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鲁棒控制和优化:效率和自治区交通系统的安全性

康涅狄格州的飞苗大学将提出学习的强大的监控和优化:下午4时自治运输系统的效率和安全性十一月1次研讨会在挖掘其运输赫斯特290纪念建筑。

抽象

在智慧城市感知无所不在启用收集实时的大型多源数据,提出了挑战,需要几个模式转变到数据驱动的网络物理系统(CPSS),集成优化控制和机器学习。例如,如何捕捉复杂性和数据分析城市规模的现象之间的动态相互作用,并采取行动以改善提高服务效率和安全性,仍然是在交通系统中一个具有挑战性的问题。在这次谈话中,我们首先提出了骑共享和拼车的自治系统,整车配套供应,当前和未来的走向都预期需求强大的数据驱动的动态资源分配框架。随着需求预测的时空不确定性,我们则易于计算验证和开发提供了系统的最坏情况和预期业绩的保证概率方法。动态定价模型是专为旅行同样的可靠性在高峰时段的时间。那我们展示了骑共享系统的性能是基于世界出租车提高了运行数据。最后,近期acerca的信息共享和决策框架,考虑到安全性和自主车的效率工作连接介绍。

周五,2019年11月1日 - 下午4:00
290赫斯特纪念矿山建设

主持人

苗菲

苗菲 is an Assistant Professor of the Department of Computer Science & Engineering, and she is also affiliated to the Department of Electrical & Computer Engineering, University of 连icut since 2017. Her research interests lie in the intersection of control, optimization, and machine learning with application in cyber-physical systems efficiency, safety, and security. She has received a couple of awards from NSF, including S&AS, CPS, and S&CC programs. She received a Ph.D. degree, and the “Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation” in Electrical and Systems Engineering, with a dual Master degree in Statistics at Wharton School from the University of Pennsylvania. She received a B.S. degree majoring in Automation from Shanghai Jiao Tong University. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of UPenn, from September 2016 to August 2017. She was a Best Paper Award Finalist at the 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2015.