In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.

Research has proven that light treatment, specifically red light radiation, can provide more clinical benefits to human health. Our investigation was firstly conducted to characterize the tissue morphology of mouse breast post 660 nm laser radiation with low power and long-term exposure. RNA sequencing results revealed that light exposure with a higher intervention dosage could cause a number of differentially expressed genes compared with a low intervention dosage. Gene ontology analysis, protein–protein interaction network analysis, and gene set enrichment analysis results suggested that 660 nm light exposure can activate more transcription-related pathways in HC11 breast epithelial cells, and these pathways may involve modulating critical gene expression. To consider the critical role of the Wnt/T-catenin pathway in light-induced modulation, we hypothesized that this pathway might play a major role in response to 660 nm light exposure. To validate our hypothesis, we conducted qRT-PCR, immunofluorescence staining, and Western blot assays, and relative results corroborated that laser radiation could promote expression levels of β-catenin and relative phosphorylation. Significant changes in metabolites and pathway analysis revealed that 660 nm laser could affect nucleotide metabolism by regulating purine metabolism. These findings suggest that the Wnt/β-catenin pathway may be the major sensor for 660 nm laser radiation, and it may be helpful to rescue drawbacks or side effects of 660 nm light exposure through relative interventional agents.

EEG signals are multi-dimensional and non-stationary time series signals, and their features are not easy to extract, so large deviations are often encountered in the classification and prediction tasks. Therefore, in this paper, an EEG eye state (open or close) recognition method is proposed based on an improved convolutional neural network and the continuous wavelet transform. First, for the high noise levels encountered during EEG signal acquisition, variational mode decomposition (VMD) is used to decompose the EEG signal into several modal components, which are then subsequently used to reconstruct the signal according to the correlation principle to obtain a noiseless signal. Secondly, the continuous wavelet transform (CWT) method is used to extract the features of the EEG signals in the time and frequency domains, and the one-dimensional signal is converted into two-dimensional images to enhance the feature-learning ability convolutional networks. Finally, a residual term is introduced on the basis of the traditional convolutional neural network, and a recognition model is constructed by combining a dual channel and spatial attention mechanism. Experimental results show that the proposed model has an accuracy rate higher than 99\%, and is more effective and with better generalizability than previous studies.