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FRAM-X Workshop

Fluids, Robotics & Applied Math X Workshop, Hangzhou, China

November 3 and 4, 2018

We are going to launch a multidisciplinary workshop on the challenging issues at the cross-area of fluid mechanics, bio-inspired robotics and their related applied mathematics. Several talks have been confirmed including, Jian Deng, Qi Gao, Zhi Lin, Jia Pan and etc. More details will be announced soon.

Optimization in Action: Unlocking Value in the Mining, Energy, and Agriculture Industries

Optimization in Action: Unlocking Value in the Mining, Energy, and Agriculture Industries

  • 时间:2018年10月19日星期五下午2点
  • 地点:浙江大学玉泉校区智能系统与控制研究所304教室
  • 演讲人:Ryan Loxton,澳大利亚科廷大学教授/ARC Future Fellow

ABSTRACT

Mathematical optimization has numerous applications in business and industry. However, there is a large mismatch between the optimization problems studied in academia (which tend to be highly structured problems) and those encountered in practice (which are non-standard, highly unstructured problems). This talk gives an overview of the presenter’s recent experiences in building optimization models and algorithms in theoil and gas, mining, and agriculture sectors. Some of this practical work has led to academic journal articles, showing that the gap between academia and industry can be overcome.

BIOGRAPHY

Ryan Loxton is a full professor and ARC Future Fellow in the School of Electrical Engineering, Computing, and Mathematical Sciences at Curtin University. His work focuses on using advanced mathematics to optimise complex processes in a wide range ofapplications such as mining, oil and gas, agriculture, and industrial process control. Ryan is a passionate advocate for industry engagement and has worked with many companies including Woodside Energy, Linkforce, and Vekta Automation. He was the 2014 West Australian Young Scientist of the Yearand he currently leads the optimisation theme in the new ARC Industrial Training Centre on Transforming Maintenance through Data Science, which is funded by the Australian Research Council (the equivalent of the NSFC in Australia) and industry partners Alcoa, BHP Billiton, and Roy Hill.

The First Workshop on Machine Learning, Optimization and Control (MLOC)

The First Workshop on Machine Learning, Optimization and Control

2018年7月9日至11日,深圳

MLOC is a forum for leading researchers and industry experts on all aspects of machine intelligence and optimization, including machine learning techniques, pattern recognition, data mining, optimization theory and control techniques. In the context of this symposium, machine learning encompasses works on concurrent deep learning techniques, their associated optimization and control techniques. Given the rise of deep learning and big data analysis, the first MLOC is particularly interested in work that addresses new optimization techniques, and control tools that attempt to improve machine learning performance in big data analytics, and work towards improved industry intelligent systems.

Specific topics of interest include (but are not limited to):

  • Deep learning techniques and applications
  • Big data analysis
  • Robust face recognition
  • Data driven control
  • Cyber Security
  • Clustering and classification techniques
  • Randomised algorithm
  • Tracking
  • Data mining and knowledge discovery
  • Computer vision and image understanding

Workshop Chair

  • Prof. Lei Zhang (张磊), The Hong Kong Polytechnic University

Workshop Organizers

  • Prof. Wanquan Liu (刘万泉), Curtin University
  • Prof. Yongsheng Ou (欧勇盛), Shenzhen Institutes of Advanced Technology, CAS
  • Prof. Aiguo Wu (吴爱国), Harbin Institute of Technology, Shenzhen
  • Prof. Chao Xu (许超), Zhejiang University

Workshop Secretaries

  • Zhiyang Wang
  • Shichao Zhou (周世超), 595689102@qq.com

日程安排

2018年7月9日 至宾馆报道


2018年7月10日
8:45 早餐后,离开宾馆,前去会场
9:25 会议开始
9:30 First Keynote

  • Title: TBA (on deep learning, optimization and control in general review)
  • Spekaer: Prof. Xiangchu Feng, Xidian University

10:15 Questions and Discussions
10:45 Second Keynote

  • Title: Implementing the ADMM to big datasets: A case study of LASSO
  • Speaker: Prof. Xiaoming Yuan, Hongkong University

11:30 Questions and Discussions
12:00 午餐
14:00 Third Keynote

  • Title: Machine learning via Wasserstein statistical manifold
  • Speaker: Prof. Wuchen Li, UCLA

