The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decompo...
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The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.
Existing stochastic configuration network (SCN)-based modeling methods are underperformed in handling multitarget regression problems. An important reason is that they ignore the intertarget correlations, which have a...
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For nonlinear Backlash systems with input constraints that are difficult to model accurately, a model-free adaptive control algorithm (MFAC)based on data-driven technology is proposed. The criterion function of MFAC a...
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Leakage accidents in natural gas pipelines bring huge property losses and pose serious safety risks. Therefore, faster and more accurate leakage localization is of great significance. In this article, a new method bas...
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A sliding mode control algorithm based on a linear extended state observer is proposed to address the multi-source uncertainty of uncalibrated visual servoing in robotic arms. Uncertainty, nonlinearity, coupling, exte...
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In the research of stock price prediction, predicting with neural network has gradually become popular. In this paper, the stock price data is processed through wavelet and Long short-term memory (LSTM) network group(...
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Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditio...
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Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa
This paper proposes a modified cockcroft-walton quasi-Z-source inverter (MCW-qZSI). The proposed inverter is conceived by embedding a boost cell reorganized and derived from the Cockcroft-Walton voltage multiplier int...
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This study addresses the issue of inaccurate resistance values of heating elements at room temperature and proposes the optimization and design of electrically heated tobacco products (eHTP) heater system. The system ...
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This paper presents a 10-bit successive approximation analog-to-digital converter (ADC) with a conversion rate of 50 MS/s and proposes a low common-mode voltage fully differential switch switching scheme. The ADC empl...
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