廉 城(能源动力专业、教授)

发布时间:2020-02-22

  

  姓名:廉城
  性别:
  邮箱:chenglian@whut.edu.cn
  职称:教授/博士生导师
  研究方向:机器学习、计算智能、模式识别、数据挖掘
  主讲课程:人工智能与机器学习(本科生课程)、机器学习(研究生课程)
  
  
 教师简介

  廉城,教授,博士生导师,武汉理工大学“15551人才工程青年拔尖人才,研究方向为机器学习算法及应用,在国内外重要期刊及会议上发表论文50余篇。作为项目负责人,主持国家自然科学基金面上项目2项,国家自然科学基金青年项目1项,中国博士后科学基金面上项目1项,湖北省自然科学基金面上项目1项,横向项目多项。

   

  

  
 学习经历
  

  2008年本科毕业于武汉理工大学自动化学院 

  

  2011年硕士毕业于武汉理工大学自动化学院 

  

  2014年博士毕业于华中科技大学自动化学院

  
  
 工作经历
  

  20169月博士后于华中科技大学电子信息与通信学院出站

   

  201610月入职武汉理工大学自动化学院任讲师

   

  20176月聘为硕士生导师

   

  20179月晋升副教授

   

  202011月聘为博士生导师

   

  2022年9月晋升教授

   

  

 教学科研情况 

  主持和参与的科研项目: 

1)国家自然科学基金面上项目面向心音心电时序信号的智能分析方法及应用。项目时间:2022/01-2025/12(主持)

2)国家自然科学基金面上项目基于多源地学时空数据分层学习的滑坡易发性动态区划。项目时间:2019/01-2022/12(主持)

3)横向项目国网江西电科院配电网低电压试验平台改造项目技术服务。项目时间:2022/04-2022/07(主持)

4)山西省重点研发计划项目面向多模态心音心电信号的心脏健康智能诊断系统研究。项目时间:2022/01-2024/01(子课题负责人)

5)武汉理工大学青年教师科学技术协会基金项目基于多任务学习的区域滑坡面状变形预测研究。项目时间:2020/01-2021/10(主持)

6)横向项目 “心音数据分割与降噪 项目时间:2020/06-2020/10(主持)

7)武汉理工大学15551青年拔尖人才科研建设项目。项目时间:2019/01-2023/12(主持)

8)国家自然科学基金青年项目基于多场信息数据驱动的滑坡演化多模式切换概率预测和控制研究。项目时间:2016/01-2018/12(主持)

9)湖北省自然科学基金面上项目基于人工神经网络多维时空数据挖掘的崩滑流灾害链预测。项目时间:2017/01-2018/12(主持)

10)中国博士后科学基金面上项目基于随机权值神经网络的滑坡位移区间预测研究项目时间:2015/01-2016/09(主持)

11)武汉理工大学自主创新研究基金项目/中央高校基本科研业务费项目 基于深度学习的多尺度时间空间预测方法研究。项目时间:2017/01-2017/12(主持)

12)国家自然科学基金国际(地区)合作与交流项目面向类脑计算的忆阻电路系统分析与设计。项目时间:2017/03-2020/02(参与)

13)国家自然科学基金面上项目智能参数变化系统的多吸引子理论及忆阻多值存储设计。项目时间:2017/01-2020/12(参与)

14)国家重点基础研究发展计划“973”项目重大工程灾变滑坡演化与控制。项目时间:2011/01-2015/12(参与)

15)国家自然科学基金杰出青年科学基金复杂系统渐近行为理论与应用。项目时间:2012/01-2015/12(参与)

16)国家自然科学基金青年项目基于忆阻动态联想网络的电路结构和算法研究。项目时间:2015/01-2017/12(参与)


 论文、专利、著作情况

     

[1] Shunxiang Yang, Cheng Lian*, Zhigang Zeng, Bingrong Xu, Junbin Zang, Zhidong Zhang, “A Multi-View Multi-Scale Neural Network for Multi-Label ECG Classification,” IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109/TETCI.2023.3235374SCI

 

[2] Yupeng Wu, Cheng Lian*, Zhigang Zeng, Bingrong Xu, Yixin Su,“An Aggregated Convolutional Transformer Based on Slices and Channels for Multivariate Time Series Classification,” IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109/TETCI.2022.3210992SCI

 

[3] Bingrong Xu, Zhigang Zeng*Cheng Lian, Zhengming Ding, “Generative Mixup Networks for Zero-Shot Learning,” IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2022.3142181SCI

 

[4] Guangyang Tian, Cheng Lian*, Bingrong Xu, Junbin Zang, Zhidong Zhang, Chenyang Xue, “Classification of Phonocardiogram Based on Multi-view Deep Network,” Neural Processing Letters, doi :10.1007/s11063-022-10771-3SCI

 

[5] Binghua Shi, Yixin Su*Cheng Lian, Chang Xiong, Yang Long, Chenglong Gong, “Obstacle type recognition in visual images via dilated convolutional neural network for unmanned surface vehicles,” Journal of Navigation, doi: https://doi.org/10.1017/S0373463321000941SCI

 

[6] Bingrong Xu, Zhigang Zeng*, Cheng Lian, Zhengming Ding, “Few-shot Domain Adaptation via Mixup Optimal Transport,” IEEE Transactions on Image Processing, vol. 31, pp. 2518-2528, 2022SCI

