29 citations · 125 across the 6 of their papers we have counts for
7 papers · 1 filter
Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
Naoki Kanda, Chihaya Abe, Shintaro Goto +5
In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image…
Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
Kumi Nakai, Takayuki Nagata, Keigo Yamada +5
The ``big'' seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones wi…
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
Takayuki Nagata, Keigo Yamada, Taku Nonomura +3
The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based…
Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
Takayuki Nagata, Keigo Yamada, Kumi Nakai +2
The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied str…
Nondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments
Kumi Nakai, Yasuo Sasaki, Takayuki Nagata +3
In this study, a nondominated-solution-based multi-objective greedy method is proposed and applied to a sensor selection problem based on the multiple indices of the optimal design…
Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
Kumi Nakai, Keigo Yamada, Takayuki Nagata +2
The selection problem of an optimal set of sensors estimating the snapshot of high-dimensional data is considered. The objective functions based on various criteria of optimal desi…