Open Access
ISSN:1526-1492(print)
ISSN:1526-1506(online)
Publication Frequency:Monthly
This journal publishes original research papers of reasonable permanent intellectual value, in the areas of computer modeling in engineering & Sciences, including, but not limited to computational mechanics, computational materials, computational mathematics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua spanning from various spatial length scales (quantum, nano, micro, meso, and macro), and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. Novel computational approaches and state-of-the-art computation algorithms, such as soft computing, artificial intelligence-based machine learning methods, and computational statistical methods are welcome.
Science Citation Index (Web of Science): 2022 Impact Factor 2.4; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2022): 3.5; SNIP (Source Normalized Impact per Paper 2022): 0.707; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649
Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 41-76, 2024, DOI:10.32604/cmes.2024.048313
Abstract The collocation method is a widely used numerical method for science and engineering problems governed by partial differential equations. This paper provides a comprehensive review of collocation methods and their applications, focused on elasticity, heat conduction, electromagnetic field analysis, and fluid dynamics. The merits of the collocation method can be attributed to the need for element mesh, simple implementation, high computational efficiency, and ease in handling irregular domain problems since the collocation method is a type of node-based numerical method. Beginning with the fundamental principles of the collocation method, the discretization process in the continuous domain is elucidated, and how… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 77-108, 2024, DOI:10.32604/cmes.2024.046758
（This article belongs to this Special Issue: The Bottleneck of Blockchain Techniques: Scalability, Security and Privacy Protection)
Abstract In recent years, the number of smart contracts deployed on blockchain has exploded. However, the issue of vulnerability has caused incalculable losses. Due to the irreversible and immutability of smart contracts, vulnerability detection has become particularly important. With the popular use of neural network model, there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts. This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts. Subsequently, it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection. These… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 109-137, 2024, DOI:10.32604/cmes.2024.047245
Abstract The centrifugal pump is a prevalent power equipment widely used in different engineering patterns, and the impeller blade wrap angle significantly impacts its performance. A numerical investigation was conducted to analyze the influence of the blade wrap angle on flow characteristics and energy distribution of a centrifugal pump evaluated as a low specific speed with a value of 69. This study investigates six impeller models that possess varying blade wrap angles (95°, 105°, 115°, 125°, 135°, and 145°) that were created while maintaining the same volute and other geometrical characteristics. The investigation of energy loss was conducted to evaluate the… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 139-170, 2024, DOI:10.32604/cmes.2024.047156
Abstract Understanding and modeling individuals’ behaviors during epidemics is crucial for effective epidemic control. However, existing research ignores the impact of users’ irrationality on decision-making in the epidemic. Meanwhile, existing disease control methods often assume users’ full compliance with measures like mandatory isolation, which does not align with the actual situation. To address these issues, this paper proposes a prospect theory-based framework to model users’ decision-making process in epidemics and analyzes how irrationality affects individuals’ behaviors and epidemic dynamics. According to the analysis results, irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 171-199, 2024, DOI:10.32604/cmes.2024.046703
Abstract The abalone shell, a composite material whose cross-section is composed of inorganic and organic layers, has high strength and toughness. Inspired by the abalone shell, several multi-layer composite plates with different layer sequences and thicknesses are studied as bullet-proof material in this paper. To investigate the ballistic performance of this multi-layer structure, the complete characterization model and related material parameters of large deformation, failure and fracture of Al2O3 ceramics and Carbon Fiber Reinforced Polymer (CFRP) are studied. Then, 3D finite element models of the proposed composite plates with different layer sequences and thicknesses impacted by a 12.7 mm armor-piercing incendiary… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 201-227, 2024, DOI:10.32604/cmes.2024.046129
Abstract In order to investigate the fatigue performance of orthotropic anisotropic steel bridge decks, this study realizes the simulation of the welding process through elastic-plastic finite element theory, thermal-structural sequential coupling, and the birth-death element method. The simulated welding residual stresses are introduced into the multiscale finite element model of the bridge as the initial stress. Furthermore, the study explores the impact of residual stress on crack propagation in the fatigue-vulnerable components of the corroded steel box girder. The results indicate that fatigue cracks at the weld toe of the top deck, the weld root of the top deck, and the… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569
Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset, which serves… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 255-273, 2024, DOI:10.32604/cmes.2024.048175
Abstract Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960
Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 305-323, 2024, DOI:10.32604/cmes.2024.047899
Abstract The Rotary Inverted Pendulum (RIP) is a widely used underactuated mechanical system in various applications such as bipedal robots and skyscraper stabilization where attitude control presents a significant challenge. Despite the implementation of various control strategies to maintain equilibrium, optimally tuning control gains to effectively mitigate uncertain nonlinearities in system dynamics remains elusive. Existing methods frequently rely on extensive experimental data or the designer’s expertise, presenting a notable drawback. This paper proposes a novel tracking control approach for RIP, utilizing a Linear Quadratic Regulator (LQR) in combination with a reduced-order observer. Initially, the RIP system is mathematically modeled using the… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 325-337, 2024, DOI:10.32604/cmes.2024.048804
