
Nagata, Takayuki
Nakai, Kumi
Tsubakino, Daisuke
Nonomura, Taku
Efficient Sensor Node Selection for Observability Gramian Optimization
title Efficient Sensor Node Selection for Observability Gramian Optimization
title_full Efficient Sensor Node Selection for Observability Gramian Optimization
title_fullStr Efficient Sensor Node Selection for Observability Gramian Optimization
title_full_unstemmed Efficient Sensor Node Selection for Observability Gramian Optimization
title_short Efficient Sensor Node Selection for Observability Gramian Optimization
title_sort efficient sensor node selection for observability gramian optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347126/
https://www.ncbi.nlm.nih.gov/pubmed/37447809
http://dx.doi.org/10.3390/s23135961
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