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  • Open AccessOpen Access

    REVIEW

    Phase-Field Simulation of Sintering Process: A Review

    Ming Xue, Min Yi*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1165-1204, 2024, DOI:10.32604/cmes.2024.049367
    Abstract Sintering, a well-established technique in powder metallurgy, plays a critical role in the processing of high melting point materials. A comprehensive understanding of structural changes during the sintering process is essential for effective product assessment. The phase-field method stands out for its unique ability to simulate these structural transformations. Despite its widespread application, there is a notable absence of literature reviews focused on its usage in sintering simulations. Therefore, this paper addresses this gap by reviewing the latest advancements in phase-field sintering models, covering approaches based on energy, grand potential, and entropy increase. The characteristics More >

  • Open AccessOpen Access

    REVIEW

    Progress in Mechanical Modeling of Implantable Flexible Neural Probes

    Xiaoli You1,2,3,, Ruiyu Bai1,2,3,4,, Kai Xue1,2,3, Zimo Zhang1,2,3, Minghao Wang5, Xuanqi Wang1,2,3, Jiahao Wang1,2,3, Jinku Guo1,2, Qiang Shen3, Honglong Chang3, Xu Long6,*, Bowen Ji1,2,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1205-1231, 2024, DOI:10.32604/cmes.2024.049047
    Abstract Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue, thus as important tools for brain science research, as well as diagnosis and treatment of brain diseases. However, the rigid neural probes, such as Utah arrays, Michigan probes, and metal microfilament electrodes, are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation, which leads to a significant degradation in the signal quality with the implantation time. In recent years, flexible neural electrodes are rapidly developed with less damage to biological tissues, excellent… More >

  • Open AccessOpen Access

    REVIEW

    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
    (This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements: Developments and Validation Using Double Edge-Notched Tensile Specimen

    Troy Myers1, Michael A. Sutton1,*, Hubert Schreier2, Alistair Tofts2, Sreehari Rajan Kattil1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1263-1298, 2024, DOI:10.32604/cmes.2024.048743
    Abstract To compare finite element analysis (FEA) predictions and stereovision digital image correlation (StereoDIC) strain measurements at the same spatial positions throughout a region of interest, a field comparison procedure is developed. The procedure includes (a) conversion of the finite element data into a triangular mesh, (b) selection of a common coordinate system, (c) determination of the rigid body transformation to place both measurements and FEA data in the same system and (d) interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates. For an aluminum Al-6061 double edge More >

  • Open AccessOpen Access

    ARTICLE

    Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

    Xingzi Yang1, Wei Gao2, Xiaodu Wang1, Xiaowei Zeng1,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1299-1313, 2024, DOI:10.32604/cmes.2024.049371
    Abstract The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model More >

  • Open AccessOpen Access

    ARTICLE

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Renewable Energy Integration: A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

    Ali S. Alghamdi1,*, Mohamed A. Zohdy2, Saad Aldoihi3,4
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1339-1370, 2024, DOI:10.32604/cmes.2024.048839
    Abstract In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Strategy of Partitioned and Monolithic Methods for Solving Strongly Coupled Analysis of Inverse and Direct Piezoelectric and Circuit Coupling

    Daisuke Ishihara*, Syunnosuke Nozaki, Tomoya Niho, Naoto Takayama
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1371-1386, 2024, DOI:10.32604/cmes.2024.049694
    Abstract The inverse and direct piezoelectric and circuit coupling are widely observed in advanced electro-mechanical systems such as piezoelectric energy harvesters. Existing strongly coupled analysis methods based on direct numerical modeling for this phenomenon can be classified into partitioned or monolithic formulations. Each formulation has its advantages and disadvantages, and the choice depends on the characteristics of each coupled problem. This study proposes a new option: a coupled analysis strategy that combines the best features of the existing formulations, namely, the hybrid partitioned-monolithic method. The analysis of inverse piezoelectricity and the monolithic analysis of direct piezoelectric More >

  • Open AccessOpen Access

    ARTICLE

    A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

    Hao Qi1, Mohamed Sharaf2, Andres Annuk3, Adrian Ilinca4, Mohamed A. Mohamed5,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1387-1404, 2024, DOI:10.32604/cmes.2024.048672
    Abstract Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

    Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281
    Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

