个人简介
刘晨亮，中南大学讲师。2024年获中南大学控制科学与工程专业博士学位（导师：王雅琳教授），2023年至2024年期间受国家留学基金委资助，在新加坡南洋理工大学进行联合培养。
主要研究方向：人工智能与深度学习、工业大数据分析与建模、工业过程智能优化决策、智能制造与工业互联网等。
主持国家自然科学基金青年科学基金项目(C类)、湖南省自然科学基金青年基金项目等国家级/省部级项目，作为技术骨干参与科技创新2030重大项目、国家重点研发计划项目、国家自然科学基金重大研究计划重点项目等。
以第一/通讯作者在IEEE/CAA Journal of Automatica Sinica、IEEE Transactions on Industrial Informatics、IEEE Transactions on Industrial Electronics、IEEE Transactions on Cybernetics、IEEE Transactions on Knowledge and Data Engineering等自动化领域国际权威期刊发表SCI论文20余篇，其中ESI 1％高被引论文3篇，谷歌学术引用900余次，担任Nature Communications、IEEE TASE、IEEE TIM、EAAI、CEP等国际期刊审稿人。
曾获中国自动化学会自然科学二等奖、湖南省人工智能学会人工智能科学一等奖、湖南省优秀毕业生、湖南省“芙蓉学子”学术科研奖、中南大学优秀毕业生、中南大学大学生年度人物等。
“工业智能感知与优化调控”课题组招收博士（每年1-2个指标）和硕士研究生（每年4-6个指标），及部分学有余力的优秀本科生！
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，欢迎有意向从事相关研究工作的同学加入课题组！
课题组负责人：王雅琳老师，教授/博士生导师
课题组联系人：刘晨亮，联系邮箱：lcliang@csu.edu.cn
【代表性论文】
[1] EKG-AC: A new paradigm for process industrial optimization based on offline reinforcement learning with expert knowledge guidance[J]. IEEE Transactions on Cybernetics, 2025.
[2] Data-driven two-stage robust optimization allocation and loading for salt lake chemical enterprise products under demand uncertainty[J]. IEEE/CAA Journal of Automatica Sinica, 2025, 12(5): 989-1003.
[3] Koopman-constrained hierarchical deep state space model for industrial quality prediction via cloud-edge collaborative framework[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025, 55(2): 1137-1150.
[4] Data-driven two-stage robust optimization allocation and loading for salt lake chemical enterprise products under demand uncertainty[J]. IEEE/CAA Journal of Automatica Sinica, 2025, 12(5): 989-1003.
[5] Attribution-aided nonlinear granger causality discovery method and its industrial application[J]. IEEE Transactions on Industrial Informatics, 2025, 21(8): 6147-6157.
[6] Interpretable switching deep markov model for industrial process monitoring via cloud-edge collaborative framework[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3501614.
[7] Two-stage hierarchical decision-making strategy for railway transportation freight stowage based on cooperative embedding optimization [J]. IEEE Transactions on Intelligent Vehicles. 2024.
[8] Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process[J]. IEEE Transactions on Cybernetics, 2024, 54(2): 974-987.
[9] Learning deep multimanifold structure feature representation for quality prediction with an industrial application[J]. IEEE Transactions on Industrial Informatics, 2022, 18(9): 5849-5858.
[10] Operating condition recognition of industrial flotation processes using visual and acoustic bimodal autoencoder with manifold learning[J]. IEEE Transactions on Industrial Informatics, 2024, 20(5): 7428-7439.
[11] Data mode related interpretable transformer network for predictive modeling and key sample analysis in industrial processes[J]. IEEE Transactions on Industrial Informatics, 2023, 19(9): 9325-9336.
[12] Adaptive attention-driven manifold regularization for deep learning networks: Industrial predictive modeling applications and beyond [J]. IEEE Transactions on Industrial Electronics, 2024, 71(10): 13439-13449.
[13] Multimodal data-driven reinforcement learning for operational decision-making in industrial processes[J]. IEEE/CAA Journal of Automatica Sinica, 2024, 11(1): 252-254.
[14] Scope-free global multi-condition-aware industrial missing data imputation framework via diffusion transformer [J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(11): 6977-6988.
[15] Blackout missing data recovery in industrial time series based on masked-former hierarchical imputation framework[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(2): 1138-1150.
[16] Revolutionizing flotation process working condition identification based on froth audio[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 9513012.
[17] Deep nonlinear dynamic feature extraction for quality prediction based on spatiotemporal neighborhood preserving SAE[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2516310.
[18] Adaptive information fusion–driven evolutionary algorithm via balancing the information from unconstrained and constrained pareto fronts[J]. Swarm and Evolutionary Computation, 2025, 98: 102150.
[19] Data-driven optimization strategy of raw material procurement under uncertainties of price fluctuations and production demand in industrial enterprises[J]. Computers & Industrial Engineering, 2025, 208: 111425.
[20] Machine scheduling optimization via multi-strategy information-aware genetic algorithm in steelmaking continuous casting industrial process[J]. Control Engineering Practice, 2025, 164: 106404.
教育经历
[1] 2020.9-2024.6
中南大学 | 控制科学与工程 | 博士学位 | 博士研究生毕业
[2] 2023.1-2024.1
新加坡南洋理工大学 | 化学、化学工程和生物技术学院 联合培养博士
[3] 2019.9-2020.6
中南大学 | 控制科学与工程 硕博连读
[4] 2015.9-2019.6
哈尔滨理工大学 | 自动化 | 学士学位 | 大学本科毕业
工作经历
[1] 2024.6-至今
中南大学
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自动化学院
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讲师
团队成员
团队名称：控制工程研究所
团队介绍：课题组隶属控制工程研究所，具有良好的科研平台，包括国家自然科学基金创新研究群体，制造过程智能控制与优化决策创新引智基地（“111”计划），工业智能与系统教育部重点实验室、有色冶金自动化教育部工程研究中心等。