Speaker: Z.Q. Zhu, Fellow of Royal Academy of Engineering, IEEE Fellow, IET Fellow,  Professor of University of SheffieldVenue: DingtalkScan the QR code of DingTalkAbstractElectrical machines and drives are a key technology for numerous market sectors, including industrial drives, domestic appliances, renewable energy, automation, robots, electrified transportation such as electric vehicles, fast trains, more electric aircrafts, and electric ships, etc. This presentation will focus on the current research developments and trends in electrical machines and drives, including high torque/high torque density, high power/high power density, high efficiency, high speed, low speed and direct drive, high reliability, high fault tolerance, high manufacturability, high system integration and design consideration of materials/power electronics/machines/ controllers, low acoustic noise and vibration, and low cost, as well as advanced cooling techniques and thermal management, etc. Worked examples will be given to illustrate the development of novel research concepts and the impact on successful commercial applications. BiographyProfessor Z.Q. Zhu is a Fellow of the Royal Academy of Engineering, a Fellow of IEEE, and a Fellow of IET. He received B.Eng. and M.Sc. degrees from Zhejiang University, China, in 1982 and 1984 respectively, and a Ph.D. degree from The University of Sheffield, U.K., in 1991. Professor Zhu was a Lecturer/Assistant Lecturer at Zhejiang University for four years before joining The University of Sheffield in 1988 where he became a professor in 2000 and has been a top-band university professor since 2014. Since 2008 he has been Head of the global well-known Electrical Machines and Drives Research Group. The group currently consists of >120 personnel (including ~100 PhD students/post-doctors), 8 research centres including the Rolls-Royce University Technology Centre in Advanced Electrical Machines, and the UK Government funded EPSRC Future Electrical Machines Manufacturing Hub. Professor Zhu is the Royal Academy of Engineering/Siemens Research Chair, Academic Director of the Sheffield Siemens Gamesa Renewable Energy Research Centre for off-shore wind power generation (2009-), Director of the Midea Electrical Machines and Control Research Centres for high performance machines and drives (2010-), and Director of the CRRC Electric Technology Centre for fast train propulsion (2014-). Prof. Zhu has made pioneering contributions to the modeling, control, new machine topologies, and applications of permanent magnet machines and drives. His innovative research works and publications have made major advances in state-of-the-art PM machine technology. These advances are now having a major impact on a wide range of applications, including electric vehicles, wind power generators and domestic appliances, leading to a major collective impact on society by reducing both energy consumption and greenhouse gas emissions as a result of significant efficiency improvements. His research activities are sponsored extensively by the UK government and the global industries, the outcomes being exploited successfully for commercialisation by Siemens, Midea, Toyota, Nissan, Valeo, Rolls-Royce, and CRRC etc. He is/was Visiting/Guest Professor of 15 universities including Zhejiang University, Tsinghua University, Chinese Academy of Science, Hong Kong University, and University of Wisconsin-Madison. He is/was a panel member of Royal Academy of Engineering, Panel 7 - Electrical & Electronic (2018-), IEEE Medal in Power Engineering Committee member (2018-), EPSRC Engineering Panel member (2013 & 2019), and NSFC Overseas Panel member (2001-2006), Editor/Associate Editor of IEEE Trans. on Energy Conversion (2014-2017); IEEE Trans. on Industry Applications (2007-2010); Journal of IEE Japan-IAS (2015-), CES Transactions on Electrical Machines and Systems (2017-) etc. He is the author/co-author of >100 patents and >400 IEEE Transactions and IET Proceedings papers. He was the recipient of the 2019 IEEE Industry Application Society Outstanding Achievement Award, 2019 Foundation Prince Albert II de Monaco Award, and 2018 Guandong Province Science and Technology Co-operation Award. He received 34 Best Paper Awards, including 6 IEEE/IET society/journal awards. He has supervised 76 PhD students who successfully obtained their PhD degrees and also 35 post-doctor Research Associates. 

