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张勇(教授)

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  • 曾获荣誉: 2023当选:北京邮电大学教学名师;
    2023当选:中国通信学会科技进步二等奖;
    2023当选:中国电子学会科技进步三等奖;
    2023当选:中国电力发展促进会科学技术奖一等奖;
    2022当选:浙江通信学会科技进步奖二等奖;
    2022当选:中国电机工程学会科技进步奖三等奖;
    2018当选:浙江省政府科技进步奖二等奖;
    2018当选:电力企业联合会创新奖一等奖;
    2017当选:电子学会科技进步奖二等奖
  • 博士生导师  硕士生导师
  • 电子邮箱:yongzhang@bupt.edu.cn
  • 所在单位:电子工程学院
  • 学历:研究生毕业
  • 办公地点:北京市海淀区西土城路10号教三楼743室
  • 性别:男
  • 联系方式:Yongzhang {at}bupt {dot}edu {dot}cn
  • 学位:博士学位
  • 在职信息:在职
  • 主要任职:未来网络与智能计算实验室主任
  • 其他任职:中国通信标准化协会移动互联网业务平台与终端工作组副组长
  • 毕业院校:北京邮电大学
  • 学科:电子科学与技术*
  • 所属院系:电子工程学院

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  • 个人简介

北京邮电大学电子工程学院教授,博士生导师,电子工程学院ICN&CAD中心主任,Fab.x人工智能联合实验室主任,中国通信标准化协会TC11 WG2副组长,北京邮电大学教学名师。2007年于北京邮电大学获得工学博士学位。2014年在美国宾夕法尼亚州立大学担任访问学者。主要研究方向:宽带无线接入网、人工智能、网络智能化等。


曾主持承担国家863项目“基于异构融合的新型无线认知网络路由及QoS 保障机制研究”,自然基金项目“基于分布式存储的分层机会网络架构及关键技术的研究”、国家重大专项“基于离散窄带频谱的宽带无线接入技术研发”。参与完成国家自然科学基金项目“下一代移动通信系统中网络融合的理论和关键技术研究”和科技部国家国际科技合作项目“Ad Hoc、传感器、Mesh 网络及协作网络:自组织无线接入网关键技术研究”等国家级科技研究项目。


当前主要研究兴趣集中在移动互联网及人工智能技术研究与应用。当前承担了主持承担了科技部重点研发课题“馆藏文物保存环境分布式智能精准监控技术研究”、国家自然基金项目“认知无线虚拟网络弹性资源管理理论与切片技术研究”、负责教育部-中国移动联合基金项目“人工智能”项目中网络智能化研究工作。课题组还参与国家电网总部科技项目,在电力无线专网、传送网的切片技术、网络规划与评估等领域开展研究。


在中国通信标准化协会负责并组织多项通信行业标准和技术报告的制定编写工作。近几年发表论文70多篇,出版个人专著“认知网络与认知无线电”、“无线网状网原理与技术”和“WiMAX技术、应用及网络规划”三本,个人译著“无线网状网:架构、协议和标准”一本,授权国家发明专利60余项、起草行业标准18项。


自2009年,国家电网开展在230MHz频段无线网络的研究以来,课题组在国家自然基金、国家电网总部科技项目、浙江电网科技项目等支持下,对电力无线专网等领域开展研究,课题负责人张勇教授还是国家电网低频段物联网技术与应用研究实验室学术委员会副主任。在该领域的工作,获得2017年度电子学会科技进步奖二等奖,2018年度电力企业联合会创新奖一等奖,2018年度浙江省科学技术奖二等奖。2022年度中国电机工程学会电力科学技术进步奖三等奖,中国电子学会科技进步三等奖,浙江省通信学会科学技术二等奖,2023年度中国电机工程学会电力创新一等奖、中国通信学会科技奖二等奖、中国电子学会科学技术奖三等奖、中国电力发展促进会科学技术奖一等奖、电工技术学会科学技术三等奖。


课题组与企业联合成立了Fab.x人工智能联合实验室。该实验室由北京邮电大学和英特尔中国、爱动超越人工智能科技(北京)有限责任公司联合成立,在人工智能理论及应用方面开展研究;物联网信息安全防御技术研究与创新;工业人员运动数据深度学习与生产安全。研究成果已成功应用于国内外多家大型企业。


2019年至2022年,课题组承担的教育部——中国移动联合基金“人工智能”研究项目,针对现网和下一代网络智能化,建设面向大规模服务的可靠、安全、灵活的高性能人工智能平台和工具,支撑中国移动人工智能应用工作。


