黄科科

教授 博士生导师 硕士生导师

入职时间:2017-04-13

所在单位:自动化学院

职务:自动化学院副院长

学历:博士研究生毕业

办公地点:中南大学校本部民主楼215

性别:男

联系方式:huangkeke@csu.edu.cn

学位:博士学位

在职信息:在职

毕业院校:清华大学

学科:控制科学与工程

曾获荣誉:

国家“万人计划”青年拔尖人才

湖南省杰出青年基金

IEEE TCSDM Young Professional Award

全球前2%顶尖科学家榜单(World’s Top 2% Scientists 2023)

中国科协青年托举人才

湖湘青年英才

湖湘青年科技创新人才

个人简介

黄科科,男,中南大学教授,博士生导师,硕士生导师,自动化学院副院长

先后入选 国家“万人计划”青年拔尖人才、湖南省杰出青年基金、中国科协青年托举人才、湖湘青年英才、中南大学创新驱动青年人才。

研究方向包括:复杂系统与复杂网络;大数据分析与处理;人工智能与机器学习;智能制造与工业互联网。

主持国家重点研发计划项目/课题、国家自然科学基金面上项目、青年基金、工信部工业互联网创新发展工程专项项目课题、中南大学创新驱动青年人才项目、校企合作重点项目等10项。

曾获中国自动化学会自然科学一等奖、中国有色金属工业科技进步一等奖、IEEE TCSDM Young Professional Award、全球前2%顶尖科学家榜单(World’s Top 2% Scientists 2023)等。在IEEE Trans.、IFAC会刊等国际期刊发表SCI论文80余篇,其中,7篇论文入选ESI高被引学术论文。授权国内外发明专利20余项。

担任IET Cyber-Physical Systems、IEEE TCCPS Newsletter、《中国有色金属学报(中英文版)》、《中南大学学报(自然科学版)》等多个国内外期刊编委和审稿人。兼任中国有色金属学会自动化学术委员会副秘书长、中国自动化学会过程控制专业委员会委员、中国自动化学会技术过程的故障诊断与安全性专委会委员、中国自动化学会青年工作委员会委员、中国自动化学会数据驱动控制学习与优化专委会委员、中国图学学会数字孪生专业委员会委员、信息技术新工科产学研联盟工业互联网工作委员会委员等。


招才引智:博士、硕士、本科生,欢迎联系。

相关学科和研究领域包括但不限于:自动化、控制、计算机、数学、通信、工业互联网、人工智能、大数据分析等。

课题组氛围融洽,经费充足,欢迎加入!


代表性论文

[1].  Adaptive multimode process monitoring based on mode-matching and similarity-preserving dictionary learning, IEEE Transactions on CyberneticsIF: 19.118

[2].  Metric learning based fault diagnosis and anomaly detection for industrial data with intra-class variance, IEEE Transactions on Neural Networks and Learning SystemsIF: 14.255

[3].  Error-triggered adaptive sparse identification for predictive control and its application to multiple operating conditions processes, IEEE Transactions on Neural Networks and Learning SystemsIF: 14.255

[4].  Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples,  IEEE Transactions on Neural Networks and Learning Systems, IF: 14.255

[5].  Cloud-edge collaborative method for industrial process monitoring based on error-triggered dictionary learning, IEEE Transactions on Industrial InformaticsIF: 11.648

[6].  Trustworthiness of process monitoring in IIoT based on self-weighted dictionary learning, IEEE Transactions on Industrial Informatics, IF: 11.648

[7].  A projective and discriminative dictionary learning for high-dimensional process monitoring with industrial applications, IEEE Transactions on Industrial Informatics, IF: 11.648

[8].  Static and dynamic joint analysis for operation condition division of industrial process with incremental learning, IEEE Internet of Things Journal, IF: 10.238

[9].  A systematic procurement supply chain optimization technique based on industrial internet of thing and application, IEEE Internet of Things Journal, IF: 10.238

[10].  LSTM-MPC: A deep learning based predictive control method for multimode process control, IEEE Transactions on Industrial ElectronicsIF: 8.162

[11].  Detecting intelligent load redistribution attack based on power load pattern learning in cyber-physical power systems, IEEE Transactions on Industrial ElectronicsIF: 8.162

[12].  Physical informed sparse learning for robust modeling of distributed parameter system and its industrial applications, IEEE Transactions on Automation Science and EngineeringIF:  6.636

[13].  Fault diagnosis of complex industrial systems based on multi-granularity dictionary learning and its application, IEEE Transactions on Automation Science and EngineeringIF:  6.636