Venue: DingTalkSpeaker: Philip T. Krein,Member of U.S. National Academy of Engineering, Executive Dean of ZJU-UIUC InstituteScan the QR code of DingTalk 

Venue: Rm. A326, Anzhong Bld. Speaker：Youchan Hwang, Ph. D., Researcher，Korea Bridge Design & Engineering Research Center，Seoul National University

Venue: Rm. A326, Anzhong Bld. Speaker：Ho-Kyung Kim, Ph. D., POSCO Chair Professor，Dept. of Civil and Environmental Engineering，Seoul National UniversityAbstract:  A platform to evaluate the vulnerability of running vehicles over the bridgedeck is proposed for the assessment of strong wind hazards on driving mobility..The platform utilizes wind tunnel tests, dynamic s imulations of nmning· vehicles, wind environmental analysis based on long-term wind data. Threeaccidents types of roll over, side slip, and rotation are considered. The criticalwind speed curves are developed according to the vehicle speeds and roadconditions by combined use of the quasi-static approach and dynamic approach,in which all the wind directions and lane positions are taken into consideration,A prototype application is demonstrated for the Gwanan Suspension Bridge composed of double-deck girders. The critical wind speeds are evaluated foreach lanc in each wind direction and the traffic control wind speeds and controlscenarios arc suggesibd. The overall vulnerability indices are also evaluated based on the accumulate data.

Location：200-9，Sir Run Run Shaw Business Administration buildingAbstract: The values of the modular j-invariant at CM points are called singular moduli. They have been known to be algebraic integers since thetime of Kronecker and Weber. Recently, Bilu-Habegger-Kühne showed that these are not units. In this talk, we will apply the results of Gross-Zagier, Gross-Kohnen-Zagier and their generalizations to give a short proof of this result.About the Speaker: Dr. Yingkun Li specializes in the area of number theory and automorphic forms, and is currently a Junior Professor Doctor in Technische Universität Darmstardt, Germany. He has wide interest in the theory of number fields and also modular forms from varying topics, and has made significant contributions to this area of research. In particular, Dr. Li has published numerously in top journals such as Duke Mathematical Journal, JEMS, Mathematische Annalen, Compositio Mathematica, and Algebra & Number Theory, etc.Contact person：Gao Fan (gaofan@zju.edu.cn)

Venue：256Lecture Hall, College of Pharmaceutical Sciences

Venue: Qiushi Hall, 4F, Qizhen Hotel, Zijingang Campus

Venue：Room 306 Ouyang Chunmei Building, Yuquan CampusAbstract：Let T(x), x\in S^2 be a real-valued Gaussian random field,  where  S^2 is the unit sphere in R^3. This talk is concerned with probabilistic and statistical properties of T. When T is isotropic, we show that many properties of T(x) such as regularity, geometric properties of the sample function x→T(x)  are explicitly determined by the high-frequency behavior of its angular power spectrum. Moreover, optimal bounds for the prediction errors of the spherical Gaussian field T are derived.  Contact Person: SU Zhonggen(suzhonggen@zju.edu.cn)     

Venue：Room 306 Ouyang Chunmei Building, Yuquan CampusAbstract：Typically M estimators of regression parameters in the linear regression model are robust against heavy tails of the error distributions, but not scale invariant. To make them scale invariant one often uses the two robust scale parameter estimators, median of the absolute residuals s1 and the median of the absolute differences of pairwise residuals s2 in the definitions of these M estimators. Since M estimators are robust against heavy tail error distributions, it is natural to know if s1 and s2 are consistent under heavy tail error distribution assumptions. This talk will present their limiting distributions when the regression errors form a linear long memory moving average stationary process with α-stable (1 < α < 2) innovations, where the moving average coefficients aj ～ j d−1 , 0 < d < 1 − 1/α. It turns out that s2 has an α∗-stable limit distribution with α∗ = α(1 − d) < α while the consistency rate of s1 is generally worse than that of s2. In the case of symmetrically distributed errors, there is no difference in their consistency rate. The proof is based on the 2nd order asymptotic expansion of the empirical process of the stated infinite variance stationary sequence.Contact Person: SU Zhonggen(suzhonggen@zju.edu.cn)