14:45 Questions and Discussions
15:15 Fourth Keynote

  • Title: TBA
  • Prof. Lei Zhang, The Hong Kong Polytechnic University

16:00 Questions and Discussions
17:00 离开会场,赴宾馆参加晚宴


2018年7月11日
8:30 早餐后,离开宾馆,前去会场
以下的每个报告将由25分钟演讲和5分钟提问讨论组成

  • 9:00 Title: TBA (on dictionary learning) | Speaker: Yong Xu, Harbin Institute of Technology (Shenzhen)
  • 9:30 人工智能中的优化问题研究 | Lingchen Kong, Beijing Jiaotong University
  • 10:00 Title: Cardiac image quantification and motion analysis based on deep neural network | Wufeng Xue, Shenzhen University
  • 10:30 Title: TBA | Daoqiang Zhang (Tony), NUAA
  • 11:00 Title: Computer image and image understanding | Wei Xie, South China University of Technology
  • 11:30 会议结束
  • 12:00 午餐

报告摘要与报告人简介(排名不分先后)

Title: Machine learning via Wasserstein statistical manifold

Abstract: In this talk, I start with reviewing several primal-dual structures in optimal transport (Wasserstein metric). Based on it, I will introduce the Wasserstein natural gradient in parametric statistical models. We pull back the L2-Wasserstein metric tensor in probability density space to parameter space, under which the parameter space become a Riemannian manifold. The gradient and Hamiltonian flows in parameter space are derived. When parameterized densities lie in 1D, we show that the induced metric tensor and gradient flow establish explicit formulas. Examples are presented to demonstrate its effectiveness in several machine learning problems.

(更多…)

Switching Time Optimization for Nonlinear Switched Systems

浙江大学玉泉校区智能系统与控制研究所二楼资料室, 9 a.m., July 2, 2018

Prof. Ryan Loxton, full professor in the School of Electrical Engineering, Computing, and Mathematics at Curtin University, Australia.

Abstract: Switched systems operate by switching among various different modes. Determining the optimal times at which the mode switches should occur is a fundamental problem in systems and control, with particular importance to the numerical solution of optimal control problems. This talk will discuss the switching time optimization problem for two classes of switched systems: those with time-dependent switching conditions (where the switches are directly controllable), and those with state-dependent switching conditions (where the switches occur when the system hits certain switching surfaces in the state space). It is widely believed that standard numerical optimization techniques struggle when applied to switching time optimization problems. In this talk we present new results showing that this challenge is over-stated; contrary to popular belief, switching times can in fact be optimized effectively using standard optimization methods. We verify this with a numerical example involving a switched system model for the production of 1,3-propanediol, an industrial polymer used in paints, adhesives, and lubricants.

Bio of the speaker: Ryan Loxton is a full professor in the School of Electrical Engineering, Computing, and Mathematics at Curtin University, Australia. His research focuses on developing new mathematical techniques to optimize complex processes in a wide range of applications such as mining, oil and gas, agriculture, and industrial process control. Ryan’s work has been recognized with several high-profile awards, including two prestigious, highly competitive fellowships from the Australian Research Council and the 2014 West Australian Young Scientist of the Year Award. A passionate advocate for industry engagement, Ryan has led many industry-funded research projects with companies such as Woodside Energy, Linkforce, Roy Hill Iron Ore, Vekta Automation, and Global Grain Handling Solutions. His mathematical algorithms underpin the Quantum software system (developed by Onesun Pty Ltd) for tracking, executing, and optimizing maintenance shutdowns in the resources sector. This technology was the winner of the 2017 South32 Designing for Excellence Innovation Award. Ryan is an Associate Editor for the Journal of Industrial and Management Optimization and has published over 70 papers in international journals and conference proceedings.

Invited to talk at the 7th WCSCM

Prof. Chao Xu is invited to deliver an aera talk (SS04: New Development of Smart Devices for Structural Control | 结构控制的智能装置新进展) at the 7th WCSCM, the World Conference on Structural Control and Monitoring (WCSCM). The title of Prof. Xu's talk is Infrastructure Cyber-Care: Challenges to Cyber-Systems, Robotics and Dada Analytics.

