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  • 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 >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

    Jiajie He1,2, Fuzheng Liu3, Xiangyi Geng3, Xifeng Liang1, Faye Zhang3,*, Mingshun Jiang3

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428

    Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the… More >

  • Open Access

    PROCEEDINGS

    The Nitsche’s Method and Applications in Isogeometric Analysis

    Qingyuan Hu1,*, Yuan Liang2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.09441

    Abstract The Nitsche’s method is originally proposed as a technique to impose boundary conditions, nowadays it is widely used for isometric analysis (IGA) and corresponding topology optimization applications. Based on our previous research [1], we present a simple way to derive the Nitsche’s formulations for different kind of boundary and interface conditions, and studied this technique in the context of IGA discretization, especially for patch coupling and contact problems. The skew-symmetric variant of the Nitsche’s method is then further studied. For linear boundary or interface conditions, the skew-symmetric formulation is parameterfree. For contact conditions, it remains stable and accurate for a… More >

  • Open Access

    PROCEEDINGS

    A Machine Learning Framework for Isogeometric Topology Optimization

    Haobo Zhang1, Ziao Zhuang1, Chen Yu2, Zhaohui Xia1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.09091

    Abstract Topology optimization (TO) is an important and powerful tool to obtain efficient and lightweight structures in conceptional design stage and a series of representative methods are implemented [1-5]. TO are mainly based on the classical finite element analysis (FEA), resulting in an inconsistency between geometric model and analytical model. Besides, there are some drawbacks of low analysis accuracy, poor continuity between adjacent elements, and high computational cost for high-order meshes. Thus, isogeometric analysis (IGA) is proposed [6] to replace FEA in TO. Using the Non-Uniform Rational B-Splines (NURBS), IGA successfully eliminates the defects of the conventional FEA and forms a… More >

  • Open Access

    ARTICLE

    Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

    Long Chen1, Yan Yu1, Yanpeng Shang1, Zhonghou Wang1,*, Jing Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 817-846, 2024, DOI:10.32604/cmes.2023.031595

    Abstract Gears are pivotal in mechanical drives, and gear contact analysis is a typically difficult problem to solve. Emerging isogeometric analysis (IGA) methods have developed new ideas to solve this problem. In this paper, a three-dimensional body parametric gear model of IGA is established, and a theoretical formula is derived to realize single-tooth contact analysis. Results were benchmarked against those obtained from commercial software utilizing the finite element analysis (FEA) method to validate the accuracy of our approach. Our findings indicate that the IGA-based contact algorithm successfully met the Hertz contact test. When juxtaposed with the FEA approach, the IGA method… More > Graphic Abstract

    Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

  • Open Access

    ARTICLE

    Utilisation du système d’information géographique et modèle numérique de terrain dans l’analyse des caractéristiques hydro-morphométriques des sous-bassins versants de la rivière Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 99-122, 2023, DOI:10.32604/rig.2023.044899

    Abstract L’analyse et la quantification des caractéristiques hydro-morphométriques sont essentielles pour une meilleure gestion des ressources en eau et une planification plus efficace des projets hydroélectriques dans le bassin de la Tshopo. Malheureusement, peu d’études ont été réalisées pour évaluer ces caractéristiques à l’échelle de ce bassin. Notre approche méthodologique consiste à utiliser les outils d’analyse des logiciels Système d’Information Géographique (SIG) appliqués au Modèle Numérique de Terrain (MNT) dérivé de l’image Advanced Land Observing Satellite (ALOS) World 3D-30m. Cela nous a permis d’extraire automatiquement le réseau hydrographique et de générer les sous-bassins versants de la Tshopo. Les résultats de cette… More >

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