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师资队伍
尚超

助理教授(特别研究员)

博士生导师

控制与决策研究所

电话: 010-62782459
地点:北京千赢国系


教育背景


2011年8月-2016年7月 千赢国系控制科学与工程专业学习,获工学博士学位

2007年8月-2011年7月 在千赢国系专业学习,获工学学士学位


工作履历


2018.10-至今 千赢国系 助理教授(特别研究员)

2016.10-2018.10 美国康奈尔大学 & 清华大学 博士后


学术兼职


IEEE Member

中国自动化学会大数据专委会 委员

中国化工学会信息技术专业委员会 青年委员

《Journal of Process Control》、《Control Engineering Practice》、《IEEE Trans. on Industrial Electronics》、《Computers & Chemical Engineering》等期刊审稿人


研究领域


[1] 大数据解析及工业应用

[2] 数据驱动的不确定规划技术及应用

[3] 过程监控与故障诊断

[4] 数据驱动的工业过程建模


研究概况


近年来,大数据蓬勃发展为控制学科带来了新的机遇与挑战。随着数据信息量与计算机运算能力的快速增长,人类处理复杂决策问题的能力正在不断增强。一方面,通过有效地收集分析数据,人们能够更好地感知并适应环境的变化,并对决策进行针对性调整;另一方面,基于大数据更深层次的不确定信息能被挖掘出来,在此基础上,不断地提高智能控制与智能决策的水平。本人研究针对数据驱动的建模、监控、诊断以及优化方法展开,并以实际工业制造过程为背景,将控制理论、人工智能以及运筹学进行有机结合,具有多学科交叉的特点。累计发表期刊论文近20篇,被引用700余次,另有5项国家发明专利已授权。


奖励与荣誉


1.1st International Conference on Industrial Artificial Intelligence Best Paper Award, 2019

2.Springer Doctorate Theses Award,2018

3.清华大学“紫荆学者”,2016

4.清华大学优秀博士论文一等奖,2016

5.北京市优秀毕业生,2016

6.清华大学教学成果奖一等奖,2016

7.清华大学“一二?九”辅导员奖,2015


学术成果


学术专著

C. Shang (2018). Dynamic Modeling of Complex Industrial Processes: Data-Driven Methods and Application Research. Springer, 2018. ISBN 978-981-10-6676-4. (143 pages)

主要论文

[J18] Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. To appear in Engineering.

[J17] Shang, C., & You, F. (2019). A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control, 75, 24-39.

[J16] Shang, C., & You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Computers & Chemical Engineering, 110, 53-68.

[J15] Shang, C., Yang, F., Huang, B., & Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Transactions on Industrial Electronics, 65(11), 8895-8905.

[J14] Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., & Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering, 130, 997-1003

[J13] Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464-479.

[J12] Gao, X., Shang, C., Huang, D., & Yang, F. (2017). A novel approach to monitoring and maintenance of industrial PID controllers. Control Engineering Practice, 64, 111-126.

[J11] Gao, X., Zhang, J., Yang, F., Shang, C., & Huang, D. (2017). Robust proportional–integral-derivative (PID) design for parameter uncertain second-order plus time delay (SOPTD) processes based on reference model approximation. Industrial & Engineering Chemistry Research, 56(41), 11903-11918.

[J10] Gao, X., Yang, F., Shang, C., & Huang, D. (2017). A novel data-driven method for simultaneous performance assessment and retuning of PID controllers. Industrial & Engineering Chemistry Research, 56(8), 2127-2139.

[J9] Shang, C., Huang, B., Yang, F., & Huang, D. (2016). Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 39, 21-34.

[J8] Guo, F., Shang, C., Huang, B., Wang, K., Yang, F., & Huang, D. (2016). Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemometrics and Intelligent Laboratory Systems, 151, 115-125.

[J7] Gao, X., Yang, F., Shang, C., & Huang, D. (2016). A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era. Chinese Journal of Chemical Engineering, 24(8), 952-962.

[J6] Shang, C., Huang, B., Yang, F., & Huang, D. (2015). Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE Journal, 2015, 61(12), 4126-4139.

[J5] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A. K., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 2015, 61(11), 3666-3682.

[J4] Shang, C., Huang, X., Suykens, J. A. K., & Huang, D. (2015) Enhancing dynamic soft sensors based on DPLS: a temporal smoothness regularization approach. Journal of Process Control, 28, 17-26.

[J3] Gao, X., Shang, C., Jiang, Y., Huang, D., & Chen, T. (2014). Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal, 60(7), 2525-2532.

[J2] Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24(3), 223-233.

[J1] Shang, C., Gao, X., Yang, F., & Huang, D. (2014). Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response. IEEE Transactions on Control Systems Technology, 22(4), 1550-1557.

发明专利

1. 黄德先,尚超,杨帆,高莘青. 基于缓慢特征回归的动态软测量方法和系统: 中国, CN104537260B. (中国专利授权号.)

2. 黄德先,尚超,杨帆,高莘青. 基于缓慢特征分析的过程监控方法和系统: 中国, CN104598681B. (中国专利授权号.)

3. 黄德先,尚超,高莘青,吕文祥. 基于贝叶斯框架的动态软测量建模方法及装置: 中国, CN103279030B. (中国专利授权号.)

4. 吴彬, 尚超, 宋晓玲, 黄德先, 夏月星, 姚佳清, 高莘青, 熊新阳, 朱绍平, 黄富铭. 乙炔法合成氯乙烯生产过程的在线预警方法: 中国, CN105204465B. (中国专利授权号.)

5. 吴彬, 尚超, 宋晓玲, 黄德先, 夏月星, 姚佳清, 高莘青, 熊新阳, 朱绍平, 黄富铭. 聚氯乙烯合成过程低沸塔尾气冷凝在线监控及报警方法: 中国, CN105404251B. (中国专利授权号.)


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