Abstract The gravitational wave spacecraft is a complex multi-input multi-output dynamic system. The gravitational wave detection mission requires the spacecraft to achieve single spacecraft with two laser links and high-precision control. Establishing one spacecraft with two laser links, compared to one spacecraft with a single laser link, requires an upgraded decoupling algorithm for the link establishment. The decoupling algorithm we designed reassigns the degrees of freedom and forces in the control loop to ensure sufficient degrees of freedom for optical axis control. In addressing the distinct dynamic characteristics of different degrees of freedom, a transfer function compensation method is used in… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 339-361, 2024, DOI:10.32604/cmes.2024.047566
Abstract This study proposes a comprehensive, coupled thermomechanical model that replaces local spatial derivatives in classical differential thermomechanical equations with nonlocal integral forms derived from the peridynamic differential operator (PDDO), eliminating the need for calibration procedures. The model employs a multi-rate explicit time integration scheme to handle varying time scales in multi-physics systems. Through simulations conducted on granite and ceramic materials, this model demonstrates its effectiveness. It successfully simulates thermal damage behavior in granite arising from incompatible mineral expansion and accurately calculates thermal crack propagation in ceramic slabs during quenching. To account for material heterogeneity, the model utilizes the Shuffle algorithm… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 363-383, 2024, DOI:10.32604/cmes.2024.049044
Abstract The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 385-408, 2024, DOI:10.32604/cmes.2024.046752
（This article belongs to this Special Issue: AI-Driven Engineering Applications)
Abstract This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit (PSE-CNN-BiGRU) fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments. Firstly, the deep convolutional network is integrated with the Mediapipe framework to extract high-precision, multi-dimensional information from the key points of the human skeleton, thereby obtaining a human posture feature set. Thereafter, a double-layer BiGRU algorithm is utilized to extract multi-layer, bidirectional temporal features from the human posture feature set, and a… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052
（This article belongs to this Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 431-460, 2024, DOI:10.32604/cmes.2024.046635
（This article belongs to this Special Issue: Numerical Modeling and Simulations on Non-Newtonian Flow Problems)
Abstract In this study, the characteristics of heat transfer on an unsteady magnetohydrodynamic (MHD) Casson nanofluid over an exponentially accelerated vertical porous plate with rotating effects were investigated. The flow was driven by the combined effects of the magnetic field, heat radiation, heat source/sink and chemical reaction. Copper oxide () and titanium oxide () are acknowledged as nanoparticle materials. The nondimensional governing equations were subjected to the Laplace transformation technique to derive closed-form solutions. Graphical representations are provided to analyze how changes in physical parameters, such as the magnetic field, heat radiation, heat source/sink and chemical reaction, affect the velocity, temperature… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 461-483, 2024, DOI:10.32604/cmes.2024.046467
（This article belongs to this Special Issue: New Trends on Meshless Method and Numerical Analysis)
Abstract In this research, we present the pure open multi-processing (OpenMP), pure message passing interface (MPI), and hybrid MPI/OpenMP parallel solvers within the dynamic explicit central difference algorithm for the coining process to address the challenge of capturing fine relief features of approximately 50 microns. Achieving such precision demands the utilization of at least 7 million tetrahedron elements, surpassing the capabilities of traditional serial programs previously developed. To mitigate data races when calculating internal forces, intermediate arrays are introduced within the OpenMP directive. This helps ensure proper synchronization and avoid conflicts during parallel execution. Additionally, in the MPI implementation, the coins… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 485-512, 2024, DOI:10.32604/cmes.2024.047907
（This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
Abstract The global population has been and will continue to be severely impacted by the COVID-19 epidemic. The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer, heart disease, and diabetes. Here, using ordinary differential equations (ODEs), two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease. After that, we highlight the stability assessments that can be applied to these models. Sensitivity analysis is used to examine how changes in certain factors impact different aspects… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 513-536, 2024, DOI:10.32604/cmes.2024.046533
（This article belongs to this Special Issue: Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond)
Abstract This study proposes a new flexible family of distributions called the Lambert-G family. The Lambert family is very flexible and exhibits desirable properties. Its three-parameter special sub-models provide all significant monotonic and non-monotonic failure rates. A special sub-model of the Lambert family called the Lambert-Lomax (LL) distribution is investigated. General expressions for the LL statistical properties are established. Characterizations of the LL distribution are addressed mathematically based on its hazard function. The estimation of the LL parameters is discussed using six estimation methods. The performance of this estimation method is explored through simulation experiments. The usefulness and flexibility of the… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174
（This article belongs to this Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an enhanced DenseNet multimodal recognition network… More >
