Effect of Carbon Fiber Paper with Thickness Gradient on Electromagnetic Shielding Performance of X-Band.

Flexible paper-based materials play a crucial role in the field of flexible electromagnetic shielding due to their thinness and controllable shape. In this study, we employed the wet paper forming technique to prepare carbon fiber paper with a thickness gradient. The electromagnetic shielding performance of the carbon fiber paper varies with the ladder-like thickness distribution.

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve.

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise.

Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven.

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms. Then, a general HSI restoration model is formulated for optimization.

Standardization of near infrared spectroscopies via sample spectral correlation equalization.

A novel method for near-infrared (NIR) spectroscopy spectra standardization is presented. NIR spectroscopies have been widely used in analytical chemistry, and many methods have been developed for NIR spectra standardization. To establish a robust standardization transformation, most existing methods require spectral data sets from both primal and secondary instruments for 1-1 correspondence validation.

Cooperated Spectral Low-Rankness Prior and Deep Spatial Prior for HSI Unsupervised Denoising.

Model-driven methods and data-driven methods have been widely developed for hyperspectral image (HSI) denoising. However, there are pros and cons in both model-driven and data-driven methods. To address this issue, we develop a self-supervised HSI denoising method via integrating model-driven with data-driven strategy.

[Orthogonal Vector Projection Algorithm for Spectral Unmixing].

Spectrum unmixing is an important part of hyperspectral technologies, which is essential for material quantity analysis in hyperspectral imagery. Most linear unmixing algorithms require computations of matrix multiplication and matrix inversion or matrix determination. These are difficult for programming, especially hard for realization on hardware.

[Effective endmembers based bilinear unmixing model].

An effective endmembers based bilinear unmixing algorithm is prompted in the present paper together with an end-member subset selection algorithm as well. Firstly, the endmembers are ranked according to their distance to the mixed pixel, involving the Euclidean distance and spectral angle. And then, an effective subset of the endmembers is abstracted considering both the ranking result and the change of error.

[Study of prediction models for oil thickness based on spectral curve].

Nowdays, oil spill accidents on sea occur frequently. It is a practical topic to estimate the amount of spilled oil, which is helpful for the subsequent processing and loss assessment. With the rapid development of hyperspectral remote sensing technology, estimating the oil thickness becomes possible.