Biao Wang¹, Yuxiang Peng^1,*, Wenhua Xu²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012061

Abstract The damage process of ship structures under near-field underwater explosions involves strong nonlinear coupling effects of multiple media, and its numerical simulation poses a serious challenge to traditional numerical algorithms. Based on previous research, this article first establishes a highly compressible multiphase flow numerical calculation model based on the high-precision Discontinuous Galerkin Method (DGM) and a ship elastic-plastic damage dynamic model based on the meshless Reproducing Kernel Particle Method (RKPM). Furthermore, we develop an algorithm for grid-independent dynamic expansion of cracks. Based on this, the Boundary Data Immersion Method (BDIM) is used to couple the More >

Yuanyuan Wang^1,2,*, Jialong Huang¹, Md Sharid Kayes Dipu¹, Hu Zhao³, Shangbing Gao^1,2, Haiyan Zhang^1,2, Pinrong Lv¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024

Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More >

Chih-Ping Wu^*, Ruei-Syuan Chang

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 917-949, 2024, DOI:10.32604/cmes.2024.052307 - 20 August 2024

Abstract This work develops a Hermitian C differential reproducing kernel interpolation meshless (DRKIM) method within the consistent couple stress theory (CCST) framework to study the three-dimensional (3D) microstructure-dependent static flexural behavior of a functionally graded (FG) microplate subjected to mechanical loads and placed under full simple supports. In the formulation, we select the transverse stress and displacement components and their first- and second-order derivatives as primary variables. Then, we set up the differential reproducing conditions (DRCs) to obtain the shape functions of the Hermitian C differential reproducing kernel (DRK) interpolant’s derivatives without using direct differentiation. The interpolant’s… More >

Ragini Thatikonda¹, Prakash Kodali^1,*, Ramalingaswamy Cheruku², Eswaramoorthy K.V³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844 - 20 June 2024

Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >

Xin Xiong¹, Jing Zhang¹, Sanli Yi¹, Chunwu Wang², Ruixiang Liu³, Jianfeng He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528 - 20 June 2024

Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158 - 20 June 2024

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Zhong Qu^1,*, Guoqing Mu¹, Bin Yuan²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 255-273, 2024, DOI:10.32604/cmes.2024.048175 - 16 April 2024

Abstract Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature… More > Graphic Abstract

Sherly Alphonse^1,*, S. Abinaya¹, Sourabh Paul²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3449-3467, 2024, DOI:10.32604/cmc.2024.046403 - 26 March 2024

Abstract The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas. The radiation cost and quality factor of the antenna are influenced by the size of the antenna. Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas. Antenna parameters have recently been predicted using machine learning algorithms in existing literature. Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters. The accuracy of the prediction will be primarily dependent on the model that is used. In… More >

Talal Albalawi, P. Ganeshkumar^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396 - 26 March 2024

Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution More >

Saima Rashid^1,2,*, Fahd Jarad^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2289-2327, 2024, DOI:10.32604/cmes.2023.028773 - 11 March 2024

Abstract Because of the features involved with their varied kernels, differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues. In this paper, we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels. In this approach, the overall population was separated into five cohorts. Furthermore, the descriptive behavior of the system was investigated, including prerequisites for the positivity of solutions, invariant domain of the solution, presence and stability of equilibrium points, and sensitivity analysis. We included a stochastic More >