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Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452
（This article belongs to this Special Issue: Intelligent Analysis of Imperfect Data in Complex Scenes: Modeling, Learning, and Optimization)
Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 577-591, 2024, DOI:10.32604/cmes.2023.043307
（This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 593-619, 2024, DOI:10.32604/cmes.2023.046319
（This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
Abstract Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace (referred to as CATT gear), a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis. In this study, a mathematical relationship between design parameters and contact stress is formulated using the Kriging Metamodel. To enhance the model’s accuracy, we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 621-645, 2024, DOI:10.32604/cmes.2024.046398
（This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
Abstract The surrounding geological conditions and supporting structures of underground engineering are often updated during construction, and these updates require repeated numerical modeling. To improve the numerical modeling efficiency of underground engineering, a modularized and parametric modeling cloud server is developed by using Python codes. The basic framework of the cloud server is as follows: input the modeling parameters into the web platform, implement Rhino software and FLAC3D software to model and run simulations in the cloud server, and return the simulation results to the web platform. The modeling program can automatically generate instructions that can run the modeling process in… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 647-663, 2024, DOI:10.32604/cmes.2024.048782
（This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
Abstract Stirred reactors are key equipment in production, and unpredictable failures will result in significant economic losses and safety issues. Therefore, it is necessary to monitor its health state. To achieve this goal, in this study, five states of the stirred reactor were firstly preset: normal, shaft bending, blade eccentricity, bearing wear, and bolt looseness. Vibration signals along x, y and z axes were collected and analyzed in both the time domain and frequency domain. Secondly, 93 statistical features were extracted and evaluated by ReliefF, Maximal Information Coefficient (MIC) and XGBoost. The above evaluation results were then fused by D-S evidence… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 665-684, 2024, DOI:10.32604/cmes.2024.048445
（This article belongs to this Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
Abstract Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630
（This article belongs to this Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 711-733, 2024, DOI:10.32604/cmes.2024.045411
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract Stiffened structures have great potential for improving mechanical performance, and the study of their stability is of great interest. In this paper, the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method, where the shape and cross section (including thickness and width) of the stiffeners can be optimized simultaneously. The grid stiffeners are a combination of many single stiffeners which are projected by the corresponding level set functions. The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 735-755, 2024, DOI:10.32604/cmes.2024.047258
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract The widespread adoption of aluminum alloy electric buses, known for their energy efficiency and eco-friendliness, faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel. This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries, necessitating robust frame protection. Our study aims to optimize the connectors of aluminum alloy bus frames, emphasizing durability, energy efficiency, and safety. This research delves into Multi-Objective Coordinated Optimization (MCO) techniques for lightweight design in aluminum alloy bus body connectors. Our goal is to enhance lightweighting, reinforce energy absorption, and improve deformation resistance in… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 757-792, 2024, DOI:10.32604/cmes.2024.048016
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design. The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads. The topology optimization formula is combined with the ordered solid isotropic material with penalization (ordered-SIMP) multi-material interpolation model. The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function. Furthermore, the sequential optimization and reliability assessment (SORA) is applied to… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 793-819, 2024, DOI:10.32604/cmes.2024.048174
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract To enhance the comprehensive performance of artillery internal ballistics—encompassing power, accuracy, and service life—this study proposed a multi-stage multidisciplinary design optimization (MS-MDO) method. First, the comprehensive artillery internal ballistic dynamics (AIBD) model, based on propellant combustion, rotation band engraving, projectile axial motion, and rifling wear models, was established and validated. This model was systematically decomposed into subsystems from a system engineering perspective. The study then detailed the MS-MDO methodology, which included Stage I (MDO stage) employing an improved collaborative optimization method for consistent design variables, and Stage II (Performance Optimization) focusing on the independent optimization of local design variables and… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 821-844, 2024, DOI:10.32604/cmes.2024.048392
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale, multifunctional, and complex orientations. The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units, and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts, leading to variations in the dynamic response of the foundation. The high-frequency units yield significantly diverse outcomes under different startup conditions and times, resulting in failure to meet operational requirements, influencing the normal function of the tunnel, and causing harm to the foundation structure, personnel, and property in severe cases. This article… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 845-862, 2024, DOI:10.32604/cmes.2024.047695
（This article belongs to this Special Issue: Structural Design and Optimization)
