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Effect of mean stress and ratcheting strain on the low cycle fatigue behavior of a wrought 316LN stainless steel

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2016-11, Vol.677, p.193-202

2016

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Two-order graph convolutional networks for semi-supervised classification

IET image processing, 2019-12, Vol.13 (14), p.2763-2771

2019

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Effects of deep cryogenic treatment on microstructure, mechanical, and corrosion of ZK60 Mg alloy

Journal of materials research and technology, 2023-09, Vol.26, p.3686-3700

2023

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A tension-torsional fatigue testing apparatus for micro-scale components

Review of scientific instruments, 2016-01, Vol.87 (1)

2016

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A graph convolutional neural network model with Fisher vector encoding and channel‐wise spatial‐temporal aggregation for skeleton‐based action recognition

IET image processing, 2022-04, Vol.16 (5), p.1433-1443

2022

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Deterioration of mechanical properties for pre-corroded AZ31 sheet in simulated physiological environment

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2014-01, Vol.593, p.153-162

2014

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Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction

Frontiers in oncology, 2021-07, Vol.11, p.700343-700343

2021

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Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Technology in cancer research & treatment, 2022, Vol.21, p.15330338221104881-15330338221104881

2022

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Evaluation of multiaxial fatigue life prediction criteria for PEEK

Theoretical and applied fracture mechanics, 2014-10, Vol.73, p.128-135

2014

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HesGCN: Hessian graph convolutional networks for semi-supervised classification

Information sciences, 2020-04, Vol.514, p.484-498

2020

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Semi-supervised classification by graph p-Laplacian convolutional networks

Information sciences, 2021-06, Vol.560, p.92-106

2021

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PEG-based bioresponsive hydrogels with redox-mediated formation and degradation

Journal of materials science. Materials in medicine, 2012-03, Vol.23 (3), p.697-710

2012

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Ratcheting of 316L stainless steel thin wire under tension-torsion loading

Frattura ed integritá strutturale, 2016-10, Vol.10 (38), p.141-147

2016

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HpLapGCN: Hypergraph p-Laplacian graph convolutional networks

Neurocomputing (Amsterdam), 2019-10, Vol.362, p.166-174

2019

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Effects of pre-compression modes on mechanical properties and fatigue behaviors of rolled ZK60 magnesium alloy

International journal of fatigue, 2024-06, Vol.183, p.108235, Article 108235

2024

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Example-feature graph convolutional networks for semi-supervised classification

Neurocomputing (Amsterdam), 2021-10, Vol.461, p.63-76

2021

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Size effect in stainless steel thin wires under tension

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2020-07, Vol.790, p.139686, Article 139686

2020

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Probing the fatigue enhancement in a thermally aged cast duplex stainless steel by in situ neutron diffraction

Scripta materialia, 2024-11, Vol.252, p.116252, Article 116252

2024

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Gradient Guided Multiscale Feature Collaboration Networks for Few-Shot Class-Incremental Remote Sensing Scene Classification

IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-12

2024

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Finding core labels for maximizing generalization of graph neural networks

Neural networks, 2024-12, Vol.180, p.106635, Article 106635

2024

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Human activity recognition by manifold regularization based dynamic graph convolutional networks

Neurocomputing (Amsterdam), 2021-07, Vol.444, p.217-225

2021

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Toward Cross-Domain Class-Incremental Remote Sensing Scene Classification

IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-13

2024

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Few-Shot Learning With Dynamic Graph Structure Preserving

IEEE transactions on industrial informatics, 2024-03, Vol.20 (3), p.1-10

2024

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Adaptive graph convolutional collaboration networks for semi-supervised classification

Information sciences, 2022-09, Vol.611, p.262-276

2022

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Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning

IEEE transaction on neural networks and learning systems, 2024-02, Vol.PP, p.1-15

2024

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Effect of mean stress and ratcheting strain on the low cycle fatigue behavior of a wrought 316LN stainless steel

Two-order graph convolutional networks for semi-supervised classification

Effects of deep cryogenic treatment on microstructure, mechanical, and corrosion of ZK60 Mg alloy

A tension-torsional fatigue testing apparatus for micro-scale components

A graph convolutional neural network model with Fisher vector encoding and channel‐wise spatial‐temporal aggregation for skeleton‐based action recognition

Deterioration of mechanical properties for pre-corroded AZ31 sheet in simulated physiological environment

Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

Evaluation of multiaxial fatigue life prediction criteria for PEEK

HesGCN: Hessian graph convolutional networks for semi-supervised classification

Semi-supervised classification by graph p-Laplacian convolutional networks

PEG-based bioresponsive hydrogels with redox-mediated formation and degradation

Ratcheting of 316L stainless steel thin wire under tension-torsion loading

HpLapGCN: Hypergraph p-Laplacian graph convolutional networks

Effects of pre-compression modes on mechanical properties and fatigue behaviors of rolled ZK60 magnesium alloy

Example-feature graph convolutional networks for semi-supervised classification

Size effect in stainless steel thin wires under tension

Probing the fatigue enhancement in a thermally aged cast duplex stainless steel by in situ neutron diffraction

Gradient Guided Multiscale Feature Collaboration Networks for Few-Shot Class-Incremental Remote Sensing Scene Classification

Finding core labels for maximizing generalization of graph neural networks

Human activity recognition by manifold regularization based dynamic graph convolutional networks

Toward Cross-Domain Class-Incremental Remote Sensing Scene Classification

Few-Shot Learning With Dynamic Graph Structure Preserving

Adaptive graph convolutional collaboration networks for semi-supervised classification

Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning

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