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  • Open Access

    ARTICLE

    Impact Performance Research of Re-Entrant Octagonal Negative Poisson’s Ratio Honeycomb with Gradient Design

    Yiyuan Li1, Yongjing Li1,2, Shilin Yan1,2,*, Pin Wen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3105-3119, 2024, DOI:10.32604/cmes.2024.051375

    Abstract Based on the traditional re-entrant honeycomb, a novel re-entrant octagon honeycomb (ROH) is proposed. The deformation mode of the honeycomb under quasi-static compression is analyzed by numerical simulation, and the results are in good agreement with the experimental ones. The deformation modes, mechanical properties, and energy absorption characteristics of ROH along the impact and perpendicular directions gradient design are investigated under different velocities. The results indicated that the deformation mode of ROH is affected by gradient design along the direction of impact and impact speed. In addition, gradient design along the direction of impact can… More >

  • Open Access

    ARTICLE

    Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

    Ahmed H. Alhadethi1,*, Ikram Smaoui2, Ahmed Fakhfakh3, Saad M. Darwish4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4825-4844, 2024, DOI:10.32604/cmc.2024.047852

    Abstract The act of transmitting photos via the Internet has become a routine and significant activity. Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced. This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images. The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression. This paper introduces… More >

  • Open Access

    ARTICLE

    Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

    Penghui Liu1,2,*, Yaning Zhang1, Yuxing Dai2, Yanzhou Sun1,3

    Energy Engineering, Vol.121, No.7, pp. 1883-1901, 2024, DOI:10.32604/ee.2024.049318

    Abstract Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product,… More >

  • Open Access

    ARTICLE

    On the Features of Thermal Convection in a Compressible Gas

    Igor B. Palymskiy1,2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.5, pp. 957-974, 2024, DOI:10.32604/fdmp.2024.048829

    Abstract The fully nonlinear equations of gas dynamics are solved in the framework of a numerical approach in order to study the stability of the steady mode of Rayleigh-Bénard convection in compressible, viscous and heat-conducting gases encapsulated in containers with no-slip boundaries and isothermal top and bottom walls. An initial linear temperature profile is assumed. A map of the possible convective modes is presented assuming the height of the region and the value of the temperature gradient as influential parameters. For a relatively small height, isobaric convection is found to take place, which is taken over… More >

  • Open Access

    ARTICLE

    Evaluation of Well Spacing for Primary Development of Fractured Horizontal Wells in Tight Sandstone Gas Reservoirs

    Fang Li1,*, Juan Wu1, Haiyong Yi2, Lihong Wu2, Lingyun Du1, Yuan Zeng1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.5, pp. 1015-1030, 2024, DOI:10.32604/fdmp.2023.043256

    Abstract Methods for horizontal well spacing calculation in tight gas reservoirs are still adversely affected by the complexity of related control factors, such as strong reservoir heterogeneity and seepage mechanisms. In this study, the stress sensitivity and threshold pressure gradient of various types of reservoirs are quantitatively evaluated through reservoir seepage experiments. On the basis of these experiments, a numerical simulation model (based on the special seepage mechanism) and an inverse dynamic reserve algorithm (with different equivalent drainage areas) were developed. The well spacing ranges of Classes I, II, and III wells in the Q gas More > Graphic Abstract

    Evaluation of Well Spacing for Primary Development of Fractured Horizontal Wells in Tight Sandstone Gas Reservoirs

  • Open Access

    ARTICLE

    L-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

    Chuandong Qin1,2, Yu Cao1,*, Liqun Meng1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228

    Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

  • Open Access

    ARTICLE

    FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption

    Hao Sun*, Xiubo Chen, Kaiguo Yuan

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159

    Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

  • Open Access

    ARTICLE

    Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

    Lyuchao Liao1,2, Hankun Xiao2,*, Pengqi Xing2, Zhenhua Gan1,2, Youpeng He2, Jiajun Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

    Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the… More >

  • Open Access

    ARTICLE

    Application of Machine Learning For Prediction Dental Material Wear

    ABHIJEET SURYAWANSHI1, NIRANJANA BEHERA2,*

    Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

    Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, More >

  • Open Access

    ARTICLE

    Experimental Investigation of a Phase-Change Material’s Stabilizing Role in a Pilot of Smart Salt-Gradient Solar Ponds

    Karim Choubani1,2,*, Ons Ghriss3, Nashmi H. Alrasheedi1, Sirin Dhaoui2, Abdallah Bouabidi2

    Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 341-358, 2024, DOI:10.32604/fhmt.2024.047016

    Abstract Faced with the world’s environmental and energy-related challenges, researchers are turning to innovative, sustainable and intelligent solutions to produce, store, and distribute energy. This work explores the trend of using a smart sensor to monitor the stability and efficiency of a salt-gradient solar pond. Several studies have been conducted to improve the thermal efficiency of salt-gradient solar ponds by introducing other materials. This study investigates the thermal and salinity behaviors of a pilot of smart salt-gradient solar ponds with (SGSP) and without (SGSPP) paraffin wax (PW) as a phase-change material (PCM). Temperature and salinity were… More >

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