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  • Open Access

    ARTICLE

    Deep Learning Model for Identifying Internal Flaws Based on Image Quadtree SBFEM and Deep Neural Networks

    Hanyu Tao1,2, Dongye Sun1,2, Tao Fang1,2, Wenhu Zhao1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 521-536, 2025, DOI:10.32604/cmes.2025.072089 - 30 October 2025

    Abstract Structural internal flaws often weaken the performance and integral stability, while traditional nondestructive testing or inversion methods face challenges of high cost and low efficiency in quantitative flaw identification. To quickly identify internal flaws within structures, a deep learning model for flaw detection is proposed based on the image quadtree scaled boundary finite element method (SBFEM) combined with a deep neural network (DNN). The training dataset is generated from the numerical simulations using the balanced quadtree algorithm and SBFEM, where the structural domain is discretized based on recursive decomposition principles and mesh refinement is automatically… 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

    Predicting COVID-19 Based on Environmental Factors With Machine Learning

    Amjed Basil Abdulkareem1, Nor Samsiah Sani1,*, Shahnorbanun Sahran1, Zaid Abdi Alkareem Alyessari1, Afzan Adam1, Abdul Hadi Abd Rahman1, Abdulkarem Basil Abdulkarem2

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 305-320, 2021, DOI:10.32604/iasc.2021.015413 - 01 April 2021

    Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet)… More >

  • Open Access

    ABSTRACT

    A New Quadtree-based Image Compression Technique using Pattern Matching Algorithm

    F. Keissarian1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.12, No.4, pp. 137-144, 2009, DOI:10.3970/icces.2009.012.137

    Abstract In this paper, a new image compression technique is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. The proposed algorithm decomposes the host image into blocks of variable sizes according to histogram analysis of the block residuals. Variable block sizes are then encoded at different rates based on their visual activity levels. To preserve edge integrity, a high-detail block is coded by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver together with More >

  • Open Access

    ARTICLE

    Fast Searching Algorithm for Candidate Satellite-node Set in NLMG

    Yufeng Nie1, Ying Liu2, Yuantong Gu3, Xiangkuo Fan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.45, No.1, pp. 31-56, 2009, DOI:10.3970/cmes.2009.045.031

    Abstract The Node-based Local Mesh Generation (NLMG) algorithm, which is free of mesh inconsistency, is one of core algorithms in the Node-based Local Finite Element Method (NLFEM) to achieve the seamless link between mesh generation and stiffness matrix calculation, and the seamless link helps to improve the parallel efficiency of FEM. Furthermore, the key to ensure the efficiency and reliability of NLMG is to determine the candidate satellite-node set of a central node quickly and accurately. This paper develops a Fast Local Search Method based on Uniform Bucket (FLSMUB) and a Fast Local Search Method based More >

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