WCSCM is a premier leading conference, under the auspices of the International Association for Structural Control and Monitoring (IACSM). The WCSCM, held every four years, is aiming at promoting advanced structural control and monitoring technology for a variety of civil, mechanical, aerospace and energy systems. The precedent conferences have been held in Pasadena - USA (1994), Kyoto - Japan (1998), Como - Italy (2002), La Jolla - USA (2006), Tokyo - Japan (2010) and Barcelona - Spain (2014).The new edition of the WCSCM, 7WCSCM, will be hosted by Harbin Institute of Technology in July 2018. The conference will provide international research community a platform to contribute to the state of the art in such multidisciplinary scientific and engineering environment with new results, fresh ideas and future perspectives.

An Optimal Control Approach to Deep Learning

An Optimal Control Approach to Deep Learning

浙江大学玉泉校区教九101演讲厅, 9:30 a.m., June 25, 2018

Dr. Qianxiao Li, Research Scientist at the Institute of High Performance Computing, A*STAR, Singapore and an adjunct assistant professor in the department of mathematics, National University of Singapore

Abstract: In this talk, we discuss a new approach to study the algorithmic and theoretical aspects of deep learning. In particular, the optimization of deep neural networks is recast as an optimal control problem, which is a classical problem that originates from the calculus of variations. Based on this viewpoint, we investigate the development of novel algorithms, as well as theoretical insights on generalization bounds of neural networks.

Bio of the speaker: Qianxiao Li is a research Scientist at the Institute of High Performance Computing, A*STAR, Singapore and an adjunct assistant professor in the department of mathematics, National University of Singapore. He graduated with a BA in Mathematics from University of Cambridge in 2010, and a PhD in applied mathematics from Princeton University in 2016. His main research interests include the theory and algorithms for deep learning, stochastic optimization and applications ofmachine learning to the physical sciences.

航天控制技术发展展望

航天控制技术发展展望:不忘初心,走向航天强国

  • 报告人:祁振强 研究员,中国运载火箭技术研究院北京航天自动控制研究所
  • 报告时间:527日周日10:00;报告地点:工控新楼501教室

讲座简介:智能控制技术成为未来航天控制技术发展的重要方向,本报告从控制理论和技术的发展历程,分析航天智能控制技术发展的需求特点,结合航天智能技术创新中心建设,阐述航天控制技术未来发展的路线图、关键技术和未来发展的能力展望。

个人简历:祁振强,工学博士,研究员,现任中国航天科技集团有限公司中国运载火箭技术研究院北京航天自动控制研究所技术发展处处长、研究院核高基重大科技专项办公室副主任,国防973项目总体组组长,国家国防科技工业局国防基础科研计划信息与控制专家组专家。担任中英先进控制系统技术联合实验室中方主任、学术委员会委员,曾任IEEE ICMC 2014程序委员会主席,IEEE CGNCC 2016程序委员会委员;是《宇航学报》、《控制与信息技术》编委和多个学术期刊审稿人。从事航天运载器导航制导控制、智能自主控制等技术研究。完成3个国家航天装备型号任务和国防973等多项科研项目研究,获得国家技术发明一等奖、国防技术发明二等奖、军队科技进步二等奖等科技奖项7项,获得授权专利33项,获颁首次探月工程有功人员奖章。主持推进国际合作,建成中英先进控制系统技术联合实验室,获批航天先进控制技术国际联合研究中心国家国际科技合作基地。

Visualizing Real World Flows

Visualizing Real World Flows

Professor Steve Wereley, Purdue University, Mechanical Engineering

  • 18:30, May 13, 2018
  • 浙江大学紫金港校区西2-309

Particle Image Velocimetry (PIV) is a laboratory tool that many fluid mechanics professionals use to measure flows in their labs where experimental conditions can be perfectly controlled. However, quite often scientists need to know about flows in the real world. One example where fluid mechanics professionals must work in the field under non-ideal conditions is that of oil spilling into the environment. While the size of many oil spills is known because a fixed volume of oil has spilled out of a damage oil tanker, for many others, the size of the spill is not. For example, the Deepwater Horizon oil spill in the USA in 2010 occurred when the oil company lost control of an oil well and oil poured unrestrainedly into the Gulf of Mexico for more than 3 months (see picture below). In order to assess the amount of damage caused by the oil spill, Professor Wereley used quantitative flow visualization to estimate the size of the oil spill. This approach was shown to be much more accurate than other methods that were used to measure the size of the oil spill.

