2021
DOI: 10.1109/access.2021.3071715
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Analysis of Quadruple Corner-Cut Ridged Elliptical Waveguide by NURBS Enhanced Scaled Boundary Finite Element Method

Abstract: The scaled boundary finite element method(SBFEM) is a semi-analysis method, combing the advantages of boundary element method and finite element method. However, in solving the quadruple corner-cut ridged elliptical(QCRE) waveguide, the traditional SBFEM employ the Lagrange polynomials as the basis functions which leads to the curved boundaries cannot be exactly represented and the continuity order across element is low. In this paper, a non-uniform rational B-spline (NURBS) enhanced SBFEM is firstly extended … Show more

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Cited by 2 publications
(2 citation statements)
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“…The model achieved an accuracy of 92.6%. The study in [38] proposes a trash classification approach using deep learning. The authors used a deep learning-based approach called ScrapNet, which uses a CNN architecture.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…The model achieved an accuracy of 92.6%. The study in [38] proposes a trash classification approach using deep learning. The authors used a deep learning-based approach called ScrapNet, which uses a CNN architecture.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In contrast to the studies in the literature, the proposed study investigated a middle eastern dataset while addressing the kingdom's waste management problem, which is first of its kind study in the kingdom. ResNEt34, VGG126, InceptionV3, DenseNet121, MobileNetV3, and GNet [43] 24,000 images from Huawei challenge cup dataset [24] GNet accuracy = 92.62% [33] YoloV5 [45] TrashNet Accuracy = 95.51% [37] CNN [44] TrashNet Accuracy = 92.6% [38] InceptionV3 [43] TrashNet Accuracy = 92.87% [14] EfficientdetD2 & EfficientnetB2 [43] 14,000 instances Accuracy = 75% [21] MLH-CNN, AlexNet, RestNet50, and VGG16, DL model with (DLSODC-GWM) [43] Benchmark datasets Precision = 95.23%, Recall = 94.29% F-score = 94.73% [28] CNN [44] TrashNet, and self-built dataset Accuracy = 96.77% [32] GCNet [43] Internet collected dataset combined with self-built dataset Accuracy = 97.54%.…”
Section: Literature Reviewmentioning
confidence: 99%