姓名：邵伟明

系属：化工装备与控制工程系

学位：博士研究生

职称：副教授

专业：控制理论与控制工程

导师类别：博士生导师

电子邮箱：shaoweiming@upc.edu.cn

通讯地址：山东省青岛市黄岛区长江西路66号

个人主页：http:/2020/0929/c15054a317410/page.htm

• ※ 研究方向

• 工业大数据驱动的过程装备智能化建模及应用

   ※教育经历
2010.9-2016.6kaiyun体育登录网页入口（华东）自动化系，研究生（博士）

2014.11-2015.11哈里法科技大学（原阿布扎比石油学院）电气工程系访问学者

2005.9-2009.7kaiyun体育登录网页入口（华东）自动化系，本科（学士）

• ※ 工作经历

2020.1至今kaiyun体育登录网页入口（华东）新能源学院装控系，副教授

2019.12kaiyun体育登录网页入口（华东）新能源学院装控系，特任副教授

2017.3-2019.11浙江大学控制科学与工程学院，博士后

2016.7-2017.2青岛鼎信通讯股份有限公司，研发工程师

• ※ 主讲课程

1.控制工程基础（本科生）

2.过程装备测控技术（本科生）

3.现代控制理论（研究生）

• ※ 学术兼职

中国自动化学会数据驱动控制、学习与优化专业委员会委员

• ※ 研究生指导情况

招收研究生方向:

1.机器学习、统计学习、深度学习等方法研究

2.工业过程与设备的关键性能指标在线智能感知、故障智能分类与诊断

• ※ 承担科研项目

1.国家自然科学基金重点基金项目（核心成员，5/18）：面向故障诊断的流程工业大数据分析与分布式建模方法2019.1-2023.12，61833014

2.中央高校基本科研业务费专项资金项目（主持）：大规模化工过程分布式建模与应用，2020.5-2022.4，20CX06010A

3.中国博士后科学基金面上项目（主持）：基于集成概率模型的半监督软测量建模方法研究,2017.11-2019.11，2017M621929

4.中国博士后科学基金特别资助项目（主持）：基于概率模型的连铸坯皮下夹渣缺陷实时预报与诊断方法，2019.6-2019.11，2019T120516

5.国家自然科学基金青年基金项目（主持）：基于半监督集成学习的化工过程自适应软测量建模方法研究2018.1-2020.12，61703367

6.工业控制技术国家重点实验室开放课题（主持）：基于混合模型的动态过程产品质量鲁棒预测与监测方法研究，2021.1-2021.12,ICT2021B50

7.国家自然科学基金面上项目（主持）：石化过程产品质量指标分布式软测量方法研究与应用验证，2022.01-2025.12，62173344

• ※ 获奖情况

1.邵伟明，田学民，宋执环,第28届中国过程控制会议优秀张贴论文奖，2017

2.LeYao, Zhiqiang Ge, Weiming Shao,Zhihuan Song,第7届数据驱动控制与学习系统会议优秀论文提名奖，2018

3.Weiming Shao, Yougao Li, Dongya Zhao, Zhiqiang Ge.第10届数据驱动控制与学习系统会议优秀论文提名奖，2021

• ※ 发表论文

  1. Weiming Shao, Xuemin Tian,Ping Wang. Local partial least squares based on line soft sensingmethod for multi-output processes with adaptive process statedivision, Chinese Journal of Chemical Engineering, 2014, 22(7):828-836. (IF: 2.627)

  2. Weiming Shao, Xuemin Tian,Ping Wang. Supervised local and non-local structure preservingprojections with application to just-in-time learning foradaptive soft sensor, Chinese Journal of Chemical Engineering,2015, 23(12): 1925-1934. (IF: 2.627)

  4. Weiming Shao, Xuemin Tian.Adaptive soft sensor for quality prediction of chemical processesbased on selective ensemble of local partial least squaresmodels, Chemical Engineering Research and Design, 2015, 95:113-132. (IF: 3.350)

  5. Weiming Shao, Igor Boiko,Ahmed Al-Durra. Control-oriented modeling of gas-lift system andanalysis of casing-heading instability, Journal of Natural GasScience and Engineering, 2016, 33: 573-586. (IF: 3.841)

  6. Weiming Shao, Igor Boiko,Ahmed Al-Durra. Plastic bag model of the artificial gas liftsystem for slug analysis, Journal of Natural Gas Science andEngineering, 2016, 29: 365-381. (IF: 3.841)

  7. Weiming Shao, Xuemin Tian.Semi-supervised selective ensemble learning based on distance tomodel for nonlinear soft sensor development, Neurocomputing,2017, 222: 91-104. (IF: 4.438)

  8.邵伟明,田学民,宋执环,基于集成学习的多产品化工过程软测量建模方法,化工学报,2018, 69(6): 2551-2559. (EI)

  9. Weiming Shao, Sheng Chen,Chris J. Harris, Adaptive Soft Sensor Development forMulti-Output Industrial Processes Based on Selective EnsembleLearning, IEEE Access, 2018,6: 55628-55642. (IF: 3.745)

  10. Jingbo Wang, WeimingShao*, Zhihuan Song, Student’s-t Mixture Regression-BasedRobust Soft Sensor Development for Multimode IndustrialProcesses, Sensors, 2018, 19(11): 3968. (IF: 3.275, top)

