Home / Journals / CMES / Vol.137, No.3, 2023
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  • Open AccessOpen Access

    REVIEW

    Research Progress of Reverse Monte Carlo and Its Application in Josephson Junction Barrier Layer

    Junling Qiu*, Huihui Sun, Shuya Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2077-2109, 2023, DOI:10.32604/cmes.2023.027353
    Abstract As indispensable components of superconducting circuit-based quantum computers, Josephson junctions determine how well superconducting qubits perform. Reverse Monte Carlo (RMC) can be used to recreate Josephson junction’s atomic structure based on experimental data, and the impact of the structure on junctions’ properties can be investigated by combining different analysis techniques. In order to build a physical model of the atomic structure and then analyze the factors that affect its performance, this paper briefly reviews the development and evolution of the RMC algorithm. It also summarizes the modeling process and structural feature analysis of the Josephson More >

  • Open AccessOpen Access

    ARTICLE

    Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

    Cho Mar Aye1, Kittinan Wansaseub2, Sumit Kumar3, Ghanshyam G. Tejani4, Sujin Bureerat1, Ali R. Yildiz5, Nantiwat Pholdee1,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2111-2128, 2023, DOI:10.32604/cmes.2023.028632
    Abstract This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization. The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints. Computational Fluid Dynamic (CFD) and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value. A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed. To validate and further assess the proposed methods, a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied More >

    Graphic Abstract

    Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

  • Open AccessOpen Access

    ARTICLE

    Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

    Qiankun Zuo1,4, Junhua Hu2, Yudong Zhang3,*, Junren Pan4, Changhong Jing4, Xuhang Chen5, Xiaobo Meng6, Jin Hong7,8,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2129-2147, 2023, DOI:10.32604/cmes.2023.028732
    Abstract The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven… More >

    Graphic Abstract

    Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

  • Open AccessOpen Access

    ARTICLE

    Strain-Rate Dependency of a Unidirectional Filament Wound Composite under Compression

    Stepan Konev1, Victor A. Eremeyev2,3, Hamid M. Sedighi4,5,*, Leonid Igumnov2, Anatoly Bragov2, Aleksandr Konstantinov2, Ayaulym Kuanyshova1, Ivan Sergeichev1
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2149-2161, 2023, DOI:10.32604/cmes.2023.028179
    Abstract This article presents the results of experimental studies concerning the dynamic deformation and failure of a unidirectional carbon fiber reinforced plastic (T700/LY113) under compression. The test samples were manufactured through the filament winding of flat plates. To establish the strain rate dependencies of the strength and elastic modulus of the material, dynamic tests were carried out using a drop tower, the Split Hopkinson Pressure Bar method, and standard static tests. The samples were loaded both along and perpendicular to the direction of the reinforcing fiber. The applicability of the obtained samples for static and dynamic… More >

  • Open AccessOpen Access

    ARTICLE

    Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

    Ali S. Alghamdi1, Mohana Alanazi2, Abdulaziz Alanazi3, Yazeed Qasaymeh1,*, Muhammad Zubair1,4, Ahmed Bilal Awan5, M. G. B. Ashiq6
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2163-2192, 2023, DOI:10.32604/cmes.2023.029453
    Abstract To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat… More >

  • Open AccessOpen Access

    ARTICLE

    Nonlinear Analysis of Organic Polymer Solar Cells Using Differential Quadrature Technique with Distinct and Unique Shape Function

    Ola Ragb1, Mokhtar Mohamed2, Mohamed S. Matbuly1, Omer Civalek3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2193-2217, 2023, DOI:10.32604/cmes.2023.028992
    Abstract Four numerical schemes are introduced for the analysis of photocurrent transients in organic photovoltaic devices. The mathematical model for organic polymer solar cells contains a nonlinear diffusion–reaction partial differential equation system with electrostatic convection attached to a kinetic ordinary differential equation. To solve the problem, Polynomial-based differential quadrature, Sinc, and Discrete singular convolution are combined with block marching techniques. These schemes are employed to reduce the problem to a nonlinear algebraic system. The iterative quadrature technique is used to solve the reduced problem. The obtained results agreed with the previous exact one and the finite More >