当前,主要研究方向致力于网络智能化,研究课题包括:

(1) 北京邮电大学——中国移动研究院联合创新基金 , 面向6G移动算网智一体的计算面技术研究与验证, 2023-07 至 2025-12

(2) 北京邮电大学——中国移动研究院联合创新基金, 面向无线网络的控制策略智能生成和效果预估技术, 2023-07 至 2023-12


课题组招收致力于人工智能、网络智能化领域的博士生、博士后,提供良好的科研发展环境。


近年来期刊论文:

1. Hu Y, Li Q, Chai Y, et al. AI Service Deployment and Resource Allocation Optimization Based on Human-Like Networking Architecture[J]. IEEE Internet of Things Journal, 2024.

2. Zhenyu Z, Yong Z, Siyu Y, et al. Resource allocation for cognitive network slicing in PD-SCMA system based on two-way deep reinforcement learning[J]. China Communications, 2024.

3. Yao L, Guo D, Wang X, et al. A double-layer attentive graph convolution networks based on transfer learning for dynamic graph classification[J]. International Journal of Machine Learning and Cybernetics, 2024, 15(3): 863-877.

4. Guo D, Luo D, Zhang Y, et al. Application of deep reinforcement learning to intelligent distributed humidity control system[J]. Applied Intelligence, 2023, 53(13): 16724-16746.

5. Zhang Y, Cheng Z, Guo D, et al. Downlink resource allocation for NOMA-based hybrid spectrum access in cognitive network[J]. China Communications, 2023.

6. Zhang Z, Li Q, Lu L, et al. Joint optimization of the partition and scheduling of dnn tasks in computing and network convergence[J]. IEEE Networking Letters, 2023.

7. Yuan S, Zhang Z, Li Q, et al. Joint optimization of dnn partition and continuous task scheduling for digital twin-aided mec network with deep reinforcement learning[J]. IEEE Access, 2023, 11: 27099-27110.

8. Li S, Zhang Y, Yuan S, et al. User scheduling and slicing resource allocation in industrial Internet of Things[J]. China Communications, 2023.

9. Zhang Z, Zhang Y, Guo D, et al. Communication-efficient federated continual learning for distributed learning system with Non-IID data[J]. Science China Information Sciences, 2023, 66(2): 122102.

10. Li Z, Zhang Y, Guo D, et al. Long-term traffic forecasting based on adaptive graph cross strided convolution network[J]. Applied Intelligence, 2023, 53(4): 3672-3686.

11. Yuan S, Zhang Y, Ma T, et al. Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing[J]. IET Communications, 2023, 17(2): 215-227.

12. Chai Y, Zhang Y, Ma T, et al. Research on multi-service slice resource allocation over licensed and unlicensed bands[J]. Wireless Networks, 2023, 29(1): 1-17.

13. Zhang Z, Zhang Y, Niu J, et al. Unknown network attack detection based on open‐set recognition and active learning in drone network[J]. Transactions on Emerging Telecommunications Technologies, 2022, 33(10): e4212.

14. Bai H, Zhang Y, Zhang Z, et al. Latency equalization policy of end-to-end network slicing based on reinforcement learning[J]. IEEE Transactions on Network and Service Management, 2022, 20(1): 88-103.

15. Zhang Z, Zhang Y, Guo D, et al. Secfednids: Robust defense for poisoning attack against federated learning-based network intrusion detection system[J]. Future Generation Computer Systems, 2022, 134: 154-169.

16. Ma T, Zhang Y, Han Z, et al. Heterogeneous RAN slicing resource allocation using mathematical program with equilibrium constraints[J]. IET Communications, 2022, 16(15): 1772-1786.

17. Li Q, Zhang Y, Hua X, et al. Structure learning‐based unsupervised root cause diagnosis for radio access networks[J]. Electronics Letters, 2022, 58(10): 414-416.

18. Zhou X, Zhang Y, Li Z, et al. Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning[J]. Neural Computing and Applications, 2022: 1-11.

19. Zhang Y, Hu Y, Ma T. Stability‐oriented RAN slicing based on joint communication and computation offloading[J]. IET Communications, 2022, 16(7): 772-785.

20. Sun Y, Zhang Y, Guo D, et al. Intelligent distributed temperature and humidity control mechanism for uniformity and precision in the indoor environment[J]. IEEE Internet of Things Journal, 2022, 9(19): 19101-19115.

