85 citations · 368 across the 12 of their papers we have counts for
12 papers
Fast Data-driven Greedy Sensor Selection for Ridge Regression
Yasuo Sasaki, Keigo Yamada, Takayuki Nagata +2
We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated…
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…
Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model
Takayuki Nagata, Kumi Nakai, Keigo Yamada +5
A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated throug…
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…
Greedy Sensor Selection for Weighted Linear Least Squares Estimation under Correlated Noise
Keigo Yamada, Yuji Saito, Taku Nonomura +1
Optimization of sensor selection has been studied to monitor complex and large-scale systems with data-driven linear reduced-order modeling. An algorithm for greedy sensor selectio…
Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
Takayuki Nagata, Taku Nonomura, Kumi Nakai +3
The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of mult…
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…
Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise
Keigo Yamada, Yuji Saito, Koki Nankai +3
A greedy algorithm is proposed for sparse-sensor selection in reduced-order sensing that contains correlated noise in measurement. The sensor selection is carried out by maximizing…
Determinant-based Fast Greedy Sensor Selection Algorithm
Yuji Saito, Taku Nonomura, Keigo Yamada +5
In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended.…