    Graphic Abstract

    Nonlinear Analysis of Organic Polymer Solar Cells Using Differential Quadrature Technique with Distinct and Unique Shape Function

  • Open AccessOpen Access

    ARTICLE

    On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications

    Hassan Okasha1,2, Mazen Nassar1,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783
    Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum More >

  • Open AccessOpen Access

    ARTICLE

    A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

    Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101
    Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this… More >

  • Open AccessOpen Access

    ARTICLE

    Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

    Jiaqun Zhu1, Hongda Chen2, Yiqing Fan1, Tongguang Ni1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2267-2283, 2023, DOI:10.32604/cmes.2023.027709
    (This article belongs to the Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract To create a green and healthy living environment, people have put forward higher requirements for the refined management of ecological resources. A variety of technologies, including satellite remote sensing, Internet of Things, artificial intelligence, and big data, can build a smart environmental monitoring system. Remote sensing image classification is an important research content in ecological environmental monitoring. Remote sensing images contain rich spatial information and multi-temporal information, but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy. To solve this problem, this study develops a transductive transfer dictionary learning (TTDL)… More >

  • Open AccessOpen Access

    ARTICLE

    Horizontal Well Interference Performance and Water Injection Huff and Puff Effect on Well Groups with Complex Fracture Networks: A Numerical Study

    Haoyu Fu1,2,3, Hua Liu1,2, Xiaohu Hu1,2, Lei Wang1,2,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2285-2309, 2023, DOI:10.32604/cmes.2023.027996
    (This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
    Abstract Well interference has become a common phenomenon with the increasing scale of horizontal well fracturing. Recent studies on well interference in horizontal wells do not properly reflect the physical model of the postfracturing well groups and the realistic fracturing process of infill wells. Establishing the correspondence between well interference causative factors and manifestations is of great significance for infill well deployment and secondary oil recovery. In this work, we develop a numerical model that considers low velocity non-Darcy seepage in shale reservoirs to study the inter-well interference phenomenon that occurs in the Santanghu field, and… More >

  • Open AccessOpen Access

    ARTICLE

    Computational Analysis of Heat and Mass Transfer in Magnetized Darcy-Forchheimer Hybrid Nanofluid Flow with Porous Medium and Slip Effects

    Nosheen Fatima1, Nabeela Kousar1, Khalil Ur Rehman2,3,*, Wasfi Shatanawi2,4,5
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2311-2330, 2023, DOI:10.32604/cmes.2023.026994
    (This article belongs to the Special Issue: Advanced Computational Methods in Fluid Mechanics and Heat Transfer)
    Abstract A computational analysis of magnetized hybrid Darcy-Forchheimer nanofluid flow across a flat surface is presented in this work. For the study of heat and mass transfer aspects viscous dissipation, activation energy, Joule heating, thermal radiation, and heat generation effects are considered. The suspension of nanoparticles singlewalled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) are created by hybrid nanofluids. However, single-walled carbon nanotubes (SWCNTs) produce nanofluids, with water acting as conventional fluid, respectively. Nonlinear partial differential equations (PDEs) that describe the ultimate flow are converted to nonlinear ordinary differential equations (ODEs) using appropriate similarity transformation.… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling and Validation of Base Pressure for Aerodynamic Vehicles Based on Machine Learning Models

    Jaimon Dennis Quadros1, Sher Afghan Khan2, Abdul Aabid3,*, Muneer Baig3
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2331-2352, 2023, DOI:10.32604/cmes.2023.028925
    (This article belongs to the Special Issue: Advanced Computational Methods in Fluid Mechanics and Heat Transfer)
    Abstract The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with cavity (βcav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for smooth flow of aerodynamic vehicles. Based… More >

    Graphic Abstract

    Modeling and Validation of Base Pressure for Aerodynamic Vehicles Based on Machine Learning Models

  • Open AccessOpen Access

    ARTICLE

    An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

    Lei Ling1, Lijun Huang2, Jie Wang2, Li Zhang2, Yue Wu2, Yizhang Jiang1, Kaijian Xia2,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828
    (This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Numerical Scheme for Biological Models in the Frame of Bernoulli Wavelets

