Research Hotspot Symposium Series of the IET Cyber-Systems and Robotics

No. 1 Fluids, Robotics & Applied Math X Workshop, Hangzhou, China

《IET智能系统与机器人》新期刊研究热点与前沿论坛系列,第一期:流动、机器人与应用数学交叉

  • November 3, 2018
  • 浙江大学玉泉校区智能系统与控制研究所304教室

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.

Speakers and presentation titles (in an alphabetic order of the initial letters of last names):

  • Shengze Cai :: 蔡声泽 (College of Control Science and Engineering, Zhejiang University), Flow visualization and measurement via computer vision technologies
  • Pakpong Chirarattananon (Department of Mechanical and Biomedical Engineering, City University of Hong Kong), Biologically Inspired Robots: Flying Like a Fly, Swimming Like a …. Fly?
  • Qi Gao :: 高琪 (School of AA, Zhejiang University), 基于流动控制方程作为物理约束的三维实验数据后处理技术
  • Tiefeng Li :: 李铁风 (School of AA, Zhejiang University), Soft Robot made of hydrogel and electroactive polymer
  • Zhi Lin :: 林智 (School of Mathematics, Zhejiang University), Stochastic Hydrodynamic Model for Biogenic Mixing
  • Lianqing Liu :: 刘连庆 (Professor Shenyang Institute of Automation, Chinese Academy of Science, China), Living Cell based Modular Micro Robot Fabricated by Robotic Micro-Manipulation System
  • Jia Pan :: 潘佳 (Department of Mechanical Engineering and Biomedical Engineering, City University of Hong Kong), Learning-based Robotic Controller Design and Optimization
  • Jianghao Wu :: 吴江浩 (School of Transportation Science and Engineering, Behang University), 仿生微型飞行器设计中的空气动力学机理及其应用

9:00 -9:10 开幕式
9:10 -9:50 报告题目:Stochastic Hydrodynamic Model for Biogenic Mixing

报告人:林智(School of Mathematics, Zhejiang University)

9:50-10:30 报告题目:Living Cell based Modular Micro Robot Fabricated by Robotic Micro-Manipulation System

报告人:刘连庆 (Professor Shenyang Institute of Automation, Chinese Academy of Science, China)

10:30-11:10 报告题目:Biologically Inspired Robots: Flying Like a Fly, Swimming Like a …. Fly?

报告人:Pakpong Chirarattananon (Department of Mechanical and Biomedical Engineering, City University of Hong Kong)

11:10-11:50  

报告题目:仿生微型飞行器设计中的空气动力学机理及其应用

报告人:吴江浩 (School of Transportation Science and Engineering, Behang University)

12:00-13:30 午餐
2018年11月3日(星期六)下午
时间 内容
13:30-14:10 报告题目:Soft Robot made of hydrogel and electroactive polymer

报告人: 李铁风 (School of AA, Zhejiang University)

14:10-14:50 报告题目:Learning-based Robotic Controller Design and Optimization

报告人:潘佳(Department of Mechanical Engineering and Biomedical Engineering, City University of Hong Kong)

14:50-15:30 报告题目:基于流动控制方程作为物理约束的三维实验数据后处理技术

报告人:高琪(School of AA, Zhejiang University)

15:30-16:10 报告题目:Flow visualization and measurement via computer vision technologies

报告人:蔡声泽 (College of Control Science and Engineering, Zhejiang University)


Schedule and abstract information:

  • Soft Robot made of hydrogel and electroactive polymer, by Tiefeng Li(李铁风)

Abstract: The softness and large strain actuation of responsive hydrogels promise the potential to fabricate soft devices, which can function as a robotic system. The key challenges lie in the fabrication of soft devices with robust actuating ability and biocompatibility to the attached organ. Here we present a solution that integrates the responsive hydrogel membrane, electro-active polymer and flexible electronics can be integrated into a electromechanical device. As an example, the actuation assisting function of this soft device for shrinking an animal bladder is presented. The mechanical behaviors of the balloon-like soft device are experimentally and theoretically investigated. The concepts are applicable to other applications such as soft implants, soft robotics, and microfluidics.


