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  • Open Access

    ARTICLE

    Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

    Mohammad Mehdi Sharifi Nevisi1, Elnaz Bashir2, Diego Martín3,*, Seyedkian Rezvanjou4, Farzaneh Shoushtari5, Ehsan Ghafourian2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875

    Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative… More >

  • Open Access

    ARTICLE

    NAMO Géoweb

    Une plateforme pour valoriser la narration et la modélisation de l’espace géographique et des territoires

    Jean-Pierre Chery1, Marie Gradeler1, Vincent Bonnal2,3,4

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 355-376, 2022, DOI:10.3166/RIG.31.355-376© 2022

    Abstract The need to enhance and share generic and original information from different research projects, led to the design of a dedicated geoweb, called NAMO. Its design uses open and free functionalities that demonstrate their flexibility in an agile and iterative approach. It is in particular the development of two dimensions of valorization and positioning in geoweb 2.0 that is highlighted: narrative mapping and systemic modeling. The use of geographic information, in particular in co-construction and open science approaches, can thus be better equipped.

    RÉSUMÉ.
    Les besoins de valoriser et de partager des informations génériques et originales de différents projets… More >

  • Open Access

    ARTICLE

    Isogeometric Analysis of Hyperelastic Material Characteristics for Calcified Aortic Valve

    Long Chen1, Ting Li1, Liang Liu1, Wenshuo Wang2,*, Xiaoxiao Du3, Wei Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2773-2806, 2024, DOI:10.32604/cmes.2024.046641

    Abstract This study explores the implementation of computed tomography (CT) reconstruction and simulation techniques for patient-specific valves, aiming to dissect the mechanical attributes of calcified valves within transcatheter heart valve replacement (TAVR) procedures. In order to facilitate this exploration, it derives pertinent formulas for 3D multi-material isogeometric hyperelastic analysis based on Hounsfield unit (HU) values, thereby unlocking foundational capabilities for isogeometric analysis in calcified aortic valves. A series of uniaxial and biaxial tensile tests is executed to obtain an accurate constitutive model for calcified active valves. To mitigate discretization errors, methodologies for reconstructing volumetric parametric models, integrating both geometric and material… More >

  • Open Access

    ARTICLE

    Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

    Jungpil Shin1,*, Md. Al Mehedi Hasan2, Abu Saleh Musa Miah1, Kota Suzuki1, Koki Hirooka1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334

    Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body movements within JSL gestures. Simultaneously,… More >

  • Open Access

    ARTICLE

    Full-Scale Isogeometric Topology Optimization of Cellular Structures Based on Kirchhoff–Love Shells

    Mingzhe Huang, Mi Xiao*, Liang Gao, Mian Zhou, Wei Sha, Jinhao Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2479-2505, 2024, DOI:10.32604/cmes.2023.045735

    Abstract Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio. In this paper, a full-scale isogeometric topology optimization (ITO) method based on Kirchhoff–Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed. This method utilizes high-order continuous nonuniform rational B-splines (NURBS) as basis functions for Kirchhoff–Love shell elements. The geometric and analysis models of thin shells are unified by isogeometric analysis (IGA) to avoid geometric approximation error and improve computational accuracy. The topological configurations of thin-shell structures are described by constructing the effective density field on the control… More >

  • Open Access

    ARTICLE

    DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

    Pengchao Li1,2,3,*, Fang Xu1,2,3,4, Jintao Wang1,2, Haibing Guo4, Mingmin Liu4, Zhenjun Du4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1755-1771, 2024, DOI:10.32604/cmc.2023.047057

    Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a high-performance deep neural network algorithm… More >

  • Open Access

    ARTICLE

    Contributions of Remote Sensing and GIS to the Inventory and Mapping of Colonial Geodetic Markers in the Katangese Copper Belt

    John Tshibangu Wa Ilunga1,*, Donatien Kamutanda Kalombo1, Olivier Ngoie Inabanza1, Dikumbwa N’landu1,2, Joseph Mukalay Muamba1,3, Patrice Amisi Mwana1, Urcel Kalenga Tshingomba1, Junior Muyumba Munganga1, Catherine Nsiami Mabiala1

    Revue Internationale de Géomatique, Vol.33, pp. 15-35, 2024, DOI:10.32604/rig.2024.046629

    Abstract The mutation of spaces observed in the Katangese Copper Belt (KCB) causes significant topographical changes. Some colonial geodetic markers are easily noticeable on many of the hills making up the KCB. These hills are subject to mining which ruins the completeness of the network of triangulations: geometric and trigonometric Katangese. In order to keep control of the latter, the study shows on the one hand the possibility of using SRTM data (Shuttle Radar Topography Mission) in the monitoring of the macro-change of the reliefs, from 442 positions, and on the other hand, an indirect (remote) inventory method of the existing… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

    S. Sofana Reka1, Ankita Bagelikar2, Prakash Venugopal2,*, V. Ravi2, Harimurugan Devarajan3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369

    Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach to Shape Optimization Problems for Flexoelectric Materials Using the Isogeometric Finite Element Method

    Yu Cheng1,2,5, Yajun Huang3, Shuai Li4, Zhongbin Zhou5, Xiaohui Yuan1,2,*, Yanming Xu5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1935-1960, 2024, DOI:10.32604/cmes.2023.045668

    Abstract A new approach for flexoelectric material shape optimization is proposed in this study. In this work, a proxy model based on artificial neural network (ANN) is used to solve the parameter optimization and shape optimization problems. To improve the fitting ability of the neural network, we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training. The isogeometric analysis-finite element method (IGA-FEM) is used to discretize the flexural theoretical formulas and obtain samples, which helps ANN to build a proxy model from the model shape to the target value. The effectiveness… More >

  • Open Access

    ARTICLE

    Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

    Kolli Ramujee1,*, Pooja Sadula1, Golla Madhu2, Sandeep Kautish3, Abdulaziz S. Almazyad4, Guojiang Xiong5, Ali Wagdy Mohamed6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1455-1486, 2024, DOI:10.32604/cmes.2023.043384

    Abstract Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal, a new approach using convolutional… More >

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