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
2016.12-2017.05University 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