[14].  Robust structure identification of industrial cyber-physical system from sparse data: a network science perspective, IEEE Transactions on Automation Science and Engineering, IF:  6.636

[15].  Outlier detection for process monitoring in industrial cyber-physical systems, IEEE Transactions on Automation Science and Engineering, IF: 6.636

[16].  Rotary kiln temperature control under multiple operating conditions: an error-triggered adaptive model predictive control solution,  IEEE Transactions on Control Systems Technology , IF:  5.418

[17].  Nonstationary industrial process monitoring based on stationary projective dictionary learning,  IEEE Transactions on Control Systems Technology ,  IF:  5.418

[18].  LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural networkNeural NetworksIF: 9.657

[19].  SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstructionNeural NetworksIF: 9.657

[20].  A geometry constrained dictionary learning method for industrial process monitoring, Information Sciences, IF: 8.233

[21].  Multi-objective adaptive optimization model predictive control: decreasing carbon emissions from a zinc oxide rotary kiln, EngineeringIF: 12.834

[22].  A robust transfer dictionary learning algorithm for industrial process monitoring, Engineering, IF: 12.834

[23].  Reconstruction of tree network via evolutionary game data analysis, IEEE Transactions on Cybernetics, IF: 19.118

[24].  Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network, Knowledge-based SystemsIF: 8.139

[25].  Adaptive process monitoring via online dictionary learning and its industrial application, ISA Transactions, IF: 5.911

[26].  A latent feature oriented dictionary learning method for closed-loop process monitoring, ISA Transactions, IF: 5.911

[27].  Structure dictionary learning-based multimode process monitoring and its application to aluminum electrolysis process, IEEE Transactions on Automation Science and Engineering, IF: 6.636

[28].  Incorporating latent constraints to enhance inference of network structure, IEEE Transactions on Network Science and Engineering, IF: 5.033

[29].  Intrusion detection of industrial internet-of-things based on reconstructed graph neural networks, IEEE Transactions on Network Science and Engineering, IF: 5.033

[30].  Multimode process monitoring based on robust dictionary learning with application to aluminium electrolysis processNeurocomputingIF: 5.779

[31].  Distributed dictionary learning for industrial process monitoring with big data, Applied Intelligence, IF: 5.019

[32].  Label propagation dictionary learning based process monitoring method for industrial process with between-mode similarity, Science China Information Sciences, IF: 7.275

[33].  Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks, Chaos Solitons and Fractals, IF: 9.922

[34].  Transfer dictionary learning method for cross-domain multimode process monitoring and fault isolation, IEEE Transactions on Instrumentation and Measurement, IF: 5.332

[35].  Unified stationary and nonstationary data representation for process monitoring in IIoT , IEEE Transactions on Instrumentation and Measurement, IF: 5.332

[36].  Reweighted compressed sensing-based smart grids topology reconstruction with application to identification of power line outage, IEEE Systems Journal, IF: 4.902

[37].  A multi-rate sampling data fusion method for fault diagnosis and its industrial applications, Journal of Process ControlIF: 3.951

[38].  Distributed dictionary learning for high-dimensional process monitoringControl Engineering PracticeIF: 4.057

[39].  Emergent inference of hidden markov models in spiking neural networks through winner-take-all, IEEE Transactions on CyberneticsIF: 19.118

教育经历

[1]   2012.8-2017.4

清华大学  |  控制科学与工程  |  博士学位  |  博士研究生毕业

[2]   2008.8-2012.7

东北大学  |  自动化  |  学士学位  |  大学本科毕业

工作经历

[1]   2021.9-至今

中南大学  |  自动化学院  |  教授

[2]   2020.9-至今

中南大学  |  自动化学院  |  博士生导师

[3]   2017.4-2021.9

中南大学  |  自动化学院  |  特聘副教授

社会兼职

  • [1]   IET Cyber-Physical Systems, Associate Editor

  • [2]   IEEE TCCPS Newsletter, Associate Editor

  • [3]   《中国有色金属学报(中英文版)》,青年编委

  • [4]   《中南大学学报(自然科学版)》,青年编委

  • [5]   中国有色金属学会自动化学术委员会,副秘书长

  • [6]   中国自动化学会过程控制专业委员会,委员

  • [7]   中国自动化学会技术过程的故障诊断与安全性专委会,委员

  • [8]   中国自动化学会数据驱动控制学习与优化专委会,委员

  • [9]   中国自动化学会青年工作委员会,委员