Te Han

发表

A novel frequency search algorithm to achieve fast locking without phase tracking in ADPLL

Bohan Wu, Weixin Gai, Te Han, 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

Adversarial Domain Adaptation for Gear Crack Level Classification Under Variable Load

Dongdong Wei, Fulei Chu, Te Han, 2020, 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM).

A 40-Gb/s low-power wireline transceiver architecture with multi-phase injection-locked clocking scheme

Te Han, Wei-Xin Gai, 2012, 2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology.

Application of Variational Mode Decomposition to Feature Isolation and Diagnosis in a Wind Turbine

Qi Zhao, Te Han, Dongxiang Jiang, 2019, Journal of Vibration Engineering & Technologies.

Dynamic characteristics of rotor system and rub-impact fault feature research based on casing acceleration

Te Han, Nanfei Wang, D. X. Jiang, 2016 .

Dynamic Characteristics and Experimental Research of Dual-Rotor System with Rub-Impact Fault

Dongxiang Jiang, Te Han, Nanfei Wang, 2016 .

Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification

Te Han, Dongxiang Jiang, Yankui Sun, 2018 .

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

Xiaochen Zhang, Te Han, Dongxiang Jiang, 2017, Sensors.

Feature dimension reduction method of rolling bearing based on quantum genetic algorithm

Xiaochen Zhang, Te Han, Dongxiang Jiang, 2016, 2016 Prognostics and System Health Management Conference (PHM-Chengdu).

Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising

Dongxiang Jiang, Te Han, 2019 .

Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm

Quan Long, Xiaochen Zhang, Te Han, 2017 .

Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

Chao Liu, Wenguang Yang, Te Han, 2018, ISA transactions.

A noise-insensitive high-resolution digitally controlled ring oscillator with multipoint-tuning technique

Li Liu, Te Han, Lu Li, 2012, 2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology.

Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

Yan Zhang, Shuai Zhao, Haifeng Xu, 2019, Mathematical Problems in Engineering.

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

Lei Wang, Qi Zhao, Te Han, 2018, Trans. Inst. Meas. Control.

An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

Chao Liu, Soumik Sarkar, Te Han, 2019, Mechanical Systems and Signal Processing.

A digital 2.4-GHz transmitter for ZigBee application

Li Li, Te Han, Wei-Xin Gai, 2012, 2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology.

Hypothesis testing with multiterminal data compression

Te Han, T. Han, 1987, IEEE Trans. Inf. Theory.

Study on the diagnosis of rub-impact fault based on finite element method and envelope demodulation

Dongxiang Jiang, Te Han, Nanfei Wang, 2016 .

Effects of Tooth Breakage Size and Rotational Speed on the Vibration Response of a Planetary Gearbox

Wenguang Yang, Te Han, Dongxiang Jiang, 2017 .

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

Chao Liu, Wenguang Yang, Te Han, 2019, Knowl. Based Syst..

Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model

Zhenyu He, Xiaochen Zhang, Chao Liu, 2020, Energies.

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Wenguang Yang, Xiaochen Zhang, Te Han, 2017, J. Sensors.

Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions.

Chao Liu, Wenguang Yang, Te Han, 2019, ISA transactions.

A 2.7-GHz digitally-controlled ring oscillator with supply sensitivity of 0.0014%-fDCO/1%-VDD using digital current-regulated tuning

Weixin Gai, Te Han, 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

Source coding with cross observations at the encoders (Corresp.)

Te Han, T. Han, 1979, IEEE Trans. Inf. Theory.

Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

Te Han, Dongxiang Jiang, D. Jiang, 2016 .

A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

Te Han, Yan-Fu Li, Min Qian, 2021, IEEE Transactions on Instrumentation and Measurement.

Deep transfer learning with limited data for machinery fault diagnosis

Te Han, Dongxiang Jiang, Chao Liu, 2021, Appl. Soft Comput..

Weighted domain adaptation networks for machinery fault diagnosis

Fulei Chu, Ming J. Zuo, Dongdong Wei, 2021 .

The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

Te Han, Dongxiang Jiang, Nanfei Wang, 2016 .