Chaoyang Jiang

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This paper presents two new greedy sensor placement algorithms, named minimum nonzero eigenvalue pursuit (MNEP) and maximal projection on minimum eigenspace (MPME), for linear inverse problems, with greater emphasis on the MPME algorithm for performance comparison with existing approaches. In both MNEP and MPME, we select the sensing locations one-by-one.(More)
This paper presents an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation(More)
This paper provides a new approach to reconstruct a fluid field from sparse sensor observations. Using the extreme learning machine (ELM) autoencoder, we can extract a dominant basis of the fluid field of interest from a database consisting of a series of fluid field snapshots obtained from offline computational fluid dynamics (CFD) simulations. The output(More)
Current CFD calibration work has mainly focused on the CFD model calibration. However no known work has considered the calibration of the CFD results. In this paper, we take inspiration from the image editing problem to develop a methodology to calibrate CFD simulation results based on sparse sensor observations. We formulate the calibration of CFD results(More)
This paper addresses the estimation problem for linear system with linear equality state constraints. We review the existing state projection method and present a simple way to find the optimal filter gain of the constrained Kalman filter. Then, we transform the constrained system into a reduced-order model and construct a reduced-order Kalman filter for(More)
This paper presents a new approach to estimate one physical field from off-line computational fluid dynamics (CFD) database and real-time sparse sensor observations. Firstly, we determine the proper orthogonal decomposition (POD) modes from the CFD database. Then, we use extreme learning machine (ELM) to build a regression model between the boundary(More)
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