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• Characteristic Tensor for Evaluation of Singular Stress Field Under Mixed-Mode Loadings
• Kei Saito^{1, 2, *}, Tei Hirashima¹, Ninshu Ma^{2, *}, Hidekazu Murakawa²
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 415-432, 2020, DOI:10.32604/cmes.2020.08847
• Abstract A characteristic tensor is defined using stress tensor averaged in a small circular domain at the crack tip and multiplied by the root of domain radius. It possesses the original stress tensor characteristics and has a simple relationship with conventional fracture-mechanics parameters. Therefore, it can be used to estimate stress intensity factors (SIFs) for cracks of arbitrary shape subjected to multiaxial stress loads. A characteristic tensor can also be used to estimate SIFs for kinked cracks. This study examines the relation between a characteristic tensor and SIFs to demonstrate the correlation between the characteristic tensor and fracture-mechanics parameters. Consequently, a… More
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• Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis
• Yingjun Wang¹, Zhongyuan Liao¹, Shengyu Shi^{1, *}, Zhenpei Wang^{2, *}, Leong Hien Poh³
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680
  (This article belongs to this Special Issue: Recent Developments of Isogeometric Analysis and its Applications in Structural Optimization)
• Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches. More
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• Reusing the Evaluations of Basis Functions in the Integration for Isogeometric Analysis
• Zijun Wu¹, Shuting Wang², Wenjun Shao^{3, *}, Lianqing Yu¹
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 459-485, 2020, DOI:10.32604/cmes.2020.08697
  (This article belongs to this Special Issue: Recent Developments of Isogeometric Analysis and its Applications in Structural Optimization)
• Abstract We propose a new approach to reuse the basis function evaluations in the numerical integration of isogeometric analysis. The concept of reusability of the basis functions is introduced according to their symmetrical, translational and proportional features on both the coarse and refined levels. Based on these features and the parametric domain regularity of each basis, we classify the bases on the original level and then reuse them on the refined level, which can reduce the time for basis calculations at integration nodes. By using the sum factorization method and the mean value theorem for the integrals, a new integration method… More
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• The m-delay Autoregressive Model with Application
• Manlika Ratchagit¹, Benchawan Wiwatanapataphee¹, Nikolai Dokuchaev^{1, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 487-504, 2020, DOI:10.32604/cmes.2020.08865
• Abstract The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an optimal delay called as the m-delay AR model. We derive the m-delay AR formula for approximating two unknown parameters based on the least squares method and develop an algorithm to determine optimal delay based on a brute-force technique. The performance of the m-delay AR model was tested by comparing with the… More
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• Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition
• Lei Chen^{1, #}, Kanghu Bo^{2, #}, Feifei Lee^{1, *}, Qiu Chen^{1, 3, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 505-523, 2020, DOI:10.32604/cmes.2020.08425
• Abstract Scene recognition is a popular open problem in the computer vision field. Among lots of methods proposed in recent years, Convolutional Neural Network (CNN) based approaches achieve the best performance in scene recognition. We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network (MultiCNN) for scene recognition. Unlike existing works that usually use individual convolutional neural network, a fusion of multiple different convolutional neural networks is applied for scene recognition. Firstly, we split training images in two directions and apply to three deep CNN model, and then extract features from the last full-connected (FC) layer… More
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• Fatigue Life Evaluation Method for Foundry Crane Metal Structure Considering Load Dynamic Response and Crack Closure Effect
• Qing Dong^{1, *}, Bin He¹, Gening Xu¹
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 525-553, 2020, DOI: 10.32604/cmes.2020.08498
• Abstract To compensate for the shortcomings of quasi-static law in anti-fatigue analysis of foundry crane metal structures, the fatigue life evaluation method of foundry crane metal structure considering load dynamic response and crack closure effect is proposed. In line with the theory of mechanical vibration, a dynamic model of crane structure during the working cycle is constructed, and dynamic coefficients under diverse actions are analysed. Calculation models of the internal force dynamic change process of dangerous cross-sections and a simulation model of first principal stress-time history are established by using the steel structure design criteria, which is utilised to extract the… More
