尚超，男，畢業於清華大學，工學博士，現任清華大學博士生導師。
基本介紹
- 中文名：尚超
- 學位/學歷：博士
- 職業：教師
- 專業方向：大數據解析及工業套用
- 任職院校：清華大學
個人經歷,研究方向,學術成果,榮譽獎項,
個人經歷
教育背景
2007年8月-2011年7月 在清華大學自動化系專業學習，獲工學學士學位
工作履歷
2018年10月-至今 清華大學自動化系 助理教授
2016年10月-2018年10月 美國康奈爾大學 & 清華大學 博士後
學術兼職
IEEE Member
中國化工學會信息技術專業委員會 青年委員
《Journal of Process Control》、《Control Engineering Practice》、《IEEE Trans. on Industrial Electronics》、《Computers & Chemical Engineering》等期刊審稿人
研究方向
[1] 大數據解析及工業套用
[2] 數據驅動的不確定規劃技術及套用
[3] 過程監控與故障診斷
[4] 數據驅動的工業過程建模
學術成果
近年來，大數據的蓬勃發展為控制學科帶來了新的機遇與挑戰。隨著數據信息量與計算機運算能力的快速增長，人類處理複雜決策問題的能力正在不斷增強。一方面，通過有效地收集分析數據，人們能夠更好地感知並適應環境的變化，並對決策進行針對性調整；另一方面，基於大數據更深層次的不確定信息能被挖掘出來，在此基礎上，不斷地提高智慧型控制與智慧型決策的水平。本人目前的研究針對數據驅動的建模、監控、診斷以及最佳化方法，並以實際工業製造過程為背景，將控制理論、人工智慧以及運籌學進行有機結合，具有多學科交叉的特點。累計發表期刊論文近20篇，被引用400餘次，其中一篇論文排名IFAC會刊Journal of Process Control高被引論文榜第二名，另有5項國家發明專利已授權。
學術專著
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)
主要論文
[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.
發明專利
1. 黃德先，尚超，楊帆，高莘青. 基於緩慢特徵回歸的動態軟測量方法和系統: 中國, CN104537260B. (中國專利授權號.)
2. 黃德先，尚超，楊帆，高莘青. 基於緩慢特徵分析的過程監控方法和系統: 中國, CN104598681B. (中國專利授權號.)
3. 黃德先，尚超，高莘青，呂文祥. 基於貝葉斯框架的動態軟測量建模方法及裝置: 中國, CN103279030B. (中國專利授權號.)
4. 吳彬, 尚超, 宋曉玲, 黃德先, 夏月星, 姚佳清, 高莘青, 熊新陽, 朱紹平, 黃富銘. 乙炔法合成氯乙烯生產過程的線上預警方法: 中國, CN105204465B. (中國專利授權號.)
5. 吳彬, 尚超, 宋曉玲, 黃德先, 夏月星, 姚佳清, 高莘青, 熊新陽, 朱紹平, 黃富銘. 聚氯乙烯合成過程低沸塔尾氣冷凝線上監控及報警方法: 中國, CN105404251B. (中國專利授權號.)
榮譽獎項
Springer Doctorate Theses Award，2018
清華大學“紫荊學者”，2016
清華大學優秀博士論文一等獎，2016
北京市優秀畢業生，2016
清華大學教學成果獎一等獎，2016
清華大學“一二·九”輔導員獎，2015
