CMES-Computer Modeling in Engineering & Sciences

ISSN: 1526-1492 (print) Impact Factor: 0.805
ISSN: 1526-1506 (online) Publication Frequency: Monthly

About the Journal

This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various 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. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.

Indexing and Abstracting

Science Citation Index (Web of Science): 2019 Impact Factor 0.805; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2019): 1.0; SNIP (Source Normalized Impact per Paper 2019): 0.499; 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...

Current Issue - Vol.125, No.2, 2020 Most Viewed

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ARTICLE
Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search
Soukaina Mjahed^1,*, Khadija Bouzaachane¹, Ahmad Taher Azar^2,3, Salah El Hadaj¹, Said Raghay¹
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791
Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More
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Dynamical Stability of Cantilevered Pipe Conveying Fluid with Inerter-Based Dynamic Vibration Absorber
Zhiyuan Liu^1,2, Xin Tan², Xiaobo Liu^1,2, Pingan Chen^1,2, Ke Yi^1,2, Tianzhi Yang^1,2, Qiao Ni^3,4, Lin Wang^3,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 495-514, 2020, DOI:10.32604/cmes.2020.012030
Abstract Cantilevered pipe conveying fluid may become unstable and flutter instability would occur when the velocity of the fluid flow in the pipe exceeds a critical value. In the present study, the theoretical model of a cantilevered fluid-conveying pipe attached by an inerter-based dynamic vibration absorber (IDVA) is proposed and the stability of this dynamical system is explored. Based on linear governing equations of the pipe and the IDVA, the effects of damping coefficient, weight, inerter, location and spring stiffness of the IDVA on the critical flow velocities of the pipe system is examined. It is shown that the stability of… More
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Modeling of an Internal Stress and Strain Distribution of an Inverted Staggered Thin-Film Transistor Based on Two-Dimensional Mass-Spring-Damper Structure
Yi Yang, Robert Nawrocki, Richard Voyles, Haiyan H. Zhang^*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 515-539, 2020, DOI:10.32604/cmes.2020.010165
Abstract Equipped with a two-dimensional topological structure, a group of masses, springs and dampers can be demonstrated to model the internal dynamics of a thin-film transistor (TFT). In this paper, the two-dimensional Mass-Spring-Damper (MSD) representation of an inverted staggered TFT is proposed to explore the TFT’s internal stress/strain distributions, and the stress-induced effects on TFT’s electrical characteristics. The 2D MSD model is composed of a finite but massive number of interconnected cellular units. The parameters, such as mass, stiffness, and damping ratios, of each cellular unit are approximated from constitutive equations of the composite materials, while the electrical properties of the… More
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ARTICLE
LES Investigation of Drag-Reducing Mechanism of Turbulent Channel Flow with Surfactant Additives
Jingfa Li¹, Bo Yu^1,*, Qianqian Shao², Dongliang Sun¹
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 541-563, 2020, DOI:10.32604/cmes.2020.011835
Abstract In this work, the drag-reducing mechanism of high-Reynoldsnumber turbulent channel flow with surfactant additives is investigated by using large eddy simulation (LES) method. An N-parallel finitely extensible nonlinear elastic model with Peterlin’s approximation (FENE-P) is used to describe the rheological behaviors of non-Newtonian fluid with surfactant. To close the filtered LES equations, a hybrid subgrid scale (SGS) model coupling the spatial filter and temporal filter is applied to compute the subgrid stress and other subfilter terms. The finite difference method and projection algorithm are adopted to solve the LES governing equations. To validate the correctness of our LES method and… More
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ARTICLE
Blood Flow Through a Catheterized Artery Having a Mild Stenosis at the Wall with a Blood Clot at the Centre
Anber Saleem^1,2, Salman Akhtar³, Sohail Nadeem^3,*, Alibek Issakhov⁴, Mehdi Ghalambaz^5,6
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 565-577, 2020, DOI:10.32604/cmes.2020.011883
Abstract The blood flow through a catheterized artery having a mild stenosis at the wall together with a blood clot at the centre is studied in the current investigation. Stenosis can occur in vessels carrying blood to brain (i.e., Carotid arteries), Renal arteries that supply blood to kidneys etc. The flow is refined in such vessels by application of catheter. We have used a Newtonian viscous fluid model and also distinct shapes of stenosis, (i.e., symmetric and non-symmetric shapes) are considered for this study. The entropy generation together with viscous dissipation is also taken into account for a complete description of… More
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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models
Xing Deng^1,2, Haijian Shao^1,2,*, Liang Shi³, Xia Wang^4,5, Tongling Xie⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920
Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Conventional Neural Networks) are provided to compare with the classification–detection approaches based on… More
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Inverse Construction Methods of Heterogeneous NURBS Object Based on Additive Manufacturing
Ting Zang¹, Dongbin Zhu^2,*, Guowang Mu¹
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 597-610, 2020, DOI:10.32604/cmes.2020.09965
（This article belongs to this Special Issue: Design & simulation in Additive Manufacturing)
