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. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

[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. At the same time, the computation costs of the algorithms increase significantly as the number of endmembers grows. Here, based on the traditional algorithm Orthogonal Subspace Projection, a new method called. Orthogonal Vector Projection is prompted using orthogonal principle. It simplifies this process by avoiding matrix multiplication and inversion. It firstly computes the final orthogonal vector via Gram-Schmidt process for each endmember spectrum. And then, these orthogonal vectors are used as projection vector for the pixel signature. The unconstrained abundance can be obtained directly by projecting the signature to the projection vectors, and computing the ratio of projected vector length and orthogonal vector length. Compared to the Orthogonal Subspace Projection and Least Squares Error algorithms, this method does not need matrix inversion, which is much computation costing and hard to implement on hardware. It just completes the orthogonalization process by repeated vector operations, easy for application on both parallel computation and hardware. The reasonability of the algorithm is proved by its relationship with Orthogonal Sub-space Projection and Least Squares Error algorithms. And its computational complexity is also compared with the other two algorithms', which is the lowest one. At last, the experimental results on synthetic image and real image are also provided, giving another evidence for effectiveness of the method.

[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. The algorithm reduces the influence of endmembers which are not component of the mixed pixel, decrease the number of endmembers involved in unmixing and improve the accuracy of abundance. The test results for simulation image prove that the algorithm would provide a lower reconstructing error. And the analysis results of actual airborne hyperspectral oil spill image further illustrate its effectiveness.

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. Specifically, an increase in thickness gradient leads to higher reflectance of the carbon fiber paper. Within the X-band frequency range (8.2-12.4 GHz), reflectivity decreases as electromagnetic wave frequency increases, indicating enhanced penetration of electromagnetic waves into the interior of the carbon fiber paper. This enhancement is attributed to an increased fiber content per unit area resulting from a greater thickness gradient, which further enhances reflection loss and promotes internal multiple reflections and scattering effects, leading to increased absorption loss. Notably, at a 5 mm thickness, our carbon fiber paper exhibits an impressive average overall shielding performance, reaching 63.46 dB. Moreover, it exhibits notable air permeability and mechanical properties, thereby assuming a pivotal role in the realm of flexible wearable devices in the foreseeable future.

[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. Firstly, a series of oil thicknesses are tested with the AvaSpec Spectrometer to get their corresponding spectral curves. And then the characteristics of the spectral curve are extracted to analyze their relationship with the oil thickness. The study shows that the oil thickness has large correlation with variables based on hyperspectral positions such as R(g), R(o), and vegetation indexes such as RDVI, TVI and Haboudane. Curve fitting, BP neural network and SVD iteration method were chosen to build the prediction models for oil thicknesses. Finally, the analysis and evaluation of each estimating model are provided.

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. Later, we comprehensively review existing HSI denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy 2-D convolutional neural network (CNN), 3-D CNN, hybrid, and unsupervised networks, to model-data-driven strategy. The advantages and disadvantages of each strategy for HSI denoising are summarized and contrasted. Behind this, we present an evaluation of the HSI denoising methods for various noisy HSIs in simulated and real experiments. The classification results of denoised HSIs and execution efficiency are depicted through these HSI denoising methods. Finally, prospects of future HSI denoising methods are listed in this technical review to guide the ongoing road for HSI denoising. The HSI denoising dataset could be found at https://qzhang95.github.io.

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. However, this limits the usage of standardization methods. This paper investigates an interesting issue, "Can spectra data in sets be arbitrarily order?" and further develops a completely different approach from existing methods in view of statistical signal processing. The key idea is to first compensate for the distortion along the wavelength and intensity of the spectra, and then transfer the second order statistic (2OS) from the primal spectra to the secondary spectra via data sphering and an inverse sphering transform so that the 2OS can be estimated regardless of the sample statistic order. To further demonstrate how the developed method can extend the usage of the NIR spectra standardization, several application-driven experiments on classification and regression are conducted for demonstration, and a comparison to the piecewise direct standardization (PDS) is also studied.

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. The proposed framework simultaneously cooperates the spectral low-rankness prior and deep spatial prior (SLRP-DSP) for HSI self-supervised denoising. SLRP-DSP introduces the Tucker factorization via orthogonal basis and reduced factor, to capture the global spectral low-rankness prior in HSI. Besides, SLRP-DSP adopts a self-supervised way to learn the deep spatial prior. The proposed method doesn't need a large number of clean HSIs as the label samples. Through the self-supervised learning, SLRP-DSP can adaptively adjust the deep spatial prior from self-spatial information for reduced spatial factor denoising. An alternating iterative optimization framework is developed to exploit the internal low-rankness prior of third-order tensors and the spatial feature extraction capacity of convolutional neural network. Compared with both existing model-driven methods and data-driven methods, experimental results manifest that the proposed SLRP-DSP outperforms on mixed noise removal in different noisy HSIs.