Sk. A. Shezan^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073418 - 27 December 2025

Abstract Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization. However, the high penetration of intermittent renewable sources often causes frequency deviations, voltage fluctuations, and poor reactive power coordination, posing serious challenges to grid stability. Conventional Interconnection Flow Controllers (IFCs) primarily regulate active power flow and fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks. To overcome these limitations, this study proposes an enhanced Interconnection Flow Controller (e-IFC) that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller (IRFC) within a unified adaptive… More >

Yuang Chen^1,2, Yong Li^1,*, Fang Lin^1,2, Shuhan Lv^1,2, Jiaze Jiang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070990 - 09 December 2025

Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature More >

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >

Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025

Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More > Graphic Abstract

Wang Gu¹, Yuanyuan Song², Zhihu Zhang³, Minggang Zheng^1,*

Energy Engineering, Vol.122, No.12, pp. 4837-4856, 2025, DOI:10.32604/ee.2025.069529 - 27 November 2025

Abstract Driven by the global “dual-carbon” goals, hydrogen fuel cell electric vehicles (FCEVs) are being rapidly promoted as a zero-emission transportation solution. However, their large-scale application is constrained by issues such as inefficient operation, poor information flow between vehicles and stations, and potential safety hazards, which are caused by insufficient intelligence of hydrogen refueling stations. This study aims to address these problems by deeply integrating Cellular Vehicle-to-Everything (C-V2X) technology with hydrogen refueling stations, thereby building a safe, efficient, and low-carbon hydrogen energy application ecosystem to promote the global transition to zero-carbon transportation. Firstly, through literature review… More >

Yifei Wang, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011121

Abstract Additive manufacturing (AM) reveals high anisotropy in mechanical properties due to the thermal accumulation induced microstructures. How to reveal the internal connection between the microstructures and the mechanical properties in additive manufacturing is a challenge. There are many methods to predict the mechanical properties based on the microstructural evolutions in additive manufacturing [1–3]. Here we summarized the main methods for the prediction of the mechanical properties in additive manufacturing, including crystal plasticity finite element method (CPFEM), dislocation dynamics (DD), and molecular dynamics (MD). We systematically examine these primary approaches for mechanical property predictions in AM,… More >

Qiji Dai¹, Jikai Li^2,*, Bohui Ning¹, Yutao Xu¹, Chang Liu², Xuan Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4061-4080, 2025, DOI:10.32604/ee.2025.067304 - 30 September 2025

Abstract Aiming at the challenge of complex load balancing coordination for a three-phase four-leg (3P4L) based multi-ended low voltage flexible DC distribution system (M-LVDC) considering unbalanced power compensation, this paper proposes a phase-split power decoupling unbalanced compensation strategy based load balancing strategy for 3P4L based M-LVDC. Firstly, the topology and operation principle of the 3P4L-based M-LVDC system is introduced, and quasi-proportional resonant (QPR) based phase-split power current control for the 3P4L converter is proposed. Secondly, a load-balancing control strategy considering unbalanced compensation for 3P4L-based M-LVDC is presented, in which the control diagrams for each 3P4L-based converter… More >

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025

Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >

Rongrong Gong^1,#, Tingxian Zhang^2,#, Yawen Wei², Dengyong Zhang², Yan Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1221-1234, 2025, DOI:10.32604/cmc.2025.066842 - 29 August 2025

Abstract Image inpainting refers to synthesizing missing content in an image based on known information to restore occluded or damaged regions, which is a typical manifestation of this trend. With the increasing complexity of image in tasks and the growth of data scale, existing deep learning methods still have some limitations. For example, they lack the ability to capture long-range dependencies and their performance in handling multi-scale image structures is suboptimal. To solve this problem, the paper proposes an image inpainting method based on the parallel dual-branch learnable Transformer network. The encoder of the proposed model More >

Jingxian Ni, Chaomeng Wang, Shiqi Sun, Yuxuan Sun, Gang Ma^*

Energy Engineering, Vol.122, No.9, pp. 3719-3736, 2025, DOI:10.32604/ee.2025.066971 - 26 August 2025

Abstract To improve the fault diagnosis accuracy of a PV grid-connected inverter, a PV grid-connected inverter data diagnosis method based on MPA-VMD-PSO-BiLSTM is proposed. Firstly, unlike the traditional VMD algorithm which relies on manual experience to set parameters (e.g., noise tolerance, penalty parameter, number of decompositions), this paper achieves adaptive optimization of parameters through MPA algorithm to avoid the problem of feature information loss caused by manual parameter tuning, and adopts the improved VMD algorithm for feature extraction of DC-side voltage data signals of PV-grid-connected inverters; and then, adopts the PSO algorithm for the Then, the… More >