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  • Open Access


    Development of a Three-Dimensional Multiscale Octree SBFEM for Viscoelastic Problems of Heterogeneous Materials

    Xu Xu1, Xiaoteng Wang1, Haitian Yang1, Zhenjun Yang2, Yiqian He1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1831-1861, 2024, DOI:10.32604/cmes.2024.048199

    Abstract The multiscale method provides an effective approach for the numerical analysis of heterogeneous viscoelastic materials by reducing the degree of freedoms (DOFs). A basic framework of the Multiscale Scaled Boundary Finite Element Method (MsSBFEM) was presented in our previous works, but those works only addressed two-dimensional problems. In order to solve more realistic problems, a three-dimensional MsSBFEM is further developed in this article. In the proposed method, the octree SBFEM is used to deal with the three-dimensional calculation for numerical base functions to bridge small and large scales, the three-dimensional image-based analysis can be conveniently conducted in small-scale and coarse… More >

  • Open Access


    Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

    Jiaxiang Luo1,2, Weien Zhou2,3, Bingxiao Du1,*, Daokui Li1, Wen Yao2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1919-1947, 2024, DOI:10.32604/cmes.2024.048118

    Abstract In recent years, there has been significant research on the application of deep learning (DL) in topology optimization (TO) to accelerate structural design. However, these methods have primarily focused on solving binary TO problems, and effective solutions for multi-material topology optimization (MMTO) which requires a lot of computing resources are still lacking. Therefore, this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design. The framework employs convolutional neural network (CNN) to construct a surrogate model for solving MMTO, and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any… More >

  • Open Access


    Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

    Deng Yang1, Chong Zhou1,*, Xuemeng Wei2, Zhikun Chen3, Zheng Zhang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049

    Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The Whale Optimization Algorithm (WOA) is… More >

  • Open Access


    Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

    Nasser Sweilam1, Seham Al-Mekhlafi2,*, Aya Ahmed3, Ahoud Alsheri4, Emad Abo-Eldahab3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1619-1645, 2024, DOI:10.32604/cmes.2024.047896

    Abstract In this paper, two crossover hybrid variable-order derivatives of the cancer model are developed. Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators. The existence, uniqueness, and stability of the proposed model are discussed. Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models. Comparative studies with generalized fifth-order Runge-Kutta method are given. Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented. We have showcased the efficiency of the proposed method and garnered robust empirical… More >

  • Open Access


    Experimental and Finite Element Analysis of Corroded High-Pressure Pipeline Repaired by Laminated Composite

    Seyed Mohammad Reza Abtahi1, Saeid Ansari Sadrabadi2,*, Gholam Hosein Rahimi1, Gaurav Singh2, Hamid Abyar3, Daniele Amato4, Luigi Federico5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1783-1806, 2024, DOI:10.32604/cmes.2024.047575

    Abstract Repairs of corroded high-pressure pipelines are essential for fluids transportation under high pressure. One of the methods used in their repairs is the use of layered composites. The composite used must have the necessary strength. Therefore, the experiments and analytical solutions presented in this paper are performed according to the relevant standards and codes, including ASME PCC-2, ASME B31.8S, ASME B31.4, ISO 24817 and ASME B31.G. In addition, the experimental tests are replicated numerically using the finite element method. Setting the strain gauges at different distances from the defect location, can reduce the nonlinear effects, deformation, and fluctuations due to… More >

  • Open Access


    Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

    Lassaad K. Smirani1, Leila Jamel2,*, Latifah Almuqren2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1315-1337, 2024, DOI:10.32604/cmes.2024.047551

    Abstract This study presents a layered generalization ensemble model for next generation radio mobiles, focusing on supervised channel estimation approaches. Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout. The model, called Stacked Generalization for Channel Estimation (SGCE), aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput. The SGCE model incorporates six machine learning methods: random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LGBM), support vector regression (SVR), extremely randomized tree (ERT), and extreme gradient boosting (XGB). By generating meta-data from five models (RF, GB, LGBM, SVR,… More >

  • Open Access


    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon1, Ye-Sol Oh1, Yeon-Ji Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239

    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In this study, we examined methods… More >

  • Open Access


    A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

    Yifan Liu1, Shancang Li1,*, Xinheng Wang2, Li Xu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1233-1261, 2024, DOI:10.32604/cmes.2024.046473

    Abstract The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest were used to… More >

  • Open Access


    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

    Wei Wu*, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174

    Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an enhanced DenseNet multimodal recognition network… More > Graphic Abstract

    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

  • Open Access


    Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

    Liang Chen1, Jingbo Zhang1, Linjie Wu1, Xingjuan Cai1,2,*, Yubin Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 363-383, 2024, DOI:10.32604/cmes.2024.049044

    Abstract The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set… More >

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