    Fei Li1, Haci Mehmet Baskonus2,*, S. Kumbinarasaiah3, G. Manohara3, Wei Gao4, Esin Ilhan5
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2381-2408, 2023, DOI:10.32604/cmes.2023.028069
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
    Abstract This article considers three types of biological systems: the dengue fever disease model, the COVID-19 virus model, and the transmission of Tuberculosis model. The new technique of creating the integration matrix for the Bernoulli wavelets is applied. Also, the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme (BWCM). All three models are in the form system of coupled ordinary differential equations without an exact solution. These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme. The numerical wave distributions of these governing models are More >

  • Open AccessOpen Access

    ARTICLE

    A Restricted SIR Model with Vaccination Effect for the Epidemic Outbreaks Concerning COVID-19

    Ibtehal Alazman1, Kholoud Saad Albalawi1, Pranay Goswami2,*, Kuldeep Malik2
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2409-2425, 2023, DOI:10.32604/cmes.2023.028674
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
    Abstract This paper presents a restricted SIR mathematical model to analyze the evolution of a contagious infectious disease outbreak (COVID-19) using available data. The new model focuses on two main concepts: first, it can present multiple waves of the disease, and second, it analyzes how far an infection can be eradicated with the help of vaccination. The stability analysis of the equilibrium points for the suggested model is initially investigated by identifying the matching equilibrium points and examining their stability. The basic reproduction number is calculated, and the positivity of the solutions is established. Numerical simulations More >

  • Open AccessOpen Access

    ARTICLE

    Dynamical Analysis of the Stochastic COVID-19 Model Using Piecewise Differential Equation Technique

    Yu-Ming Chu1, Sobia Sultana2, Saima Rashid3,*, Mohammed Shaaf Alharthi4
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2427-2464, 2023, DOI:10.32604/cmes.2023.028771
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
    Abstract Various data sets showing the prevalence of numerous viral diseases have demonstrated that the transmission is not truly homogeneous. Two examples are the spread of Spanish flu and COVID-19. The aim of this research is to develop a comprehensive nonlinear stochastic model having six cohorts relying on ordinary differential equations via piecewise fractional differential operators. Firstly, the strength number of the deterministic case is carried out. Then, for the stochastic model, we show that there is a critical number that can predict virus persistence and infection eradication. Because of the peculiarity of More >

  • Open AccessOpen Access

    ARTICLE

    Investigation of the Severity of Modular Construction Adoption Barriers with Large-Scale Group Decision Making in an Organization from Internal and External Stakeholder Perspectives

    Muzi Li*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

  • Open AccessOpen Access

    ARTICLE

    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He1,*, 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
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    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.… More >

  • Open AccessOpen Access

    ARTICLE

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

    B. Ramesh, Kuruva Lakshmanna*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this… More >

    Graphic Abstract

    Multi Head Deep Neural Network Prediction Methodology for High-Risk Cardiovascular Disease on Diabetes Mellitus

  • Open AccessOpen Access

    ARTICLE

    A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

    Mahmood Hussain Mir1,*, Sanjay Jamwal1, Ummer Iqbal2, Abolfazl Mehbodniya3, Julian Webber3, Umar Hafiz Khan4
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173
    (This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
    Abstract The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework… More >

  • Open AccessOpen Access

    ARTICLE

    Improved RRT Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

    Chong Xu1, Hao Zhu1, Haotian Zhu2, Jirong Wang1, Qinghai Zhao1,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2567-2591, 2023, DOI:10.32604/cmes.2023.029152
    (This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract A new and improved RRT algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother More >

    Graphic Abstract

    Improved RRT<sup>∗</sup> Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

  • Open AccessOpen Access

    ARTICLE

    Peridynamic Study on Fracture Mode and Crack Propagation Path of a Plate with Multiple Cracks Subjected to Uniaxial Tension

    Zeyuan Zhou, Ming Yu, Xinfeng Wang*, Zaixing Huang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2593-2620, 2023, DOI:10.32604/cmes.2023.027384
    (This article belongs to the Special Issue: Peridynamics and its Current Progress)
    Abstract How to simulate fracture mode and crack propagation path in a plate with multiple cracks is an attractive but dicult issue in fracture mechanics. Peridynamics is a recently developed nonlocal continuum formulation that can spontaneously predict the crack nucleation, branch and propagation in materials and structures through a meshfree discrete technique. In this paper, the peridynamic motion equation with boundary traction is improved by simplifying the boundary transfer functions. We calculate the critical cracking load and the fracture angles of the plate with multiple cracks under uniaxial tension. The results are consistent with those predicted More >

