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
- 演讲人：Ryan Loxton，澳大利亚科廷大学教授／ARC Future Fellow
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.
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.
World Robotic Sailing Championship
"The 3rd place of WRSC 2018 Micro-sailboat class goes to Zhejiang University! As first time participants, the made their boat within 2 months. Well done and congratulations!"
Congratulations to the ZMART / Sailing Group!
After several years explorations and research in robotics, we currently have new definition for ZMART, which was named in 2012 as ZJU's Micro-Aerial Robotics Team when we first attend the IARC (the International Aerial Robotics Competition).
In 2018, we organized two more groups to attend the other two exciting competitions, including the ICRA 2018 DJI RoboMaster AI Challenge Overview and the 11th World Robotic Sailing Championship.
Based on the new activities, the BLUe Lab is going to give a new meaning for the ZMART, ZJU's Micro Agents and Robotics Team. Then, ZMART / Aerial / Ground / Sailing represents different teams corresponding to IARC, DJI RoboMaster and WRSC, respectively.
News featured in the media include:
- 电视频道报道：国际空中机器人（IARC）大赛及青少年附加赛在中国教育电视台-1 频道播出，Youku链接
- College of Control Science & Engineering, Zhejiang University, "历时5年，浙大队ZMART勇夺国际空中机器人大赛世界冠军，终结第七代任务"
- China Automation Association (中国自动化学会), "【2018 IARC】国际空中机器人大赛将于8月26日在北京航空航天大学体育馆开幕！大赛背景介绍与参赛队伍简介一睹为快！"
- INTERNATIONAL AERIAL ROBOTICS COMPETITION Mission 7 (2014-2018), http://www.aerialroboticscompetition.org/mission7.php
- THE INTERNATIONAL AERIAL ROBOTICS COMPETITION'S 27 Year History, http://www.aerialroboticscompetition.org/pastmissions.php
"Mission 7 took a monumental leap by requiring autonomous aerial robots to interact with and control autonomous ground robots. Teams were tasked with developing systems to herd ground robots out one end of an arena in the absence of 3D cues such as walls. The ground robots could only be interacted with by touch. A top touch would command a 45° clockwise turn and a blocking action would result in a 180° turn. To complicate matters, the ground robots do a 180° turn every 20 seconds and add up to 15 degrees of trajectory noise every 5 seconds. The ground robots also impact one another and quickly devolve into non-deterministic travel. In the midst of the arena were four obstacle robots to complicate navigation and obstacle avoidance. The aerial robots had to dynamically determine a best course of action to keep the ground robots from exiting on three of four sides of the arena. In the top performances, which were replicated multiple times, the Zhejiang University team showed that its autonomous aerial robot could track individual ground robots, redirect them in either 45° or 180° increments while at the same time staying within the arena boundaries and avoiding the mobile obstacles circulating within the arena."
The First Workshop on Machine Learning, Optimization and Control
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
- Data mining and knowledge discovery
- Computer vision and image understanding
- Prof. Lei Zhang (张磊), The Hong Kong Polytechnic University
- 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
- Zhiyang Wang
- Shichao Zhou (周世超), firstname.lastname@example.org
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
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
- 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
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.
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.
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
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.
- 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
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。