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  • Open Access

    ARTICLE

    Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

    Jianfeng Zhao1,2, Yunliang Huo1,2, Ji Xiong1,*, Junbo Liu1,2, Zhixing Guo1, Qingxian Li3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1663-1678, 2024, DOI:10.32604/cmes.2023.030584

    Abstract To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform, a case-based reasoning framework is proposed. Specifically, a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties. In addition, AHP (Analytic Hierarchy Process) and entropy weight method are integrated to provide features weight, where both user preferences and comprehensive impact of the index have been concerned. Grey relation analysis is used to obtain the similarity of a new problem and alternative cases. Finally, a platform is also… More >

  • Open Access

    ARTICLE

    Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

    Ehab Bahaudien Ashary1, Sahar Jambi2, Rehab B. Ashari2, Mahmoud Ragab3,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2953-2969, 2023, DOI:10.32604/csse.2023.041523

    Abstract Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste, enhancing waste management and reducing… More >

  • Open Access

    ARTICLE

    HybridHR-Net: Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

    Muhammad Naeem Akbar1,*, Seemab Khan2, Muhammad Umar Farooq1, Majed Alhaisoni3, Usman Tariq4, Muhammad Usman Akram1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289

    Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep… More >

  • Open Access

    PROCEEDINGS

    A Data-Fusion Method for Uncertainty Quantification of Mechanical Property of Bi-Modulus Materials: An Example of Graphite

    Liang Zhang1,*, Zigang He1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09713

    Abstract The different elastic properties of tension and compression are obvious in many engineering materials, especially new materials. Materials with this characteristic, such as graphite, ceramics, and composite materials, are called bi-modulus materials. Their mechanical properties such as Young’s modulus have randomness in tension and compression due to different porosity, microstructure, etc. To calibrate the mechanical properties of bi-modulus materials by bridging FEM simulation results and scarce experimental data, the paper presents a data-fusion computational method. The FEM simulation is implemented based on Parametric Variational Principle (PVP), while the experimental result is obtained by Digital Image Correlation (DIC) technology. To deal… More >

  • Open Access

    PROCEEDINGS

    Deformation Behaviour and Strengthening Mechanism of High-Entropy Alloys Using Model and Simulation

    Jia Li1, Yang Chen1, Baobin Xie1, Weizheng Lu1, Qihong Fang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010064

    Abstract The high-profile high-entropy alloy shows outstanding mechanical properties. However, the accurate and reasonable models for describing the mechanical behavior of HEAs are still scarce due to their distinctive characteristics such as serious lattice distortion, which limit the engineering application. We have developed a new general framework combining atomic simulation, discrete dislocation dynamics and crystal plasticity finite element method, to study the deformation behaviour and strengthening mechanism of HEAs, and realized the influence of complex cross-scale factors on material deformation [1-3]. Compared with the classic crystal plasticity finite element, the bottom-up hierarchical multiscale model could couple the underlying physical mechanisms from… More >

  • Open Access

    PROCEEDINGS

    On the Fatigue Damage of GH4169 Based on Thermodynamic Entropy Generation

    Shuiting Ding1, Liangliang Zuo2, Guo Li2,*, Zhenlei Li3, Shuyang Xia2, Shaochen Bao3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.1, pp. 1-1, 2023, DOI: 10.32604/icces.2023.09911

    Abstract This paper presents the assessment of fatigue damage for GH4169 under cyclic loading based on thermodynamic entropy generation at elevated temperature. According to the second law of thermodynamics, fatigue crack propagation is an irreversible thermodynamic dissipative process in which damage accumulates and entropy generates with each cycle until fracture occurs. In this work, crack growth process is simulated by commercial finite element software ABAQUS, and the concept of cyclic entropy generation rate (CEGR) is proposed to present the entropy generation of the crack tip region in a single loading cycle, where the calculation of CEGR is dependent on the evolution… More >

  • Open Access

    ARTICLE

    On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications

    Hassan Okasha1,2, Mazen Nassar1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783

    Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum likelihood estimators of the model’s… More >

  • Open Access

    ARTICLE

    Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy

    Zhixian Qi1,2,*, Shuohe Wang1,2, Qiang Xue1,2, Haiting Mi3, Jian Wang1,2

    Energy Engineering, Vol.120, No.9, pp. 2059-2077, 2023, DOI:10.32604/ee.2023.028595

    Abstract A current identification method based on optimized variational mode decomposition (VMD) and sample entropy (SampEn) is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current. This method uses the principle of energy difference to optimize the optimal mode decomposition number k of VMD; the optimal VMD for DC feeder current is decomposed into the intrinsic modal function (IMF) of different frequency bands. The sample entropy algorithm is used to perform feature extraction of each IMF, and then the eigenvalues of… More >

  • Open Access

    ARTICLE

    Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

    Xinfei Li2, Xiaolan Xie1,2,*, Yigang Tang2, Qiang Guo1,2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2707-2724, 2023, DOI:10.32604/csse.2023.037351

    Abstract Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used… More >

  • Open Access

    ARTICLE

    MAGNETOCONVECTION AND IRREVERSIBILITY PHENOMENA WITHIN A LID DRIVEN CAVITY FILLED WITH LIQUID METAL UNDER MAGNETIC FIELD

    Fakher Oueslatia,b,†, Brahim Ben-Beyab

    Frontiers in Heat and Mass Transfer, Vol.8, pp. 1-11, 2017, DOI:10.5098/hmt.8.38

    Abstract The current study deals with a numerical investigation of magnetoconvection and entropy generation within a lid driven square cavity subject to uniform magnetic field and filled with liquid metal. Effects of multiple parameters namely; the Prandtl, Hartmann and Richardson numbers were predicted and analyzed using a numerical methodology based on the finite volume method and a full multigrid technique. The numerical outcome of the present study shows that, the enhancement of Hartmann number declines the heat transfer rate for all liquid metals considered. Moreover, it is observed that augmenting the Richardson number leads to acceleration of the flow with a… More >

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