    Graphic Abstract

    Peridynamic Study on Fracture Mode and Crack Propagation Path of a Plate with Multiple Cracks Subjected to Uniaxial Tension

  • Open AccessOpen Access

    ARTICLE

    An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

    Jing Xin1,*, Kenan Du1, Jiale Feng1, Mao Shan2
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467
    (This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud More >

  • Open AccessOpen Access

    ARTICLE

    Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

    Lilan Zou1, Bo Liang1, Xu Cheng2, Shufa Li1,*, Cong Lin1,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2641-2659, 2023, DOI:10.32604/cmes.2023.028037
    (This article belongs to the Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment. In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment, we proposed a more effective and robust target detection framework based on deep learning, which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box fusion method is adopted to generate a fusion box by weighted… More >

    Graphic Abstract

    Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

  • Open AccessOpen Access

    ARTICLE

    Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

    Baofeng Ji1,2,3,*, Mingkun Zhang1,2, Weixing Wang1, Song Chen4
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2661-2673, 2023, DOI:10.32604/cmes.2023.028762
    (This article belongs to the Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
    Abstract The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles (IoV). Routing transmission solved the limitation of transmission distance to a certain extent. Traditional routing algorithm cannot adapt to complex traffic environment, resulting in low transmission efficiency. In order to improve the transmission success rate and quality of vehicle network routing transmission, make the routing algorithm more suitable for complex traffic environment, and reduce transmission power consumption to improve energy efficiency, a comprehensive optimized routing transmission algorithm is proposed. Based on the… More >

  • Open AccessOpen Access

    ARTICLE

    Fairness-Aware Harvested Energy Efficiency Algorithm for IRS-Aided Intelligent Sensor Networks with SWIPT

    Yingying Chen1, Weiqiang Tan2, Shidang Li3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2675-2691, 2023, DOI:10.32604/cmes.2023.028533
    (This article belongs to the Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
    Abstract In this paper, a novel fairness-aware harvested energy efficiency-based green transmission scheme for wireless information and power transfer (SWIPT) aided sensor networks is developed for active beamforming of multiantenna transmitter and passive beamforming at intelligent reflecting surfaces (IRS). By optimizing the active beamformer assignment at the transmitter in conjunction with the passive beamformer assignment at the IRS, we aim to maximize the minimum harvested energy efficiency among all the energy receivers (ER) where information receivers (IR) are bound to the signal-interference-noise-ratio (SINR) and the maximum transmitted power of the transmitter. To handle the non-convex problem, More >

  • Open AccessOpen Access

    ARTICLE

    Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

    Shuilong He1,2, Yang Liu1, Shanchao Wang2,*, Liangying Hu1, Fei Xiao2, Chao Li2
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2693-2714, 2023, DOI:10.32604/cmes.2023.028631
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The reduction of fuel consumption in engines is always considered of vital importance. Along these lines, in this work, this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy. More specifically, at first, a general first principles model for heavy-duty commercial vehicles and a transient fuel consumption model for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data. The accuracy of the proposed model was demonstrated from the stage and the final results. Next, the control optimization problem resulting in low fuel consumption in heavy commercial… More >

    Graphic Abstract

    Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

  • Open AccessOpen Access

    ARTICLE

    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

    Yaling Xu, Congjun Rao*, Xinping Xiao, Fuyan Hu*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2715-2742, 2023, DOI:10.32604/cmes.2023.029023
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to… More >