21. Ma T, Zhang Y, Yuan S, et al. Cognitive ran slicing resource allocation based on stackelberg game[J]. China Communications, 2021, 19(5): 12-23.

22. Li J, Zhu L, Zhang Y, et al. Attention-based multi-scale prediction network for time-series data[J]. China Communications, 2021, 19(5): 286-301.

23. Zhang Z, Zhang Y, Guo D, et al. A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks[J]. International Journal of Machine Learning and Cybernetics, 2021, 12(6): 1649-1665.

24. Yuan S, Zhang Y, Qie W, et al. Deep reinforcement learning for resource allocation with network slicing in cognitive radio network[J]. Computer Science and Information Systems, 2021, 18(3): 979-999.


近年来会议论文:

1. World Model aided Parameter Adjustment Decisionand Evaluation System for Radio Access Network, ICC2024 (accepted)

2. Accelerating Distributed Model Training through Intelligent Node Selection and Data Allocation Strategies in 6G network, ICC2024 DDINS WKSP (accepted)

3. CDVD: Causal Dynamic Variational Deconfounder for Estimating Parameter Adjusting Effect, ICC2024 DDINS WKSP (accepted)

4. Label-Related Adaptive Graph Construction Based on Attention for Multi-Label Text Classification, ICIC2024 (accepted)

5. Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level, NAACL2024 (accepted)

6. Li Y, Sun Y, Liu Z, et al. Indoor Temperature and Humidity Control System Based on Transfer Deep Reinforcement Learning[C]//2023 China Automation Congress (CAC). IEEE, 2023: 5720-5725.

7. Zhang Y, Li Q, Hua X, et al. DCDN: Estimating Handover Parameter Adjusting Effect with Causal Inference[C]//2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall). IEEE, 2023: 1-5.

8. Sun B, Yao L, Liu Z, et al. Predictability of Cellular Network Traffic Based on Conditional Entropy[C]//2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS). IEEE, 2023: 132-137.

9. Hu L, Chai Y, Li Q, et al. Multi-source dnn task offloading strategy based on in-network computing[C]//2023 25th International Conference on Advanced Communication Technology (ICACT). IEEE, 2023: 226-231.

10. Zhang Y, Zhang Y, Zhang Z, et al. Evaluation of data poisoning attacks on federated learning-based network intrusion detection system[C]//2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). IEEE, 2022: 2235-2242.

11. Yao L, Guo D, Wang X, et al. Spatial-Temporal Adaptive Convolutional Network with External Factors for Cellular Traffic Prediction[C]//2022 IEEE 8th International Conference on Computer and Communications (ICCC). IEEE, 2022: 1010-1016.

12. Zhang J, Zu Y, Zhang Y, et al. Dynamic RAN Slicing with Effective Isolation under Imperfect CSI[C]//2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET). IEEE, 2022: 238-242.

13. Li Z, Zhang Y, Zhang Z, et al. Adaptive Spatial-Temporal Convolution Network for Traffic Forecasting[C]//International Conference on Knowledge Science, Engineering and Management. Cham: Springer International Publishing, 2022: 287-299.

14. Hu Y, Ma D, Sui J, et al. Dynamic allocation of FlexE discrete resource based on regional traffic prediction[C]//2022 3rd Information Communication Technologies Conference (ICTC). IEEE, 2022: 86-90.

15. Zhu X, Zhang Y, Zhang Z, et al. Interpretability evaluation of botnet detection model based on graph neural network[C]//IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2022: 1-6.

16. Wu D, Xin P, Liu L, et al. Routing policy for balanced management of slices using flexible Ethernet[C]//2022 7th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2022: 537-542.

17. Zhang Y, Yuan S, Hu L, et al. Slice Resource Allocation Technology of Cognitive Wireless Network Based on NOMA[C]//2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE). IEEE, 2021: 169-173.

18. Zhang Z, Zheng W, Shao W, et al. Resource Allocation for Multi-service NOMA System Based on Deep Reinforcement Learning[C]//International Conference on Human Centered Computing. Cham: Springer Nature Switzerland, 2021: 219-231.

19. Zhang Y, Niu J, He G, et al. Network intrusion detection based on active semi-supervised learning[C]//2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, 2021: 129-135.

20. Guo D, Xia X, Zhu L, et al. Dynamic modification neural network model for short-term traffic prediction[J]. Procedia Computer Science, 2021, 187: 134-139.


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