  • Biologically Inspired Robots: Flying Like a Fly, Swimming Like a …. Fly?, by Pakpong Chirarattananon, Assistant Professor, Department of Biomedical Engineering City University of Hong Kong (Email: pakpong.c@cityu.edu.hk; Web: http://ris.mbe.cityu.edu.hk)

Abstract: Using only tiny nervous system, flying insects are able to perform superlative aerodynamic feats such as deftly avoiding a striking hand or landing on flowers buffeted by wind. These amazing creatures inspire scientists and engineers to understand and translate this ubiquitous form of locomotion into man-made machines. In this talk, I will discuss challenges that arise in the fabrication and control and exciting research outcomes related to millimeter-scale biologically-inspired flapping-wing robots. Challenges for the development of such small robots are associated to actuation, power and complex fluid-structure interactions. Through multiple iterations of designs, experiments and theoretical modeling, over the years, not only did we demonstrate a stable tethered flight of an insect-scale robot, but complex maneuvers have also been realized. The flapping-wing robot is capable of flying, landing on a wall, or swimming and transitioning out of the water.

Biography: Pakpong Chirarattananon received his Bachelor and Master degrees from University of Cambridge, UK, and a Ph.D. degree from Harvard University under the supervision of Professor Robert Wood in 2014. His graduate work was centred on the dynamics and control for insect-scale flapping-wing robots. In December 2014, he joined the Department of Mechanical and Biomedical Engineering as an Assistant Professor and founded the Robotic and Intelligent Systems Laboratory at City University of Hong Kong. Dr Chirarattananon is broadly interested in applying control and dynamic system theories to study aerial and biologically-inspired robotic systems. He has published in Science, Science Robotics, and has been nominated as the best student paper award finalist at the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and the best paper award finalist at the 2014 IEEE International Conference on Biomedical Robotics and Biomechatronics. Dr Chirarattananon is serving as the publication co-chair for the upcoming 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).


  • Stochastic Hydrodynamic Model for Biogenic Mixing, by Zhi Lin 林智

Abstract: Biogenic mixing (the mixing and transport due to the swimming of marine species) has attracted a significant amount of interdisciplinary research efforts in recent years. In previous work, the applicant has contributed in deriving a stochastic hydrodynamic model that incorporates both deterministic fluid dynamics models and statistic description for stochastic swimming with a bottom-up approach and leads to a formula for the effective diffusivity. This model provides a new approach for the study of biogenic mixing and similar phenomena in other areas of science and engineering. Recently, we are seeking to improve the model and its simulation by considering various complicated factors, such as anisotropic swimming, nonlinear coupling by massive schooling.

Education:

  • Ph.D.(2007), Mathematics, University of North Carolina, Chapel Hill, North Carolina, USA :: Thesis: “Scalar Intermittency in Random Flows: Modelling and Simulation” • Advisors: Professor Richard McLaughlin and Professor Roberto Camassa
  • B.Sc.(2002), Applied Mathematics and Applied Software, South China University of Technology, China

Appointments:

  • 2018/01 ~ Present, Professor, School of Mathematical Sciences, Zhejiang University, Hangzhou, China
  • 2011/08 ~ 2017/12, Special-Termed Professor, Department of Mathematics, Zhejiang University, Hangzhou, China
  • 2009/09 ~ 2011/08, Postdoctoral Fellow, Institute for Mathematics and its Applications, Minneapolis, Minnesota, USA
  • 2007/09 ~ 2009/05, Postdoctoral Assistant Professor, Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA

  • 基于流动控制方程作为物理约束的三维实验数据后处理技术, by Qi Gao 高琪

Abstract: 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,实验能实现三维非定常复杂流动的多物理量耦合测量。而针对时间解析的三维实验数据处理和分析也出现一个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,通过不可压缩连续性方程来抑制速度场误差,通过压力梯度无旋方程来实现压力场误差的抑制,通过涡量方程来实现三维数据时空分辨率的提高,以及以实验数据作为流场初边值条件通过求解动量方程来实现流场整体的数据优化。本报告将针对实验数据后处理技术来介绍其发展的最新动向。


