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:人工智能系副主任

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:

湖南省杰出青年基金

IEEE TCSDM Young Professional Award

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,人工智能系副主任。

先后获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。

主持和参与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、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等。在IEEEIFAC汇刊等国际期刊发表SCI论文60余篇,其中,5篇论文入选ESI高被引学术论文,授权发明专利10余项。

担任IET Cyber-Physical SystemsIEEE 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、中国有色金属学会智能制造联盟秘书、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。


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].  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

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

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

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

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

[8].  SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction, Neural Networks, IF: 9.657

[9].  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

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

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

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

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

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

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

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

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

[18]. Intrusion Detection of Industrial Internet-of-Things Based on Reconstructed Graph Neural Networks, IEEE Transactions on Network Science and Engineering, IF: 5.033

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

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

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

[22]. Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks, Chaos solitons and fractals, IF: 9.922

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

[24]. Unified Stationary and Nonstationary Data Representation for Process Monitoring in IIoT , IEEE Transactions on Instrumentation and Measurement, IF: 5.332

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

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

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

[28]. 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]   中国自动化学会技术过程的故障诊断与安全性专委会,委员

  • [7]   中国自动化学会青年工作委员会,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