CMES-Computer Modeling in Engineering & Sciences

ISSN: 1526-1492 (print) Impact Factor: 1.593
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): 2020 Impact Factor 1.593; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2020): 1.6; SNIP (Source Normalized Impact per Paper 2020): 0.59; 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.128, No.2, 2021 Most Viewed

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EDITORIAL
Introduction to the Special Issue on Recent Advances on Deep Learning for Medical Signal Analysis
Yu-Dong Zhang^1,*, Zhengchao Dong², Juan Manuel Gorriz^3,4, Carlo Cattani⁵, Ming Yang⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 399-401, 2021, DOI:10.32604/cmes.2021.017472
（This article belongs to this Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract This article has no abstract. More
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ARTICLE
A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention
Zhongqin Bi¹, Shiyang Wang¹, Yan Chen^2,*, Yongbin Li¹, Jung Yoon Kim^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 403-426, 2021, DOI:10.32604/cmes.2021.016729
（This article belongs to this Special Issue: Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce)
Abstract With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper… More
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ARTICLE
MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks
Juhong Tie^1,2,*, Hui Peng², Jiliu Zhou^1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 427-445, 2021, DOI:10.32604/cmes.2021.014107
（This article belongs to this Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))
Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number… More
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REVIEW
A Contemporary Review on Drought Modeling Using Machine Learning Approaches
Karpagam Sundararajan¹, Lalit Garg^2,*, Kathiravan Srinivasan^4,*, Ali Kashif Bashir³, Jayakumar Kaliappan⁴, Ganapathy Pattukandan Ganapathy⁵, Senthil Kumaran Selvaraj⁶, T. Meena⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 447-487, 2021, DOI:10.32604/cmes.2021.015528
Abstract Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the… More
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REVIEW
Multi-Disease Prediction Based on Deep Learning: A Survey
Shuxuan Xie, Zengchen Yu, Zhihan Lv^*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 489-522, 2021, DOI:10.32604/cmes.2021.016728
Abstract In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some… More
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ARTICLE
Multi-Material Topology Optimization of Structures Using an Ordered Ersatz Material Model
Baoshou Liu^1,2, Xiaolei Yan¹, Yangfan Li³, Shiwei Zhou⁴, Xiaodong Huang^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 523-540, 2021, DOI:10.32604/cmes.2021.017211
（This article belongs to this Special Issue: Novel Methods of Topology Optimization and Engineering Applications)
Abstract This paper proposes a new element-based multi-material topology optimization algorithm using a single variable for minimizing compliance subject to a mass constraint. A single variable based on the normalized elemental density is used to overcome the occurrence of meaningless design variables and save computational cost. Different from the traditional material penalization scheme, the algorithm is established on the ordered ersatz material model, which linearly interpolates Young's modulus for relaxed design variables. To achieve a multi-material design, the multiple floating projection constraints are adopted to gradually push elemental design variables to multiple discrete values. For the convergent element-based solution, the multiple… More
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ARTICLE
Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm
Jiaxiu Dong^1,2,3, Jianhua Liu⁴, Niannian Wang^1,2,3,*, Hongyuan Fang^1,2,3, Jinping Zhang¹, Haobang Hu^1,2,3, Duo Ma^1,2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 541-564, 2021, DOI:10.32604/cmes.2021.015875
Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a region of interest (RoI) Align… More
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ARTICLE
Stability Reliability of the Lateral Vibration of Footbridges Based on the IEVIE-SA Method
Buyu Jia, Siyi Mao, Quansheng Yan, Xiaolin Yu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 565-582, 2021, DOI:10.32604/cmes.2021.015183
Abstract Research on the lateral vibrational stability of footbridges has attracted increasing attention in recent years. However, this stability contains a series of complex mechanisms, such as nonlinear vibration, random excitation, and random stability. The Lyapunov method is regarded as an effective tool for analyzing random vibrational stability; however, it is a qualitative method and can only provide a binary judgment for stability. This study proposes a new method, IEVIE–SA, which combines the energy method based on the comparison between the input energy and the variation of intrinsic energy (IEVIE) and the stochastic averaging (SA) method. The improved Nakamura model was… More
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ARTICLE
Computational Analysis of Airflow in Upper Airway under Light and Heavy Breathing Conditions for a Realistic Patient Having Obstructive Sleep Apnea
W. M. Faizal^1,2, N. N. N. Ghazali^2,*, C. Y. Khor¹, M. Z. Zainon², Irfan Anjum Badruddin^3,4,*, Sarfaraz Kamangar⁴, Norliza Binti Ibrahim⁵, Roziana Mohd Razi⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 583-604, 2021, DOI:10.32604/cmes.2021.015549
Abstract Background: Obstructive sleep apnea is a sleeping disorder that has troubled a sizeable population. There is an active area of research on obstructive sleep apnea that intends to better understand airflow behaviors and therefore treat patients more effectively. This paper aims to investigate the airflow characteristics of the upper airway in an obstructive sleep apnea (OSA) patient under light and heavy breathing conditions by using Turbulent Kinetic Energy (TKE), an accurate method in expressing the flow concentration mechanisms of sleeping disorders. It is important to visualize the concentration of flow in the upper airway in order to identify the severity… More
