Jiayun Ding, Bin Chen^*, Yutong Lei, Wei Zhang

Energy Engineering, Vol.122, No.11, pp. 4561-4577, 2025, DOI:10.32604/ee.2025.067574 - 27 October 2025

Abstract In the field of low-carbon building systems, the combination of renewable energy and hydrogen energy systems is gradually gaining prominence. However, the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling. In light of this, this study focuses on electro-thermal-hydrogen trigeneration systems, first modelling the system’s scheduling optimization problem as a Markov decision process, thereby transforming it into a sequential decision problem. Based on this, this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement, aiming to minimize system operating… More >

Diyar Wirya Omar Ameenulhakeem^*, Osman Nuri Uçan

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5671-5702, 2025, DOI:10.32604/cmc.2025.070422 - 23 October 2025

Abstract Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in… More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025

Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >

Qin Qin¹, Xuan Jiang^1,*, Jinhua Jiang¹, Dongfang Zhao¹, Zimei Tu¹, Zhiwei Shen²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4849-4867, 2025, DOI:10.32604/cmc.2025.067919 - 23 October 2025

Abstract The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion, surface defects, and low contrast, which hinder the extraction of reliable visual information. The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments. This makes conventional OCR techniques and standard deep learning models unreliable. To address these limitations, this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs. A Biorthogonal 4.4 (bior4.4) wavelet transform is adaptively tuned using DE to enhance character… More >

Yifan Zhu^1,2, Fengxiang Xu^1,2,*, Zhen Zou^1,2, Zhengpao Liu^1,2, Xiaokun Dai^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.010641

Abstract Conventional energy-absorbing mechanical metamaterials primarily dissipate energy through irreversible plastic deformation, buckling, or fragmentation. Their applications are limited by structural fractures caused by 45° shear stresses and their suitability only for single-use impact protection, lacking the capability for repeated energy absorption. Inspired by the cancellous bone of the human skull, a Tangent Arc Curve Structure (TACS) was proposed in this study, followed by the modeling and fabrication of four types of 3D-TACSs: tensile, tensile-rotational, orthogonal, and diagonal. The shear resistance and repeatable energy absorption capabilities of TACS were systematically investigated through theoretical analysis, compression experiments,… More >

Mohammed Albekairi¹, Radhia Khdhir^2,*, Amina Magdich³, Somia Asklany^4,*, Ghulam Abbas⁵, Amr Yousef ^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681 - 30 September 2025

Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

Yiming Guo, Hongyu Ma^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025

Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

Thai-Viet Dang^1,*, Dinh-Manh-Cuong Tran¹, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2651-2680, 2025, DOI:10.32604/cmc.2025.067500 - 23 September 2025

Abstract Precise and robust three-dimensional object detection (3DOD) presents a promising opportunity in the field of mobile robot (MR) navigation. Monocular 3DOD techniques typically involve extending existing two-dimensional object detection (2DOD) frameworks to predict the three-dimensional bounding box (3DBB) of objects captured in 2D RGB images. However, these methods often require multiple images, making them less feasible for various real-time scenarios. To address these challenges, the emergence of agile convolutional neural networks (CNNs) capable of inferring depth from a single image opens a new avenue for investigation. The paper proposes a novel ELDE-Net network designed to… More >