周晓君

2022级博士研究生，主要从事神经架构搜索、强化学习、特征选择等方面的理论研究工作，及流程工业过程智能监控、AI辅助医学诊断等应用研究，目前已发表的论文有：

[1] Du, Y., Zhou, X., Huang, T., & Yang, C.  A hierarchical evolution of neural architecture search method based on state transition algorithm[J]. International Journal of Machine Learning and Cybernetics, 2023, 14: 2723–2738. 

[2] Du Y, Zhou X, Yang C, et al. An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data[J]. Knowledge-Based Systems, 2023: 111102.

2023博士研究生，主要从事多目标优化、多属性决策、流程工业智能优化决策等方面研究, 目前已发表的学术论文有：

[1] Yan Sun, Xiaojun ZHOU, etc. Optimal PID controller design for AVR system based on multi-objective optimization and multi-attribute decision making[C]. 2021, 中国控制与决策会议，accepted. 

[2] Zhou X, Sun Y, Huang Z, et al. Dynamic multi-objective optimization and fuzzy AHP for copper removal process of zinc hydrometallurgy[J]. Applied Soft Computing, 2022: 109613.

[3] Sun Y, Zhou X, et al. A visual analytics approach for multi-attribute decision making based on intuitionistic fuzzy AHP and UMAP[J], Information Fusion, 2023, 96: 269-280.

[4] Zhou X, Tan W, Sun Y, et al. Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS[J], Expert Systems With Applications, 2024, 240: 122539.

2023级硕士生，主要从事组合优化、强化学习、启发式算法等在无人机路径规划方面应用的研究工作。

2023级硕士生，主要从事组合优化、强化学习、深度学习等在无人机任务分配方面应用的研究工作。

2023级硕士生，主要从事多目标优化、约束优化、机器学习等方面的研究工作。

2023级硕士生，主要从事多目标优化、鲁棒优化、控制与决策等方面的研究工作。

2022级硕士生，主要从事机器学习、群体多属性决策、集成学习方面的研究工作。

2022级硕士生，主要从事流程工业智能监控、特征选择和可视化方面的研究工作。 

2022级硕士生，主要从事流程工业智能优化决策、不确定优化与决策方面的研究工作。

2022级硕士生，主要从事约束优化、无人机路径规划方面的研究工作，目前已经发表的论文有：

[1]  X Zhou, Z Tang, N Wang, C Yang, T Huang. A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning[J]. Expert Systems with Applications, 2024, 248: 123481.

2021级硕士研究生，主要从事不确定优化、综合能源系统等方面研究工作, 目前已经发表的论文有:

[1] Jiaming Hu, Xiaojun Zhou, Chaojie Li. Chance Constrained Weighted Goal Programming Based Optimal Operation for Integrated Energy System Considering Wind Power Uncertainty[C].  2022 International Conference on Intelligent Systems and Computational lntelligence(ICISCl), pp. 56-61. 

2021级硕士研究生，主要从事多目标优化、模糊多属性决策、综合能源系统等方面研究工作，目前已发表的论文有：

[1] Wan Tan, Xiaojun Zhou, Yan Sun.  Evaluation of wireless sensor networks based on state transition algorithm and fuzzy TOPSIS[C],  2022, 中国控制与决策会议，accepted. 

[2]  Xiaojun Zhou, Wan Tan, Yan Sun, etc.  Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS[J],  Expert Systems With Applications, 2023, accepted. 

2021级硕士研究生，主要从事可解释机器学习、博弈优化、综合能源系统等方面研究工作，目前已发表的论文有:

[1] Z Wang, C Li, X Zhou, R Xie, X Li, Z Dong. Stochastic bidding for VPPs enabled ancillary services: A case study [J].  Applied Energy, 2023, 352, 121918.

2020级硕士研究生(已硕士毕业)，主要从事特征工程、图像处理、流程工业智能监控等方面研究工作。

2020级硕士研究生(已硕士毕业)，主要从事双层优化、机器学习、供应链优化等方面的研究，目前已经发表的学术论文有：

[1] Zeyu Wang, Xiaojun Zhou, Jituo Tian, Tingwen Huang.  Hierarchical parameter optimization based support vector regression for power load forecasting [J].  Sustainable Cities and Society , accepted. 

[2] Xiaojun Zhou, Jituo Tian, etc. A fast constrained state transition algorithm[J]. Neurocomputing, 2021, 455, 202-214.

[3] Xiaojun Zhou, Jituo Tian, Zeyu Wang, etc. Nonlinear bilevel programming approach for decentralized supply chain using a hybrid state transition algorithm[J], 2022, accepted. 

2020级硕士研究生(已硕士毕业)，主要从事模糊优化、机会约束、流程工业智能优化控制等方面的研究，目前已发表的学术论文有:

[1] Lin F, Zhou X, Li C, et al. Data-driven state transition algorithm for fuzzy chance-constrained dynamic optimization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, accepted. 