Views:479 Downloads:201 Download PDF

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ARTICLE
Deep Learning-Based Surrogate Model for Flight Load Analysis
Haiquan Li¹, Qinghui Zhang^2,*, Xiaoqian Chen³
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 605-621, 2021, DOI:10.32604/cmes.2021.015747
Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, a deep neural network (DNN)… More
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ARTICLE
Steganalysis of Low Embedding Rate CNV-QIM in Speech
Wanxia Yang^*, Miaoqi Li, Beibei Zhou, Yan Liu, Kenan Liu, Zhiyu Hu
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 623-637, 2021, DOI:10.32604/cmes.2021.015629
Abstract To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods… More
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ARTICLE
Numerical Implementation of a Unified Viscoplastic Model for Considering Solder Joint Response under Board-Level Temperature Cycling
Hung-Chun Yang, Tz-Cheng Chiu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 639-668, 2021, DOI:10.32604/cmes.2021.016159
Abstract An implicit integration scheme was developed for simulating the viscoplastic constitutive behavior of Sn3.0Ag0.5Cu solder and programmed into a user material subroutine of the finite element software ANSYS. The numerical procedure first solves the essential state variables by using a three-level iterative procedure, and updates the remaining stress and state variables accordingly. The numerical implementation was applied to consider the responses of solder joints in an electronic assembly under temperature cycling condition. The viscoplastic strain energy density accumulation over one temperature cycle was identified as a feasible parameter for evaluating the thermomechanical reliability of the solder joints. More
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ARTICLE
A Computational Study on Lateral Flight Stability of the Cranefly in Hover
Na Xu¹, Shuaizhi Zhou¹, Chunchen Zhang¹, Xiaolei Mou^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 669-685, 2021, DOI:10.32604/cmes.2021.016269
Abstract The dynamic flight stability of hovering insects includes the longitudinal and lateral motion. Research results have shown that for the majority of hovering insects the same longitudinal natural modes are identified and the hovering flight in longitudinal is unstable. However, in lateral, the modal structure for hovering insects could be different and the stability property of lateral disturbance motion is not as robust as that of longitudinal motion. The cranefly possesses larger aspect ratio and lower Reynolds number, and such differences in morphology and kinematics may make the lateral dynamic stability different. In this paper, the lateral flight stability of… More
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ARTICLE
An Efficient Meshless Method for Hyperbolic Telegraph Equations in (1 + 1) Dimensions
Fuzhang Wang^1,2, Enran Hou^2,*, Imtiaz Ahmad³, Hijaz Ahmad⁴, Yan Gu⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 687-698, 2021, DOI:10.32604/cmes.2021.014739
（This article belongs to this Special Issue: Modeling Real World Problems with Mathematics)
Abstract Numerical solutions of the second-order one-dimensional hyperbolic telegraph equations are presented using the radial basis functions. The purpose of this paper is to propose a simple novel direct meshless scheme for solving hyperbolic telegraph equations. This is fulfilled by considering time variable as normal space variable. Under this scheme, there is no need to remove time-dependent variable during the whole solution process. Since the numerical solution accuracy depends on the condition of coefficient matrix derived from the radial basis function method. We propose a simple shifted domain method, which can avoid the full-coefficient interpolation matrix easily. Numerical experiments performed with… More
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ARTICLE
High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes
Luciano Pereira da Silva^1,*, Bruno Benato Rutyna¹, Aline Roberta Santos Righi², Marcio Augusto Villela Pinto³
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 699-715, 2021, DOI:10.32604/cmes.2021.014239
（This article belongs to this Special Issue: Intelligent Computing for Engineering Applications)
Abstract In this article, we improve the order of precision of the two-dimensional Poisson equation by combining extrapolation techniques with high order schemes. The high order solutions obtained traditionally generate non-sparse matrices and the calculation time is very high. We can obtain sparse matrices by applying compact schemes. In this article, we compare compact and exponential finite difference schemes of fourth order. The numerical solutions are calculated in quadruple precision (Real * 16 or extended precision) in FORTRAN language, and iteratively obtained until reaching the round-off error magnitude around 1.0E −32. This procedure is performed to ensure that there is no… More
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ARTICLE
A Binomial Model Approach: Comparing the R₀ Values of SARS-CoV-2 rRT-PCR Data from Laboratories across Northern Cyprus
Nazife Sultanoglu^1,2,*, Nezihal Gokbulut³, Tamer Sanlidag², Evren Hincal^2,3, Bilgen Kaymakamzade^2,3, Murat Sayan^2,4
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 717-729, 2021, DOI:10.32604/cmes.2021.016297
（This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)
