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Found 10 papers, 2 papers with code
Fast Data-driven Greedy Sensor Selection for Ridge Regression
Sensor selection which prevents the overfitting of the resulting estimator can be realized by setting a positive regularization parameter.
Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
In the wind tunnel test, the PIV measurement and real-time measurement using SPPIV were conducted for the flow velocity field around the NACA0015 airfoil model.
Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method.
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations.
Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods.
Nondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments
Specifically, a new index is iteratively added to the nondominated solutions of sets, and the multi-objective functions are evaluated for new sets.
Greedy Sensor Selection for Weighted Linear Least Squares Estimation under Correlated Noise
A noise model is given using truncated modes in reduced-order modeling, and sensor positions that are optimal for generalized least squares estimation are selected.
Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
The objective functions based on various criteria of optimal design are adopted to the greedy method: D-optimality, A-optimality, and E-optimality, which maximizes the determinant, minimize the trace of inverse, and maximize the minimum eigenvalue of the Fisher information matrix, respectively.
Determinant-based Fast Greedy Sensor Selection Algorithm
The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach.
Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm
A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper.
