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  • Open Access


    A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

    Huile Zhang1,2,*, Shikang Li2, Yurui Wu3, Pengpeng Zhi1, Wei Wang1,4, Zhonglai Wang1,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1975-1996, 2024, DOI:10.32604/cmes.2024.050185

    Abstract Carbon fiber composites, characterized by their high specific strength and low weight, are becoming increasingly crucial in automotive lightweighting. However, current research primarily emphasizes layer count and orientation, often neglecting the potential of microstructural design, constraints in the layup process, and performance reliability. This study, therefore, introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic (CFRP) drive shafts. Initially, parametric modeling of the microscale cell was performed, and its elastic performance parameters were predicted using two homogenization methods, examining the impact of fluctuations in microscale cell parameters on composite material performance. A finite element model of the CFRP… More >

  • Open Access


    Development of a Three-Dimensional Multiscale Octree SBFEM for Viscoelastic Problems of Heterogeneous Materials

    Xu Xu1, Xiaoteng Wang1, Haitian Yang1, Zhenjun Yang2, Yiqian He1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1831-1861, 2024, DOI:10.32604/cmes.2024.048199

    Abstract The multiscale method provides an effective approach for the numerical analysis of heterogeneous viscoelastic materials by reducing the degree of freedoms (DOFs). A basic framework of the Multiscale Scaled Boundary Finite Element Method (MsSBFEM) was presented in our previous works, but those works only addressed two-dimensional problems. In order to solve more realistic problems, a three-dimensional MsSBFEM is further developed in this article. In the proposed method, the octree SBFEM is used to deal with the three-dimensional calculation for numerical base functions to bridge small and large scales, the three-dimensional image-based analysis can be conveniently conducted in small-scale and coarse… More >

  • Open Access


    An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

    Syed Ayaz Ali Shah1,*, Aamir Shahzad1,*, Musaed Alhussein2, Chuan Meng Goh3, Khursheed Aurangzeb2, Tong Boon Tang4, Muhammad Awais5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2565-2583, 2024, DOI:10.32604/cmc.2024.047597

    Abstract Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, especially in the presence of different pathologies. Furthermore, the speed of the retinal vessel segmentation system is of utmost importance. With the surge of now available big data, the speed of the algorithm becomes increasingly important, carrying almost equivalent weightage to the accuracy of the algorithm. To address these challenges, we present a novel approach for retinal… More > Graphic Abstract

    An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

  • Open Access


    CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

    Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187

    Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small scales to capture global contextual… More >

  • Open Access


    Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

    Dingping Chen1, Zhiheng Zhu2, Jinyang Fu1,3, Jilin He1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1679-1703, 2024, DOI:10.32604/cmc.2024.049048

    Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >

  • Open Access


    An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

    Zhihua Liu, Shengquan Liu*, Jian Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

    Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

  • Open Access


    An Efficient Peridynamics Based Statistical Multiscale Method for Fracture in Composite Structure with Randomly Distributed Particles

    Zihao Yang1, Shaoqi Zheng1, Fei Han2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09250

    Abstract This paper proposes a peridynamics-based statistical multiscale (PSM) framework to simulate the macroscopic structure fracture with high efficiency. The heterogeneities of composites, including the shape, spatial distribution and volume fraction of particles, are characterized within the representative volume elements (RVEs), and their impact on structure failure are extracted as two types of peridynamic parameters, namely, statistical critical stretch and equivalent micromodulus. At the microscale level, a bondbased peridynamic (BPD) model with energy-based micromodulus correction technique is introduced to simulate the fracture in RVEs, and then the computational model of statistical critical stretch is established through micromechanical analysis. Moreover, based on… More >

  • Open Access


    RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

    Tian Ma, Chenhui Fu*, Jiayi Yang, Jiehui Zhang, Chuyang Shang

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1103-1122, 2023, DOI:10.32604/cmc.2023.042416

    Abstract Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a… More >

  • Open Access


    PAN-DeSpeck: A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

    Saima Yasmeen1, Muhammad Usman Yaseen1,*, Syed Sohaib Ali2, Moustafa M. Nasralla3, Sohaib Bin Altaf Khattak3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3671-3689, 2023, DOI:10.32604/cmc.2023.041195

    Abstract SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based… More >

  • Open Access


    Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite: Multiscale Design and Experimental Verification

    Xiaoyu Zhang1,2, Huizhong Zeng2, Shaohui Zhang2, Yan Zhang3,*, Mi Xiao4, Liping Liu2, Hao Zhou2,*, Hongyou Chai2, Liang Gao4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 201-220, 2024, DOI:10.32604/cmes.2023.029389

    Abstract Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting from the sandwich effect. Such structures can be fabricated by metallic additive manufacturing technique, such as selective laser melting (SLM). However, the maximum dimensions of actual structures are usually in a sub-meter scale, which results in restrictions on their appliance in aerospace and other fields. In this work, a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall. The… More >

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