Home / Journals / CMES / Vol.136, No.3, 2023
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  • Open AccessOpen Access

    ARTICLE

    Interactive Restoration of Three-Dimensional Implicit Surface with Irregular Parts

    Jiayu Ren1,*, Yoshihisa Fujita2, Susumu Nakata2
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2111-2125, 2023, DOI:10.32604/cmes.2023.025970
    Abstract Implicit surface generation based on the interpolation of surface points is one of the well-known modeling methods in the area of computer graphics. Several methods for the implicit surface reconstruction from surface points have been proposed on the basis of radial basis functions, a weighted sum of local functions, splines, wavelets, and combinations of them. However, if the surface points contain errors or are sparsely distributed, irregular components, such as curvature-shaped redundant bulges and unexpectedly generated high-frequency components, are commonly seen. This paper presents a framework for restoring irregular components generated on and around surfaces. Users are assumed to specify… More >

  • Open AccessOpen Access

    REVIEW

    A Survey of Convolutional Neural Network in Breast Cancer

    Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang*
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2127-2172, 2023, DOI:10.32604/cmes.2023.025484
    (This article belongs to this Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract Problems: For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims: A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more… More >

    Graphic Abstract

    A Survey of Convolutional Neural Network in Breast Cancer

  • Open AccessOpen Access

    REVIEW

    Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review

    Jiaqi Shao1,#, Shuwen Chen1,2,3,#,*, Jin Zhou1,#, Huisheng Zhu1, Ziyi Wang1, Mackenzie Brown4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2173-2219, 2023, DOI:10.32604/cmes.2023.025499
    (This article belongs to this Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
    Abstract As a mainstream research direction in the field of image segmentation, medical image segmentation plays a key role in the quantification of lesions, three-dimensional reconstruction, region of interest extraction and so on. Compared with natural images, medical images have a variety of modes. Besides, the emphasis of information which is conveyed by images of different modes is quite different. Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors. Therefore, large quantities of automated medical image segmentation methods have been developed. However, until now, researchers have not developed a universal method for all… More >

  • Open AccessOpen Access

    ARTICLE

    Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

    Jipeng Gu1, Weijie Zhang1, Youbing Zhang1,*, Binjie Wang1, Wei Lou2, Mingkang Ye3, Linhai Wang3, Tao Liu4
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2221-2236, 2023, DOI:10.32604/cmes.2023.025396
    Abstract An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend change of load in the… More >

  • Open AccessOpen Access

    ARTICLE

    Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

    Ying Li1,2, Guanghong Gong1, Dan Wang1, Ni Li1,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2237-2265, 2023, DOI:10.32604/cmes.2023.025193
    Abstract There are two types of methods for image segmentation. One is traditional image processing methods, which are sensitive to details and boundaries, yet fail to recognize semantic information. The other is deep learning methods, which can locate and identify different objects, but boundary identifications are not accurate enough. Both of them cannot generate entire segmentation information. In order to obtain accurate edge detection and semantic information, an Adaptive Boundary and Semantic Composite Segmentation method (ABSCS) is proposed. This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances. It includes adaptively dividing and modifying the… More >

    Graphic Abstract

    Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

  • Open AccessOpen Access

    ARTICLE

    A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

    Abdelazim G. Hussien1,2, Guoxi Liang3, Huiling Chen4,*, Haiping Lin5,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2267-2289, 2023, DOI:10.32604/cmes.2023.024247
    Abstract Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may fail in sub-optimal regions. In this study, an enhanced version of SCA named RDSCA is suggested… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

    Abdullah Ahmed Al-Dulaimi1,*, Muhammet Tahir Guneser1, Alaa Ali Hameed2, Mohammad Shukri Salman3
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2291-2319, 2023, DOI:10.32604/cmes.2023.026065
    Abstract Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures.… More >

    Graphic Abstract

    Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

  • Open AccessOpen Access

    ARTICLE

    A Systematic Approach for Exploring Underground Environment Using LiDAR-Based System

    Tareq Alhmiedat1,2,*, Ashraf M. Marei1,2, Saleh Albelwi1,2, Anas Bushnag2, Wassim Messoudi2, Abdelrahman Osman Elfaki2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2321-2344, 2023, DOI:10.32604/cmes.2023.025641
    Abstract Agricultural projects in different parts of the world depend on underground water wells. Recently, there have been many unfortunate incidents in which children have died in abandoned underground wells. Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them, especially since most of these wells become buried over time. Many solutions have been developed recently, most with the aim of exploring these well areas. However, these systems suffer from several limitations, including high complexity, large size, or inefficiency. This paper focuses on the development of a smart exploration unit that is… More >

  • Open AccessOpen Access

    ARTICLE

    Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

    Dong Qing1,*, Qi Song1, Shuangyun Huang2, Gening Xu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2345-2370, 2023, DOI:10.32604/cmes.2023.026552
    Abstract For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time, an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed. The heterogeneous kernel relevance vector machine model (HKRVM) with comprehensive expression ability is established using the complementary advantages of various kernel functions. The combination strategy consisting of refraction reverse learning, golden sine, and Cauchy mutation + logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm (TSSA), thus optimizing the relevant parameters of HKRVM. The adaptive updating mechanism of the heterogeneous kernel RVM model under the multi-strategy improved… More >

  • Open AccessOpen Access

    ARTICLE

    On a Novel Extended Lomax Distribution with Asymmetric Properties and Its Statistical Applications

    Aisha Fayomi1, Christophe Chesneau2,*, Farrukh Jamal3, Ali Algarni1
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2371-2403, 2023, DOI:10.32604/cmes.2023.027000
    Abstract In this article, we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax distribution. It is called the extended Lomax distribution. The considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis purposes. As a result, its cumulative distribution has the same functional basis as that of the Lomax distribution, but with a novel special logarithmic term depending on several parameters. The modulation of this logarithmic term reveals new types of asymetrical shapes, implying a… More >

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