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  • Open Access

    ARTICLE

    Intelligent Scheduling of Virtual Power Plants Based on Deep Reinforcement Learning

    Shaowei He, Wenchao Cui*, Gang Li, Hairun Xu, Xiang Chen, Yu Tai

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 861-886, 2025, DOI:10.32604/cmc.2025.063979 - 09 June 2025

    Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >

  • Open Access

    ARTICLE

    DNEFNET: Denoising and Frequency Domain Feature Enhancement Event Fusion Network for Image Deblurring

    Kangkang Zhao1, Yaojie Chen1,*, Jianbo Li2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 745-762, 2025, DOI:10.32604/cmc.2025.063906 - 09 June 2025

    Abstract Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur. Existing event-based deblurring methods still face challenges when facing high-speed moving objects. We conducted an in-depth study of the imaging principle of event cameras. We found that the event stream contains excessive noise. The valid information is sparse. Invalid event features hinder the expression of valid features due to… More >

  • Open Access

    ARTICLE

    Reinforcement Learning for Solving the Knapsack Problem

    Zhenfu Zhang1, Haiyan Yin2, Liudong Zuo3, Pan Lai1,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 919-936, 2025, DOI:10.32604/cmc.2025.062980 - 09 June 2025

    Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >

  • Open Access

    ARTICLE

    CerfeVPR: Cross-Environment Robust Feature Enhancement for Visual Place Recognition

    Lingyun Xiang1, Hang Fu1, Chunfang Yang2,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 325-345, 2025, DOI:10.32604/cmc.2025.062834 - 09 June 2025

    Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

  • Open Access

    ARTICLE

    A Data-Enhanced Deep Learning Approach for Emergency Domain Question Intention Recognition in Urban Rail Transit

    Yinuo Chen1, Xu Wu1, Jiaxin Fan1, Guangyu Zhu2,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

    Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

  • Open Access

    ARTICLE

    Image Style Transfer for Exhibition Hall Design Based on Multimodal Semantic-Enhanced Algorithm

    Qing Xie*, Ruiyun Yu

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1123-1144, 2025, DOI:10.32604/cmc.2025.062712 - 09 June 2025

    Abstract Although existing style transfer techniques have made significant progress in the field of image generation, there are still some challenges in the field of exhibition hall design. The existing style transfer methods mainly focus on the transformation of single dimensional features, but ignore the deep integration of content and style features in exhibition hall design. In addition, existing methods are deficient in detail retention, especially in accurately capturing and reproducing local textures and details while preserving the content image structure. In addition, point-based attention mechanisms tend to ignore the complexity and diversity of image features… More >

  • Open Access

    ARTICLE

    Numerical Study on the Influence of Rectifier Grid on the Performances of a Cement Kiln’s SCR (Selective Catalytic Reduction) Denitrification Reactor

    Liang Ai1, Mingyue Li2, Lumin Chen1, Yihua Gao2, Yi Sun1, Yue Wu1, Fuping Qian1,*, Jinli Lu2, Naijin Huang3

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1171-1190, 2025, DOI:10.32604/fdmp.2025.055985 - 30 May 2025

    Abstract In this study, Computational Fluid Dynamics (CFD) together with a component transport model are exploited to investigate the influence of dimensionless parameters, involving the height of the rectifier grid and the installation height of the first catalyst layer, on the flow field and the overall denitration efficiency of a cement kiln’s SCR (Selective catalytic reduction) denitrification reactor. It is shown that accurate numerical results can be obtained by fitting the particle size distribution function to the actual cement kiln fly ash and implementing a non-uniform particle inlet boundary condition. The relative error between denitration More >

  • 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

    Leveraging AI for Advancements in Qualitative Research Methodology

    Ilyas Haouam*

    Journal on Artificial Intelligence, Vol.7, pp. 85-114, 2025, DOI:10.32604/jai.2025.064145 - 27 May 2025

    Abstract This study investigates the integration of Artificial Intelligence (AI) technologies—particularly natural language processing and machine learning—into qualitative research (QR) workflows. Our research demonstrates that AI can streamline data collection, coding, theme identification, and visualization, significantly improving both speed and accuracy compared to traditional manual methods. Notably, our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency, accuracy, and usability across various QR tasks. By presenting and discussing studies on some AI & generative AI models, we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its… More >

  • Open Access

    ARTICLE

    Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

    Peiying Zhang1,2,*, Yihong Yu1,2, Jing Liu3, Chong Lv1,2, Lizhuang Tan4,5, Yulin Zhang6,7,8

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654 - 19 May 2025

    Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

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