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  • Open Access

    ARTICLE

    Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

    Li Wang1,*, Xiaoyong Wang2

    Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

    Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

  • Open Access

    PROCEEDINGS

    Integrated Multiscale Unified Phase-Field Modellings (UPFM)

    Yuhong Zhao1,2,3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012951

    Abstract For a long time, the phase-field method has been considered as a mesoscale phenomenological method lacking physical accuracy and unable to be associated with the mechanical/functional properties of materials, etc. Some misunderstandings existing in these viewpoints need to be clarified. Therefore, it is necessary to propose or adopt the perspective of “unified or unifying phase-field modeling (UPFM)” to address these issues, which means that phase-field modeling has multiple unifications. Specifically, the phase-field method is the perfect unity of thermodynamics and kinetics, the unity of multi-scale models from micro- to meso- and then to macroscopic scale, More >

  • Open Access

    ARTICLE

    Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty

    Qingrong Zeng, Xiaochen Liu, Xuefeng Zhu*, Xiangkui Zhang, Ping Hu

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2065-2085, 2024, DOI:10.32604/cmes.2024.052620 - 31 October 2024

    Abstract Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we More >

  • Open Access

    PROCEEDINGS

    Ultrafast Self-Transport of Multi-Scale Droplets Driven by Laplace Pressure Difference and Capillary Suction

    Fujian Zhang1, Ziyang Wang1, Xiang Gao1, Zhongqiang Zhang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011736

    Abstract Spontaneous droplet transport has broad application prospects in fields such as water collection and microfluidic chips. Despite extensive research in this area, droplet self-transport is still limited by issues such as slow transport velocity, short distance, and poor integrity. Here, a novel cross-hatch textured cone (CHTC) with multistage microchannels and circular grooves is proposed to realize ultrafast directional long-distance self-transport of multi-scale droplets. The CHTC triggers two modes of fluid transport: Droplet transport by Laplace pressure difference and capillary suction pressure-induced fluid transfer in microchannels on cone surfaces. By leveraging the coupling effect of the… More >

  • Open Access

    ARTICLE

    Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

    Milos Dobrojevic1,4, Luka Jovanovic1, Lepa Babic3, Miroslav Cajic5, Tamara Zivkovic6, Miodrag Zivkovic2, Suresh Muthusamy7, Milos Antonijevic2, Nebojsa Bacanin2,4,8,9,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4997-5027, 2024, DOI:10.32604/cmc.2024.054459 - 12 September 2024

    Abstract Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones, computers, or tablets. It can occur through various channels, such as social media, text messages, online forums, or gaming platforms. Cyberbullying involves using technology to intentionally harm, harass, or intimidate others and may take different forms, including exclusion, doxing, impersonation, harassment, and cyberstalking. Unfortunately, due to the rapid growth of malicious internet users, this social phenomenon is becoming more frequent, and there is a huge need to address this issue. Therefore, the main goal of the research… More >

  • Open Access

    ARTICLE

    An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

    Cong Shen1,*, Wei Zhang1,2,*, Tanping Zhou1,2, Yiming Zhang1, Lingling Zhang3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

    Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

  • Open Access

    ARTICLE

    Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss Optimization

    Zhiyi Ding, Lei Sun*, Xiuqing Mao, Leyu Dai, Ruiyang Ding

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4387-4412, 2024, DOI:10.32604/cmc.2024.052196 - 12 September 2024

    Abstract Object detection finds wide application in various sectors, including autonomous driving, industry, and healthcare. Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples. This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems. Most existing adversarial attack strategies focus primarily on image classification problems, failing to fully exploit the unique characteristics of object detection models, thus resulting in widespread deficiencies in their transferability. Furthermore, previous research has predominantly concentrated on… More >

  • Open Access

    ARTICLE

    Droplet Condensation and Transport Properties on Multiple Composite Surface: A Molecular Dynamics Study

    Haowei Hu1,2,*, Qi Wang1, Xinnuo Chen1, Qin Li3, Mu Du4, Dong Niu5,*

    Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1245-1259, 2024, DOI:10.32604/fhmt.2024.054223 - 30 August 2024

    Abstract To investigate the microscopic mechanism underlying the influence of surface-chemical gradient on heat and mass recovery, a molecular dynamics model including droplet condensation and transport process has been developed to examine heat and mass recovery performance. This work aimed at identify optimal conditions for enhancing heat and mass recovery through the combination of wettability gradient and nanopore transport. For comprehensive analysis, the structure in the simulation was categorized into three distinct groups: a homogeneous structure, a small wettability gradient, and a large wettability gradient. The homogeneous surface demonstrated low efficiency in heat and mass transfer, More >

  • Open Access

    ARTICLE

    Droplet Self-Driven Characteristics on Wedge-Shaped Surface with Composite Gradients: A Molecular Dynamics Study

    Haowei Hu1,2,*, Xinnuo Chen1, Qi Wang1, Qin Li3, Dong Niu4, Mu Du5,*

    Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1071-1085, 2024, DOI:10.32604/fhmt.2024.054218 - 30 August 2024

    Abstract The self-driven behavior of droplets on a functionalized surface, coupled with wetting gradient and wedge patterns, is systematically investigated using molecular dynamics (MD) simulations. The effects of key factors, including wedge angle, wettability, and wetting gradient, on the droplet self-driving effect is revealed from the nanoscale. Results indicate that the maximum velocity of droplets on hydrophobic wedge-shaped surfaces increases with the wedge angle, accompanied by a rapid attenuation of driving force; however, the average velocity decreases with the increased wedge angle. Conversely, droplet movement on hydrophilic wedge-shaped surfaces follows the opposite trend, particularly in terms… More >

  • Open Access

    ARTICLE

    Three-Dimensional Convection in an Inclined Porous Layer Subjected to a Vertical Temperature Gradient

    Ivan Shubenkov1,2,*, Tatyana Lyubimova1,2, Evgeny Sadilov1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 1957-1970, 2024, DOI:10.32604/fdmp.2024.050167 - 23 August 2024

    Abstract In this paper, we study the onset and development of three-dimensional convection in a tilted porous layer saturated with a liquid. The layer is subjected to a gravitational field and a strictly vertical temperature gradient. Typically, problems of thermal convection in tilted porous media saturated with a liquid are studied by assuming constant different temperatures at the boundaries of the layer, which prevent these systems from supporting conductive (non-convective) states. The boundary conditions considered in the present work allow a conductive state and are representative of typical geological applications. In an earlier work, we carried… More > Graphic Abstract

    Three-Dimensional Convection in an Inclined Porous Layer Subjected to a Vertical Temperature Gradient

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