16 citations · 16 across the 1 of their papers we have counts for
7 papers
Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points
Tomoki Inoue, Yu Matsuda, Tsubasa Ikami +3
We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at opt…
Effect of Oxygen Mole Fraction on Static Properties of Pressure-Sensitive Paint
Tomohiro Okudera, Takayuki Nagata, Miku Kasai +3
The effects of oxygen mole fraction on the static properties of pressure-sensitive paint (PSP) were investigated. Sample coupon tests using a calibration chamber were conducted for…
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…
Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm
Yuji Saito, Taku Nonomura, Koki Nankai +4
A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sen…
Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising
Taku Nonomura, Hisaichi Shibata, Ryoji Takaki
A new dynamic mode decomposition (DMD) method is introduced for simultaneous online system identification and denoising in conjunction with the adoption of an extended Kalman filte…