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• Effect of RANS Model on the Aerodynamic Characteristics of a Train in Crosswinds Using DDES
• Tian Li^{1, *}, Zhiyuan Dai¹, Weihua Zhang¹
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 555-570, 2020, DOI:10.32604/cmes.2020.08101
• Abstract Detached eddy simulation has been widely applied to simulate the flow around trains in recent years. The Reynolds-averaged Navier-Stokes (RANS) model for delayed detached eddy simulation (DDES) is an essential user input. The effect of the RANS model for DDES on the aerodynamic characteristics of a train in crosswinds is investigated in this study. Three different DDES models are used, based on the Spalart-Allmaras model (SA), the realizable k-ε model (RKE), and the shear stress transport k-ω model (SST). Results show that all DDES models can give relatively accurate predictions of pressure coefficient on almost all surfaces. There are only… More
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• Beam Approximation for Dynamic Analysis of Launch Vehicles Modelled as Stiffened Cylindrical Shells
• Siyang Piao¹, Huajiang Ouyang^{1, 2}, Yahui Zhang^{1, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 571-591, 2020, DOI: 10.32604/cmes.2020.08789
• Abstract A beam approximation method for dynamic analysis of launch vehicles modelled as stiffened cylindrical shells is proposed. Firstly, an initial beam model of the stiffened cylindrical shell is established based on the cross-sectional area equivalence principle that represents the shell skin and its longitudinal ribs as a beam with annular cross-section, and the circumferential ribs as lumped masses at the nodes of the beam elements. Then, a fine finite element model (FE model) of the stiffened cylindrical shell is constructed and a modal analysis is carried out. Finally, the initial beam model is improved through model updating against the natural… More
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• Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning
• Dongjie Liu¹, Jin Zhao^{1, *}, Axin Xi², Chao Wang¹, Xinnian Huang¹, Kuncheng Lai¹, Chang Liu¹
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 593-617, 2020, DOI:10.32604/cmes.2020.08641
• Abstract With the advent of deep learning, self-driving schemes based on deep learning are becoming more and more popular. Robust perception-action models should learn from data with different scenarios and real behaviors, while current end-to-end model learning is generally limited to training of massive data, innovation of deep network architecture, and learning in-situ model in a simulation environment. Therefore, we introduce a new image style transfer method into data augmentation, and improve the diversity of limited data by changing the texture, contrast ratio and color of the image, and then it is extended to the scenarios that the model has been… More
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• Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis
• Kemal Akyol^{1, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 619-632, 2020, DOI:10.32604/cmes.2020.07632
  (This article belongs to this Special Issue: Data Science and Modeling in Biology, Health, and Medicine)
• Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order… More
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• Experimental Simulation and Numerical Modeling of Deformation and Damage Evolution of Pre-Holed Sandstones After Heat Treatment
• Shuo Yang¹, Yuanhai Li^{1, 2, ∗}, Xiaojie Tang^{1, 2}, Jinshan Liu^{1, 2}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 633-659, 2020, DOI:10.32604/cmes.2020.07919
  (This article belongs to this Special Issue: Modeling and Simulation of Fluid flows in Fractured Porous Media: Current Trends and Prospects)
• Abstract The deformation and damage evolution of sandstone after heat treatment greatly influence the efficient and safe development of deep geothermal energy extraction. To investigate this issue, laboratory confined compression tests and numerical simulations were conducted on pre-holed sandstone specimens after heat treatment. The laboratory test results show that the failure modes are closely related to the heat treatment temperature, with increasing treatment temperature, the failure modes change from mixed and shear modes to a splitting mode. The cracks always initiate from the sidewalls of the hole and then propagate. The failure process inside the hole proceeds as follows: calm period,… More
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• Stress Analysis of Printed Circuit Board with Different Thickness and Composite Materials Under Shock Loading
• Kuan-Ting Liu¹, Chun-Lin Lu¹, Nyan-Hwa Tai², Meng-Kao Yeh^{1, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 661-674, 2020, DOI:10.32604/cmes.2020.07792
  (This article belongs to this Special Issue: Data Computation in Advanced Composites: Characterization, Machining, and Waste Management)
