Shearlet regularization and dimensionality reduction for the temperature distribution sensing

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Shearlet regularization and dimensionality reduction for the temperature distribution sensing

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作者：J. Lei^a ; ^{leijingdr@126.com" class="auth_mail" title="E-mail the corresponding author} ; W.Y. Liu^a ; Q.B. Liu^b ; X.Y. Wang^b ; S. Liu^a
关键词：ALM ; augmented Lagrangian multipliers ; ANN ; artificial neural network ; CS ; compressed sensing ; DRTDS ; dimensionality reduction based temperature distribution sensing ; DST ; discrete Shearlet transform ; GPOD ; gappy proper orthogonal decomposition ; LRMC ; low rank matrix completion ; LSSVM ; least squares support vector machine ; MRE ; mean relative error ; NMF ; nonnegative matrix factorization ; NMS ; Nelder&ndash ; Mead simplex ; PCA ; principle component analysis ; POD ; proper orthogonal decomposition ; PNMF
刊名：Measurement
出版年：2016
出版时间：March 2016
年：2016
卷：82
期：Complete
页码：176-187
全文大小：1579 K

文摘

•: A dimensionality reduction based method is proposed to model the TDS problem.
•: The PNMF method is developed to extract the basis vectors from a set of snapshots.
•: An objective functional is proposed to cast the TDS task as a minimization problem.
•: An iteration scheme is developed to solve the proposed objective functional.
•: The feasibility of the proposed method is numerically validated.

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