邱才明
发表时间:2018-03-16阅读次数:1862次

Caiming Qiu

教授,上海交通大学电气工程系

 

研究领域

High-dimensional statistics, artificial intelligence, smart grid

 

联系方式

地址:上海市闵行区 东川路800号 上海交通大学 智能电网中心楼505室

邮编:200240

联系电话:website: bdc.sjtu.edu.cn

电子邮件:rcqiu@sjtu.edu.cn;  

 

教育背景

1983.09—1987.06   B.S., Electrical Engineering, Xidian University

1987.08—1990.03   M.S., Electromagnetic Engineering and Microwave Technology,          University of Electronic Science and Technology of China

1992.08—1995.07   Ph.D., Electrical Engineering, New York University

 

工作经历

1995.10—1997.04   Staff.                                              GTE Labs Inc.,         GTE Corp.

1997.05—2000.04   Staff.                                              Bell Labs,

2014.06—Now          Endowed-chair-professor, Shanghai Jiaotong University

 

成果与获奖

1、Best paper award of IEEE International Conference on Communications (ICC), 2011 – “Decoding

the ‘Nature Encoded’ Messages for Distributed Energy Generation Control in Microgrid” (3/1092)

 

主要论文

[1]     He X, Ai Q, Qiu R, et al. A big data architecture design for smart grids based on random matrix theory[J]. IEEE Transactions on Smart Grid, 2017, 8(2): 674–686. Online: http://arxiv.org/pdf/1501.07329.pdf

[2]     He X, Qiu R, Ai Q, et al. Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices[J]. IEEE Access, 2016, (4): 3557–3568. Online: http://arxiv.org/pdf/1512.07082.pdf

[3]     Xu X, He X, Ai Q, Qiu R. A Correlation Analysis Method for Power Systems Based on Random Matrix Theory[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 1811–1820. Online: http://arxiv.org/pdf/1506.04854.pdf

[4]     He X, Chu L, Ai Q, Qiu R. A Novel Data-Driven Situation Awareness Approach for Future Grids—Using Large Random Matrices for Big Data Modeling[J]. Accepted by IEEE Access, 2018/2/10. Online: http://arxiv.org/pdf/1610.05076.pdf

[5]     He X, Qiu R, Chu L, el al. Detection and Estimation of the Invisible Units Using Utility Data Based on Random Matrix Theory[J]. Submitted to IEEE Transactions on Power Systems, 2018. Online: http://arxiv.org/pdf/1710.10745.pdf

[6]     Chu L, Qiu R, He X, Ling Z, Liu Y. Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests[J]. IEEE Transactions on Big Data, 2018, 4(1): 55–64. Online:http://arxiv.org/pdf/1609.03301.pdf

[7]     Yang F, He X, Qiu R, Ling Z. A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model[J]. Submitted to IEEE Transactions on Smart Grid, 2018. Online: http://arxiv.org/pdf/1712.08871.pdf

[8]     Ling Z, Qiu R, He X. A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location[J]. Submitted to IEEE Transactions on Big Data, 2018. Online: http://arxiv.org/pdf/1802.03503.pdf

[9]     Ling Z, Qiu R, Jin Z, Zhang Y, He X. An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images[J]. 2018. Online: http://arxiv.org/pdf/1801.05143.pdf

[10]   Shi X, Qiu R, He X, et al. Incipient Fault Detection and Location in Distribution Networks: A Data-Driven Approach[J]. Submitted to IEEE Transactions on Power Delivery, 2018. Online: http://arxiv.org/pdf/1801.01669.pdf

[11]   Qiu R, Chu L, He X, et al. Spatio-Temporal Big Data Analysis for Smart Grids Based on Random Matrix Theory: A Comprehensive Study[Z]. Book chapter23 for the book ”Transportation and Power Grid in Smart Cities: Communication Networks and Services”. Online: http://arxiv.org/pdf/1708.04935.pdf

[12]   Zhang C, Qiu R C. Data Modeling with Large Random Matrices in a Cognitive Radio Network Testbed: Initial Experimental Demonstrations with 70 Nodes[J]. ArXiv eprints, 2014. http://arxiv.org/pdf/1404.3788.pdf.

专著

[1]   Qiu R, Hu Z, Li H, et al. Cognitive radio communication and networking: Principles and practice[M]. [S.l.] : John Wiley & Sons, 2012.

[2]   Qiu R, Wicks M. Cognitive Networked Sensing and Big Data[M]. [S.l.] : Springer, 2014.

[3]   Qiu R C, Antonik P. Smart grid using big data analytics: a random matrix theory approach[M]. [S.l.] : John Wiley & Sons, 2017

 

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