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  • Open Access

    ARTICLE

    A Low Light Image Enhancement Method Based on Dehazing Physical Model

    Wencheng Wang1,2,*, Baoxin Yin1,2, Lei Li2,*, Lun Li1, Hongtao Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1595-1616, 2025, DOI:10.32604/cmes.2025.063595 - 30 May 2025

    Abstract In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the… More >

  • Open Access

    ARTICLE

    Ultrashort-Term Power Prediction of Distributed Photovoltaic Based on Variational Mode Decomposition and Channel Attention Mechanism

    Zhebin Sun1, Wei Wang1, Mingxuan Du2, Tao Liang1, Yang Liu1, Hailong Fan3, Cuiping Li2, Xingxu Zhu2, Junhui Li2,*

    Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

    Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

  • Open Access

    ARTICLE

    TSMS-InceptionNeXt: A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization

    Huiling Yu1, Xibei Jia2, Yongfeng Niu1, Yizhuo Zhang1,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4329-4352, 2025, DOI:10.32604/cmc.2025.061882 - 19 May 2025

    Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >

  • Open Access

    ARTICLE

    Numerical Study of Multi-Factor Coupling Effects on Energy Conversion Performance of Nanofluidic Reverse Electrodialysis

    Hao Li1, Cunlu Zhao2, Jinhui Zhou1, Jun Zhang3, Hui Wang1, Yanmei Jiao1,*, Yugang Zhao4,5,*

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 507-528, 2025, DOI:10.32604/fhmt.2025.063359 - 25 April 2025

    Abstract Based on the rapid advancements in nanomaterials and nanotechnology, the Nanofluidic Reverse Electrodialysis (NRED) has attracted significant attention as an innovative and promising energy conversion strategy for extracting sustainable and clean energy from the salinity gradient energy. However, the scarcity of research investigating the intricate multi-factor coupling effects on the energy conversion performance, especially the trade-offs between ion selectivity and mass transfer in nanochannels, of NRED poses a great challenge to achieving breakthroughs in energy conversion processes. This numerical study innovatively investigates the multi-factor coupling effect of three critical operational factors, including the nanochannel configuration,… More >

  • Open Access

    ARTICLE

    Flow Boiling Heat Transfer and Pressure Gradient of R410A in Micro-Channel Flat Tubes at 25°C and 30°C

    Bo Yu1,2, Yuye Luo3, Luyao Guo4, Long Huang4,*

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 553-575, 2025, DOI:10.32604/fhmt.2025.062851 - 25 April 2025

    Abstract This study investigates the flow boiling heat transfer coefficient and pressure gradient of refrigerant R410A in micro-channel flat tubes. Experiments were conducted at saturation temperatures ranging from 25°C to 30°C, mass fluxes between 198 and 305 kg/m2s, and heat fluxes from 9.77 to 20.18 kW/m2, yielding 99 sets of local heat transfer coefficient data. The results show that increasing heat flux and mass flux enhances the heat transfer coefficient, although the rate of enhancement decreases with increasing vapor quality. Conversely, higher saturation temperatures slightly reduce the heat transfer coefficient. Additionally, the experimental findings reveal discrepancies in More >

  • Open Access

    ARTICLE

    Experimental and Numerical Study on Flow and Heat Transfer Characteristics in Rectangular Channels with Leaf-Shaped Pin Fins

    Chao Zhang1, Runze Yan1, Honghui Li1, Qingheng Tang1, Qinghai Zhao1,2,*

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 421-440, 2025, DOI:10.32604/fhmt.2025.061469 - 25 April 2025

    Abstract The growing need for enhanced heat dissipation is compelling the development of more effective heat exchangers. Innovation inspired by nature bionics, four types of leaf-shaped pin fins were proposed and four combinations of them were considered. The leaf-shaped design of the cooling pin fin enhances uniformity and synergy, effectively creating an optimized flow path that boosts cooling performance. Eight three-dimensional conjugate heat transfer models in staggered arrangement were developed using ANSYS-Fluent software. Aluminum 6061 material was used as the heat sink material and single-phase liquid water flowed through the rectangular channel where the Reynolds (Re) number… More >

  • Open Access

    ARTICLE

    Conjugate Usage of Experimental for and Theoretical Models Aqua Carboxymethyl Cellulose Nanofluid Flow in Convergent-Divergent Shaped Microchannel

    Shervin Fateh Khanshir1, Saeed Dinarvand2,*, Ramtin Fateh Khanshir3

    Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 663-684, 2025, DOI:10.32604/fhmt.2025.060559 - 25 April 2025

    Abstract This article aims to model and analyze the heat and fluid flow characteristics of a carboxymethyl cellulose (CMC) nanofluid within a convergent-divergent shaped microchannel (Two-dimensional). The base fluid, water + CMC (0.5%), is mixed with CuO and Al2O3 nanoparticles at volume fractions of 0.5% and 1.5%, respectively. The research is conducted through the conjugate usage of experimental and theoretical models to represent more realistic properties of the non-Newtonian nanofluid. Three types of microchannels including straight, divergent, and convergent are considered, all having the same length and identical inlet cross-sectional area. Using ANSYS FLUENT software, Navier-Stokes equations… More > Graphic Abstract

    Conjugate Usage of Experimental for and Theoretical Models Aqua Carboxymethyl Cellulose Nanofluid Flow in Convergent-Divergent Shaped Microchannel

  • Open Access

    ARTICLE

    A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

    Xiaoying Qiu1,*, Xiaoyu Ma1, Guangxu Zhao1, Jinwei Yu2, Wenbao Jiang1, Zhaozhong Guo1, Maozhi Xu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869 - 16 April 2025

    Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >

  • Open Access

    ARTICLE

    Effect of Surface Tension on the Dynamics of an Oscillating Interface in a Vertical Slotted Channel

    Veronika Dyakova1,2,*, Olga Vlasova1, Victor Kozlov1

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.3, pp. 493-508, 2025, DOI:10.32604/fdmp.2025.060577 - 01 April 2025

    Abstract An experimental investigation of the dynamics of the interface between two low-viscosity fluids with high density contrast oscillating in a fixed vertical slotted channel has been conducted. It has been found that as the amplitude of the liquid column oscillations increases, parametric oscillations of the interface are excited in the form of a standing wave located in the channel plane. In particular, depending on the interfacial tension, the standing waves have a frequency equal to that of liquid piston oscillations (harmonic response), or half of the frequency of oscillations of the liquid column in the… More >

  • Open Access

    ARTICLE

    CE-CDNet: A Transformer-Based Channel Optimization Approach for Change Detection in Remote Sensing

    Jia Liu1, Hang Gu1, Fangmei Liu1, Hao Chen1, Zuhe Li1, Gang Xu2, Qidong Liu2, Wei Wang2,*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025

    Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection More >

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