Te Han Sr.

Personal Details

First Name:	Te
Middle Name:
Last Name:	Han
Suffix:	Sr.
RePEc Short-ID:	pha1518
	[This author has chosen not to make the email address public]
	https://scholar.google.com/citations?hl=zh-CNuser=HqIb9_EAAAAJview_op=list_works

Affiliation

Center for Energy and Environmental Policy Research (CEEP)
Beijing Institute of Technology

Beijing, China
http://ceep.bit.edu.cn/
RePEc:edi:cebitcn (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).

   Cited by:

   1. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
   2. Nak-Hun Choi & Jung Woo Sohn & Jong-Seok Oh, 2023. "Defect Detection Model Using CNN and Image Augmentation for Seat Foaming Process," Mathematics, MDPI, vol. 11(24), pages 1-13, December.
   3. Bao, Xinyuan & Chen, Liping & Lopes, António M. & Li, Xin & Xie, Siqiang & Li, Penghua & Chen, YangQuan, 2023. "Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries," Energy, Elsevier, vol. 278(C).

2. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

   Cited by:

   1. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
   2. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

3. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

   Cited by:

   1. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2023. "Reliable neural networks for regression uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   2. Xue, Gang & Liu, Shifeng & Ren, Long & Gong, Daqing, 2024. "Risk assessment of utility tunnels through risk interaction-based deep learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
   3. Rombach, Katharina & Michau, Gabriel & Fink, Olga, 2023. "Controlled generation of unseen faults for Partial and Open-Partial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   4. Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   5. Zhang, Xingwu & Zhao, Yu & Yu, Xiaolei & Ma, Rui & Wang, Chenxi & Chen, Xuefeng, 2023. "Weighted domain separation based open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
   6. Yu, Xiaolei & Zhao, Zhibin & Zhang, Xingwu & Chen, Xuefeng & Cai, Jianbing, 2023. "Statistical identification guided open-set domain adaptation in fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
   7. Zhou, Taotao & Zhang, Xiaoge & Droguett, Enrique Lopez & Mosleh, Ali, 2023. "A generic physics-informed neural network-based framework for reliability assessment of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   8. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   9. Yuan, Zixia & Xiong, Guojiang & Fu, Xiaofan & Mohamed, Ali Wagdy, 2023. "Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
   10. Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Xie, Chenlin & Chen, Ke & Kou, Lei, 2023. "Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   11. Xu, Jinjin & Wang, Rongxi & Liang, Zeming & Liu, Pengpeng & Gao, Jianmin & Wang, Zhen, 2023. "Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
   12. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   13. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
   14. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   15. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
   16. Yang, Zhen & Dong, Xiaobin & Guo, Li, 2023. "Scenario inference model of urban metro system cascading failure under extreme rainfall conditions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   17. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   18. Li, Xin & Li, Yong & Yan, Ke & Shao, Haidong & (Jing) Lin, Janet, 2023. "Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

4. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

   Cited by:

   1. Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   2. Yu, Xiaolei & Zhao, Zhibin & Zhang, Xingwu & Chen, Xuefeng & Cai, Jianbing, 2023. "Statistical identification guided open-set domain adaptation in fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
   3. Xia, Pengcheng & Huang, Yixiang & Tao, Zhiyu & Liu, Chengliang & Liu, Jie, 2023. "A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
   4. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   5. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   6. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
   7. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   8. Cao, Yudong & Jia, Minping & Zhao, Xiaoli & Yan, Xiaoan & Feng, Ke, 2024. "Complex augmented representation network for transferable health prognosis of rolling bearing considering dynamic covariate shift," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
   9. Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
   10. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   11. Ma, Yulin & Yang, Jun & Li, Lei, 2023. "Gradient aligned domain generalization with a mutual teaching teacher-student network for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
   12. Wang, Huan & Li, Yan-Fu, 2023. "Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
   13. Li, Xin & Li, Yong & Yan, Ke & Shao, Haidong & (Jing) Lin, Janet, 2023. "Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   14. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

5. Li, Xin & Zhong, Xiang & Shao, Haidong & Han, Te & Shen, Changqing, 2021. "Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

   Cited by:

   1. Xue, Gang & Liu, Shifeng & Ren, Long & Gong, Daqing, 2024. "Risk assessment of utility tunnels through risk interaction-based deep learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
   2. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   3. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
   4. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
   5. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
   6. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
   7. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Bian, Wenbin, 2023. "Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
   8. Na, Kyumin & Yoon, Heonjun & Kim, Jaedong & Kim, Sungjong & Youn, Byeng D., 2023. "PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   9. Li, Xin & Li, Yong & Yan, Ke & Shao, Haidong & (Jing) Lin, Janet, 2023. "Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
   10. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
   11. Zhang, Xinwei & Feng, Yong & Chen, Jinglong & Liu, Zijun & Wang, Jun & Huang, Hong, 2024. "Knowledge distillation-optimized two-stage anomaly detection for liquid rocket engine with missing multimodal data," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

6. Zhenyu He & Xiaochen Zhang & Chao Liu & Te Han, 2020. "Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model," Energies, MDPI, vol. 13(18), pages 1-20, September.

   Cited by:

   1. Kuei-Hsiang Chao & Chen-Hou Ke, 2020. "Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives," Energies, MDPI, vol. 13(23), pages 1-25, November.
   2. Tarek Berghout & Mohamed Benbouzid & Leïla-Hayet Mouss, 2021. "Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
   3. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.

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