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  • Open Access

    ARTICLE

    Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

    Mohammad Mehedi Hassan1,*, Salman A. AlQahtani2, Mabrook S. AlRakhami1, Ahmed Zohier Elhendi3
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.047940
    (This article belongs to this Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
    Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    Research on the Generation Mechanism and Suppression Method of Aerodynamic Noise in Expansion Cavity Based on Hybrid Method

    Haitao Liu1,2,*, Jiaming Wang1, Xiuliang Zhang1, Yanji Jiang2, Qian Xiao1
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.047129
    Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high level of consistency with experimental… More >

  • Open Access

    ARTICLE

    Generative Multi-Modal Mutual Enhancement Video Semantic Communications

    Yuanle Chen1, Haobo Wang1, Chunyu Liu1, Linyi Wang2, Jiaxin Liu1, Wei Wu1,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046837
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it,… More >

  • Open Access

    ARTICLE

    Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

    Kuosheng Jiang1, Chengrui Han1, Yasheng Chang2,3,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.046729
    (This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045417
    (This article belongs to this Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

  • Open Access

    ARTICLE

    KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy

    Dan Zhao1, Yang You2, Chuanwen Luo3,*, Ting Chen4,*, Yang Liu5
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045400
    (This article belongs to this Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
    Abstract In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.… More >

  • Open Access

    ARTICLE

    NFT Security Matrix: Towards Modeling NFT Ecosystem Threat

    Peng Liao1, Chaoge Liu2, Jie Yin1,3,*, Zhi Wang2, Xiang Cui2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.043855
    (This article belongs to this Special Issue: The Bottleneck of Blockchain Techniques: Scalability, Security and Privacy Protection)
    Abstract Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens (NFTs). To be specific, the total trading volume of digital assets reached an astounding $55.5 billion in 2022. Nevertheless, numerous security concerns have been raised by the rapid expansion of the NFT ecosystem. NFT holders are exposed to a plethora of scams and traps, putting their digital assets at risk of being lost. However, academic research on NFT security is scarce, and the security issues have aroused rare attention. In this study, the NFT ecological process is comprehensively explored. This process falls into five different… More >

  • Open Access

    REVIEW

    A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

    Yuming Tang1,#, Yitian Zhang2,#, Tao Niu1, Zhen Li2,3,*, Zijian Zhang1,3, Huaping Chen4, Long Zhang4
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.030084
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which… More >

  • Open Access

    REVIEW

    Recent Advances on Deep Learning for Sign Language Recognition

    Yanqiong Zhang, Xianwei Jiang*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045731
    Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the… More >

  • Open Access

    ARTICLE

    Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

    Rui Wang1, Yao Zhou3,*, Guangchun Luo1, Peng Chen2, Dezhong Peng3,4
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.047065
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

  • Open Access

    REVIEW

    A Review of the Tuned Mass Damper Inerter (TMDI) in Energy Harvesting and Vibration Control: Designs, Analysis and Applications

    Xiaofang Kang1,2,*, Qiwen Huang1, Zongqin Wu1, Jianjun Tang1, Xueqin Jiang1, Shancheng Lei3
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.043936
    (This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract Tuned mass damper inerter (TMDI) is a device that couples traditional tuned mass dampers (TMD) with an inertial device. The inertial device produces resistance proportional to the relative acceleration at its two ends through its “inertial” constant. Due to its unique mechanical properties, TMDI has received widespread attention and application in the past twenty years. As different configurations are required in different practical situations, TMDI is still active in the research on vibration control and energy harvesting in structures. This paper provides a comprehensive review of the research status of TMDI. This work first examines the generation and important vibration… More >

  • Open Access

    ARTICLE

    Gyroscope Dynamic Balance Counterweight Prediction Based on Multi-Head ResGAT Networks

    Wuyang Fan, Shisheng Zhong*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046951
    Abstract The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model, which… More >

  • Open Access

    ARTICLE

    Dynamic Response Impact of Vehicle Braking on Simply Supported Beam Bridges with Corrugated Steel Webs Based on Vehicle-Bridge Coupled Vibration Analysis

    Yan Wang*, Siwen Li, Na Wei
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.046454
    (This article belongs to this Special Issue: Recent Advances in Computational Methods for Performance Assessment of Engineering Structures and Materials against Dynamic Loadings)
    Abstract A novel approach for analyzing coupled vibrations between vehicles and bridges is presented, taking into account spatiotemporal effects and mechanical phenomena resulting from vehicle braking. Efficient modeling and solution of bridge vibrations induced by vehicle deceleration are realized using this method. The method’s validity and reliability are substantiated through numerical examples. A simply supported beam bridge with a corrugated steel web is taken as an example and the effects of parameters such as the initial vehicle speed, braking acceleration, braking location, and road surface roughness on the mid-span displacement and impact factor of the bridge are analyzed. The results show… More >

  • Open Access

    ARTICLE

    Nonlinear Study on the Mechanical Performance of Built-Up Cold-Formed Steel Concrete-Filled Columns under Compression

    Oulfa Harrat1,*, Yazid Hadidane1, S. M. Anas2,*, Nadhim Hamah Sor3,4, Ahmed Farouk Deifalla5, Paul O. Awoyera6, Nadia Gouider1
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.044950
    (This article belongs to this Special Issue: Recent Advances in Computational Methods for Performance Assessment of Engineering Structures and Materials against Dynamic Loadings)
    Abstract Given their numerous functional and architectural benefits, such as improved bearing capacity and increased resistance to elastic instability modes, cold-formed steel (CFS) built-up sections have become increasingly developed and used in recent years, particularly in the construction industry. This paper presents an analytical and numerical study of assembled CFS two single channel-shaped columns with different slenderness and configurations (back-to-back, face-to-face, and box). These columns were joined by double-row rivets for the back-to-back and box configurations, whereas they were welded together for the face-to-face design. The built-up columns were filled with ordinary concrete of good strength. Finite element models were applied,… More >
    Graphic Abstract

    Nonlinear Study on the Mechanical Performance of Built-Up Cold-Formed Steel Concrete-Filled Columns under Compression

  • Open Access

    ARTICLE

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

    Xianfeng Cao1, Meihua Yao2, Yahui Zhang3,*, Xiaofeng Hu4, Chuanxun Wu3
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.030773
    Abstract As the take-off of China’s macro economy, as well as the rapid development of infrastructure construction, real estate industry, and highway logistics transportation industry, the demand for heavy vehicles is increasing rapidly, the competition is becoming increasingly fierce, and the digital transformation of the production line is imminent. As one of the most important components of heavy vehicles, the transmission front and middle case assembly lines have a high degree of automation, which can be used as a pilot for the digital transformation of production. To ensure the visualization of digital twins (DT), consistent control logic, and real-time data interaction,… More >
    Graphic Abstract

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

  • Open Access

    ARTICLE

    Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

    Saima Rashid1,2,*, Fahd Jarad3,4
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.028773
    (This article belongs to this Special Issue: Recent Developments on Computational Biology-I)
    Abstract Because of the features involved with their varied kernels, differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues. In this paper, we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels. In this approach, the overall population was separated into five cohorts. Furthermore, the descriptive behavior of the system was investigated, including prerequisites for the positivity of solutions, invariant domain of the solution, presence and stability of equilibrium points, and sensitivity analysis. We included a stochastic element in every cohort and… More >

  • Open Access

    REVIEW

    Saddlepoint Approximation Method in Reliability Analysis: A Review

    Debiao Meng1,2,*, Yipeng Guo1,2, Yihe Xu3, Shiyuan Yang1,2,*, Yongqiang Guo4, Lidong Pan4, Xinkai Guo2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.047507
    (This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract The escalating need for reliability analysis (RA) and reliability-based design optimization (RBDO) within engineering challenges has prompted the advancement of saddlepoint approximation methods (SAM) tailored for such problems. This article offers a detailed overview of the general SAM and summarizes the method characteristics first. Subsequently, recent enhancements in the SAM theoretical framework are assessed. Notably, the mean value first-order saddlepoint approximation (MVFOSA) bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation (MVSOSA); the latter serves as an auxiliary approach to the former. Their distinction is rooted in the varying expansion orders of the performance function as… More >

  • Open Access

    ARTICLE

    An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

    Younghoon Ban, Myeonghyun Kim, Haehyun Cho*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046658
    (This article belongs to this Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum, capitalizing on the ambiguities present… More >

  • Open Access

    ARTICLE

    Investigation of Projectile Impact Behaviors of Graphene Aerogel Using Molecular Dynamics Simulations

    Xinyu Zhang1, Wenjie Xia2, Yang Wang3,4, Liang Wang1,*, Xiaofeng Liu1
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046922
    (This article belongs to this Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
    Abstract Graphene aerogel (GA), as a novel solid material, has shown great potential in engineering applications due to its unique mechanical properties. In this study, the mechanical performance of GA under high-velocity projectile impacts is thoroughly investigated using full-atomic molecular dynamics (MD) simulations. The study results show that the porous structure and density are key factors determining the mechanical response of GA under impact loading. Specifically, the impact-induced penetration of the projectile leads to the collapse of the pore structure, causing stretching and subsequent rupture of covalent bonds in graphene sheets. Moreover, the effects of temperature on the mechanical performance of… More >

  • Open Access

    ARTICLE

    Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

    Yifan Huang1, Zikang Zhou1,2, Mingyu Li1, Xuedong Luo1,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.045947
    (This article belongs to this Special Issue: Bio-inspired Optimization in Engineering and Sciences)
    Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of determination (R2), root mean square… More >

  • Open Access

    ARTICLE

    Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

    Jungpil Shin1,*, Md. Al Mehedi Hasan2, Abu Saleh Musa Miah1, Kota Suzuki1, Koki Hirooka1
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046334
    Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body movements within JSL gestures. Simultaneously,… More >

  • Open Access

    ARTICLE

    Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

    Yan Wang*, You Lu, Yuqing Zhou, Zhijian Zhao
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046743
    Abstract Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise,… More >

  • Open Access

    ARTICLE

    A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

    Kai Jiang, Bin Cao*, Jing Fan
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046348
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

  • Open Access

    ARTICLE

    Discrete Element Modelling of Damage Evolution of Concrete Considering Meso-Structure of ITZ

    Weiliang Gao1, Shixu Jia2, Tingting Zhao2,3,*, Zhiyong Wang2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046188
    Abstract The mechanical properties of interfacial transition zones (ITZs) have traditionally been simplified by reducing the stiffness of cement in previous simulation methods. A novel approach based on the discrete element method (DEM) has been developed for modeling concrete. This new approach efficiently simulates the meso-structure of ITZs, accurately capturing their heterogeneous properties. Validation against established uniaxial compression experiments confirms the precision of this model. The proposed model can model the process of damage evolution containing cracks initiation, propagation and penetration. Under increasing loads, cracks within ITZs progressively accumulate, culminating in macroscopic fractures that traverse the mortar matrix, forming the complex,… More >

  • Open Access

    ARTICLE

    Full-Scale Isogeometric Topology Optimization of Cellular Structures Based on Kirchhoff–Love Shells

    Mingzhe Huang, Mi Xiao*, Liang Gao, Mian Zhou, Wei Sha, Jinhao Zhang
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045735
    Abstract Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio. In this paper, a full-scale isogeometric topology optimization (ITO) method based on Kirchhoff–Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed. This method utilizes high-order continuous nonuniform rational B-splines (NURBS) as basis functions for Kirchhoff–Love shell elements. The geometric and analysis models of thin shells are unified by isogeometric analysis (IGA) to avoid geometric approximation error and improve computational accuracy. The topological configurations of thin-shell structures are described by constructing the effective density field on the control… More >

  • Open Access

    ARTICLE

    CAW-YOLO: Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

    Weiya Shi1,*, Shaowen Zhang2, Shiqiang Zhang2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.044863
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

  • Open Access

    ARTICLE

    A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

    Shujiang Xu1,2,*, Ziye Wang1,2, Lianhai Wang1,2, Miodrag J. Mihaljević1,2,3, Shuhui Zhang1,2, Wei Shao1,2, Qizheng Wang1,2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046164
    (This article belongs to this Special Issue: The Bottleneck of Blockchain Techniques: Scalability, Security and Privacy Protection)
    Abstract Blockchain technology, with its attributes of decentralization, immutability, and traceability, has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes. However, transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain. Due to its inability to meet the demands of high-frequency trading, blockchain cannot be adopted in many scenarios. To improve the transaction capacity, researchers have proposed some on-chain scaling technologies, including lightning networks, directed acyclic graph technology, state channels, and sharding mechanisms, in which sharding emerges as a potential scaling technology. Nevertheless, excessive cross-shard transactions and uneven shard… More >
    Graphic Abstract

    A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

  • Open Access

    ARTICLE

    Sub-Homogeneous Peridynamic Model for Fracture and Failure Analysis of Roadway Surrounding Rock

    Shijun Zhao1, Qing Zhang2, Yusong Miao1, Weizhao Zhang3, Xinbo Zhao1, Wei Xu1,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045015
    (This article belongs to this Special Issue: New Trends on Meshless Method and Numerical Analysis)
    Abstract The surrounding rock of roadways exhibits intricate characteristics of discontinuity and heterogeneity. To address these complexities, this study employs non-local Peridynamics (PD) theory and reconstructs the kernel function to represent accurately the spatial decline of long-range force. Additionally, modifications to the traditional bond-based PD model are made. By considering the micro-structure of coal-rock materials within a uniform discrete model, heterogeneity characterized by bond random pre-breaking is introduced. This approach facilitates the proposal of a novel model capable of handling the random distribution characteristics of material heterogeneity, rendering the PD model suitable for analyzing the deformation and failure of heterogeneous layered… More >

  • Open Access

    ARTICLE

    Optimal Shape Factor and Fictitious Radius in the MQ-RBF: Solving Ill-Posed Laplacian Problems

    Chein-Shan Liu1, Chung-Lun Kuo1, Chih-Wen Chang2,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046002
    (This article belongs to this Special Issue: New Trends on Meshless Method and Numerical Analysis)
    Abstract To solve the Laplacian problems, we adopt a meshless method with the multiquadric radial basis function (MQ-RBF) as a basis whose center is distributed inside a circle with a fictitious radius. A maximal projection technique is developed to identify the optimal shape factor and fictitious radius by minimizing a merit function. A sample function is interpolated by the MQ-RBF to provide a trial coefficient vector to compute the merit function. We can quickly determine the optimal values of the parameters within a preferred rage using the golden section search algorithm. The novel method provides the optimal values of parameters and,… More >

  • Open Access

    ARTICLE

    An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

    Tarek Sheltami1,*, Gamil Ahmed1, Ansar Yasar2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.044973
    Abstract Recently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoD deployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoD deployment strategy that maximizes the coverage region while minimizing interference between neighboring drones. In deployment problem, the interference increases as the… More >

  • Open Access

    ARTICLE

    PAL-BERT: An Improved Question Answering Model

    Wenfeng Zheng1, Siyu Lu1, Zhuohang Cai1, Ruiyang Wang1, Lei Wang2, Lirong Yin2,*
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.046692
    Abstract In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language model. Firstly, a first-order network… More >

  • Open Access

    ARTICLE

    Synergistic Swarm Optimization Algorithm

    Sharaf Alzoubi1, Laith Abualigah2,3,4,5,6,7,8,*, Mohamed Sharaf9, Mohammad Sh. Daoud10, Nima Khodadadi11, Heming Jia12
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.045170
    Abstract This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm (SSOA). The SSOA combines the principles of swarm intelligence and synergistic cooperation to search for optimal solutions efficiently. A synergistic cooperation mechanism is employed, where particles exchange information and learn from each other to improve their search behaviors. This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities. Furthermore, adaptive mechanisms, such as dynamic parameter adjustment and diversification strategies, are incorporated to balance exploration and exploitation. By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation, the SSOA… More >

  • Open Access

    ARTICLE

    Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

    Wangchen Yan1,*, Jinbao Yang1, Xin Luo2
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.044709
    Abstract Transfer learning could reduce the time and resources required for the training of new models and be therefore important in generalized applications of the trained machine learning algorithms. In this study, a transfer learningenhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the… More >