Abstract Northern Cyprus has implemented relatively strict measures in the battle against the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measures were introduced at the beginning of the COVID-19 pandemic, in order to prevent the spread of the disease. One of these measures was the use of two separate real-time reverse transcription polymerase chain reaction (rRT-PCR) tests for SARS-CoV-2 referred to as the double screening procedure, which was adopted following the re-opening of the sea, air and land borders for passengers after the first lockdown. The rRT-PCR double screening procedure involved reporting a negative rRT-PCR test which was… More
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ARTICLE
Construction of Design Guidelines for Optimal Automotive Frame Shape Based on Statistical Approach and Mechanical Analysis
Masanori Honda^1,3, Chikara Kawamura^1,3, Isamu Kizaki¹, Yoichi Miyajima¹, Akihiro Takezawa^2,*, Mitsuru Kitamura³
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 731-742, 2021, DOI:10.32604/cmes.2021.016181
（This article belongs to this Special Issue: New Trends in Structural Optimization)
Abstract A body frame composed of thin sheet metal is a crucial structure that determines the safety performance of a vehicle. Designing a correct weight and high-performance automotive body is an emerging engineering problem. To improve the performance of the automotive frame, we attempt to reconstruct its design criteria based on statistical and mechanical approaches. At first, a fundamental study on the frame strength is conducted and a cross-sectional shape optimization problem is developed for designing the cross-sectional shape of an automobile frame having a very high mass efficiency for strength. Shape optimization is carried out using the nonlinear finite element… More
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ARTICLE
Decision Making Algorithmic Approaches Based on Parameterization of Neutrosophic Set under Hypersoft Set Environment with Fuzzy, Intuitionistic Fuzzy and Neutrosophic Settings
Atiqe Ur Rahman^1,*, Muhammad Saeed¹, Sultan S. Alodhaibi², Hamiden Abd El-Wahed Khalifa^3,4
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 743-777, 2021, DOI:10.32604/cmes.2021.016736
（This article belongs to this Special Issue: Advances in Neutrosophic and Plithogenic Sets for Engineering and Sciences: Theory, Models, and Applications (ANPSESTMA))
Abstract Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set. This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes. The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function. Firstly, we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set, intuitionistic fuzzy set and neutrosophic set along with some of their elementary properties… More
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ARTICLE
An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision
Jian Yang, Lang Xin^#, Haihui Huang^*,#, Qiang He
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 779-802, 2021, DOI:10.32604/cmes.2021.014993
（This article belongs to this Special Issue: Machine Learning based Methods for Mechanics)
Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression (NMS)… More
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ARTICLE
Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN
Huizhi Gou^1,2,*, Yuncai Ning¹
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922
（This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More
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ARTICLE
Validating the Correct Wearing of Protection Mask by Taking a Selfie: Design of a Mobile Application “CheckYourMask” to Limit the Spread of COVID-19
Karim Hammoudi^1,2,*, Adnane Cabani³, Halim Benhabiles⁴, Mahmoud Melkemi^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1049-1059, 2020, DOI:10.32604/cmes.2020.011663
（This article belongs to this Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)
Abstract In a context of a virus that is transmissive by sputtering, wearing masks appear necessary to protect the wearer and to limit the propagation of the disease. Currently, we are facing the 2019–2020 coronavirus pandemic. Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. The symptom of COVID-19 was reported first in China and very quickly spreads to the rest of the world. The COVID-19 contagiousness is known to be high by comparison with the flu. In this paper, we propose a design of a mobile application for permitting everyone having a smartphone and… More
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REVIEW
Review on Application of Artificial Intelligence in Civil Engineering
Youqin Huang¹, Jiayong Li¹, Jiyang Fu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653
Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be known. It is shown that… More
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ARTICLE
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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ARTICLE
ALCencryption: A Secure and Efficient Algorithm for Medical Image Encryption
Jiao Ge^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1083-1100, 2020, DOI:10.32604/cmes.2021.013039
（This article belongs to this Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)
Abstract With the rapid development of medical informatization and the popularization of digital imaging equipment, DICOM images contain the personal privacy of patients, and there are security risks in the process of storage and transmission, so it needs to be encrypted. In order to solve the security problem of medical images on mobile devices, a safe and efficient medical image encryption algorithm called ALCencryption is designed. The algorithm first analyzes the medical image and distinguishes the color image from the gray image. For gray images, the improved Arnold map is used to scramble them according to the optimal number of iterations,… More
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ARTICLE
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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ARTICLE
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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