Te Han / 韩特

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	Sve	Od 2020.
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Suautori

Chao LiuAssociate Professor, Department of Energy and Power Engineering, Tsinghua University, ChinaPotvrđena adresa e-pošte na tsinghua.edu.cn
Haidong ShaoAssociate Professor, College of Mechanical and Vehicle Engineering, Hunan University,Potvrđena adresa e-pošte na hnu.edu.cn
Yan-Fu LiProfessor, Tsinghua University, ChinaPotvrđena adresa e-pošte na tsinghua.edu.cn
Taotao ZhouAssociate Professor, China University of Petroleum-BeijingPotvrđena adresa e-pošte na cup.edu.cn
Min XieChair Professor of Industrial Engineering, City University of Hong KongPotvrđena adresa e-pošte na cityu.edu.hk
Enrique Lopez DroguettUniversity of California, Los AngelesPotvrđena adresa e-pošte na ucla.edu
Yi-Ming Wei (魏一鸣)Center of Energy and Environmental Policy Research, Beijing Institute of TechnologyPotvrđena adresa e-pošte na bit.edu.cn
Zhonghao Chang / 常中豪Beijing Institute of Technology, ChinaPotvrđena adresa e-pošte na bit.edu.cn
Jinpeng TianThe Hong Kong Polytechnic UniversityPotvrđena adresa e-pošte na polyu.edu.hk
Huan Wang / 王欢Department of Industrial Engineering, Tsinghua UniversityPotvrđena adresa e-pošte na mails.tsinghua.edu.cn
Ming J ZuoUniversity of AlbertaPotvrđena adresa e-pošte na ualberta.ca
Soumik SarkarDirector, Translational AI Center, Professor, Iowa State UniversityPotvrđena adresa e-pošte na iastate.edu
Linjiang WuSr. System EngineerPotvrđena adresa e-pošte na continental-corporation.com
A. MOSLEHUniversity of California Los Angeles (UCLA)Potvrđena adresa e-pošte na ucla.edu
Ke FengProfessor, Marie Curie Fellow, Xi'an Jiaotong UniversityPotvrđena adresa e-pošte na xjtu.edu.cn
PRADEEP KUNDUAssistant Professor, KU LeuvenPotvrđena adresa e-pošte na kuleuven.be
Konstantinos C. GrylliasProfessor, Department of Mechanical Engineering, KU Leuven, BelgiumPotvrđena adresa e-pošte na kuleuven.be
Shen YinNorwegian University of Science and TechnologyPotvrđena adresa e-pošte na ntnu.no
Rong-Gang CongBeijing Institute of TechnologyPotvrđena adresa e-pošte na bit.edu.cn

Prati

Te Han / 韩特

Ostala imenaT. Han, Han Te, Han T.

Associate Professor, Beijing Institute of Technology, China

Potvrđena adresa e-pošte na bit.edu.cn - Početna stranica

Sustainable Energy Smart Energy Prognostics and Health Management Machine Learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application T Han, C Liu, W Yang, D Jiang ISA Transactions 97, 269-281, 2020	508	2020
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults T Han, C Liu, W Yang, D Jiang Knowledge-Based Systems 165, 474-487, 2019	460	2019
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery T Han, D Jiang, Q Zhao, L Wang, K Yin Transactions of the Institute of Measurement and Control 40 (8), 2681-2693, 2018	339	2018
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions T Han, YF Li, M Qian IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021	208	2021
Deep transfer learning with limited data for machinery fault diagnosis T Han, C Liu, R Wu, D Jiang Applied Soft Computing 103, 107150, 2021	187	2021
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework T Zhou, T Han, EL Droguett Reliability Engineering & System Safety 224, 108525, 2022	182	2022
Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions T Han, C Liu, W Yang, D Jiang ISA Transactions 93, 341-353, 2019	177	2019
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems T Han, C Liu, L Wu, S Sarkar, D Jiang Mechanical Systems and Signal Processing 117, 170-187, 2019	172	2019
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles T Han, YF Li Reliability Engineering & System Safety 226, 108648, 2022	164	2022
Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data J Yao, T Han Energy 271, 127033, 2023	143	2023
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine T Han, W Xie, Z Pei Information Sciences 648, 119496, 2023	129	2023
Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis H Meng, M Geng, T Han Reliability Engineering & System Safety 236, 109288, 2023	128	2023
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer Y Xiao, H Shao, M Feng, T Han, J Wan, B Liu Journal of Manufacturing Systems 70, 186-201, 2023	121	2023
End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation T Han, Z Wang, H Meng Journal of Power Sources 520, 230823, 2022	108	2022
A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network T Zhou, S Jiang, T Han, SP Zhu, Y Cai International Journal of Fatigue 166, 107234, 2023	88	2023
Weighted domain adaptation networks for machinery fault diagnosis D Wei, T Han, F Chu, MJ Zuo Mechanical Systems and Signal Processing 158, 107744, 2021	86	2021
Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression X Li, X Zhong, H Shao, T Han, C Shen Reliability Engineering & System Safety 216, 108018, 2021	77	2021
Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification T Han, D Jiang, Y Sun, N Wang, Y Yang Measurement 118, 181-193, 2018	77	2018
Rolling Bearing Fault Diagnostic Method Based on VMD‐AR Model and Random Forest Classifier T Han, D Jiang Shock and Vibration 2016 (1), 5132046, 2016	59	2016
Challenges and opportunities for battery health estimation: Bridging laboratory research and real-world applications T Han, J Tian, CY Chung, YM Wei Journal of Energy Chemistry 89, 434-436, 2024	56	2024

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