    Hela Elmannai1,*, Monia Hamdi1, Souham Meshoul1, Amel Ali Alhussan2, Manel Ayadi3, Amel Ksibi3
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1429-1457, 2024, DOI:10.32604/cmes.2024.048557
    Abstract Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision. Glaucoma ranks as the second most prevalent cause of permanent blindness. Traditional glaucoma diagnosis requires a highly experienced specialist, costly equipment, and a lengthy wait time. For automatic glaucoma detection, state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio. Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features. Localizing the optic disc (OD) is one of the key features in retinal images for detecting retinal diseases,… More >

  • Open AccessOpen Access

    ARTICLE

    Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

    Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1459-1483, 2024, DOI:10.32604/cmes.2024.049756
    Abstract The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for… More >

  • Open AccessOpen Access

    ARTICLE

    Design and Performance Analysis of HMDV Dynamic Inertial Suspension Based on Active Disturbance Rejection Control

    Xiaofeng Yang1,3,4, Wei Wang1,3,4,*, Yujie Shen2,4, Changning Liu1,3,4, Tianyi Zhang1,4
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1485-1506, 2024, DOI:10.32604/cmes.2024.049837
    Abstract This paper addresses the impact of vertical vibration negative effects, unbalanced radial forces generated by the static eccentricity of the hub motor, and road excitation on the suspension performance of Hub Motor Driven Vehicle (HMDV). A dynamic inertial suspension based on Active Disturbance Rejection Control (ADRC) is proposed, combining the vertical dynamic characteristics of dynamic inertial suspension with the features of ADRC, which distinguishes between internal and external disturbances and arranges the transition process. Firstly, a simulation model of the static eccentricity of the hub motor is established to simulate the unbalanced radial electromagnetic force… More >

    Graphic Abstract

    Design and Performance Analysis of HMDV Dynamic Inertial Suspension Based on Active Disturbance Rejection Control

  • Open AccessOpen Access

    ARTICLE

    Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

    Haijian Shao1,2,*, Suqin Lei1, Chenxu Yan3, Xing Deng1, Yunsong Qi1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1507-1537, 2024, DOI:10.32604/cmes.2024.050140
    Abstract This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy, specifically tailored for environments characterized by markedly low luminance levels. Conventional methodologies struggle with the challenges posed by luminosity fluctuations, especially in settings characterized by diminished radiance, further exacerbated by the utilization of suboptimal imaging instrumentation. The envisioned approach mandates a departure from the conventional YOLOX model, which exhibits inadequacies in mitigating these challenges. To enhance the efficacy of this approach in low-light conditions, the dehazing algorithm undergoes refinement, effecting a discerning regulation of the transmission rate at the pixel… More >

    Graphic Abstract

    Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

  • Open AccessOpen Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475
    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More >

    Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

    Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398
    (This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
    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 More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic Characteristics of Functionally Graded Timoshenko Beams by Improved Differential Quadrature Method

    Xiaojun Huang1, Liaojun Zhang2,*, Hanbo Cui1, Gaoxing Hu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1647-1668, 2024, DOI:10.32604/cmes.2024.049124
    (This article belongs to the Special Issue: Recent Advances in Computational Methods for Performance Assessment of Engineering Structures and Materials against Dynamic Loadings)
    Abstract This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method (DQM) for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution. Firstly, based on the first-order shear deformation theory, the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement, transverse displacement, and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section. Then, ignoring the shear deformation of the… More >

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    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… More >

  • Open AccessOpen Access

    ARTICLE

    An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

    Md. Abdur Rahim1, Fahmid Al Farid2, Abu Saleh Musa Miah3, Arpa Kar Puza1, Md. Nur Alam4, Md. Najmul Hossain5, Sarina Mansor2, Hezerul Abdul Karim2,6,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

  • Open AccessOpen Access

    ARTICLE

    Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

    Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

  • Open AccessOpen Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More >

    Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open AccessOpen Access

    ARTICLE

    Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

    Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1757-1781, 2024, DOI:10.32604/cmes.2024.049208
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
    Abstract This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and… More >

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Advances in Structural Analysis of Energy and Freight Transport Infrastructures)
    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, More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

