Results 231 to 240 of about 2,969,144 (286)

A Facial Foundation Model for Clinical Biomarker Prediction and Real‐World Mobile Deployment

Advanced Science, EarlyView.
MedicalFaceFound is a facial foundation model developed through three‐stage progressive pre‐training, adapting representations from general vision to facial structure and clinical phenotypes. Using a single facial image, it estimates 62 biomarkers across multiple physiological systems and supports downstream risk prediction for cardiovascular ...
Tingfeng Xu, Huixuan Xu, Li Lin, Fei Wang, Yaodong Ding, Shuangqiao Liao, Xingyu Chen, Jichen Wen, Hongqiang Zhao, Jing Hao, Weixiang Zhao, Gong Zhang, Shiguang Shan, Yong Zeng, Hu Han, Minxian Wang   +15 more
wiley   +1 more source

A target-domain augmentation and class-wise alignment framework for few-shot cross-condition bearing fault diagnosis. [PDF]

PLoS One
Hu Y, Gu Y, Su Y, Zhao C.
europepmc   +1 more source

A Hybrid VMD-Transformer-BiLSTM Framework with Cross-Attention Fusion for Aileron Fault Diagnosis in UAVs. [PDF]

Sensors (Basel)
Song Y, Zheng W, Zhang X, Guo R.
europepmc   +1 more source

Hierarchical Residual Attribution with Supervised Channel Shortlisting for Sensor-Local and Process Fault Diagnosis in Multivariate Sensor Time Series. [PDF]

Sensors (Basel)
Wang Y, Lu X, Wang J.
europepmc   +1 more source

Physics-guided cross-domain adaptation: a hierarchical hybrid transformer framework with contrastive learning for robust fault diagnosis under variable working conditions. [PDF]

Sci Rep
Liu L, Hao C, Xiong L, Lv X.
europepmc   +1 more source

A hybrid convolutional-transformer neural network model for photovoltaic fault detection and localization. [PDF]

Sci Rep
Ramadan EA, Moawad NM, Abouzalam BA, Abouzaid WF, El-Banby GM.   +4 more
europepmc   +1 more source

A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines. [PDF]

Sensors (Basel)
Xu Z, Lu N, Jin L.
europepmc   +1 more source

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An unsupervised approach for fault diagnosis of power transformers

Quality and Reliability Engineering International, 2021
AbstractElectrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility.
Luis Dias, Miguel Ribeiro, Armando Leitão, Luís Guimarães, Leonel M. Carvalho, Manuel A. Matos, Ricardo J. Bessa   +6 more
openaire   +2 more sources