Variational mode decomposition-based abnormal wheel-rail relationship detection in distributed acoustic sensing.

The distributed acoustic sensing system can obtain the vibration signal caused by the vibration of the train. By analyzing these wheel-rail vibration signals, an abnormal wheel-rail relationship identification scheme is proposed. The variational mode decomposition is employed for signal decomposition, thereby obtaining intrinsic mode functions with prominent abnormal fluctuations. The kurtosis value of each intrinsic mode function is calculated, which is compared with the threshold value for the identification of trains with abnormal wheel-rail relationship. And the extreme point of the abnormal intrinsic mode function is used to locate the bogie with an abnormal wheel-rail relationship. Experimental demonstration verifies that the proposed scheme can identify the train and locate the bogie with an abnormal wheel-rail relationship.

Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning.

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.