    Aadel M. Alatwi1,2, Abualkasim Bakeer3, Sherif A. Zaid1,*, Ibrahem E. Atawi1, Hani Albalawi1,4, Ahmed M. Kassem5
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1807-1830, 2024, DOI:10.32604/cmes.2024.048521
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract An autonomous microgrid that runs on renewable energy sources is presented in this article. It has a superconducting magnetic energy storage (SMES) device, wind energy-producing devices, and an energy storage battery. However, because such microgrids are nonlinear and the energy they create varies with time, controlling and managing the energy inside them is a difficult issue. Fractional-order proportional integral (FOPI) controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance. The suggested dedicated control for the SMES comprises two loops: the outer loop, which uses the FOPI to regulate… More >

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Advances on Mesh and Dimension Reduction Methods)
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Constitutive Behavior of the Interface between UHPC and Steel Plate without Shear Connector: From Experimental to Numerical Study

    Zihan Wang1, Boshan Zhang2, Hui Wang1,*, Qing Ai1, Xingchun Huang1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1863-1888, 2024, DOI:10.32604/cmes.2024.048217
    (This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract The application of ultra-high performance concrete (UHPC) as a covering layer for steel bridge decks has gained widespread popularity. By employing a connection without a shear connector between the steel plate and UHPC, namely, the sandblasted interface and the epoxy adhesive with sprinkled basalt aggregate interface, the installation cannot only be simplified but also the stress concentration resulting from the welded shear connectors can be eliminated. This study develops constitutive models for these two interfaces without shear connectors, based on the interfacial pull-off and push-out tests. For validation, three-point bending tests on the steel-UHPC composite More >

  • Open AccessOpen Access

    ARTICLE

    A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

    Jingyu Wang1, Ping Jiang1,*, Jianjun Qi2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1889-1918, 2024, DOI:10.32604/cmes.2024.049813
    (This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract The unmanned aerial vehicle (UAV) swarm plays an increasingly important role in the modern battlefield, and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm. Due to the high cost and long duration of operational tests, it is essential to plan the test in advance. To solve the problem of planning UAV swarm operational test, this study considers the multi-stage feature of a UAV swarm mission, composed of launch, flight and combat stages, and proposes a method to find test plans that can maximize mission reliability.… More >

  • Open AccessOpen Access

    ARTICLE

    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
    (This article belongs to the Special Issue: Structural Design and Optimization)
    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… More >

  • Open AccessOpen Access

    ARTICLE

    Topology Optimization of Two Fluid Heat Transfer Problems for Heat Exchanger Design

    Kun Yan1, Yunyu Wang2, Jun Yan3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1949-1974, 2024, DOI:10.32604/cmes.2024.048877
    (This article belongs to the Special Issue: Structural Design and Optimization)
    Abstract Topology optimization of thermal-fluid coupling problems has received widespread attention. This article proposes a novel topology optimization method for laminar two-fluid heat exchanger design. The proposed method utilizes an artificial density field to create two permeability interpolation functions that exhibit opposing trends, ensuring separation between the two fluid domains. Additionally, a Gaussian function is employed to construct an interpolation function for the thermal conductivity coefficient. Furthermore, a computational program has been developed on the OpenFOAM platform for the topology optimization of two-fluid heat exchangers. This program leverages parallel computing, significantly reducing the time required for More >

  • Open AccessOpen Access

    ARTICLE

    A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

    Huile Zhang1,2,*, Shikang Li2, Yurui Wu3, Pengpeng Zhi1, Wei Wang1,4, Zhonglai Wang1,4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1975-1996, 2024, DOI:10.32604/cmes.2024.050185
    (This article belongs to the Special Issue: Structural Design and Optimization)
    Abstract Carbon fiber composites, characterized by their high specific strength and low weight, are becoming increasingly crucial in automotive lightweighting. However, current research primarily emphasizes layer count and orientation, often neglecting the potential of microstructural design, constraints in the layup process, and performance reliability. This study, therefore, introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic (CFRP) drive shafts. Initially, parametric modeling of the microscale cell was performed, and its elastic performance parameters were predicted using two homogenization methods, examining the impact of fluctuations in microscale cell parameters on composite material performance. A finite… More >

  • Open AccessOpen Access

    ARTICLE

    Proactive Caching at the Wireless Edge: A Novel Predictive User Popularity-Aware Approach

