Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (5,724)
  • Open Access

    ARTICLE

    Research on Effect of Icing Degree on Performance of NACA4412 Airfoil Wind Turbine

    Yuhao Jia1, Bin Cheng1,2,*, Xiyang Li1,2, Hui Zhang1,2, Yinglong Dong1

    Energy Engineering, Vol.117, No.6, pp. 413-427, 2020, DOI:10.32604/EE.2020.012019 - 16 October 2020

    Abstract In order to study the effect of icing on the wind turbine blade tip speed ratio and wind energy utilization coefficient under working conditions, it is important to better understand the growth characteristics of wind turbine blade icing under natural conditions. In this paper, the icing test of the NACA4412 airfoil wind turbine was carried out using the natural low temperature wind turbine icing test system. An evaluation model of icing degree was established, and the influence of wind speed and icing degree on the performance parameters of wind turbines was compared and analyzed. It… More >

  • Open Access

    ARTICLE

    Improved Thermal Efficiency of Salinity Gradient Solar Pond by Suppressing Surface Evaporation Using an Air Layer

    Asaad H. Sayer1, Hameed B. Mahood2,*

    Energy Engineering, Vol.117, No.6, pp. 367-379, 2020, DOI:10.32604/EE.2020.011156 - 16 October 2020

    Abstract Salinity gradient solar ponds (SGSPs) provide a tremendous way to collect and store solar radiation as thermal energy, and can help meet the critical need for sustainable ways of producing fresh water. However, surface evaporation results in the loss of both water and heat. This study therefore theoretically investigates the effect on temperatures within an SGSP when its surface is covered with a layer of air encased in a nylon bag. An earlier SGSP model was slightly modified to add the air layer and to estimate the temperature distributions of the upper layer or the… More >

  • Open Access

    ARTICLE

    Design of Nonlinear Uncertainty Controller for Grid-Tied Solar Photovoltaic System Using Sliding Mode Control

    D. Menaga1, M. Premkumar2, R. Sowmya1,*, S. Narasimman3

    Energy Engineering, Vol.117, No.6, pp. 481-495, 2020, DOI:10.32604/EE.2020.013282 - 16 October 2020

    Abstract The proposed controller accompanies with different sliding surfaces. To understand maximum power point extraction as opposed to nonlinear uncertainties and unknown disturbance of a grid-connected photovoltaic system to various control inputs (ud, uq) is designed. To extract maximum power from a solar array and maintain unity power flow in a grid by controlling the voltage across the dclink capacitor (Vpvdc) and reactive current (iq). A multiple input-output with multiple uncertainty constraints have considered designing proposed sliding mode controllers to validated their robustness performance. An innovative controller verifies uncertain inputs, constant and changes in irradiances, and temperature of More >

  • Open Access

    ARTICLE

    Smart Contract Fuzzing Based on Taint Analysis and Genetic Algorithms

    Zaoyu Wei1,*, Jiaqi Wang2, Xueqi Shen1, Qun Luo1

    Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 35-45, 2020, DOI:10.32604/jihpp.2020.010331 - 15 October 2020

    Abstract Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm More >

  • Open Access

    ARTICLE

    Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

    Weiwen Kong1, Baowei Wang1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 849-863, 2020, DOI:10.32604/cmes.2020.012818 - 12 October 2020

    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… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

    Baoyin Sun1, 2, Yantai Zhang3, Caigui Huang4, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632 - 12 October 2020

    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 More >

  • Open Access

    ARTICLE

    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 - 12 October 2020

    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… More >

  • Open Access

    ARTICLE

    Reliability Analysis Based on Optimization Random Forest Model and MCMC

    Fan Yang1,2,3,*, Jianwei Ren1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889 - 12 October 2020

    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 >

  • Open Access

    ARTICLE

    A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

    Xing Deng1,2, Haijian Shao1,2,*, Liang Shi3, Xia Wang4,5, Tongling Xie6

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920 - 12 October 2020

    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 (Convolutional Neural Networks) are provided to compare with More >

  • Open Access

    ARTICLE

    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 - 12 October 2020

    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… More >

Displaying 4061-4070 on page 407 of 5724. Per Page