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Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time–frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time–frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time–frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

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The aim of this article is to study T-helper (Th) cell differentiation in the progression of acute, subacute, and chronic atopic dermatitis. Skin biopsies from 48 patients with acute, subacute, and chronic atopic dermatitis were studied using immunohistochemistry with antibodies to TARC/CCL17, CTACK/CCL27, and RANTES/CCL5. Peripheral blood mononuclear cells were studied in 17 patients using flow cytometry to measure the content of Th1/Th2 cells and Th17/Treg cells. Levels of interferon (IFN)-γ, interleukin (IL)-4, IL-17A, and transforming growth factor (TGF)-β1 were evaluated with enzyme-linked immunosorbent assay (ELISA). Distinctive expressions of T-cell-specific chemokines TARC/CCL17, CTACK/CCL27, and RANTES/CCL5 were observed at different stages of atopic dermatitis, which were consistent with the differentiation of the Th cell subsets, Th2/Th1, and Th17/Treg. Th2 and Th17 were acute-phase subsets, while Th1 and Treg were chronic-phase subsets. At an early stage of atopic dermatitis, Th17 and Th2 cells were found in peripheral blood mononuclear cells, followed by Th1 cells, Treg cells, and eosinophils; in late-stage or subacute and chronic atopic dermatitis, Th17 and Th2 cell numbers decreased. The levels of the IFN-γ and TGF-β1 increased during the progression of atopic dermatitis from acute to chronic forms. The levels of IL-17A and IL-4 decreased during the progression of atopic dermatitis from acute to chronic forms. The differentiation of Th subsets at distinct phases in atopic dermatitis may form the basis for further studies on the classification or control of this increasingly common clinical condition.

With the rapidly growing demand for large-scale online education and the advent of big data, numerous research works have been performed to enhance learning quality in e-learning environments. Among these studies, adaptive learning has become an increasingly important issue. The traditional classification approaches analyze only the surface characteristics of students but fail to classify students accurately in terms of deep learning features. Meanwhile, these approaches are unable to analyze these high-dimensional learning behaviors in massive amounts of data. Hence, we propose a learning style classification approach based on the deep belief network (DBN) for large-scale online education to identify students’ learning styles and classify them. The first step is to build a learning style model and identify indicators of learning style based on the experiences of experts; then, relate the indicators to the different learning styles. We improve the DBN model and identify a student’s learning style by analyzing each individual’s learning style features using the improved DBN. Finally, we verify the DBN result by conducting practical experiments on an actual educational dataset. The various learning styles are determined by soliciting questionnaires from students based on the ILS theory by Felder and Soloman (1996) and the Readiness for Education At a Distance Indicator. Then, we utilized those data to train our DBNLS model. The experimental results indicate that the proposed DBNLS method has better accuracy than do the traditional approaches.

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