85 citations · 697 across the 21 of their papers we have counts for
21 papers
Unsteady large-scale wake structure behind levitated freestream-aligned circular cylinder
Sho Yokota, Taku Nonomura
The relationships between characteristic large-scale wake structures appearing behind a freestream-aligned circular cylinder are investigated and discussed from the velocity field…
Flow instability and momentum exchange in separation control by a synthetic jet
Yoshiaki Abe, Taku Nonomura, Kozo Fujii
This study investigates a mechanism of controlling separated flows around an airfoil using a synthetic jet (SJ). A large-eddy simulation (LES) was performed for a leading-edge sepa…
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…
Spatiotemporal Superresolution Measurement based on POD and Sparse Regression applied to a Supersonic Jet measured by PIV and Near-field Microphone
Yuta Ozawa, Takayuki Nagata, Taku Nonomura
The present study proposed the framework of the spatiotemporal superresolution measurement based on the sparse regression with dimensionality reduction using the proper orthogonal…
Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
Ryoma Inoba, Kazuki Uchida, Yuto Iwasaki +5
This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven o…
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 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…
Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem
Taku Nonomura, Shunsuke Ono, Kumi Nakai +1
The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex for…
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.…
Quantitative Evaluation of a Linear Reduced-order Model based on Particle-image-velocimetry Data of Separated Flow Field around Airfoil
Taku Nonomura, Koki Nankai, Yuto Iwasaki +2
A quantitative evaluation method for a reduced-order model of the flow field around an NACA0015 airfoil based on particle image velocimetry (PIV) data is proposed in the present pa…
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…
Dynamic mode decomposition using a Kalman filter for parameter estimation
Taku Nonomura, Hisaichi Shibata, Ryoji Takaki
A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combinatio…