Towards Greater Efficiency and Autonomy of VTOL UAVs: Design, Modeling and Control

垂直起降无人机设计、建模与控制(海报pdf版本

欢迎广大师生前来参加学术报告!

  • 2018年4月27日(周五)下午3点半
  • 地点:浙江大学玉泉校区智能系统与控制研究所304教室
  • 请金数据登记报名:https://jinshuju.net/f/q4Qs1H

Abstract: Over last decade, small-size unmanned aerial vehicles (UAVs) have received unprecedented research interests and created extensive applications as well as market opportunities. Currently dominant UAV platforms, such as quadrotors and hexarotors, although exhibits great maneuverability (i.e. vertical takeoff and landing, hovering capability), is inherently energy inefficient by its flight mechanics. This drawback has increasingly limited their application in range- and/or endurance-demanding tasks such as surveying and mapping. Motivated by this, hybrid aerial vehicles such as tail-sitters, tilt-rotors, tilt-props, or dual-propulsion systems can transform between multirotor mode and fixed-wing mode, thus inheriting both benefits. Though having been actively explored in the aviation history, low-cost, small-size hybrid UAVs with increased intelligence and autonomy still poses a grand challenge. In this talk, I will present the development of a portable hybrid vertical takeoff and landing (VTOL) UAV. From a system point of view, three topics will be covered: design, modeling and control. Through the design, implementation and intensive tests, I will show how recent advances in low cost actuator (e.g. motor, propeller, ESCs), computation units and sensors have enabled the development of such small-size hybrid VTOL UAVs, and the arisen opportunities and challenges.

Bio: Dr. Fu Zhang received the B.E. (with honor) from the Department of Automation, University of Science and Technology of China, Hefei, China, in 2011. Then he studied at Department of Mechanical Engineering, the University of California, with full scholarship. He received the Ph.D. degree there in 2015, focusing on the dynamics modeling and control of ultra-high precision machining systems and high performance micro-scale rate integrating gyros funded by DARPA. Dr. Fu Zhang is now a research assistant professor with the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). His current research interests include the dynamics, control and navigation of unmanned aerial vehicles (UAVs), deep reinforcement learning and optimization.

三维流场测量技术发展动态

三维流场测量技术发展动态(海报PDF版本

  • 时间:2018年4月23日星期一上午9:50
  • 地点:曹光彪楼二期204教室

演讲人:高琪,男,1979年出生,浙大航空航天学院流体工程研究所副教授。《实验流体力学》编委,中国力学学会科学普及工作委员会委员。目前从事三维流场观测的实验研究:研制了国内第一套具有自主知识产权的层析粒子图像测速(PIV)系统,研发了单相机层析PIV技术以及三脉冲速度场/压力场耦合测量的系统。

教育经历

  • 浙江大学工程力学系                       学士
  • 清华大学工程力学系                       硕士
  • 明尼苏达大学航空工程与力学系     博士

报告摘要:近年来,实验流体力学测量技术在很多方面有了显著的发展。人们不再满足对单一物理量的单点或者平面数据进行实验测量,而向往和数值计算结果一样,获得多物理场耦合的三维空间体内的实验测量结果。为了突破对简单流动工况测量的局限性,人们针对非定常复杂流动,采用声、光、电和磁等多种物理测量手段来实现流场测量。在实验测量技术的复杂化过程中,出现了两种较新的三维测量技术:层析粒子图像测速(TPIV)和磁共振测速(MRV)。这两种技术分别采用光学和磁场成像技术来实现流场的测量。另一方面,在实验数据处理和分析过程中出现一种让人眼前一亮的趋势,就是通过引入物理约束,或者说利用流动控制方程来优化实验结果、进行杂交的数值模拟计算和对流场的预测。这一发展方向上,最具代表性的就是通过时间解析的三维速度场来重构流场三维压力场的技术。本报告将从测量技术和数据处理这两方面来介绍三维流场测量技术发展的最新动向。