• Abstract In this study, the deformation and stress distribution of printed circuit board (PCB) with different thickness and composite materials under a shock loading were analyzed by the finite element analysis. The standard 8-layer PCB subjected to a shock loading 1500 g was evaluated first. Moreover, the finite element models of the PCB with different thickness by stacking various number of layers were discussed. In addition to changing thickness, the core material of PCB was replaced from woven E-glass/epoxy to woven carbon fiber/epoxy for structural enhancement. The non-linear material property of copper foil was considered in the analysis. The results indicated… More
•   Views:2732       Downloads:1479       Cited by:1        Download PDF

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• Mixed Noise Parameter Estimation Based on Variance Stable Transform
• Ling Ding^{1, 2}, Huyin Zhang^{1, *}, Jinsheng Xiao³, Junfeng Lei³, Fang Xu³, Shejie Lu²
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 675-690, 2020, DOI:10.32604/cmes.2020.07987
  (This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
• Abstract The ultimate goal of image denoising from video is to improve the given image, which can reduce noise interference to ensure image quality. Through denoising technology, image quality can have effectively optimized, signal-to-noise ratio can have increased, and the original mage information can have better reflected. As an important preprocessing method, people have made extensive research on image denoising algorithm. Video denoising needs to take into account the various level of noise. Therefore, the estimation of noise parameters is particularly important. This paper presents a noise estimation method based on variance stability transformation, which estimates the parameters of variance stability… More
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• Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network
• Yanli Ji^{1, *}, Weidong Wang², Yinghai Zhang²
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 691-701, 2020, DOI:10.32604/cmes.2020.07861
  (This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
• Abstract In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels, this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks. After adding idle cognitive users for detection, the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time. Both theoretical analysis and simulation results show that using idle cognitive users can reduce… More
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• A Numerical Study on Hydraulic Fracturing Problems via the Proper Generalized Decomposition Method
• Daobing Wang^{1, *}, Sergio Zlotnik^{2, *}, Pedro Díez², Hongkui Ge³, Fujian Zhou³, Bo Yu⁴
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 703-720, 2020, DOI:10.32604/cmes.2020.08033
  (This article belongs to this Special Issue: Advances in Modeling and Simulation of Complex Heat Transfer and Fluid Flow)
• Abstract The hydraulic fracturing is a nonlinear, fluid-solid coupling and transient problem, in most cases it is always time-consuming to simulate this process numerically. In recent years, although many numerical methods were proposed to settle this problem, most of them still require a large amount of computer resources. Thus it is a high demand to develop more effificient numerical approaches to achieve the real-time monitoring of the fracture geometry during the hydraulic fracturing treatment. In this study, a reduced order modeling technique namely Proper Generalized Decomposition (PGD), is applied to accelerate the simulations of the transient, non-linear coupled system of hydraulic… More
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• Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG
• Jingmin Guo¹, Xiu Cheng¹, Duanpo Wu^{2, 3, *}
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 721-741, 2020, DOI:10.32604/cmes.2020.07470
  (This article belongs to this Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)
• Abstract The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch… More
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• Analytical and Numerical Investigation for the DMBBM Equation
• Abdulghani Alharbi¹, Mahmoud A. E. Abdelrahman^{1, 2, *}, M. B. Almatrafi¹
• CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 743-756, 2020, DOI:10.32604/cmes.2020.07996
  (This article belongs to this Special Issue: Numerical Methods for Differential and Integral Equations)
• Abstract The nonlinear dispersive modified Benjamin-Bona-Mahony (DMBBM) equation is solved numerically using adaptive moving mesh PDEs (MMPDEs) method. Indeed, the exact solution of the DMBBM equation is obtained by using the extended Jacobian elliptic function expansion method. The current methods give a wider applicability for handling nonlinear wave equations in engineering and mathematical physics. The adaptive moving mesh method is compared with exact solution by numerical examples, where the explicit solutions are known. The numerical results verify the accuracy of the proposed method. More
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