Xiaoding Wei^1,*, Wenqing Zhu¹, Junjie Liu²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09998

Abstract Fiber-reinforced polymer composites with excellent impact energy absorption properties play a pivotal role in the safety of spacecraft, protection of military personnel and equipment, as well as high-speed transportation. Research on the impact performance of composite materials has always relied mainly on expensive experiments and large-scale simulations. In this talk, we will introduce the “dynamic shear-lag model” by extending the classical shear-lag model to the dynamic domain. The dynamic shear-lag model reveals the transfer characteristics of impact energy in the microstructure scale of composite materials, and establishes a quantitative relationship between the " composition-microstructure-performance" of More >

Shiwei Zhao^1,*, Hao Chen², Jidong Zhao¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09965

Abstract With global warming, thaw-induced landslides occur more frequently in permafrost, which not only threaten the safety of infrastructures as general geohazards but also worsen global warming due to carbon release. This work presents a novel computational framework to model thaw-induced landslides from a multiscale perspective. The proposed approach can capture the thermal-mechanical (TM) response of frozen soils at the particulate scale by using discrete element method (DEM). The micromechanics-based TM model is superior to capturing the sudden crash of soil skeletons caused by thaw-induced cementation loss between soil grains. The DEM-simulated TM response is then More >

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 - 26 June 2023

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… More >

Wenchao Ma^*

Energy Engineering, Vol.120, No.7, pp. 1685-1699, 2023, DOI:10.32604/ee.2023.025404 - 04 May 2023

Abstract The power output state of photovoltaic power generation is affected by the earth's rotation and solar radiation intensity. On the one hand, its output sequence has daily periodicity; on the other hand, it has discrete randomness. With the development of new energy economy, the proportion of photovoltaic energy increased accordingly. In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation, this paper proposes the short-term prediction of photovoltaic power generation based on the improved multi-scale permutation entropy, local mean decomposition… More >

Wen Long^*, Bin Zhu, Huaizheng Li, Yan Zhu, Zhiqiang Chen, Gang Cheng

Energy Engineering, Vol.120, No.5, pp. 1253-1269, 2023, DOI:10.32604/ee.2023.026395 - 20 February 2023

Abstract There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and… More >

Jufeng Wang¹, Yong Wu¹, Ying Xu¹, Fengxin Sun^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 341-356, 2023, DOI:10.32604/cmes.2022.023140 - 29 September 2022

Abstract By introducing the dimensional splitting (DS) method into the multiscale interpolating element-free Galerkin (VMIEFG) method, a dimension-splitting multiscale interpolating element-free Galerkin (DS-VMIEFG) method is proposed for three-dimensional (3D) singular perturbed convection-diffusion (SPCD) problems. In the DS-VMIEFG method, the 3D problem is decomposed into a series of 2D problems by the DS method, and the discrete equations on the 2D splitting surface are obtained by the VMIEFG method. The improved interpolation-type moving least squares (IIMLS) method is used to construct shape functions in the weak form and to combine 2D discrete equations into a global system More >

Y. Nishio^1,*, B. Janssens¹, K. Limam², J. van Beeck³

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.3, pp. 743-753, 2023, DOI:10.32604/fdmp.2022.021848 - 29 September 2022

Abstract A LES model is proposed to predict the dispersion of particles in the atmosphere in the context of Chemical, Biological, Radiological and Nuclear (CBRN) applications. The code relies on the Finite Element Method (FEM) for both the fluid and the dispersed solid phases. Starting from the Navier-Stokes equations and a general description of the FEM strategy, the Streamline Upwind Petrov-Galerkin (SUPG) method is formulated putting some emphasis on the related assembly matrix and stabilization coefficients. Then, the Variational Multiscale Method (VMS) is presented together with a detailed illustration of its algorithm and hierarchy of computational More >

Raqinah Alrabiah, Muhammad Hussain^*, Hatim A. AboAlSamh

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2941-2962, 2023, DOI:10.32604/iasc.2023.030036 - 17 August 2022

Abstract The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing… More >

Shouming Hou¹, Chaolan Jia¹, Kai Li¹, Liya Fan², Jincheng Guo^3,*, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 81-94, 2022, DOI:10.32604/cmes.2022.019006 - 02 June 2022

Abstract Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The… More >

Weiwei Cai^1,2, Yaping Song¹, Huan Duan¹, Zhenwei Xia¹, Zhanguo Wei^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785 - 19 April 2022

Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics… More >