Abstract According to the requirement of heterogeneous object modeling in additive manufacturing (AM), the Non-Uniform Rational B-Spline (NURBS) method has been applied to the digital representation of heterogeneous object in this paper. By putting forward the NURBS material data structure and establishing heterogeneous NURBS object model, the accurate mathematical unified representation of analytical and free heterogeneous objects have been realized. With the inverse modeling of heterogeneous NURBS objects, the geometry and material distribution can be better designed to meet the actual needs. Radical Basis Function (RBF) method based on global surface reconstruction and the tensor product surface interpolation method are combined… More
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A Simplified Model for Buckling and Post-Buckling Analysis of Cu Nanobeam Under Compression
Jiachen Guo^1,2, Yunfei Xu², Zhenyu Jiang^1,*, Xiaoyi Liu², Yang Cai^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 611-623, 2020, DOI:10.32604/cmes.2020.011148
Abstract Both of Buckling and post-buckling are fundamental problems of geometric nonlinearity in solid mechanics. With the rapid development of nanotechnology in recent years, buckling behaviors in nanobeams receive more attention due to its applications in sensors, actuators, transistors, probes, and resonators in nanoelectromechanical systems (NEMS) and biotechnology. In this work, buckling and post-buckling of copper nanobeam under uniaxial compression are investigated with theoretical analysis and atomistic simulations. Different cross sections are explored for the consideration of surface effects. To avoid complicated high order buckling modes, a stressbased simplified model is proposed to analyze the critical strain for buckling, maximum deflection,… More
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A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images
S. Velliangiri^1,*, J. Premalatha²
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 625-645, 2020, DOI:10.32604/cmes.2020.010869
（This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
Abstract Different devices in the recent era generated a vast amount of digital video. Generally, it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice. Many kinds of researches on forensic detection have been presented, and it provides less accuracy. This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network (CNN). In the initial stage, the input video is taken as of the dataset and then converts the videos into image frames. Next, perform pre-sampling using the… More
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ARTICLE
Dynamic Characteristics Analysis of Ice-Adhesion Transmission Tower-Line System under Effect of Wind-Induced Ice Shedding
Yongping Yu¹, Lihui Chen¹, Juanjuan Wang¹, Guoji Liu^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 647-670, 2020, DOI:10.32604/cmes.2020.011067
Abstract The tower line system will be in an unsafe status due to uniform or uneven fall of ice coating which is attached to the surface of tower and lines. The fall of ice could be caused by wind action or thermal force. In order to study the dynamic characteristics of the self-failure of the transmission line under the action of dynamic wind load, a finite element model of the two-span transmission tower line system was established. The birth and death element methods are used to simulate the icing and shedding of the line. Tensile failure strength is the shedding criterion… More
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An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing
S. Jerald Nirmal Kumar^1,*, S. Ravimaran², M. M. Gowthul Alam³
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 671-697, 2020, DOI:10.32604/cmes.2020.09361
Abstract Nowadays, succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers. Hence, to secure both data and keys ensuring secured data storage and access, our proposed work designs a Novel Quantum Key Distribution (QKD) relying upon a non-commutative encryption framework. It makes use of a Novel Quantum Key Distribution approach, which guarantees high level secured data transmission. Along with this, a shared secret is generated using Diffie Hellman (DH) to certify secured key generation at reduced time complexity. Moreover, a non-commutative approach is used, which effectively allows the users to store… More
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Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm
Xianghui Lu¹, Junliang Fan², Lifeng Wu^1,*, Jianhua Dong³
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004
Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET₀) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET₀ using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More
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Experimental and Numerical Study on Anchorage Strength and Deformation Properties of Blocky Rock Mass
Junfu Zhu¹, Qian Yin^1,2,*, Hongwen Jing¹, Xinshuai Shi¹, Minliang Chen^1,3
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 725-753, 2020, DOI:10.32604/cmes.2020.012648
（This article belongs to this Special Issue: Modeling and Simulation of Fluid flows in Fractured Porous Media: Current Trends and Prospects)
Abstract This study experimentally and numerically investigated the anchorage properties, bolt force evolution, deformation and stress fields of blocky rock mass with various dip angles of joint surfaces under an applied axial load. The results show that due to bolt reinforcement, the axial stress-strain curves of anchorage blocky rock mass show typical strain-hardening characteristics, and compared with models without anchorage, the peak strength and elastic modulus increase by 21.56% and 20.0%, respectively. With an increase in axial stress, the lateral strain continuously increases, and restriction effects of bolts reduce the overall deformation of model surfaces. The axial stressstrain curves of anchorage… More
