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  • Open Access

    ARTICLE

    Analysis of Color Landscape Characteristics in “Beautiful Village” of China Based on 3D Real Scene Models

    Yiyi Cen1,3, Wenzheng Jia2, Wen Dai3,*, Chun Wang4, He Wu1

    Revue Internationale de Géomatique, Vol.33, pp. 93-109, 2024, DOI:10.32604/rig.2024.050273

    Abstract Color, as a significant element of village landscapes, serves various functions such as enhancing aesthetic appeal and attractiveness, conveying emotions and cultural values. To explore the three-dimensional spatial characteristics of color landscapes in beautiful villages, this study conducted a comparative experiment involving eight provincial-level beautiful villages and eight ordinary villages in Jinzhai County. Landscape pattern indices were used to analyze the color landscape patterns on the facades of these villages, complemented by a quantitative analysis of color attributes using the Munsell color system. The results indicate that (1) Natural landscape colors in beautiful villages are primarily concentrated in the yellow-red… More >

  • Open Access

    ARTICLE

    NFHP-RN: A Method of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

    Tao Yi1,3, Xingshu Chen1,2,*, Mingdong Yang3, Qindong Li1, Yi Zhu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793

    Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >

  • Open Access

    ARTICLE

    A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

    Yong Ma1, Han Zhao2, Kunyin Guo3,*, Yunni Xia3,*, Xu Wang4, Xianhua Niu5, Dongge Zhu6, Yumin Dong7

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 907-927, 2024, DOI:10.32604/cmes.2024.048759

    Abstract Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially… More >

  • Open Access

    ARTICLE

    DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

    Chengzhang Zhu1,2, Renmao Zhang1, Yalong Xiao1,2,*, Beiji Zou1, Xian Chai1, Zhangzheng Yang1, Rong Hu3, Xuanchu Duan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453

    Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address… More >

  • Open Access

    ARTICLE

    Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

    Xue-Qin Li1, Lu-Kai Song2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 665-684, 2024, DOI:10.32604/cmes.2024.048445

    Abstract Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of… More >

  • Open Access

    REVIEW

    Review of Collocation Methods and Applications in Solving Science and Engineering Problems

    Weiwu Jiang1, Xiaowei Gao1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 41-76, 2024, DOI:10.32604/cmes.2024.048313

    Abstract The collocation method is a widely used numerical method for science and engineering problems governed by partial differential equations. This paper provides a comprehensive review of collocation methods and their applications, focused on elasticity, heat conduction, electromagnetic field analysis, and fluid dynamics. The merits of the collocation method can be attributed to the need for element mesh, simple implementation, high computational efficiency, and ease in handling irregular domain problems since the collocation method is a type of node-based numerical method. Beginning with the fundamental principles of the collocation method, the discretization process in the continuous domain is elucidated, and how… More >

  • Open Access

    ARTICLE

    Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

    Haosong Gou1, Gaoyi Zhang1, Renê Ripardo Calixto2, Senthil Kumar Jagatheesaperumal3, Victor Hugo C. de Albuquerque2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1077-1102, 2024, DOI:10.32604/cmes.2024.047806

    Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally,… More >

  • Open Access

    ARTICLE

    Enhancing Ulcerative Colitis Diagnosis: A Multi-Level Classification Approach with Deep Learning

    Hasan J. Alyamani*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1129-1142, 2024, DOI:10.32604/cmes.2024.047756

    Abstract The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition with significant clinical implications. However, endoscopic assessment is susceptible to inherent variations, both within and between observers, compromising the reliability of individual evaluations. This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity. To initiate this endeavor, a multi-faceted approach is embarked upon. The dataset is meticulously preprocessed, enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization (CLAHE). A diverse array of data augmentation… More > Graphic Abstract

    Enhancing Ulcerative Colitis Diagnosis: A Multi-Level Classification Approach with Deep Learning

  • Open Access

    REVIEW

    A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

    Xianwei Jiang1, Yanqiong Zhang1,*, Juan Lei1, Yudong Zhang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

    Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >

  • Open Access

    ARTICLE

    Identifying Brand Consistency by Product Differentiation Using CNN

    Hung-Hsiang Wang1, Chih-Ping Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

    Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >

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