    Yunye Wan1, Peng Chen2, Yunni Xia1,*, Yong Ma3, Dongge Zhu4, Xu Wang5, Hui Liu6, Weiling Li7, Xianhua Niu2, Lei Xu8, Yumin Dong9
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1997-2017, 2024, DOI:10.32604/cmes.2024.048723
    (This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Mobile Edge Computing (MEC) is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible. In an MEC environment, servers are deployed closer to mobile terminals to exploit storage infrastructure, improve content delivery efficiency, and enhance user experience. However, due to the limited capacity of edge servers, it remains a significant challenge to meet the changing, time-varying, and customized needs for highly diversified content of users. Recently, techniques for caching content at the edge are becoming popular for addressing the above challenges. It is capable of filling… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling the Interaction between Vacancies and Grain Boundaries during Ductile Fracture

    Mingjian Li, Ping Yang*, Pengyang Zhao
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2019-2034, 2024, DOI:10.32604/cmes.2024.048334
    (This article belongs to the Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
    Abstract The experimental results in previous studies have indicated that during the ductile fracture of pure metals, vacancies aggregate and form voids at grain boundaries. However, the physical mechanism underlying this phenomenon remains not fully understood. This study derives the equilibrium distribution of vacancies analytically by following thermodynamics and the micromechanics of crystal defects. This derivation suggests that vacancies cluster in regions under hydrostatic compression to minimize the elastic strain energy. Subsequently, a finite element model is developed for examining more general scenarios of interaction between vacancies and grain boundaries. This model is first verified and More >

  • Open AccessOpen Access

    ARTICLE

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

    Sandeep Dasari, Rajesh Kaluri*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152
    (This article belongs to the Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
    Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More >

    Graphic Abstract

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

  • Open AccessOpen Access

    ARTICLE

    Generalized nth-Order Perturbation Method Based on Loop Subdivision Surface Boundary Element Method for Three-Dimensional Broadband Structural Acoustic Uncertainty Analysis

    Ruijin Huo1,2,3, Qingxiang Pei1,2,3, Xiaohui Yuan1,*, Yanming Xu3
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2053-2077, 2024, DOI:10.32604/cmes.2024.049185
    (This article belongs to the Special Issue: Integration of Physical Simulation and Machine Learning in Digital Twin and Virtual Reality)
    Abstract In this paper, a generalized th-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems. The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field, and the th-order discretization formulation of the boundary integral equation is derived. In addition, the computation of loop subdivision surfaces and the subdivision rules are introduced. In order to confirm the effectiveness of the algorithm, the computed results are contrasted and analyzed with the results under Monte More >

  • Open AccessOpen Access

    ARTICLE

    Sensitivity Analysis of Electromagnetic Scattering from Dielectric Targets with Polynomial Chaos Expansion and Method of Moments

    Yujing Ma1,4, Zhongwang Wang2, Jieyuan Zhang3, Ruijin Huo1,4, Xiaohui Yuan1,4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2079-2102, 2024, DOI:10.32604/cmes.2024.048488
    (This article belongs to the Special Issue: Integration of Physical Simulation and Machine Learning in Digital Twin and Virtual Reality)
    Abstract In this paper, an adaptive polynomial chaos expansion method (PCE) based on the method of moments (MoM) is proposed to construct surrogate models for electromagnetic scattering and further sensitivity analysis. The MoM is applied to accurately solve the electric field integral equation (EFIE) of electromagnetic scattering from homogeneous dielectric targets. Within the bistatic radar cross section (RCS) as the research object, the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate model. The corresponding sensitivity results are given by the further derivative operation, which is compared with those of More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Analysis of Perforation during Hydraulic Fracture Initiation Based on Continuous–Discontinuous Element Method

    Rui Zhang1, Lixiang Wang2,*, Jing Li1,4, Chun Feng2, Yiming Zhang1,3,4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2103-2129, 2024, DOI:10.32604/cmes.2024.049885
    (This article belongs to the Special Issue: Multiscale, Multifield, and Continuum-Discontinuum Analysis in Geomechanics )
    Abstract Perforation is a pivotal technique employed to establish main flow channels within the reservoir formation at the outset of hydraulic fracturing operations. Optimizing perforation designs is critical for augmenting the efficacy of hydraulic fracturing and boosting oil or gas production. In this study, we employ a hybrid finite-discrete element method, known as the continuous–discontinuous element method (CDEM), to simulate the initiation of post-perforation hydraulic fractures and to derive enhanced design parameters. The model incorporates the four most prevalent perforation geometries, as delineated in an engineering technical report. Real-world perforations deviate from the ideal cylindrical shape, More >

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