    Graphic Abstract

    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

  • Open AccessOpen Access

    ARTICLE

    RAISE: A Resilient Anonymous Information Sharing Environment

    Ning Hu1, Ling Liu1, Xin Liu3, Kaijun Wu2, Yue Zhao2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2743-2759, 2023, DOI:10.32604/cmes.2023.026939
    (This article belongs to the Special Issue: Emerging Trends on Blockchain: Architecture and Dapp Ecosystem)
    Abstract With the widespread application of cloud computing and network virtualization technologies, more and more enterprise applications are directly deployed in the cloud. However, the traditional TCP/IP network transmission model does not fully consider the information security issues caused by the uncontrollable internet environment. Network security communication solutions represented by encrypted virtual private networks (VPN) are facing multiple security threats. In fact, during the communication process, the user application needs to protect not only the content of the communication but also the behavior of the communication, such as the communication relationship, the communication protocol, and so… More >

  • Open AccessOpen Access

    ARTICLE

    Reliability Analysis of HEE Parameters via Progressive Type-II Censoring with Applications

    Heba S. Mohammed1, Mazen Nassar2,3, Refah Alotaibi1, Ahmed Elshahhat4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2761-2793, 2023, DOI:10.32604/cmes.2023.028826
    (This article belongs to the Special Issue: Application of Computer Tools in the Study of Mathematical Problems)
    Abstract A new extended exponential lifetime model called Harris extended-exponential (HEE) distribution for data modelling with increasing and decreasing hazard rate shapes has been considered. In the reliability context, researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample size. So, this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring strategy. Under the premise of independent gamma priors, the Bayesian estimation is created using the squared-error and… More >

  • Open AccessOpen Access

    ARTICLE

    Pythagorean Fuzzy Einstein Aggregation Operators with Z-Numbers: Application in Complex Decision Aid Systems

    Shahzad Noor Abbasi1, Shahzaib Ashraf1,*, M. Shazib Hameed1, Sayed M. Eldin2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2795-2844, 2023, DOI:10.32604/cmes.2023.028963
    (This article belongs to the Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
    Abstract The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability and reliability in their decision-making. The PFS is known to address the levels of participation and non-participation. To begin, we introduce the novel concept of a PFZN, which is a hybrid structure of Pythagorean fuzzy sets and the ZN. The PFZN is graded in terms of membership and non-membership, as well as reliability, which provides a strong advice in real-world decision support concerns. The PFZN is a… More >

  • Open AccessOpen Access

    ARTICLE

    Experimental and Numerical Investigation on High-Pressure Centrifugal Pumps: Ultimate Pressure Formulation, Fatigue Life Assessment and Topological Optimization of Discharge Section

    Abdourahamane Salifou Adam1, Hatem Mrad1, Haykel Marouani2,*, Yasser Fouad3
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2845-2865, 2023, DOI:10.32604/cmes.2023.030777
    (This article belongs to the Special Issue: Structural Design and Optimization)
    Abstract A high percentage of failure in pump elements originates from fatigue. This study focuses on the discharge section behavior, made of ductile iron, under dynamic load. An experimental protocol is established to collect the strain under pressurization and depressurization tests at specific locations. These experimental results are used to formulate the ultimate pressure expression function of the strain and the lateral surface of the discharge section and to validate finite element modeling. Fe-Safe is then used to assess the fatigue life cycle using different types of fatigue criteria (Coffin-Manson, Morrow, Goodman, and Soderberg). When the… More >

  • Open AccessOpen Access

    ARTICLE

    Computer Modelling of Compact 28/38 GHz Dual-Band Antenna for Millimeter-Wave 5G Applications

    Amit V. Patel1, Arpan Desai1, Issa Elfergani2,3,*, Hiren Mewada4, Chemseddine Zebiri5, Keyur Mahant1, Jonathan Rodriguez2, Raed Abd-Alhameed3
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2867-2879, 2023, DOI:10.32604/cmes.2023.026200
    (This article belongs to the Special Issue: Green IoE for Smart 5G and beyond (5GB) Applications)
    Abstract A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed for millimeter-wave communication systems in this paper. The multiple-input-multiple-output (MIMO) antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back. The overall size of the four elements MIMO antenna is 2.24λ × 2.24λ (at 27.12 GHz). The prototype of four-element MIMO resonator is designed and printed using Rogers RT Duroid 5880 with εr = 2.2 and loss tangent = 0.0009 and having a thickness of More >

  • Open AccessOpen Access

    ARTICLE

    Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

    Zekai Li1, Wenfeng Wang2,3,4,5,6,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated More >

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