2020级硕士研究生(已硕士毕业)，主要从事时间序列预测、智能交通方面的研究，目前已经发表的学术论文有：

[1] Zeyu Wang, Xiaojun Zhou, Jituo Tian, Tingwen Huang.  Hierarchical parameter optimization based support vector regression for power load forecasting [J].  Sustainable Cities and Society , accepted. 

19级硕士研究生（已硕士毕业），主要从事多任务学习、多目标优化、流程工业智能优化决策等方面的研究，目前已发表的学术论文有：

[1] 周晓君， 高媛等. 基于多目标优化多任务学习的端到端车牌识别方法[J]. 控制理论与应用，已录用

[2] 周晓君， 高媛等. A multiple gradient descent design for multi-task learning on edge computing: multi-objective machine learning approach[J]. IEEE Transactions on Network Science and Engineering, accepted. 

[3] 周晓君， 高媛等. A multiobjective state transition algorithm based on modified decomposition method[J]. Applied Soft Computing, 2022, 119: 108553. 

19级硕士生（已硕士毕业）, 主要从事医学图像处理、机器学习等方面的研究, 目前已发表的学术论文有：

[1] 周晓君，耿传玉等. 基于状态转移算法和U-net的医学图像分割, 中南大学学报（自然科学版）, 已录用。

19级硕士生（已硕士毕业），主要从事集成学习、多属性决策、流程工业智能优化决策等方面的研究，目前已发表的学术论文有：

[1] Jingyi He，Xiaojun Zhou, etc.  An ensemble learning framework based on group decision making[C]. 2020, 中国控制与决策会议，accepted. 

[2] Xiaojun Zhou, Jingyi He, etc. An ensemble learning method based on deep neural network and group decision making[J]. Knowledge-Based Systems, accepted. 

18级硕士生（已硕士毕业），主要从事数据驱动优化、贝叶斯学习、RNN等方面的研究, 目前已发表的学术论文有：

[1] 张云祥，周晓君等.  Data-driven based state transition algorithm for dynamic optimization [C]. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, pp. 1091--1096.

18级硕士生（已硕士毕业），主要从事不确定优化、机会约束优化、流程工业智能优化控制等方面的研究，目前已发表的学术论文有：

[1] 王湘月，周晓君等.  不确定条件下的湿法炼锌除铜过程机会约束优化控制[J]. 化工学报, 2020, 71: 1226--1233.

[2]  周晓君，王湘月等.  Hybrid Intelligence Assisted Sample Average Approximation Method for Chance Constrained Dynamic Optimization[J]. IEEE Transactions on Industrial Informatics,  DOI: 10.1109/TII.2020.3006514

19级博士生，主要从事时间序列分析、分布式优化、约束优化算法等方面的研究，目前已经发表的学术论文有：

[1] C Xu, G Chen, X Zhou. Temporal pattern attention-based sequence to sequence model for multistep individual load forecasting[C]. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, accepted. 

[2] Xu C, Liao Z, Li C, Zhou X, et al. Review on interpretable machine learning in smart grid[J]. Energies, 2022, 15(12): 4427.

[3] Xu C, Li C, Zhou X. Interpretable LSTM based on mixture attention mechanism for multi-step residential load forecasting[J]. Electronics, 2022, 11(14): 2189.

17级硕士生（已硕士毕业）, 主要从事图像处理、机器学习、特征选择等方面的研究, 目前已发表的学术论文有：
[1] 王启安，黄淼，周晓君. Feature selection in froth flotation for production condition recognition [J]. IFAC-PapersOnLine, 2018, 51(21): 123-128.

[2] 周晓君，王启安等，A hybrid feature selection method for production condition recognition in froth flotation with noisy labels[J]. Minerals Engineering, 2020, https://doi.org/10.1016/j.mineng.2020.106201

17级硕士生（已硕士毕业）, 主要从事智能优化、约束优化、数据驱动优化等方面的研究，目前已发表的学术论文有：
[1] 周晓君，龙建朋，徐冲冲等. An external archive-based constrained state transition algorithm for optimal power dispatch [J]. Complexity, 2019, https://doi.org/10.1155/2019/4727168

17级硕士生（已硕士毕业）, 主要从事社区发现、聚类分析、机器学习等方面的研究，目前已发表的学术论文有：
[1] 周晓君，杨珂等. A novel modularity-based discrete state transition algorithm for community detection in networks [J]. Neurocomputing, 2019, 34：89--99. 