  11. Jingbo Wang, WeimingShao*, Zhihuan Song*, Semi-supervised Variational BayesianStudent's t Mixture Regression and Robust Inferential SensorApplication, Control Engineering Practice, 2019, 92: 104155. (IF:3.193)

  12. Jingbo Wang, WeimingShao*, Zhihuan Song*, Robust Inferential Sensor Development Basedon Variational Bayesian Student's-t Mixture Regression,Neurocomputing, 2019, 369: 11-28. (IF: 4.438)

  13. Weiming Shao, HongweiZhang, Zhihuan Song, Robust supervised probabilistic factoranalysis and its application to industrial soft sensor modeling,IEEEAccess,2019,7: 184038-184052. (IF: 3.745)

  14. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Semi-supervised Mixture of Latent Factor AnalysisModels with Application to Online Key Variable Estimation,Control Engineering Practice,2019, 84: 32-47. (IF: 3.232)

  15. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Kai Wang, Nonlinear Industrial Soft SensorDevelopment Based on Semi-supervised Probabilistic Mixture ofExtreme Learning Machines, Control Engineering Practice,2019,91:104098, (IF: 3.193)

  16. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Soft-Sensor Development for Processes With MultipleOperating Modes Based on Semisupervised Gaussian MixtureRegression, IEEE Transactions on Control Systems andTechnology,2019,27(5):2169-2181. (IF: 5.312)

  17. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Quality Variable Prediction for Chemical ProcessesBased on Semisupervised Dirichlet Process Mixture of Gaussians,Chemical Engineering Science, 2019, 193: 394-410. (IF: 3.871,top)

  18. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Parallel Computing and SGD Based DPMM For SoftSensor Development With Large-Scale Semisupervised Data, IEEETransactions on Industrial Electronics,2019, 66(8): 6362-6373.(IF: 7.515, top)

  19. ChihangWei, Weiming Shao,Zhihuan Song, Virtual sensor development for multi-outputnonlinear processes based on bilinear neighborhood preservingregression model with localized construction, IEEE Transactionson Industrial Informatics, 2021, 17(4): 2500-2510 (IF: 9.112,top)

  20. LeYao, Weiming Shao,Zhiqiang Ge, Hierarchical Quality Monitoring for Large-ScaleIndustrial Plants With Big Process Data, IEEE Transactions onNeural Networks and Learning Systems, 2020, DOI:10.1109/TNNLS.2019.2958184 (IF: 8.793, top)

  21.LiuKang, Weiming Shao*,Guoming Chen, Autoencoder-based nonlinear Bayesian locallyweighted regression for soft sensor development, ISATransactions,2020, 103: 143-155 (IF: 4.305, top)

  22. Weiming Shao, Zhiqiang Ge,Le Yao, Zhihuan Song, Bayesian Nonlinear Gaussian MixtureRegression and its Application to Virtual Sensing for MultimodeIndustrial Processes, IEEE Transactions on Automation Science andEngineering, 2020, 17(3): 871-885 (IF: 4.938)

  23. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Bayesian Just-in-time learning and its applicationto industrial soft sensing, IEEE Transactions on IndustrialInformatics,2020,16(4): 2787-2798 (IF: 9.112, top)

  24. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Jingbo Wang, Semi-supervised Robust Modeling ofMultimode Industrial Processes for Quality Variable PredictionBased on Student's t Mixture Model, IEEE Transactions onIndustrial Informatics, 2020,16(5): 2965-2976 (IF: 9.112, top)

  25. Weiming Shao, Zhiqiang Ge,Zhihuan Song, Semisupervised Bayesian Gaussian Mixture Models fornon-Gaussian Soft sensor, IEEE Transactions on Cybenetrics, 2020,DOI: 10.1109/TCYB.2019.2947622 (IF: 11.079, top)

  26. Jingbo Wang, WeimingShao*,Xinmin Zhang, Zhihuan Song*.Dynamic Variational Bayesian Student's T Mixture Regression WithHidden Variables Propagation for Industrial Inferential SensorDevelopment. IEEE Transactions on Industrial Informatics, 2021,16(4): 2787-2798 (IF: 9.112, top)

  27. Jingbo Wang, WeimingShao*,Xinmin Zhang, Jinchuan Qian, Zhihuan Song*,Zhiping Peng. Nonlinear variational Bayesian Student’s-tmixture regression and inferential sensor application withsemisupervised data. Journal of Process Control, 2021, 105:141-159. (IF: 3.666)

• ※ 专利

  1.邵伟明，宋执环，一种基于半监督混合模型的聚丙烯熔融指数预测方法，发明专利，201711236164.4，已授权

  2.邵伟明，宋执环，一种基于半监督贝叶斯高斯混合模型的合成氨过程一段炉氧气含量在线估计方法,发明专利,201810338582.2,已授权

  3.邵伟明,宋执环,一种基于鲁棒混合模型的化工过程浓度变量在线估计方法，发明专利，201810678469.9，公开

  4.邵伟明,宋执环.基于集成学习的化工过程软测量建模方法仿真与测试平台V1.0，软件著作权，2019，登记号:2019SR 1169860