Yinghua Song^1,2, Hairong Lyu^1,2, Wei Zhang^1,2,*

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249

Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy, by setting… More >

Weijun Wang^1,*, Min Chen¹, Hui Yin¹, Yuan Li²

Energy Engineering, Vol.120, No.10, pp. 2433-2448, 2023, DOI:10.32604/ee.2023.028620

Abstract To identify the parameters of the extended Debye model of XLPE cables, and therefore evaluate the insulation performance of the samples, the sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well the basics and processes of its application were explained. The amplitude vector based on polarization current was first calculated. Based on the non-zero elements of the vector, the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived. Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried… More >

Shoutao Li^1,2,*, Ruyi Qu¹, Yu Zhang¹, Dingli Yu³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and… More >

Shourong Hao¹, Yongxing Shen^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09336

Abstract Dynamic fracture is an important class of damage widely present in engineering materials and structures, e.g., high-speed impact and explosion. In recent years, the phase field approach to fracture proposed by Bourdin et al. [1] becomes popular for complicated fracture problems for its ability to simulate crack nucleation, propagation, branching, and merging without extra criteria, and the crack path does not need to be tracked, which makes the implementation straightforward and the calculation efficient. However, one of the major issues of the phase field method is the high computational cost due to the need of a very fine mesh, which… More >

Zhiheng Luo¹, Lin Chen², Nan Wang¹, Bin Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2022.08813

Abstract Anisotropy is inherent in many materials, either because of the manufacturing process, or due to their microstructure, and can markedly influence the failure behavior. Anisotropic materials obviously possess both anisotropic elasticity and anisotropic fracture surface energy. Phase-field methods are elegant and mathematically well-grounded, and have become popular for simulating isotropic and anisotropic brittle fracture. Here, we developed a variational phase-field model for strongly anisotropic fracture, which accounts for the anisotropy both in elastic strain energy and in fracture surface energy, and the asymmetric behavior of cracks in traction and in compression. We implement numerically our higher-order phase-field model with mixed… More >

Zikun Li¹, Jing Tang¹, Xiaobao Tian¹, Qingyuan Wang¹, Wentao Jiang¹, Haidong Fan^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09968

Abstract Magnesium is a lightweight structural metal but the industrial application is limited by its poor intrinsic ductility. Pyramidal dislocations are believed to be responsible for the ductility enhancement whereas the dislocation plasticity of magnesium was not well studied, especially the pyramidal dislocations. In this work, molecular dynamics simulations were performed to investigate the pyramidal disloation behaviors including the decomposition of pyramidal dislocations on both pyramidal-I and pyramidal-II planes and the interactions between themselves and other dislocations in Mg. The pyramidal-I dislocations are decomposed into and dislocations under shear stress at 0-400K, which all reside on basal plane.… More >

Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

Xinfei Li², Xiaolan Xie^1,2,*, Yigang Tang², Qiang Guo^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351

Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used… More >

Jiaojiao Yuan¹, Su Tuo¹, Yangyang Liu¹, Jing He¹, Shao-Hwa Hu¹, Junling Tu^2,*

Journal of Renewable Materials, Vol.11, No.8, pp. 3371-3385, 2023, DOI:10.32604/jrm.2023.028046

Abstract

Due to the existence of poly-hydroxyl structures, the temperature may have an effect on the thermal stability of oleuropein for its applications. In the current study, the thermal decomposition process and kinetics behavior of oleuropein from the olive resource were researched by thermogravimetric theoretical analysis methods and non-isothermal kinetics simulation. The results of thermogravimetry analysis showed the whole thermal decomposition process of oleuropein involved two stages, with 21.22% of residue. It was also revealed that high heating rates of more than 20 K min⁻¹ led to significant thermal hysteresis and inhibited the whole thermal decomposition behavior of oleuropein. Moreover, an… More > Graphic Abstract

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >