Home / Journals / CMES / Vol.138, No.3, 2024
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  • Open AccessOpen Access

    EDITORIAL

    Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

    Danial Jahed Armaghani1,*, Ahmed Salih Mohammed2,3, Ramesh Murlidhar Bhatawdekar4, Pouyan Fakharian5, Ashutosh Kainthola6, Wael Imad Mahmood7
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2023-2027, 2024, DOI:10.32604/cmes.2023.031701
    (This article belongs to this Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    REVIEW

    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

    Sheetal Sharma1,2, Kamali Gupta1, Deepali Gupta1, Shalli Rani1,*, Gaurav Dhiman3,4,5,6,7,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997
    Abstract The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and… More >

    Graphic Abstract

    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

  • Open AccessOpen Access

    REVIEW

    Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

    Hong Zhang1,*, Haijian Shao2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2061-2102, 2024, DOI:10.32604/cmes.2023.030649
    Abstract OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in the realm of disease detection.… More >

    Graphic Abstract

    Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

  • Open AccessOpen Access

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509
    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road… More >

  • Open AccessOpen Access

    ARTICLE

    Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

    Lianghao Hua1,2, Jianfeng Zhang1,*, Dejie Li3, Xiaobo Xi1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2129-2157, 2024, DOI:10.32604/cmes.2023.030535
    Abstract With the increasing prevalence of high-order systems in engineering applications, these systems often exhibit significant disturbances and can be challenging to model accurately. As a result, the active disturbance rejection controller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmanned aerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances and the possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address these issues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neural network (RBFNN) with a second-order ADRC and leverages a… More >

    Graphic Abstract

    Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

  • Open AccessOpen Access

    ARTICLE

    Enriched Constant Elements in the Boundary Element Method for Solving 2D Acoustic Problems at Higher Frequencies

    Zonglin Li1,2, Zhenyu Gao2, Yijun Liu2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2159-2175, 2024, DOI:10.32604/cmes.2023.030920
    Abstract The boundary element method (BEM) is a popular method for solving acoustic wave propagation problems, especially those in exterior domains, owing to its ease in handling radiation conditions at infinity. However, BEM models must meet the requirement of 6–10 elements per wavelength, using the conventional constant, linear, or quadratic elements. Therefore, a large storage size of memory and long solution time are often needed in solving higher-frequency problems. In this work, we propose two new types of enriched elements based on conventional constant boundary elements to improve the computational efficiency of the 2D acoustic BEM. The first one uses a… More >

    Graphic Abstract

    Enriched Constant Elements in the Boundary Element Method for Solving 2D Acoustic Problems at Higher Frequencies

  • Open AccessOpen Access

    ARTICLE

    Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

    Ying Zhang1, Wei Li1,*, Guidong Yang1, Snezana Kirin2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2177-2199, 2024, DOI:10.32604/cmes.2023.030728
    Abstract The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model with noise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian white noise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. As follows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPT is obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrence of the tumor from the extinction state to the tumor-present state is more concerned in this paper. A more efficient algorithm of Back-Propagation Neural… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

    Thi-Kien Dao1, Trong-The Nguyen1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2201-2237, 2024, DOI:10.32604/cmes.2023.029880
    Abstract Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism (SWOA) for node localization in… More >

  • Open AccessOpen Access

    ARTICLE

    Lightweight Multi-Resolution Network for Human Pose Estimation

    Pengxin Li1, Rong Wang1,2,*, Wenjing Zhang1, Yinuo Liu1, Chenyue Xu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2239-2255, 2024, DOI:10.32604/cmes.2023.030677
    Abstract Human pose estimation aims to localize the body joints from image or video data. With the development of deep learning, pose estimation has become a hot research topic in the field of computer vision. In recent years, human pose estimation has achieved great success in multiple fields such as animation and sports. However, to obtain accurate positioning results, existing methods may suffer from large model sizes, a high number of parameters, and increased complexity, leading to high computing costs. In this paper, we propose a new lightweight feature encoder to construct a high-resolution network that reduces the number of parameters… More >

  • Open AccessOpen Access

    ARTICLE

    Simulation of Corrosion-Induced Cracking of Reinforced Concrete Based on Fracture Phase Field Method

    Xiaozhou Xia1, Changsheng Qin1, Guangda Lu2, Xin Gu1,*, Qing Zhang1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2257-2276, 2024, DOI:10.32604/cmes.2023.031238
    Abstract Accurate simulation of the cracking process caused by rust expansion of reinforced concrete (RC) structures plays an intuitive role in revealing the corrosion-induced failure mechanism. Considering the quasi-brittle fracture of concrete, the fracture phase field driven by the compressive-shear term is constructed and added to the traditional brittle fracture phase field model. The rationality of the proposed model is verified by a mixed fracture example under a shear displacement load. Then, the extended fracture phase model is applied to simulate the corrosion-induced cracking process of RC. The cracking patterns caused by non-uniform corrosion expansion are discussed for RC specimens with… More >

  • Open AccessOpen Access

    ARTICLE

    The Optimization Design of the Nozzle Section for the Water Jet Propulsion System Applied in Jet Skis

    Cheng-Yeh Li, Jui-Hsiang Kao*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2277-2304, 2024, DOI:10.32604/cmes.2023.030215
    Abstract The performance of a water jet propulsion system is related to the inlet duct, rotor, stator, and nozzle. Generally, the flow inlet design must fit the bottom line of the hull, and the design of the inlet duct is often limited by stern space. The entire section, from the rotor to the nozzle through the stator, must be designed based on system integration in that the individual performance of these three components will influence each other. Particularly, the section from the rotor to the nozzle significantly impacts the performance of a water jet propulsion system. This study focused on nozzle… More >

  • Open AccessOpen Access

    ARTICLE

    Finite Element Simulations on Failure Behaviors of Granular Materials with Microstructures Using a Micromechanics-Based Cosserat Elastoplastic Model

    Chenxi Xiu1,2,*, Xihua Chu2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2305-2338, 2024, DOI:10.32604/cmes.2023.030194
    Abstract This paper presents a micromechanics-based Cosserat continuum model for microstructured granular materials. By utilizing this model, the macroscopic constitutive parameters of granular materials with different microstructures are expressed as sums of microstructural information. The microstructures under consideration can be classified into three categories: a medium-dense microstructure, a dense microstructure consisting of one-sized particles, and a dense microstructure consisting of two-sized particles. Subsequently, the Cosserat elastoplastic model, along with its finite element formulation, is derived using the extended Drucker-Prager yield criteria. To investigate failure behaviors, numerical simulations of granular materials with different microstructures are conducted using the ABAQUS User Element (UEL)… More >

  • Open AccessOpen Access

    ARTICLE

    Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

    R. Saravanan1,*, R. Muthaiah1, A. Rajesh2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2339-2356, 2024, DOI:10.32604/cmes.2023.030898
    Abstract This study develops an Enhanced Threshold Based Energy Detection approach (ETBED) for spectrum sensing in a cognitive radio network. The threshold identification method is implemented in the received signal at the secondary user based on the square law. The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing. Additionally, the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems. In the dynamic threshold, the signal ratio-based threshold is fixed. The threshold is computed by considering the Modified Black Widow Optimization Algorithm (MBWO). So, the proposed… More >

    Graphic Abstract

    Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

  • Open AccessOpen Access

    ARTICLE

    An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

    Shude Fu1,2, Xinye Wu1,2,*, Wenjie Wang3, Yixin Hu1,3,*, Zhengke Li1, Feng Jiang3
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2357-2382, 2024, DOI:10.32604/cmes.2023.031118
    Abstract In this paper, given the shortcomings of jellyfish search algorithm with low search ability in the early stage and easy to fall into local optimal solution, this paper introduces adaptive weight function and elite strategy, improving the global search scope in the early stage and the ability to refine the local development in the later stage. In the numerical study, the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted, and the corresponding penalty method is used for constraint treatment. The test results show that the improved… More >

    Graphic Abstract

    An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

  • Open AccessOpen Access

    ARTICLE

    An Innovative Finite Element Geometric Modeling of Single-Layer Multi-Bead WAAMed Part

    Xiangman Zhou1,*, Jingping Qin1, Zichuan Fu1, Min Wang1, Youlu Yuan1, Junjian Fu1, Haiou Zhang2, Seyed Reza Elmi Hosseini3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2383-2401, 2024, DOI:10.32604/cmes.2023.029249
    Abstract Finite element (FE) coupled thermal-mechanical analysis is widely used to predict the deformation and residual stress of wire arc additive manufacturing (WAAM) parts. In this study, an innovative single-layer multi-bead profile geometric modeling method through the isosceles trapezoid function is proposed to build the FE model of the WAAM process. Firstly, a straight-line model for overlapping beads based on the parabola function was established to calculate the optimal center distance. Then, the isosceles trapezoid-based profile was employed to replace the parabola profiles of the parabola-based overlapping model to establish an innovative isosceles trapezoid-based multi-bead overlapping geometric model. The rationality of… More >

  • Open AccessOpen Access

    ARTICLE

    Development and Application of a Power Law Constitutive Model for Eddy Current Dampers

    Longteng Liang1,2,3, Zhouquan Feng2,4,*, Hongyi Zhang2,4, Zhengqing Chen2,4, Changzhao Qian1,3
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2403-2419, 2024, DOI:10.32604/cmes.2023.031260
    Abstract Eddy current dampers (ECDs) have emerged as highly desirable solutions for vibration control due to their exceptional damping performance and durability. However, the existing constitutive models present challenges to the widespread implementation of ECD technology, and there is limited availability of finite element analysis (FEA) software capable of accurately modeling the behavior of ECDs. This study addresses these issues by developing a new constitutive model that is both easily understandable and user-friendly for FEA software. By utilizing numerical results obtained from electromagnetic FEA, a novel power law constitutive model is proposed to capture the nonlinear behavior of ECDs. The effectiveness… More >

  • Open AccessOpen Access

    ARTICLE

    Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

    Fady Samann1,*, Shavan Askar2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2421-2444, 2024, DOI:10.32604/cmes.2023.031265
    Abstract Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms. K-clustering algorithms are simplistic, with fast performance and relative accuracy. However, their implementation depends on the initial selection of clusters number (K), the initial clusters’ centers, and the clustering metric. This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time (LET) as a clustering metric. Realistic traffic flows were considered for three maps, namely Highway, Traffic Light junction, and Roundabout junction, to study the effect of road layout on… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Simulation of Surrounding Rock Deformation and Grouting Reinforcement of Cross-Fault Tunnel under Different Excavation Methods

    Duan Zhu1,2, Zhende Zhu1,2, Cong Zhang1,2,*, Lun Dai1,2, Baotian Wang1,2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2445-2470, 2024, DOI:10.32604/cmes.2023.030847
    Abstract Tunnel construction is susceptible to accidents such as loosening, deformation, collapse, and water inrush, especially under complex geological conditions like dense fault areas. These accidents can cause instability and damage to the tunnel. As a result, it is essential to conduct research on tunnel construction and grouting reinforcement technology in fault fracture zones to address these issues and ensure the safety of tunnel excavation projects. This study utilized the Xianglushan cross-fault tunnel to conduct a comprehensive analysis on the construction, support, and reinforcement of a tunnel crossing a fault fracture zone using the three-dimensional finite element numerical method. The study… More >

  • Open AccessOpen Access

    ARTICLE

    Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

    Wimonmas Bamrungsetthapong1, Pramote Charongrattanasakul2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2471-2495, 2024, DOI:10.32604/cmes.2023.031433
    Abstract This study presents the design of a modified attributed control chart based on a double sampling (DS) np chart applied in combination with generalized multiple dependent state (GMDS) sampling to monitor the mean life of the product based on the time truncated life test employing the Weibull distribution. The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing. Three control limit levels are used: the warning control limit, inner control limit, and outer control limit. Together, they enhance the capability for variation detection. A genetic algorithm can be used… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

    Nasser Alalwan1,*, Ahmed I. Taloba2, Amr Abozeid3, Ahmed Ibrahim Alzahrani1, Ali H. Al-Bayatti4
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2497-2517, 2024, DOI:10.32604/cmes.2023.029910
    (This article belongs to this Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid method for the automatic classification… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati1,2, Ali Louati3,*, Elham Kariri3, Slim Bechikh2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806
    (This article belongs to this Special Issue: Intelligent Biomedical Image Processing and Computer Vision)
    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression of a convolutional neural network… More >

  • Open AccessOpen Access

    ARTICLE

    An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

    Hong Sun1, Fangquan Yang2, Peiwen Zhang3,*, Yang Jiao4, Yunxiang Zhao5
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2549-2569, 2024, DOI:10.32604/cmes.2023.030131
    (This article belongs to this Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
    Abstract With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on the normal distribution characteristics of… More >

  • Open AccessOpen Access

    ARTICLE

    Computational Analysis of Novel Extended Lindley Progressively Censored Data

    Refah Alotaibi1, Mazen Nassar2,3, Ahmed Elshahhat4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2571-2596, 2024, DOI:10.32604/cmes.2023.030582
    (This article belongs to this Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
    Abstract A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing, bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied. In this research, using a progressive Type-II censored, various inferences of the MOL model parameters of life are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of the model parameters and various reliability measures are investigated. Against both symmetric and asymmetric loss functions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with the assumption of independent gamma priors. From the Fisher information… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

    Zhihui Xu1, Shuwen Shang2, Yuntong Pu3, Xiaoyan Su2,*, Hong Qian2, Xiaolei Pan2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2597-2617, 2024, DOI:10.32604/cmes.2023.031247
    (This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract Cognitive Reliability and Error Analysis Method (CREAM) is widely used in human reliability analysis (HRA). It defines nine common performance conditions (CPCs), which represent the factors that may affect human reliability and are used to modify the cognitive failure probability (CFP). However, the levels of CPCs are usually determined by domain experts, which may be subjective and uncertain. What’s more, the classic CREAM assumes that the CPCs are independent, which is unrealistic. Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation. To address the issue… More >

    Graphic Abstract

    An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

  • Open AccessOpen Access

    ARTICLE

    Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

    Jinxi Guo1, Kai Chen1,2, Jiehui Liu1, Yuhao Ma2, Jie Wu2,*, Yaochun Wu2, Xiaofeng Xue3, Jianshen Li1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360
    (This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
    Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstly converted into frequency domain… More >

  • Open AccessOpen Access

    ARTICLE

    A Formal Model for Analyzing Fair Exchange Protocols Based on Event Logic

    Ke Yang1, Meihua Xiao2,*, Zehuan Li1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2641-2663, 2024, DOI:10.32604/cmes.2023.031458
    (This article belongs to this Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
    Abstract Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure manner. These protocols are widely used in electronic payment systems and electronic contract signing, ensuring the reliability and security of network transactions. In order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols, this paper proposes a formal model for analyzing such protocols. The proposed model begins with a thorough analysis of fair exchange protocols, followed by the formal definition of fairness. This definition accurately captures the inherent requirements of… More >

  • Open AccessOpen Access

    ARTICLE

    Web Layout Design of Large Cavity Structures Based on Topology Optimization

    Xiaoqiao Yang, Jialiang Sun*, Dongping Jin
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2665-2689, 2024, DOI:10.32604/cmes.2023.031482
    (This article belongs to this Special Issue: Structural Design and Optimization)
    Abstract Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades and wings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumption has become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topology optimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By using the variable density method, lightweight design is achieved without compromising structural strength. The optimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivity filtering and projection to obtain a robust optimized configuration. The mechanical properties are… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Convolutional Neural Network for Traffic Scene Segmentation

    Fuliang Xu, Yong Luo, Chuanlong Sun, Hong Zhao*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2691-2708, 2024, DOI:10.32604/cmes.2023.030940
    (This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and short-term memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm’s capability to process both global and local information. Additionally, to increase the network’s initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset.… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

    Guilin Wu1,2, Shenghua Huang1, Tingting Liu3, Zhuoni Yang3, Yuesong Wu2, Guihong Wei1, Peng Yu1,*, Qilin Zhang4, Jun Feng4, Bo Zeng5,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2709-2725, 2024, DOI:10.32604/cmes.2023.031399
    (This article belongs to this Special Issue: Machine Learning Based Computational Mechanics)
    Abstract Esophageal disease is a common disorder of the digestive system that can severely affect the quality of life and prognosis of patients. Esophageal stenting is an effective treatment that has been widely used in clinical practice. However, esophageal stents of different types and parameters have varying adaptability and effectiveness for patients, and they need to be individually selected according to the patient’s specific situation. The purpose of this study was to provide a reference for clinical doctors to choose suitable esophageal stents. We used 3D printing technology to fabricate esophageal stents with different ratios of thermoplastic polyurethane (TPU)/(Poly-ε-caprolactone) PCL polymer,… More >

  • Open AccessOpen Access

    ARTICLE

    Influences of the Fresh Air Volume on the Removal of Cough-Released Droplets in a Passenger Car of a High-Speed Train Using CFD

    Jun Xu1, Kai Bi1, Yibin Lu2,*, Tiantian Wang2,3, Hang Zhang2, Zeyuan Zheng3, Fushan Shi3, Yaxin Zheng3, Xiaoying Li2, Jingping Yang3
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2727-2748, 2024, DOI:10.32604/cmes.2023.031341
    (This article belongs to this Special Issue: Computer Modeling in Vehicle Aerodynamics)
    Abstract The spread and removal of pollution sources, namely, cough-released droplets in three different areas (front, middle, and rear areas) of a fully-loaded passenger car in a high-speed train under different fresh air flow volume were studied using computational fluid dynamics (CFD) method. In addition, the structure of indoor flow fields was also analysed. The results show that the large eddies are more stable and flow faster in the air supply under Mode 2 (fresh air volume: 2200 m3/h) compared to Mode 1 (fresh air volume: 1100 m3/h). By analysing the spreading process of droplets sprayed at different locations in the passenger car… More >

  • Open AccessOpen Access

    ARTICLE

    New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

    Shimaa M. Amer1, Ashraf A. M. Khalaf2, Amr H. Hussein3,4, Salman A. Alqahtani5, Mostafa H. Dahshan6, Hossam M. Kassem3,4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s excitations while the GA optimizes… More >

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    ARTICLE

    Fault Identification for Shear-Type Structures Using Low-Frequency Vibration Modes

    Cuihong Li1, Qiuwei Yang2,3,*, Xi Peng2,3
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2769-2791, 2024, DOI:10.32604/cmes.2023.030908
    (This article belongs to this Special Issue: Failure Detection Algorithms, Methods and Models for Industrial Environments)
    Abstract Shear-type structures are common structural forms in industrial and civil buildings, such as concrete and steel frame structures. Fault diagnosis of shear-type structures is an important topic to ensure the normal use of structures. The main drawback of existing damage assessment methods is that they require accurate structural finite element models for damage assessment. However, for many shear-type structures, it is difficult to obtain accurate FEM. In order to avoid finite element modeling, a model-free method for diagnosing shear structure defects is developed in this paper. This method only needs to measure a few low-order vibration modes of the structure.… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

    Se Li1, Tiantang Yu1,*, Tinh Quoc Bui2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2793-2808, 2024, DOI:10.32604/cmes.2023.030278
    (This article belongs to this Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures)
    Abstract Isogeometric analysis (IGA) is known to show advanced features compared to traditional finite element approaches. Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functional grading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward a deep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complex IGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trained using the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationship between the outputs and the inputs… More >

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    ARTICLE

    Attention-Based Residual Dense Shrinkage Network for ECG Denoising

    Dengyong Zhang1,2, Minzhi Yuan1,2, Feng Li1,2, Lebing Zhang3,*, Yanqiang Sun4, Yiming Ling5
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2809-2824, 2024, DOI:10.32604/cmes.2023.029181
    (This article belongs to this Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by a shallow feature extraction network… More >

  • Open AccessOpen Access

    ARTICLE

    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan1, Binqiang Chen1,*, Bin Yao1, Wangpeng He2
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378
    (This article belongs to this Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when the cutting conditions undergo any… More >

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    ARTICLE

    An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

    Raghunathan Krishankumar1, Dhruva Sundararajan2, K. S. Ravichandran2, Edmundas Kazimieras Zavadskas3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2845-2872, 2024, DOI:10.32604/cmes.2023.029438
    (This article belongs to this Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)
    Abstract Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation’s growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights… More >

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    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu1, Shuai Huang1, Danial Jahed Armaghani2, Biswajeet Pradhan3, Annan Zhou4, Jian Zhou1,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a… More >

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    ARTICLE

    Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

    Ayla Ocak1, Ümit Işıkdağ2, Gebrail Bekdaş1,*, Sinan Melih Nigdeli1, Sanghun Kim3, Zong Woo Geem4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2899-2924, 2024, DOI:10.32604/cmes.2023.030418
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator structure was estimated and compared… More >

  • Open AccessOpen Access

    ARTICLE

    Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

    Rabar H. Faraj1,*, Hemn Unis Ahmed2,3, Hardi Saadullah Fathullah4, Alan Saeed Abdulrahman2, Farid Abed5
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2925-2954, 2024, DOI:10.32604/cmes.2023.029392
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Plain concrete is strong in compression but brittle in tension, having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures, even when steel reinforcing is present. In order to address these challenges, short polymer fibers are randomly dispersed in a cement-based matrix to form a highly ductile engineered cementitious composite (ECC). This material exhibits high ductility under tensile forces, with its tensile strain being several hundred times greater than conventional concrete. Since concrete is inherently weak in tension, the tensile strain capacity (TSC) has become one of the most extensively researched properties. As a… More >

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    ARTICLE

    An Effective Meshless Approach for Inverse Cauchy Problems in 2D and 3D Electroelastic Piezoelectric Structures

    Ziqiang Bai1, Wenzhen Qu2,*, Guanghua Wu3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2955-2972, 2024, DOI:10.32604/cmes.2023.031474
    (This article belongs to this Special Issue: New Trends on Meshless Method and Numerical Analysis)
    Abstract In the past decade, notable progress has been achieved in the development of the generalized finite difference method (GFDM). The underlying principle of GFDM involves dividing the domain into multiple sub-domains. Within each sub-domain, explicit formulas for the necessary partial derivatives of the partial differential equations (PDEs) can be obtained through the application of Taylor series expansion and moving-least square approximation methods. Consequently, the method generates a sparse coefficient matrix, exhibiting a banded structure, making it highly advantageous for large-scale engineering computations. In this study, we present the application of the GFDM to numerically solve inverse Cauchy problems in two-… More >

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    ARTICLE

    A Stochastic Model to Assess the Epidemiological Impact of Vaccine Booster Doses on COVID-19 and Viral Hepatitis B Co-Dynamics with Real Data

    Andrew Omame1,2,*, Mujahid Abbas3,6, Dumitru Baleanu4,5,6
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2973-3012, 2024, DOI:10.32604/cmes.2023.029681
    (This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
    Abstract A patient co-infected with COVID-19 and viral hepatitis B can be at more risk of severe complications than the one infected with a single infection. This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19. The model is fitted to real COVID-19 data from Pakistan. The proposed model incorporates logistic growth and saturated incidence functions. Rigorous analyses using the tools of stochastic calculus, are performed to study appropriate conditions for the existence of unique global solutions, stationary distribution in the sense of ergodicity and disease… More >

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    ARTICLE

    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088
    (This article belongs to this Special Issue: Information Security and Trust Issues in the Digital World)
    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly includes: (1) Construct a smart… More >

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    ARTICLE

    Euler’s First-Order Explicit Method–Peridynamic Differential Operator for Solving Population Balance Equations of the Crystallization Process

    Chunlei Ruan1,2,*, Cengceng Dong1, Kunfeng Liang3, Zhijun Liu1, Xinru Bao1
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3033-3049, 2024, DOI:10.32604/cmes.2023.030607
    (This article belongs to this Special Issue: Peridynamics and its Current Progress)
    Abstract Using Euler’s first-order explicit (EE) method and the peridynamic differential operator (PDDO) to discretize the time and internal crystal-size derivatives, respectively, the Euler’s first-order explicit method–peridynamic differential operator (EE–PDDO) was obtained for solving the one-dimensional population balance equation in crystallization. Four different conditions during crystallization were studied: size-independent growth, size-dependent growth in a batch process, nucleation and size-independent growth, and nucleation and size-dependent growth in a continuous process. The high accuracy of the EE–PDDO method was confirmed by comparing it with the numerical results obtained using the second-order upwind and HR-van methods. The method is characterized by non-oscillation and high… More >

    Graphic Abstract

    Euler’s First-Order Explicit Method–Peridynamic Differential Operator for Solving Population Balance Equations of the Crystallization Process

  • Open AccessOpen Access

    ARTICLE

    Enhanced Temporal Correlation for Universal Lesion Detection

    Muwei Jian1,2,*, Yue Jin1, Hui Yu3
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3051-3063, 2024, DOI:10.32604/cmes.2023.030236
    (This article belongs to this Special Issue: Deep Learning based Computational Methods for Abnormality Detection in Human Medical Images)
    Abstract Universal lesion detection (ULD) methods for computed tomography (CT) images play a vital role in the modern clinical medicine and intelligent automation. It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks. However, 3D CT blocks necessitate significantly higher hardware resources during the learning phase. Therefore, efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks. In this paper, we propose a ULD network with the enhanced temporal correlation for this purpose, named TCE-Net. The designed TCE module is applied to enrich the discriminate… More >

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