Abstract This paper focuses on the effective utilization of data augmentation techniques for 3D lidar point clouds to enhance the performance of neural network models. These point clouds, which represent spatial information through a collection of 3D coordinates, have found wide-ranging applications. Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities. Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds. However, there has been a lack of focus on making the most of the numerous existing… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 863-885, 2024, DOI:10.32604/cmes.2024.047295
（This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Users and edge servers are not fully mutually trusted in mobile edge computing (MEC), and hence blockchain can be introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in both MEC and blockchain, processing users’ tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users’ offloaded tasks and blockchain tasks simultaneously. Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the MEC is crucial for the… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140
（This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core of the methodology lies in… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 907-927, 2024, DOI:10.32604/cmes.2024.048759
（This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793
（This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 957-974, 2024, DOI:10.32604/cmes.2024.047327
（This article belongs to this Special Issue: Machine Learning Based Computational Mechanics)
Abstract Structural damage in heterogeneous materials typically originates from microstructures where stress concentration occurs. Therefore, evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial. Repeating unit cells (RUCs) are commonly used to represent microstructural details and homogenize the effective response of composites. This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs. The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters, including volume fraction, fiber/matrix property ratio, fiber shapes, and loading direction. Subsequently, the conditional generative adversarial network… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 975-996, 2024, DOI:10.32604/cmes.2024.047664
（This article belongs to this Special Issue: Computer Modeling in Vehicle Aerodynamics)
Abstract The safety and stability of high-speed maglev trains traveling on viaducts in crosswinds critically depend on their aerodynamic characteristics. Therefore, this paper uses an improved delayed detached eddy simulation (IDDES) method to investigate the aerodynamic features of high-speed maglev trains with different marshaling lengths under crosswinds. The effects of marshaling lengths (varying from 3-car to 8-car groups) on the train’s aerodynamic performance, surface pressure, and the flow field surrounding the train were investigated using the three-dimensional unsteady compressible Navier-Stokes (N-S) equations. The results showed that the marshaling lengths had minimal influence on the aerodynamic performance of the head and middle… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 997-1015, 2024, DOI:10.32604/cmes.2024.048640
（This article belongs to this Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
Abstract Strain localization frequently occurs in cohesive materials with friction (e.g., composites, soils, rocks) and is widely recognized as a fundamental cause of progressive structural failure. Nonetheless, achieving high-fidelity simulation for this issue, particularly concerning strong discontinuities and tension-compression-shear behaviors within localized zones, remains significantly constrained. In response, this study introduces an integrated algorithm within the finite element framework, merging a coupled cohesive zone model (CZM) with the nonlinear augmented finite element method (N-AFEM). The coupled CZM comprehensively describes tension-compression and compression-shear failure behaviors in cohesive, frictional materials, while the N-AFEM allows nonlinear coupled intra-element discontinuities without necessitating extra nodes or… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1017-1028, 2024, DOI:10.32604/cmes.2024.048797
（This article belongs to this Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
Abstract For media with inclusions (e.g., precipitates, voids, reinforcements, and others), the difference in lattice parameter and the elastic modulus between the matrix and inclusions cause stress concentration at the interfaces. These stress fields depend on the inclusions’ size, shape, and distribution and will respond instantly to the evolving microstructure. This study develops a phase-field model concerning modulus heterogeneity. The effect of modulus heterogeneity on the growth process and equilibrium state of the α plate in Ti-6Al-4V during precipitation is evaluated. The α precipitate exhibits strong anisotropy in shape upon cooling due to the interplay of the elastic strain and interfacial… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1029-1044, 2024, DOI:10.32604/cmes.2024.047665
（This article belongs to this Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
Abstract A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653
（This article belongs to this Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1077-1102, 2024, DOI:10.32604/cmes.2024.047806
（This article belongs to this Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally,… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453
（This article belongs to this Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1129-1142, 2024, DOI:10.32604/cmes.2024.047756
（This article belongs to this Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition with significant clinical implications. However, endoscopic assessment is susceptible to inherent variations, both within and between observers, compromising the reliability of individual evaluations. This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity. To initiate this endeavor, a multi-faceted approach is embarked upon. The dataset is meticulously preprocessed, enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization (CLAHE). A diverse array of data augmentation… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549
（This article belongs to this Special Issue: Integration of Physical Simulation and Machine Learning in Digital Twin and Virtual Reality)
Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations… More >
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