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  • Open Access


    An Interpolation Method for Karhunen–Loève Expansion of Random Field Discretization

    Zi Han1,*, Zhentian Huang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 245-272, 2024, DOI:10.32604/cmes.2023.029708

    Abstract In the context of global mean square error concerning the number of random variables in the representation, the Karhunen–Loève (KL) expansion is the optimal series expansion method for random field discretization. The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem (IEVP). The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares (MLS), least squares (LS), and finite element method (FEM) to solve the IEVP. Compared with the Galerkin method based on finite element or Legendre polynomials, the main advantage of the… More > Graphic Abstract

    An Interpolation Method for Karhunen–Loève Expansion of Random Field Discretization

  • Open Access


    TPMS-Based Topology Optimization Design with Multiple Materials via MMC Method

    Sinuo Zhang1, Daicong Da2, Yingjun Wang1,3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.2, pp. 1-2, 2023, DOI:10.32604/icces.2023.09085

    Abstract Topology optimization (TO) designs involving multiple materials suffer either difficult interface modeling or finding physically meaningful transition domains with an accurate structural representation. Simple interpolation approaches are usually used in multi-material designs to represent the overlapped regions of different materials, which cannot solve either of these problems. In this paper, a moving morphable component (MMC)-based TO is developed to overcome this issue by leveraging the triply periodic minimal surfaces (TPMS). The TMPS-based architecture will serve as the infilling microstructure to accurately represent the overlapped domains of different materials. A TPMS function interpolation scheme is used to generate new microstructures for… More >

  • Open Access


    A Three-Dimensional Model for the Formation Pressure in Wellbores under Uncertainty

    Jiawei Zhang*, Qing Wang, Hongchun Huang, Haige Wang, Guodong Ji, Meng Cui, Hongyuan Zhang

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2305-2314, 2023, DOI:10.32604/fdmp.2023.026304

    Abstract Formation pressure is the key parameter for the analysis of wellbore safety. With increasing drilling depth, however, the behavior of this variable becomes increasingly complex. In this work, a 3D model of the formation pressure under uncertainty is presented. Moreover a relevant algorithm is elaborated. First, the logging data of regional key drilling wells are collected and a one-dimensional formation pressure profile along the well depth is determined. Then, a 3D model of regional formation pressure of the hierarchical group layer is defined by using the Kriging interpolation algorithm relying on a support vector machine (SVM) and the formation pressure… More >

  • Open Access


    Interpolation Technique for the Underwater DEM Generated by an Unmanned Surface Vessel

    Shiwei Qin, Zili Dai*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3157-3172, 2023, DOI:10.32604/cmes.2023.026874

    Abstract High-resolution underwater digital elevation models (DEMs) are important for water and soil conservation, hydrological analysis, and river channel dredging. In this work, the underwater topography of the Panjing River in Shanghai, China, was measured by an unmanned surface vessel. Five different interpolation methods were used to generate the underwater DEM and their precision and applicability for different underwater landforms were analyzed through cross-validation. The results showed that there was a positive correlation between the interpolation error and the terrain surface roughness. The five interpolation methods were all appropriate for the survey area, but their accuracy varied with different surface roughness.… More >

  • Open Access


    Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

    S. Vidhya1,*, V. Kalaivani2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2673-2692, 2023, DOI:10.32604/csse.2023.027724

    Abstract In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored… More >

  • Open Access


    Building 3-D Human Data Based on Handed Measurement and CNN

    Bich Nguyen1, Binh Nguyen1, Hai Tran2, Vuong Pham1, Le Nhi Lam Thuy1, Pham The Bao1,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2431-2441, 2023, DOI:10.32604/cmc.2023.029618

    Abstract 3-dimension (3-D) printing technology is growing strongly with many applications, one of which is the garment industry. The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics. This paper proposes a method to construct 3-D human models by applying deep learning. We calculate the location of the main slices of the human body, including the neck, chest, belly, buttocks, and the rings of the extremities, using pre-existing information. Then, on the positioning frame, we find the key points (fixed and unaltered) of these key slices and update… More >

  • Open Access


    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710

    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open Access


    Peridynamic Shell Model Based on Micro-Beam Bond

    Guojun Zheng1,2, Zhaomin Yan1, Yang Xia1,2, Ping Hu1,2, Guozhe Shen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1975-1995, 2023, DOI:10.32604/cmes.2022.021415

    Abstract Peridynamics (PD) is a non-local mechanics theory that overcomes the limitations of classical continuum mechanics (CCM) in predicting the initiation and propagation of cracks. However, the calculation efficiency of PD models is generally lower than that of the traditional finite element method (FEM). Structural idealization can greatly improve the calculation efficiency of PD models for complex structures. This study presents a PD shell model based on the micro-beam bond via the homogenization assumption. First, the deformations of each endpoint of the micro-beam bond are calculated through the interpolation method. Second, the micro-potential energy of the axial, torsional, and bending deformations… More >

  • Open Access


    LaNets: Hybrid Lagrange Neural Networks for Solving Partial Differential Equations

    Ying Li1, Longxiang Xu1, Fangjun Mei1, Shihui Ying2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 657-672, 2023, DOI:10.32604/cmes.2022.021277

    Abstract We propose new hybrid Lagrange neural networks called LaNets to predict the numerical solutions of partial differential equations. That is, we embed Lagrange interpolation and small sample learning into deep neural network frameworks. Concretely, we first perform Lagrange interpolation in front of the deep feedforward neural network. The Lagrange basis function has a neat structure and a strong expression ability, which is suitable to be a preprocessing tool for pre-fitting and feature extraction. Second, we introduce small sample learning into training, which is beneficial to guide the model to be corrected quickly. Taking advantages of the theoretical support of traditional… More >

  • Open Access


    Super-Resolution Based on Curvelet Transform and Sparse Representation

    Israa Ismail1,*, Mohamed Meselhy Eltoukhy1,2, Ghada Eltaweel1

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 167-181, 2023, DOI:10.32604/csse.2023.028906

    Abstract Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary learning in the sparse representation… More >

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