Home / Journals / CMES / Vol.128, No.2, 2021
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The topic of this paper is to enhance the semantic information of dialogue through external knowledge graph. Knowledge graph is a graph structure composed of entities and relations between entities. The size change of nodes is used in the graph to indicate the different degrees of mutual influence between nodes.

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  • Open Access

    EDITORIAL

    Introduction to the Special Issue on Recent Advances on Deep Learning for Medical Signal Analysis

    Yu-Dong Zhang1,*, Zhengchao Dong2, Juan Manuel Gorriz3,4, 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 >

  • Open Access

    ARTICLE

    A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention

    Zhongqin Bi1, Shiyang Wang1, Yan Chen2,*, Yongbin Li1, Jung Yoon Kim3,*
    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 >

  • Open Access

    ARTICLE

    MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

    Juhong Tie1,2,*, Hui Peng2, Jiliu Zhou1,3,*
    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 >

  • Open Access

    REVIEW

    A Contemporary Review on Drought Modeling Using Machine Learning Approaches

    Karpagam Sundararajan1, Lalit Garg2,*, Kathiravan Srinivasan4,*, 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 >

  • Open Access

    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 >

  • Open Access

    ARTICLE

    Multi-Material Topology Optimization of Structures Using an Ordered Ersatz Material Model

    Baoshou Liu1,2, Xiaolei Yan1, Yangfan Li3, Shiwei Zhou4, Xiaodong Huang3,*
    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 >

  • Open Access

    ARTICLE

    Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm

    Jiaxiu Dong1,2,3, Jianhua Liu4, Niannian Wang1,2,3,*, Hongyuan Fang1,2,3, Jinping Zhang1, Haobang Hu1,2,3, Duo Ma1,2,3
    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 >

  • Open Access

    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 >

  • Open Access

    ARTICLE

    Computational Analysis of Airflow in Upper Airway under Light and Heavy Breathing Conditions for a Realistic Patient Having Obstructive Sleep Apnea

    W. M. Faizal1,2, N. N. N. Ghazali2,*, C. Y. Khor1, M. Z. Zainon2, Irfan Anjum Badruddin3,4,*, 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 >

  • Open Access

    ARTICLE

    Deep Learning-Based Surrogate Model for Flight Load Analysis

    Haiquan Li1, Qinghui Zhang2,*, 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 >

  • Open Access

    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 >

  • Open Access

    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 >

  • Open Access

    ARTICLE

    A Computational Study on Lateral Flight Stability of the Cranefly in Hover

    Na Xu1, Shuaizhi Zhou1, Chunchen Zhang1, Xiaolei Mou2,*
    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 >

  • Open Access

    ARTICLE

    An Efficient Meshless Method for Hyperbolic Telegraph Equations in (1 + 1) Dimensions

    Fuzhang Wang1,2, Enran Hou2,*, 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 >

  • Open Access

    ARTICLE

    High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes

    Luciano Pereira da Silva1,*, 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 >

  • Open Access

    ARTICLE

    A Binomial Model Approach: Comparing the R0 Values of SARS-CoV-2 rRT-PCR Data from Laboratories across Northern Cyprus

    Nazife Sultanoglu1,2,*, Nezihal Gokbulut3, Tamer Sanlidag2, Evren Hincal2,3, Bilgen Kaymakamzade2,3, Murat Sayan2,4
    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 >

  • Open Access

    ARTICLE

    Construction of Design Guidelines for Optimal Automotive Frame Shape Based on Statistical Approach and Mechanical Analysis

    Masanori Honda1,3, Chikara Kawamura1,3, Isamu Kizaki1, Yoichi Miyajima1, Akihiro Takezawa2,*, 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 >

  • Open Access

    ARTICLE

    Decision Making Algorithmic Approaches Based on Parameterization of Neutrosophic Set under Hypersoft Set Environment with Fuzzy, Intuitionistic Fuzzy and Neutrosophic Settings

    Atiqe Ur Rahman1,*, Muhammad Saeed1, Sultan S. Alodhaibi2, Hamiden Abd El-Wahed Khalifa3,4
    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 >

  • Open Access

    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 >

  • Open Access

    ARTICLE

    Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

    Huizhi Gou1,2,*, 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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