2024/12/02 更新

写真a

ササキ ヤスオ
佐々木 康雄
SASAKI Yasuo
所属
大学院工学研究科 航空宇宙工学専攻 飛行・制御 助教
職名
助教
外部リンク
 

論文 1

  1. Efficient Sensor Node Selection for Observability Gramian Optimization

    Yamada, K; Sasaki, Y; Nagata, T; Nakai, K; Tsubakino, D; Nonomura, T

    SENSORS   23 巻 ( 13 )   2023年7月

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    記述言語:英語   出版者・発行元:Sensors  

    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.

    DOI: 10.3390/s23135961

    Web of Science

    Scopus

    PubMed