视觉合成重点实验室
机构队伍

固定研究人员

首 页 > 机构队伍 > 固定研究人员 > 正文

伍元凯

日期:2022年07月18日 编辑:管理员 点击:

伍元凯 男 研究员/硕士生导师

邮箱:wuyk0@scu.edu.cn

招生方向:电子信息-人工智能  软件工程-软件智能

研究方向:机器学习、时空大数据分析、智能决策与控制

个人简介

伍元凯,博士,四川大学计算机学院研究员。2019年于北京理工大学获博士学位,2019年到2022在麦吉尔大学进行博士后研究,主要研究方向是时空大数据分析、机器学习、智能网联交通系统、智慧城市。获加拿大数据价值化研究所(IVADO)博士后基金项目,已在计算机科学、交通工程等领域发表论文50余篇(IEEE TNNLS IEEE TITS IEEE TIIIEEE TVTTransportation Research Part C(最高被引用论文)、AAAI会议,Google Scholar引用超1600次),申请三项美国专利、获批一项、获中国电子学会科技进步二等奖,目前作为审稿人服务于30余种SCI期刊,担任多个期刊的客座编辑。

欢迎计算机、数学、交通、自动化等专业方向的同学报考本人的研究生!

研究及参与项目

2022.03至今 四川大学科研启动经费, (主持. 50 万元).

2020.02 — 2022.02 Ivado博士后基金, (主持. 140,000 加币), Deep Spatiotemporal Modeling for Urban Traffic Data

2019.12 — 2022.02 加拿大 Mitacs 基金会, 参与, Develop reinforcement learning platform for traffic signal control based on real-world traffic data and scenarios.

2021.04 — 2022.02 加拿大太空局地球系统数据分析项目, 参与, Dynamic flood inundation modelling in regional earth system models guide by space-based observations and machine learning.

2018.01 — 2019.08 国家自然科学基金, 国际 (地区) 合作与交流重点项目, 参与, 面向高维多源耦合大数据的多张量网络理论及其实证研究.

代表性论文

时空大数据分析方向

[1] Wu, Y., Zhuang, D., Labbe, A. and Sun, L., 2021, May. Inductive Graph Neural Networks for Spatiotemporal Kriging. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4478-4485).CCF A类会议)

[2] Zhang, H., Wu, Y.*, Tan, H., Dong, H., Ding, F. and Ran, B., 2020. Understanding and modeling urban mobility dynamics via disentangled representation learning. IEEE Transactions on Intelligent Transportation Systems.(中科院1Top

[3] Wu, Y., Teufel, B., Sushama, L., Belair, S. and Sun, L., 2021. Deep LearningBased SuperResolution Climate SimulatorEmulator Framework for Urban Heat Studies. Geophysical Research Letters, 48(19), p.e2021GL094737.(中科院2Top

[4] Wu, Y., Tan, H., Qin, L., Ran, B. and Jiang, Z., 2018. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, pp.166-180.(中科院1TopESI高被引论文)

[5] Wu, Y., Tan, H., Li, Y., Zhang, J. and Chen, X., 2018. A fused CP factorization method for incomplete tensors. IEEE Transactions on Neural Networks and Learning Systems, 30(3), pp.751-764.(中科院1Top

[6] Wu, Y., Tan, H., Li, Y., Li, F. and He, H., 2017. Robust tensor decomposition based on Cauchy distribution and its applications. Neurocomputing, 223, pp.107-117.(中科院2Top

[7] Tan, H., Wu, Y., Shen, B., Jin, P.J. and Ran, B., 2016. Short-term traffic prediction based on dynamic tensor completion. IEEE Transactions on Intelligent Transportation Systems, 17(8), pp.2123-2133.(中科院1Top)

智能决策与控制方向

[1] Wang, Y., Tan, H., Wu, Y.* and Peng, J., 2020. Hybrid electric vehicle energy management with computer vision and deep reinforcement learning. IEEE Transactions on Industrial Informatics, 17(6), pp.3857-3868.(中科院1Top

[2] Wu, Y., Tan, H., Qin, L. and Ran, B., 2020. Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm. Transportation research part C: emerging technologies, 117, p.102649.(中科院1Top

[3] Lian, R., Tan, H., Peng, J., Li, Q. and Wu, Y.*, 2020. Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management. IEEE Transactions on Vehicular Technology, 69(8), pp.8367-8380.(中科院2Top)

[4] Lian, R., Peng, J., Wu, Y.*, Tan, H. and Zhang, H., 2020. Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle. Energy, 197, p.117297.(中科院1Top)

[5] Wu, Y., Tan, H., Peng, J., Zhang, H. and He, H., 2019. Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus. Applied energy, 247, pp.454-466.(中科院1TopESI高被引论文)