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Baijie Qiao
Researcher at Xi'an Jiaotong University
Publications - 78
Citations - 1356
Baijie Qiao is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Vibration & Computer science. The author has an hindex of 14, co-authored 53 publications receiving 777 citations. Previous affiliations of Baijie Qiao include University of Massachusetts Lowell.
Papers
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Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis
TL;DR: A novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed, based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults.
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Sparse regularization for force identification using dictionaries
TL;DR: Considering the sparsity of force in the time domain or in other basis space, a general sparse regularization method based on minimizing l 1 -norm of the coefficient vector of basis functions was developed in this article.
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Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction
TL;DR: In this article, the primal-dual interior point method (PDIPM) is proposed to solve the large-scale ill-posed inverse problem in moderate computational cost, where minimizing the l 2 -norm is replaced by minimizing the L 1 -norm.
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Impact-force sparse reconstruction from highly incomplete and inaccurate measurements
TL;DR: Wang et al. as discussed by the authors developed a general sparse methodology based on minimizing l1-norm for solving the highly underdetermined model of impact-force reconstruction, which can not only determine the actual impact location from many candidate sources but also simultaneously reconstruct the time history of impact force.
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A force identification method using cubic B-spline scaling functions
TL;DR: In this paper, an efficient basis function expansion method based on wavelet multi-resolution analysis using cubic B-spline scaling functions as basis functions is proposed for identifying force history with high accuracy, which can overcome the deficiency of the illposed problem.