Research graph
References from Wear evolution analysis of artificial joints based on wear particle recognition: A study under insufficient and imbalanced sample scenarios. Local targets link to admitted publications; unresolved targets remain external evidence.
Knee osteoarthritis
10.7326/annals-24-01249 · 2024 · External reference
Systematic narrative review of modalities in physiotherapy for managing pain in hip and knee osteoarthritis: A review
10.1097/md.0000000000038225 · 2024 · External reference
Coupling mechanism of bioinspired artificial composite synovial fluid on the tribological behavior of artificial joints
10.26599/frict.2025.9440908 · 2025 · External reference
Fretting-corrosion mechanisms of Ti6Al4V against CoCrMo in simulated body fluid under various fretting states
10.1007/s40544-024-0909-0 · 2024 · External reference
Wear mechanism and wear debris characterization of ULWPE in multidirectional motion
10.1007/s11249-024-01928-3 · 2024 · External reference
In-vitro investigation on the tribological properties of nickel–titanium (NiTi) alloy used in braided vascular stents
10.1080/10402004.2024.2401411 · 2024 · External reference
Running-in friction of hip joint replacements can be significantly reduced: The effect of surface-textured acetabular cup
10.1007/s40544-019-0351-x · 2020 · External reference
Mechanical and bio-tribological behaviours of UHMWPE based composites for prosthetic hip joint implant application
10.1016/j.triboint.2024.109945 · 2024 · External reference
Computer image analysis for identification of wear particles
10.1016/0043-1648(91)90165-q · 1991 · External reference
Three-dimensional reconstruction of wear particle surfaces using multi-focus image sequence
10.1016/j.measurement.2024.114925 · 2024 · External reference
Coupling influence between environmental factors and synovial fluid components on tribological behavior of hip joint materials
10.1016/j.triboint.2024.110464 · 2025 · External reference
Characteristics of VE/HXLPE wear particle morphology: A comparative analysis with UHMWPE in an in vitro artificial tibial insert simulation test
10.1016/j.wear.2024.205390 · 2024 · External reference
The morphological attributes of wear particles—their role in identifying wear mechanisms
10.1016/0043-1648(94)90174-0 · 1994 · External reference
Characterization of wear particles for comprehension of wear mechanisms: Case of PTFE against cast iron
10.1016/j.wear.2008.04.036 · 2008 · External reference
Probability-weighted ensemble support vector machine for intelligent recognition of moving wear debris from joint implant
10.1016/j.triboint.2023.108583 · 2023 · External reference
Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
10.1007/s40544-021-0516-2 · 2022 · External reference
Study on wear debris characteristics of different polyethylene under reciprocating sliding and compound motion modes
10.1016/j.wear.2023.205062 · 2023 · External reference
A hybrid convolutional neural network for intelligent wear particle classification
10.1016/j.triboint.2019.05.029 · 2019 · External reference
WP-DRnet: A novel wear particle detection and recognition network for automatic ferrograph image analysis
10.1016/j.triboint.2020.106379 · 2020 · External reference
Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor
10.1007/s40544-023-0800-4 · 2024 · External reference
A wear debris recognition method based on dual-channel electrostatic signal and prominence of cross-correlation function
10.1016/j.triboint.2023.109020 · 2023 · External reference
Intelligent classification of online wear particle in lubricating oil using optical direct imaging method and convolutional neural network for rotating machinery
10.1016/j.triboint.2023.109015 · 2023 · External reference
TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion
10.1038/s41598-024-82961-z · 2024 · External reference
Imbalanced data preprocessing techniques for machine learning: A systematic mapping study
10.1007/s10115-022-01772-8 · 2023 · External reference
Cost-sensitive learning of deep feature representations from imbalanced data
10.1109/tnnls.2017.2732482 · 2018 · External reference
10.1109/iccv.2017.324
10.1109/iccv.2017.324 · External reference
10.1007/978-3-030-89010-0_4
10.1007/978-3-030-89010-0_4 · External reference
A broad review on class imbalance learning techniques
10.1016/j.asoc.2023.110415 · 2023 · External reference
SMOTE: Synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
Learning from imbalanced data
10.1109/tkde.2008.239 · 2009 · External reference
Generative adversarial networks in medical image augmentation: A review
10.1016/j.compbiomed.2022.105382 · 2022 · External reference
Wear mechanism and debris analysis of PEEK as an alternative to CoCrMo in the femoral component of total knee replacement
10.1007/s40544-022-0700-z · 2023 · External reference
10.1109/iros47612.2022.9982240
10.1109/iros47612.2022.9982240 · External reference
Wear particle analysis—Utilization of quantitative computer image analysis: A review
10.1016/j.triboint.2005.03.013 · 2005 · External reference
10.1145/3543081.3543087
10.1145/3543081.3543087 · External reference
10.1109/iccv.2015.123
10.1109/iccv.2015.123 · External reference
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · External reference
Despeckling of SAR images using residual twin CNN and multi-resolution attention mechanism
10.3390/rs15143698 · 2023 · External reference
10.1109/igarss47720.2021.9553597
10.1109/igarss47720.2021.9553597 · External reference
10.1109/icdl55364.2023.10364530
10.1109/icdl55364.2023.10364530 · External reference
