秦岩，教授/博导，重庆大学弘深优秀学者，国家青年高层次人才（2023）。

个人主页：https://sites.google.com/view/cquqinyan

教育背景

2007.09-2011.07 信息工程大学，工学学士

2011.09-2013.07 东北大学，工学硕士

2013.09-2018.06 浙江大学，工学博士

2016.12-2017.05 University of Alberta，访学

2019.04-2021.05 新加坡科学设计大学，博士后

2021.05-2022.12 新加坡南洋理工大学，博士后

2023.03-2023.10 重庆大学，副教授/硕导

2023.11- 重庆大学，教授/博导

专业领域：

工业大数据分析技术，人工智能技术

主要研究方向：

近年来，以“复杂工业过程（连续化工、批次制造、机械加工等）的高效过程监测及质量预测与自愈控制”、“工控系统网络攻击检测与防御机制”及“工业物联网设备异常监测和剩余寿命分析”为主题，针对多时段、非平稳等工业特性，利用机器学习方法致力于基础研究和应用基础研究工作。

学术兼职和荣誉：

现（曾）任多部国际学术期刊Associate Editor，包括：

· SN Computer Science, Associate Editor

· SCI期刊Processes（IF=3.352），客座主编，“Machine learning in model predictive control and optimal control”

· 会议专题主席，49th Annual Conference of the IEEE Industrial Electronics Society，“Industrial Internet of Things (IoT) for Industrial Process Health Prognosis in the era of Artificial Intelligence”, 2023

· 会议专题主席，2022 IEEE Conference on Smart Data Workshop，“Digital twin and edge computing for cyber physical system: modeling, communication, and learning”, 2022

· 会议专题主席，2022 Asia Control Conference，“Security, privacy, and optimization of industrial intelligent systems” , 2022

· 特邀报告，The 11th Asian Conference on Electrochemical Power Sources, “Transfer learning for health prognosis for Lithium-ion batteries” , 2022

科研情况简介：

面向数字化变革在智能制造中的前沿问题展开研究，在国际知名期刊和学术会议发表论文40余篇，其中以一作/唯一通讯作者发表/录用智能制造领域主流SCI期刊论文20余篇，包括中科院一区及Top期刊9篇（IEEE Trans. Industr. Inform.、IEEE Trans. Cybern.、IEEE/ASME Trans. Mechatron.、IEEE Trans. Veh. Technol.）和自动化领域顶级与知名期刊8篇（AIChE J.、J. Process Control、Chem. Eng. Sci.、Ind. Eng. Chem. Res.等）。在上述研究成果中，面向制造过程的多时段划分研究获2015年中国过程控制会议张仲俊院士优秀论文奖（该年度唯一）；申请人参与的发明专利“一种强磁选别过程运行控制方法”获第二届辽宁省专利奖一等奖（排名5/6）。此外，申请人受邀在新加坡举办的学术会议11th Asian Conference on Electrochemical Power Sources做Keynote报告“Transfer learning for health prognosis for lithium-ion batteries”。

论文（选录）

[1] Anushiya Arunan, Yan Qin（秦岩）*, Xiaoli Li, and Chau Yuen*. A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction [J]. IEEE Transactions on Automation Science and Engineering. Accepted, 2023.

[2] K.Q. Zhou, Y. Qin（秦岩）*, and C. Yuen*. Lithium-ion battery online knee onset detection by matrix profile [J]. IEEE Transactions on Transportation Electrification. Early Access, 2023.

[3] Y. Qin（秦岩）, Yuen Chau*, Yongliang Guan. Capsule neural network enabled vehicle trajectory prediction in the V2X network [J]. IEEE Transaction on Vehicular Technology. Early Access, 2023.

[4] Y. Qin（秦岩）, A. Auran, C. Yuen*. Digital twin for real-time Li-ion battery state of health estimation with partially discharged cycling data [J]. IEEE Transaction on Information Informatics. In press, 2023.

[5] Chintaka, Y. Qin（秦岩）*, C. Yuen, and et al. A hybrid deep learning model based remaining useful life estimation for reed relay with degradation pattern clustering [J]. IEEE Transaction on Information Informatics, Early Access, 2022.

[6] Y. Qin（秦岩）, C. Yuen*, X. Yin, H. Biao. A transferable multi-stage model with cycling discrepancy learning for Lithium-ion battery state of health estimation [J]. IEEE Transaction on Information Informatics, In press, 2022, DOI: 10.1109/TII.2022.3205942.

[7] K.Q. Zhou, Y. Qin（秦岩）*, and C. Yuen. Transfer learning-based state of health estimation for Lithium-ion battery with cycle synchronization [J]. IEEE/ASME Transactions on Mechatronics, In press, 2022, DOI: 10.1109/TMECH.2022.3201010.

[8] Y. Qin（秦岩）, C. Yuen*, Y.M. Shao, B. Qin, and X. Li. Slow-varying dynamics-assisted temporal capsule network for machinery remaining useful life estimation [J]. IEEE Transaction on Cybernetics, vol. 53, no. 1, pp. 592-606, Jan. 2023.

[9] Y. Qin（秦岩）*, W. Li, C. Yuen, W. Tushar, and T.K. Saha. IIoT-enabled health monitoring for integrated heat pump system using mixture slow feature analysis [J]. IEEE Transaction on Information Informatics, vol. 18, no. 7, pp. 4725-4736, 2022.

[10] Y. Qin（秦岩）*, S. Adams, and C. Yuen. A transfer learning-based state of charge estimation for Lithium-ion battery at varying ambient temperatures [J]. IEEE Transaction on Information Informatics, vol. 17, no. 11, pp. 7304-7315, 2021.

[11] Y. Qin（秦岩）, C.H. Zhao. A comprehensive process decomposition based on quality-relevant slow feature regression for soft sensor modelling [J]. Journal of Process Control. 2019. 77: 141-154.

[12] Y. Qin（秦岩）, C.H. Zhao, B. Huang. A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic process [J]. Chemical Engineering Science. 2019. 199: 28-39.

[13] Y. Qin（秦岩）, C.H. Zhao, F.R. Gao. An intelligent non-optimality self-recovery method based on reinforcement learning with small data in big data era [J]. Chemometrics and Intelligent Laboratory Systems. 2018. 176: 89-100.

[14] Y. Qin（秦岩）, C.H. Zhao, F.R. Gao. An iterative two-step sequential phase partition (ITSPP) method for batch process modelling and online monitoring [J]. AIChE Journal. 2016. 62(7): 2358-2373.

[15] 秦岩, 代伟, 杨杰, 周平. 基于软PLC技术的磨矿运行控制仿真系统的设计与实现[J]. 东北大学学报(EI期刊). 2015. 36(3): 309-313.

联系方式：

yan.qin@cqu.edu.cn; zdqinyan@gmail.com

重庆市沙坪坝区大学城中路20号重庆大学自动化学院信息大楼A418