姓名：邵伟明

职称：副教授/硕士生导师
系属：化工装备与控制工程系
最高学位：博士
学科：动力工程及工程热物理
所学专业：控制理论与控制工程
电子邮箱：shaoweiming@upc.edu.cn
地址：山东省青岛市黄岛区长江西路66号中国石油大学（华东）新能源学院

学习与工作经历
2010.9-2016.6中国石油大学（华东）自动化系，研究生（博士）
2014.11-2015.11哈里发科技大学（原阿布扎比石油学院）电气工程系访问****
2005.9-2009.7中国石油大学（华东）自动化系，本科（学士）

2020.1至今中国石油大学（华东）新能源学院装控系，副教授
2019.12中国石油大学（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

研究方向
工业大数据驱动的智能化建模与应用

讲授课程
1.现代控制理论（研究生）
2.控制工程基础（本科生）

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虚拟测量、故障分类与诊断

承担科研课题
1.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，**
2.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
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
5.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，**

主要论文
1.WeimingShao,Zhiqiang Ge, Zhihuan Song, Semisupervised Bayesian Gaussian MixtureModels for non-Gaussian Soft sensor, IEEETransactions on Cybenetrics,2020, DOI: 10.1109/TCYB.2019.**(SCI,IF: 11.079, top)
2.WeimingShao,Zhiqiang Ge, Zhihuan Song, Jingbo Wang, Semi-supervised RobustModeling of Multimode Industrial Processes for Quality VariablePrediction Based on Student's t Mixture Model, IEEETransactions on Industrial Informatics,2020,16(5): 2965-2976 (SCI,IF: 9.112, top)
3.WeimingShao,Zhiqiang Ge, Zhihuan Song, Bayesian Just-in-time learning and itsapplication to industrial soft sensing, IEEETransactions on Industrial Informatics,2020,16(4): 2787-2798 (SCI, IF: 9.112, top)
4.WeimingShao,Zhiqiang Ge, Le Yao, Zhihuan Song, Bayesian Nonlinear GaussianMixture Regression and its Application to Virtual Sensing forMultimode Industrial Processes, IEEETransactions on Automation Science and Engineering,2020,17(3): 871-885 (SCI,IF: 4.938)
5.LiuKang, WeimingShao*,Guoming Chen, Autoencoder-based nonlinear Bayesian locally weightedregression for soft sensor development, ISATransactions,2020, 103: 143-155 (SCI, IF: 4.305, top)
6.LeYao, WeimingShao,Zhiqiang Ge, Hierarchical Quality Monitoring for Large-ScaleIndustrial Plants With Big Process Data, IEEETransactions on Neural Networks and Learning Systems,2020, DOI: 10.1109/TNNLS.2019.**(SCI,IF: 8.793,top)
7.ChihangWei, WeimingShao,Zhihuan Song, Virtual sensor development for multi-output nonlinearprocesses based on bilinear neighborhood preserving regression modelwith localized construction, IEEETransactions on Industrial Informatics,2020,DOI: 10.1109/TII.2020.**(SCI, IF: 9.112, top)
8.WeimingShao,Zhiqiang Ge, Zhihuan Song, ParallelComputing and SGD Based DPMM For Soft Sensor Development WithLarge-Scale Semisupervised Data,IEEETransactions on Industrial Electronics,2019, 66(8): 6362-6373. (SCI,IF: 7.515, top)
9.WeimingShao,Zhiqiang Ge, Zhihuan Song, QualityVariable Prediction for Chemical Processes Based on SemisupervisedDirichlet Process Mixture of Gaussians, ChemicalEngineering Science,2019, 193: 394-410. (SCI,IF: 3.871, top)
10.WeimingShao,Zhiqiang Ge, Zhihuan Song, Soft-SensorDevelopment for Processes With Multiple Operating Modes Based onSemisupervised Gaussian Mixture Regression, IEEETransactions on Control Systems and Technology,2019,27(5):2169-2181. (SCI,IF: 5.312)
11.WeimingShao,Zhiqiang Ge, Zhihuan Song, Kai Wang, Nonlinear Industrial SoftSensor Development Based on Semi-supervised Probabilistic Mixture ofExtreme Learning Machines, ControlEngineering Practice,2019,91: 104098, (SCI,IF: 3.193)
12.WeimingShao,Zhiqiang Ge, Zhihuan Song, Semi-supervised Mixture of Latent FactorAnalysis Models with Application to Online Key Variable Estimation,ControlEngineering Practice,2019, 84: 32-47. (SCI,IF: 3.232)
13.WeimingShao,Hongwei Zhang, Zhihuan Song,Robustsupervised probabilistic factor analysis and its application toindustrial soft sensor modeling, IEEEAccess,2019,7: 184038-184052. (SCI,IF: 3.745)
14.JingboWang, WeimingShao*,Zhihuan Song*, Robust Inferential Sensor Development Based onVariational Bayesian Student's-t Mixture Regression, Neurocomputing,2019,369: 11-28. (SCI,IF: 4.438)
15.JingboWang, WeimingShao*,Zhihuan Song*, Semi-supervised Variational Bayesian Student's tMixture Regression and Robust Inferential Sensor Application,ControlEngineering Practice,2019, 92: 104155. (SCI,IF: 3.193)
16.JingboWang, WeimingShao*,Zhihuan Song, Student’s-tMixture Regression-Based Robust Soft Sensor Development forMultimode Industrial Processes, Sensors,2018,19(11): 3968.(SCI,IF: 3.275,top)
17.WeimingShao,Sheng Chen, Chris J. Harris,AdaptiveSoft Sensor Development for Multi-Output Industrial Processes Basedon Selective Ensemble Learning, IEEEAccess,2018,6: 55628-55642. (SCI,IF: 3.745)
18.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)
19.WeimingShao,Xuemin Tian. Semi-supervisedselective ensemble learning based on distance to model fornonlinearsoftsensordevelopment,Neurocomputing,2017, 222: 91-104. (SCI, IF: 4.438)
20.WeimingShao,Igor Boiko, Ahmed Al-Durra. Plastic bag model of the artificial gaslift system for slug analysis, Journalof Natural Gas Science and Engineering,2016, 29: 365-381. (SCI, IF: 3.841)
21.WeimingShao,Igor Boiko, Ahmed Al-Durra. Control-oriented modeling of gas-liftsystem and analysis of casing-heading instability, Journalof Natural Gas Science and Engineering,2016, 33: 573-586. (SCI,IF: 3.841)
22.WeimingShao,Xuemin Tian. Adaptive soft sensor for quality prediction of chemicalprocesses based on selective ensemble of local partial least squaresmodels,ChemicalEngineering Research and Design,2015, 95: 113-132. (SCI,IF: 3.350)
23.WeimingShao,Xuemin Tian, Ping Wang, Xiaogang Deng, Sheng Chen. Online softsensor design using local partial least squares models with adaptiveprocess state partition, Chemometricsand Intelligent Laboratory Systems,2015, 144: 108-121. (SCI,IF: 2.895)
24.WeimingShao,Xuemin Tian, Ping Wang. Supervised local and non-local structurepreserving projections with application to just-in-time learning foradaptive soft sensor, ChineseJournal of Chemical Engineering,2015, 23(12): 1925-1934. (SCI,IF: 2.627)
25.WeimingShao,Xuemin Tian, Ping Wang. Local partial least squares based onlinesoft sensing method for multi-output processes with adaptive processstate division, ChineseJournal of Chemical Engineering,2014, 22(7): 828-836. (SCI,IF: 2.627)

专利与软件著作权
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，4.4，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,2.2, 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，9.9，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 **

奖励
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, WeimingShao,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