This paper combines dynamic modeling, vibration error monitoring, and machine tool information acquisition to propose a new method for the process perception of vibration errors in CNC machine tool spindle systems. A ...
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With the increasing demand for supplier evaluation in the power industry, the traditional evaluation system is particularly inadequate in dealing with the complexity of data and intelligent requirements. Existing meth...
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Existing time series correlation research methods often rely too much on overall statistical characteristics and ignore the dynamic changes of data in the time dimension, especially in the identification of trend turn...
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Accurate dimensionality reduction models are crucial for constructing real-time computational digital twin systems for process equipment. To deepen the understanding of acoustic resonance flow mechanisms and optimize ...
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Accurate dimensionality reduction models are crucial for constructing real-time computational digital twin systems for process equipment. To deepen the understanding of acoustic resonance flow mechanisms and optimize equipment design, a high-precision prediction method for acoustic resonance flow fields is required. This study introduces a non-intrusive reduced-order modeling (ROM) approach to learn fluid motion patterns and forecast flow field evolution. The original dataset is derived from an experimentally validated computational fluid dynamics model. The flow field snapshots are decomposed into spatial modes and temporal coefficients using proper orthogonal decomposition. High-complexity temporal coefficients, identified through sample entropy analysis, undergo secondary decomposition using methods such as variational mode decomposition. A specialized BiLSTM-attention network is then employed to learn and predict each component's behavior. By integrating secondary decomposition techniques commonly used in time series prediction with ROM technology, this paper presents a non-intrusive flow field prediction method. Compared to the control method without secondary decomposition, this approach significantly enhances prediction accuracy, with the coefficient of determination for individual components improving by up to 27%. This advancement is instrumental for the development of high-fidelity digital twins in acoustic resonance devices, providing a robust basis for real-time computational process equipment. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://***/licenses/by/4.0/).
Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper s...
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As an important branch of mathematical statistics, multivariate statistical analysis has developed rapidly, its theory is more rigorous, its content is more solid, and it has a wide range of practical application valu...
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Chemical looping pyrolysis (CLPy) is an innovative thermochemical conversion technique for transforming solid waste into valuable resources. In this paper, thermogravimetry (TG) experiments were conducted on blends of...
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Chemical looping pyrolysis (CLPy) is an innovative thermochemical conversion technique for transforming solid waste into valuable resources. In this paper, thermogravimetry (TG) experiments were conducted on blends of biogas residue (BR) and Fe2NiO4 oxygen carriers (OCs) at ratios (B:O) of 1:0, 0.7:0.3 and 0.5:0.5, under an N2 atmosphere at heating rates of 10, 15, 20, and 25 degrees C/min. Beside, a neural network model was employed to predict the mass loss curves under various conditions. Experimental results revealed that the pyrolysis process of BR during CLPy occurs in three main stages, with the most prominent peak in the DTG curve emerging in the second stage. Higher heating rates resulted in delayed pyrolysis reactions, and ultimately increasing the BR mass after pyrolysis. The BR and OCs blends exhibited a suppresses effect on the GLPy process, resulting in a decrease in the comprehensive pyrolysis index from 1.52 x 10-4 to 3.04 x 10-5, and a decrease in the maximum DTG from 5.41 %/min to 2.90 %/min as the B:O ratio increase from 1:0 to 0.5:0.5. The average activation energy calculated by FWO method and KAS method is 161.65 kJ/mol and 176.93 kJ/mol, respectively. In particular, the optimized artificial neural network (ANN) model, with 10 hidden layer nodes, a learning rate of 0.01 and minimum error of training target of 1.0 x 10-5, achieves highest R 2 of 0.9997 in cross-validation. This model demonstrated superior performance in predicting TG data. These findings provide essential technical support and a scientific foundation for the industrial application of BR energy and the optimization of the CLPy process.
In the realm of high-energy physics experiments, the ability of software to visualize data plays a pivotal role. It supports the design of detectors, aids in dataprocessing, and enhances the potential to refine physi...
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With the gradual deepening of the development of mineral resources, people's demand for accurate detection of mineral components is also increasing. High-precision electron probe technology has become an important...
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Given research work is devoted to the development of an automated system model for generating a supply plan and is aimed at optimizing inventory management and supply processes. The work examines the main elements of ...
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