17级硕士生（已硕士毕业）, 主要从事模糊聚类、模糊决策等方面的研究，目前已发表的学术论文有：

[1] 周晓君，张润东等，Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem[J]. Soft Computing, 2020，https://link.springer.com/article/10.1007/s00500-020-04879-8

[1] 周晓君，张润东等,  Using hybrid normalization technique and state transition algorithm to VIKOR method for influence maximization problem[J]. Neurocomputing, 2020, 410:41-50.

16级硕士生（已硕士毕业）, 主要从事多目标优化、智能优化算法等方面的研究, 目前已发表的学术论文有：
[1] 周晓君，周佳佳，阳春华等. Set-point tracking and multi-objective optimization-Based PID control for the goethite process [J]. IEEE ACCESS, 2018, 6: 36683-36698.

16级硕士生（已硕士毕业）, 主要从事动态优化、机器学习等方面的研究,目前已发表的学术论文有：
[1] 黄淼, 周晓君*, T.W. Huang等. Dynamic optimization based on state transition algorithm for copper removal process [J]. Neural Computing and Applications, 2017, DOI 10.1007/s00521-017-3232-0
[2] 周晓君, 黄淼, T.W. Huang等. Dynamic optimization for copper removal process with continuous production constraints [J]. IEEE Transactions on Industrial Informatics, 2019, DOI 10.1109/TII.2019.2943500

17级博士生（已博士毕业）, 主要从事工业过程建模与优化控制等方面的研究, 目前已发表的学术论文有：
[1] 张凤雪, 阳春华, 周晓君*, 桂卫华. Fractional-order PID controller tuning using continuous state transition algorithm [J]. Neural Computing and Applications, 2018, 29(10): 795-804.
[2] 张凤雪, 阳春华, 周晓君, 桂卫华. Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy[J]. Hydrometallurgy, 2018, 178: 60-76.
[3] 张凤雪, 阳春华, 周晓君*, 桂卫华. Optimal setting and control strategy for industrial process based on discrete-time fractional-order PID[J], IEEE Access, 2019, 7:47747--47761.

http://faculty.csu.edu.cn/jie_HAN/zh_CN/index/160667/list/index.htm

16级博士生（已博士毕业）, 主要从事不确定优化、动态多目标优化、图像处理等方面的研究，目前已发表的学术论文有：

[1] 韩洁, 董天雪, 周晓君*, 阳春华, 桂卫华. State transition algorithm for constrained optimization problems[C]. in the 33rd Chinese Control Conference (CCC), 2014, pp. 7543-7548.
[2] 韩洁, 阳春华, 周晓君*, 桂卫华. Entropy-based estimation of bubble size distributions in froth flotation using B-spline functions [J]. IFAC-PapersOnLine, 2016, 49(20): 96–101;
[3] 韩洁, 阳春华, 周晓君*, 桂卫华. A new multi-threshold image segmentation approach using state transition algorithm [J]. Applied Mathematical Modelling, 2017, 44: 588–601;
[4] 韩洁, 阳春华, 周晓君, 桂卫华. Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy [J]. Hydrometallurgy, 2017, 173: 134–148.
[5] 韩洁, 阳春华, 周晓君, 桂卫华. A two-stage state transition algorithm for constrained engineering optimization problems[J]. Int. Journal of Control Automation and Systems, 2018, 16(2): 522-534.

[6] 韩洁, 阳春华, CC Lim, 周晓君等. Power scheduling optimization under single-valued neutrosophic uncertainty[J].  Neurocomputing,  2020, 382: 12--20

[7] 韩洁, 阳春华, CC Lim, 周晓君等. Stackelberg-Nash game approach for constrained robust optimization with fuzzy variables[J]. IEEE Transactions on Fuzzy Systems,  DOI: 10.1109/TFUZZ.2020.3025697

15级博士生（已博士毕业）, 主要从事不平衡数据处理、双层规划、数据挖掘等方面的研究, 目前已发表的学术论文有：
[1] 黄兆可, 阳春华, 周晓君, 桂卫华. A novel cognitively-inspired state transition algorithm for solving the linear bi-level programming problem[J]. Cognitive Computation, 2018, DOI: 10.1007/s12559-018-9561-1.
[2] 黄兆可, 阳春华, 周晓君等. A hybrid feature selection method based on binary state transition algorithm and ReliefF[J],IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2018.2872811
[3] 黄兆可，阳春华, 周晓君等. Energy consumption forecasting for the nonferrous metallurgy industry using hybrid support vector regression with an adaptive state transition algorithm[J], Cognitive Computation, https://doi.org/10.1007/s12559-019-09644-0
[4] 黄兆可，阳春华，陈晓方等. Adaptive over-sampling method for classification with application to imbalanced datasets in aluminum electrolysis[J], Neural Computing and Applications, https://doi.org/10.1007/s00521-019-04208-7

[5] 黄兆可，阳春华，陈晓方，周晓君等. Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification[J]. Applied Soft Computing, accepted.