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Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames
Baoyin Sun^{1, 2}, Yantai Zhang³, Caigui Huang^{4, *}
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632
（This article belongs to this Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can be trained using calculated damage… More
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A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data
Jiaqi He¹, Yangjun Luo^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 777-800, 2020, DOI:10.32604/cmes.2020.010688
（This article belongs to this Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract For structures that only the predicted bounds of uncertainties are available, this study proposes a Bayesian method to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data. According to the given interval ranges of uncertainties, we determine the initial characteristic parameters of a multi-ellipsoid convex set. Moreover, to update the plausibility of characteristic parameters, a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed. Then, an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be achieved. The credible non-probabilistic reliability… More
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Reliability Analysis Based on Optimization Random Forest Model and MCMC
Fan Yang^1,2,3,*, Jianwei Ren^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889
（This article belongs to this Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)
Abstract Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy. More
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A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon
Panagiotis G. Asteris^1,*, Maria G. Douvika¹, Chrysoula A. Karamani¹, Athanasia D. Skentou¹, Katerina Chlichlia², Liborio Cavaleri³, Tryfon Daras⁴, Danial J. Armaghani⁵, Theoklis E. Zaoutis⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 815-828, 2020, DOI:10.32604/cmes.2020.013280
（This article belongs to this Special Issue: Soft Computing Techniques in Materials Science and Engineering)
Abstract The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the… More
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Numerical Simulation of Bone Remodeling Coupling the Damage Repair Process in Human Proximal Femur
Chuanyong Qu^*, Hui Yuan
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 829-847, 2020, DOI:10.32604/cmes.2020.012407
（This article belongs to this Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)
Abstract Microdamage is produced in bone tissue under the long-term effects of physiological loading, as well as age, disease and other factors. Bone remodeling can repair microdamage, otherwise this damage will undermine bone quality and even lead to fractures. In this paper, the damage variable was introduced into the remodeling algorithm. The new remodeling algorithm contains a quadratic term that can simulate reduction in bone density after large numbers of loading cycles. The model was applied in conjunction with the 3D finite element method (FEM) to the remodeling of the proximal femur. The results showed that the initial accumulation of fatigue… More
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Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things
Weiwen Kong¹, Baowei Wang^1,2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 849-863, 2020, DOI:10.32604/cmes.2020.012818
（This article belongs to this Special Issue: Intelligent Models for Security and Resilience in Cyber Physical Systems)
Abstract Internet of Things (IoT) is a network that connects things in a special union. It embeds a physical entity through an intelligent perception system to obtain information about the component at any time. It connects various objects. IoT has the ability of information transmission, information perception,andinformationprocessing.Theairqualityforecastinghasalways been an urgent problem, which affects people’s quality of life seriously. So far, many air quality prediction algorithms have been proposed, which can be mainly classifed into two categories. One is regression-based prediction, the other is deep learning-based prediction. Regression-based prediction is aimed to make use of the classical regression algorithm and the various… More
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Improvement of Orbit Prediction Algorithm for Spacecraft Through Simplified Precession-Nutation Model Using Cubic Spline Interpolation Method
Gen Xu, Danhe Chen, Xiang Zhang, Wenhe Liao^*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 865-878, 2020, DOI:10.32604/cmes.2020.012844
（This article belongs to this Special Issue: Modeling and Analysis of Autonomous Intelligence)
Abstract For the on-orbit flight missions, the model of orbit prediction is critical for the tasks with high accuracy requirement and limited computing resources of spacecraft. The precession-nutation model, as the main part of extended orbit prediction, affects the efficiency and accuracy of on-board operation. In this paper, the previous research about the conversion between the Geocentric Celestial Reference System and International Terrestrial Reference System is briefly summarized, and a practical concise precession-nutation model is proposed for coordinate transformation computation based on Celestial Intermediate Pole (CIP). The idea that simplifying the CIP-based model with interpolation method is driven by characteristics of… More
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3D Multilayered Turtle Shell Models for Image Steganography
Ji-Hwei Horng¹, Juan Lin^2,*, Yanjun Liu³, Chin-Chen Chang^13,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 879-906, 2020, DOI:10.32604/cmes.2020.09355
