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  • Open Access

    ARTICLE

    An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

    Abidhan Bardhan1,*, Raushan Kumar Singh2, Mohammed Alatiyyah3, Sulaiman Abdullah Alateyah4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467

    Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was… More >

  • Open Access

    ARTICLE

    Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

    Deng Qin1, Xing Du2, Tian Li1,*, Jiye Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2155-2173, 2024, DOI:10.32604/cmes.2023.044460

    Abstract Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly, energy efficient and rapid advances in train technology. Using computational fluid dynamics theory and the K-FWH acoustic equation, a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs. A component optimization method is proposed as a possible solution to the problem of aerodynamic drag and noise in high-speed pantographs. The results of the study indicate that the panhead, base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs. Therefore, a gradual optimization process is implemented to… More >

  • Open Access

    ARTICLE

    Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart Grids

    Fengyong Li1,*, Weicheng Shen1, Zhongqin Bi1, Xiangjing Su2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431

    Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to… More >

  • Open Access

    ARTICLE

    A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

    Jingyu Zhang1,2, Pian Zhou1, Jin Wang1, Osama Alfarraj3, Saurabh Singh4, Min Zhu5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1613-1633, 2024, DOI:10.32604/cmes.2023.044418

    Abstract Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system. This technology has been widely used and has developed rapidly in big data systems across various fields. An increasing number of users are participating in application systems that use blockchain as their underlying architecture. As the number of transactions and the capital involved in blockchain grow, ensuring information security becomes imperative. Addressing the verification of transactional information security and privacy has emerged as a critical challenge. Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations. However,… More >

  • Open Access

    ARTICLE

    Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

    Yujie Duan1, Jianguo Liang1,*, Jianglin Liu1, Haifeng Gao1, Yinhui Li2, Jinzhu Zhang1, Xinyu Wen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1239-1261, 2024, DOI:10.32604/cmes.2023.044400

    Abstract In the fiber winding process, strong disturbance, uncertainty, strong coupling, and fiber friction complicate the winding constant tension control. In order to effectively reduce the influence of these problems on the tension output, this paper proposed a tension fluctuation rejection strategy based on feedforward compensation. In addition to the bias harmonic curve of the unknown state, the tension fluctuation also contains the influence of bounded noise. A tension fluctuation observer (TFO) is designed to cancel the uncertain periodic signal, in which the frequency generator is used to estimate the critical parameter information. Then, the fluctuation signal is reconstructed by a… More >

  • Open Access

    ARTICLE

    Influences of Co-Flow and Counter-Flow Modes of Reactant Flow Arrangement on a PEMFC at Start-Up

    Qianqian Shao1, Min Wang2,*, Nuo Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1337-1356, 2024, DOI:10.32604/cmes.2023.044313

    Abstract To investigate the influences of co-flow and counter-flow modes of reactant flow arrangement on a proton exchange membrane fuel cell (PEMFC) during start-up, unsteady physical and mathematical models fully coupling the flow, heat, and electrochemical reactions in a PEMFC are established. The continuity equation and momentum equation are solved by handling pressure-velocity coupling using the SIMPLE algorithm. The electrochemical reaction rates in the catalyst layers (CLs) of the cathode and anode are calculated using the Butler-Volmer equation. The multiphase mixture model describes the multiphase transport process of gas mixtures and liquid water in the fuel cell. After validation, the influences… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

    Ahmed Barnawi1,*, Krishan Kumar2, Neeraj Kumar1, Bander Alzahrani1, Amal Almansour1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2117-2137, 2024, DOI:10.32604/cmes.2023.044184

    Abstract Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach, landmine removal remains a challenging… More >

  • Open Access

    ARTICLE

    Interaction Mechanisms between Natural Debris Flow and Rigid Barrier Deflectors: A New Perspective for Rational Design and Optimal Arrangement

    Yu Huang1, Beilei Liu1, Dianlei Feng2,*, Hao Shi1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1679-1699, 2024, DOI:10.32604/cmes.2023.044094

    Abstract Rigid barrier deflectors can effectively prevent overspilling landslides, and can satisfy disaster prevention requirements. However, the mechanisms of interaction between natural granular flow and rigid barrier deflectors require further investigation. To date, few studies have investigated the impact of deflectors on controlling viscous debris flows for geological disaster prevention. To investigate the effect of rigid barrier deflectors on impact mechanisms, a numerical model using the smoothed particle hydrodynamics (SPH) method with the Herschel–Bulkley model is proposed to simulate the interaction between natural viscous flow and single/dual barriers with and without deflectors. This model was validated using laboratory flume test data… More > Graphic Abstract

    Interaction Mechanisms between Natural Debris Flow and Rigid Barrier Deflectors: A New Perspective for Rational Design and Optimal Arrangement

  • Open Access

    ARTICLE

    Advancing Wound Filling Extraction on 3D Faces: An Auto-Segmentation and Wound Face Regeneration Approach

    Duong Q. Nguyen1, Thinh D. Le3, Phuong D. Nguyen3, Nga T. K. Le2, H. Nguyen-Xuan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2197-2214, 2024, DOI:10.32604/cmes.2023.043992

    Abstract Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting… More > Graphic Abstract

    Advancing Wound Filling Extraction on 3D Faces: An Auto-Segmentation and Wound Face Regeneration Approach

  • Open Access

    REVIEW

    Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques: A Comprehensive Review and Open Challenges

    Samina Amin1, Muhammad Ali Zeb1, Hani Alshahrani2,*, Mohammed Hamdi2, Mohammad Alsulami2, Asadullah Shaikh3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

    Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >

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