Te Han
Orcid: 0000-0002-6559-1986
According to our database1,
Te Han
authored at least 30 papers
between 1979 and 2024.
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Bibliography
2024
Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection.
Remote. Sens., February, 2024
Unsupervised multimodal change detection based on adaptive optimization of structured graph.
Int. J. Appl. Earth Obs. Geoinformation, February, 2024
FFT-Trans: Enhancing Robustness in Mechanical Fault Diagnosis With Fourier Transform-Based Transformer Under Noisy Conditions.
IEEE Trans. Instrum. Meas., 2024
2023
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine.
Inf. Sci., November, 2023
Attention-aware temporal-spatial graph neural network with multi-sensor information fusion for fault diagnosis.
Knowl. Based Syst., October, 2023
Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis.
Reliab. Eng. Syst. Saf., August, 2023
Time-Efficient Neural Architecture Search for Autonomous Underwater Vehicle Fault Diagnosis.
IEEE Trans. Instrum. Meas., 2023
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications.
Reliab. Eng. Syst. Saf., 2023
A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems.
Eng. Appl. Artif. Intell., 2023
IEEE Access, 2023
Spatial Feature Regularization and Label Decoupling Based Cross-Subject Motor Imagery EEG Decoding.
Proceedings of the Pattern Recognition and Computer Vision - 6th Chinese Conference, 2023
2022
Positive-Unlabeled Learning-Based Hybrid Deep Network for Intelligent Fault Detection.
IEEE Trans. Ind. Informatics, 2022
Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework.
Reliab. Eng. Syst. Saf., 2022
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles.
Reliab. Eng. Syst. Saf., 2022
RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking.
Remote. Sens., 2022
A fault diagnosis method in industrial processes with integrated feature space and optimized random forest.
Proceedings of the 31st IEEE International Symposium on Industrial Electronics, 2022
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022
2021
A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions.
IEEE Trans. Instrum. Meas., 2021
Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression.
Reliab. Eng. Syst. Saf., 2021
Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM).
Remote. Sens., 2021
Appl. Soft Comput., 2021
2019
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults.
Knowl. Based Syst., 2019
2018
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery.
Trans. Inst. Meas. Control, 2018
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application.
CoRR, 2018
2017
Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.
Sensors, 2017
Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine.
J. Sensors, 2017
2013
A novel frequency search algorithm to achieve fast locking without phase tracking in ADPLL.
Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2013
A 2.7-GHz digitally-controlled ring oscillator with supply sensitivity of 0.0014%-fDCO/1%-VDD using digital current-regulated tuning.
Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2013
1979
IEEE Trans. Inf. Theory, 1979