个人简介
孙备，中南大学自动化学院教授、博士生导师。2015年12月获中南大学控制科学与工程专业工学博士学位，纽约大学联合培养博士(2012-2014)，芬兰阿尔托大学博士后(2016-2018)，中国科协青年托举人才、湖南省荷尖人才、湖湘青年英才、中南大学创新驱动人才。主要从事工业人工智能研究工作，主持国家重点研发计划课题，国家自然科学基金面上项目、青年基金，湖南省自然科学基金青年基金等项目。近5年第一/通讯作者发表论文31篇，合作出版英文专著1部，参编教材1部，申请/授权国家发明专利40项，获批软件著作权8项。兼任国际自动控制联合会TC6.2技术委员会委员，中国有色金属学会自动化学术委员会秘书长，中国自动化学会应用专业委员会和标准化工作委员会委员，International Journal of Minerals, Metallurgy and Materials(IJMMM)青年编委。曾获教育部技术发明奖一等奖1项，湖南省科学技术创新团队奖1项，中国自动化学会自然科学奖一等奖1项，中国有色金属工业协会科学技术奖一等奖2项。获全国有色金属优秀青年科技奖、中国过程控制会议张钟俊院士优秀论文奖。指导学生获湖南省优秀硕士论文，以及中国自动化大会最佳应用论文奖、中国有色金属学术年会优秀论文奖等奖项。
研究方向
1.工业智能
2.系统建模与辨识
3.模式识别与机器学习
4.最优控制与强化学习
招生：有1-2名博士生、3名硕士研究生招生指标，请对上述研究方向感兴趣、踏实上进的同学与我联系，Email：sunbei@csu.edu.cn。
代表性论著
专著、教材：
1. 《Modeling, optimization and control of zinc hydrometallurgical purification processes》. Chunhua Yang, Bei Sun. London: Elsevier, 2021.
2. 《智能控制: 方法与应用》第十八章：流程工业过程智能控制. 周晓君, 孙备. 北京: 中国科学技术出版社, 2020.
论文：
1. 复杂生产流程协同优化与智能控制[J]. 自动化学报, 2023, 49(3): 528-539.
2. A dynamics-learning multirate estimation approach for the feeding condition perception of complex industry processes[J]. IEEE Transactions on Cybernetics, 2023, doi: 10.1109/TCYB.2023.3263571.
3. A spatial-temporal structural estimation model based on GATE-PCGRU for multirate industrial process[J]. IEEE Transactions on Instrumentation and Measurement, 2023, doi: 10.1109/TIM.2023.3291796.
4. A multimode structured prediction model based on dynamic attribution graph attention network for complex industrial processes[J]. Information Sciences, 2023, 640, no. 119001.
5. A multimode mechanism-guided product quality estimation approach for multi-rate industrial processes[J]. Information Sciences, 2022, 596: 489-500.
6. Process monitoring of abnormal working conditions in the zinc roasting process with an ALD-based LOF-PCA method[J]. Process Safety and Environmental Protection, 2022, 161: 640-650.
7. Multi-models and dual-sampling periods quality prediction with time-dimensional K-means and state transition-LSTM network[J]. Information Sciences, 2021, 580: 917-933.
8. An integrated multi-mode model of froth flotation cell based on fusion of flotation kinetics and froth image features[J]. Minerals Engineering, 2021, 172: no. 107169.
9. An efficient operation optimization method for the series-parallel fractionation system of industrial hydrocracking[J]. Chemical Engineering Research and Design, 2021, 171: 111-124.
10. Decentralized PCA modeling based on relevance and redundancy variable selection and its application to large-scale dynamic process monitoring[J]. Process Safety and Environmental Protection, 2021, 151: 85-100.
11. A trend-based event-triggering fuzzy controller for the stabilizing control of a large-scale zinc roaster[J]. Journal of Process Control, 2021, 97: 59-71.
12. A chance-constrained programming approach for a zinc hydrometallurgy blending problem under uncertainty[J]. Computers and Chemical Engineering, 2020, 140: no. 106893.
13. Multi-stage intelligent operation optimization for a hydrocracking fractionation system with a multi-fractionator series-parallel structure[J]. Canadian Journal of Chemical Engineering, 2020, 98(11): 2342-2359.
14. Optimizing zinc electrowinning processes with current switching via Deep Deterministic Policy Gradient learning[J]. Neurocomputing, 2020, 380: 190-200.
15. A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes[J]. Journal of Process Control, 2020, 86: 30-43.