 

[7] Wei Yao, Cheng Lian*, Lorenzo Bruzzone, “A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification,” Remote Sensing, vol. 14, no. 10, 4982, 2022SCI

 

[8] Guangyang Tian, Cheng Lian*, Zhigang Zeng, Bingrong Xu, Yixin Su, Junbin Zang, Zhidong Zhang, Chenyang Xue, “Imbalanced heart sound signal classification based on two-stage trained DsaNet,” Cognitive Computation, vol. 14, pp. 1378-1391, 2022SCI

 

[9] Xiaoyang Yu, Cheng Lian*, Yixin Su, Bingrong Xu, Xiaoping Wang, Wei Yao, Huiming Tang, “Selective ensemble deep bidirectional RVFLN for landslide displacement prediction,” Natural Hazards, vol. 112, pp. 725-745, 2022SCI

 

[10] Shu Sun, Xiaoping Wang*, Junnan Li, Cheng Lian, “Landslide evolution state prediction and down-level control based on multi-task learning,” Knowledge-Based Systems, vol. 238, 107884, 2022SCI

 

[11] Bingrong Xu, Zhigang Zeng*, Cheng Lian, Zhengming Ding, “Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning,” IEEE Transactions on Image Processing, vol. 30, pp. 2207-2219, 2021SCI

 

[12] Wei Zhou, Cheng Lian*, Zhigang Zeng, Bingrong Xu, Yixin Su, “Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples,” Neural Processing Letters, vol. 53, pp. 3427-3443, 2021SCI

 

[13] Wei Yao*, Cheng Lian, Lorenzo Bruzzone, “ClusterCNN: Clustering based feature learning for hyperspectral image classification,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 11, pp. 1991-1995, 2021SCI

 

[14] Cheng Lian, Zhigang Zeng*, Xiaoping Wang, Wei Yao, Yixin Su, Huiming Tang, “Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization,” Neural Networks, vol.130, pp.286-296, 2020SCI

 

[15] Wei Zhou, Cheng Lian*, Zhigang Zeng, Yixin Su, “Mutual improvement between temporal ensembling and virtual adversarial training,” Neural Processing Letters, vol. 51, pp. 1111-1124, 2020SCI

 

[16] Lingzi Zhu, Cheng Lian*, Zhigang Zeng, Yixin Su, “A broad learning system with ensemble and classification methods for multi-step-ahead wind speed prediction,” Cognitive Computation, vol. 12, pp. 654-666, 2020SCI

 

[17] Zhihao Liu, Zhigang Zeng*, Cheng Lian, “Multidomain features fusion for zero-shot learning,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 6, pp. 764-773, 2020SCI

 

[18] Cheng Lian, Lingzi Zhu, Zhigang Zeng*, Yixin Su, Wei Yao, Huiming Tang, “Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched,” Neurocomputing, vol. 291, pp. 1-10, 2018SCI

 

[19] Wei Yao, Zhigang Zeng*, Cheng Lian, Huiming Tang, “Pixel-wise regression using U-Net and its application on pansharpening,” Neurocomputing, vol.312, pp.364-371, 2018SCI

 

[20] Wei Yao, Zhigang Zeng*, Cheng Lian, “Generating probabilistic predictions using mean-variance estimation and echo state network,” Neurocomputing, vol.219, pp.536-547,2017SCI

 

[21] Cheng Lian, Zhigang Zeng*, Wei Yao, Huiming Tang, C. L. Philip Chen, “Landslide displacement prediction with uncertainty based on neural networks with random hidden weights,” IEEE Transactions on Neural Networks and Learning Systemsvol. 27, no. 12, pp. 2683-2695, 2016SCI

 

[22] Cheng Lian*, C. L. Philip Chen, Zhigang Zeng, Wei Yao, Huiming Tang, “Prediction intervals for landslide displacement based on switched neural networks,” IEEE Transactions on Reliabilityvol. 65, no. 3, pp. 1483-1495, 2016SCI

 

[23] Cheng Lian, Zhigang Zeng*, Wei Yao, Huiming Tang, “Multiple neural networks switched prediction for landslide displacement,” Engineering Geology, vol. 186, pp. 91-99, 2015SCI

 

[24] Wei Yao, Zhigang Zeng*, Cheng Lian, Huiming Tang, “Training enhanced reservoir computing predictor for landslide displacement,” Engineering Geology, vol. 188, pp. 101-109, 2015SCI

 

[25] Cheng Lian, Zhigang Zeng*, Wei Yao, Huiming Tang, “Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level,” Stochastic Environmental Research and Risk Assessment, vol.28, no.8, pp.1957-1972, 2014SCI


[26] Cheng Lian*, Zhigang Zeng, Wei Yao, Huiming Tang, “Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis,” Neural Computing and Applications, vol.24, no.1, pp.99-107, 2014SCI


[27] Cheng Lian, Zhigang Zeng*, Wei Yao, Huiming Tang, “Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine,” Natural Hazards, vol. 66, no. 2, pp.759-771, 2013SCI

   

  

 其他

  

  欢迎具有机器学习基础,熟练掌握Python,在国际主流期刊/CCF推荐会议上发表过机器学习领域相关学术论文的硕士生报考博士;欢迎计算机、自动化、数学专业的本科生报考硕士。