（This article belongs to this Special Issue: Information Hiding and Multimedia Security)
Abstract By embedding secret data into cover images, image steganography can produce non-discriminable stego-images. The turtle shell model for data hiding is an excellent method that uses a reference matrix to make a good balance between image quality and embedding capacity. However, increasing the embedding capacity by extending the area of basic structures of the turtle shell model usually leads to severe degradation of image quality. In this research, we innovatively extend the basic structure of the turtle shell model into a three-dimensional (3D) space. Some intrinsic properties of the original turtle shell model are well preserved in the 3D version.… More
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Conceptual Modular Design of Auto Body Frame Based on Hybrid Optimization Method
Yonghong Zhao¹, Changsheng Wang², Huanquan Yuan¹, Yongcheng Li¹, Chunlai Shan², Wenbin Hou^{2, *}
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 351-376, 2020, DOI:10.32604/cmes.2020.08058
Abstract This article presents a systematic research methodology of modular design for conceptual auto body frame by hybrid optimization method. A modified graph-based decomposition optimization algorithm is utilized to generate an optimal BIW assembly topo model composed of “potential modules”. The consistency constraint function in collaborative optimization is extended to maximize the commonality of modules and minimize the performance loss of all car types in the same product family simultaneously. A novel screening method is employed to select both “basic structures” and “reinforcement” modules based on the dimension optimization of the manufacturing elements and the optimal assembly mode; this allows for… 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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Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm
Tinghuai Ma^1,*, Honghao Zhou¹, Dongdong Jia¹, Abdullah Al-Dhelaan², Mohammed Al-Dhelaan², Yuan Tian³
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 569-592, 2019, DOI:10.32604/cmes.2019.07758
Abstract Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In this article, a feature selection algorithm with local search strategy based on the forest optimization algorithm, namely FSLSFOA, is proposed. The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest. Next, the fitness function is improved, which not only considers the classification accuracy, but also considers the size of the feature subset. To avoid… More
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Vibration Analysis of a Drillstring in Horizontal Well
Xiaohua Zhu^1,*, Li Zeng¹, Bo Li¹
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 631-660, 2019, DOI:10.32604/cmes.2019.06755
Abstract The complicated geological conditions will bring great challenges to the drillstring of horizontal wells for the reason that the increase of the well depth in SichuanChongqing region. Since drillstring failure and friction during drilling are generally caused by drillstring vibration, great importance must be attached to computer simulation methods for the prediction of drillstring vibration. A finite element model considering axial, lateral and torsional vibration is established. In order to verify the established numerical model, an indoor experimental device based on the similarity principle was established. The vibration characteristics of three shale gas horizontal wells drillstrings are described. The reasons… More
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Numerical Simulations of the Ice Load of a Ship Navigating in Level Ice Using Peridynamics
Yanzhuo Xue¹, Renwei Liu¹, Yang Liu^1,*, Lingdong Zeng¹, Duanfeng Han¹
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 523-550, 2019, DOI:10.32604/cmes.2019.06951
（This article belongs to this Special Issue: Recent Developments on Peridynamics)
Abstract In this study, a numerical method was developed based on peridynamics to determine the ice loads for a ship navigating in level ice. Convergence analysis of threedimensional ice specimen with tensile and compression loading are carried out first. The effects of ice thickness, sailing speed, and ice properties on the mean ice loads were also investigated. It is observed that the ice fragments resulting from the icebreaking process will interact with one another as well as with the water and ship hull. The ice fragments may rotate, collide, or slide along the ship hull, and these ice fragments will eventually… More
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A Robust Roll Stabilization Controller with Aerodynamic Disturbance and Actuator Failure Consideration
Qiancai Ma¹, Fengjie Gao², Yang Wang³, Qiuxiong Gou³, Liangyu Zhao^{1, *}
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 109-130, 2020, DOI:10.32604/cmes.2020.08109
（This article belongs to this Special Issue: Nonlinear Computational and Control Methods in Aerospace Engineering)
Abstract Combining adaptive theory with an advanced second-order sliding mode control algorithm, a roll stabilization controller with aerodynamic disturbance and actuator failure consideration for spinning flight vehicles is proposed in this paper. The presented controller is summarized as an “observer-controller” system. More specifically, an adaptive second-order sliding mode observer is presented to select the proper design parameters and estimate the knowledge of aerodynamic disturbance and actuator failure, while the proposed roll stabilization control scheme can drive both roll angle and rotation rate smoothly converge to the desired value. Theoretical analysis and numerical simulation results demonstrate the effectiveness of the proposed controller. More
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