  • Living Cell based Modular Micro Robot Fabricated by Robotic Micro-Manipulation System, by Lianqing Liu 刘连庆 (Professor Shenyang Institute of Automation, Chinese Academy of Science, China)

Abstract: Micro-robots have a great application prospect in the biomedical field due to the feature of small size. To solve the issues of energy supply and bio-compatibility of micro-robots, the bio-syncretic micro-robots actuated by living cells have been studied widely. However, the fabrication, assembling and control of the bio-syncretic micro-robots are the main challenges for the development of the bio-syncretic micro-robots. In this talk, manufacture and control of the living cells based modular micro robots will be discussed. Firstly, a robotic micro-manipulation system will be introduced to implement the high-throughput fabrication of the biological modules of micro robots, and to execute the on-line flexible assembling of the bio-syncretic robots by the fabricated living cells based modules. Then, an approach based on circularly distributed multiple electrodes (CE) will be induced to improve the differentiation of myoblast cells and characterize the electro-responsive beating behavior of living myotubes for the development of bio-syncretic micro robots. Moreover, a biomimetic bio-syncretic crawler actuated by living myotubes was demonstrated to move under the control of the CE. This talk will not only be related to the micro-robots, but will be also informative for biological tissue engineering and drugs screening.


  • Learning-based Robotic Controller Design and Optimization, by Jia Pan 潘佳

Abstract: Artificial intelligence has recently been used to design and optimize robotic controllers in a data-driven manner. This talk will introduce our recent work on how to use machine learning techniques to compute high-quality feedback controllers for several typical applications: the cloth assembly, the multi-robot warehouse scheduling, and the robot navigation in dense crowds.

Biography: Jia Pan received his Ph.D. degree in Computer Science from the University of North Carolina at Chapel Hill in 2013. He has been with the City University of Hong Kong since 2015. His research interests include robotics and artificial intelligence.


  • 仿生微型飞行器设计中的空气动力学机理及其应用, by Jianghao Wu 吴江浩

Abstract: 自然界中的昆虫和较小的鸟类展现出了卓越的机动飞行能力,这得益于其充分利用了翅膀拍动过程中产生的诸多非定常高升力气动原理。在仿生微小型飞行器上应用和融合这些高升力机理、实现高性能飞行是研究者们的梦想。近年来研究人员借鉴蜻蜓与蜂鸟等生物飞行的空气动力学原理提出了微型扑旋翼及仿蜂鸟扑翼两类代表性的仿生微型飞行器。其中,前者的翼主动竖直拍动、被动旋转,其翼的竖直拍动产生推力驱动翼旋转,借助拍动及旋转耦合效应使扑旋翼能够兼顾高升力产生及高气动效率;后者借鉴蜂鸟飞行原理实现仿生飞行及控制。本报告将介绍这两类仿生微型飞行器的空气动力学机理及其在气动增升、控制及飞行器总体设计等方面的应用进展。


  • Flow visualization and measurement via computer vision technologies, by Shengze Cai 蔡声泽

Abstract: Particle image velocimetry (PIV), as a flow visualization technology, can provide non-intrusive quantitative measurement of the velocity fields. This technique has been studied intensively and plays an increasingly important role in experimental fluid mechanics. To go deeper insight into complex flow phenomena, precise motion estimation methods dedicated to fluid mechanics and towards real-time measurement are being investigated in this talk. On the one hand, variational optical flow computation coupled with the stochastic transport theory is proposed for turbulent flow estimation. This formulation can extract accurate velocity fields from particle images as well as scalar images. On the other hand, deep learning technique is also introduced to PIV estimation with high efficiency, which is promising for real-time measurement and active flow control.

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.

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.

Nonlinear Control of Transport PDE-ODE Interconnections

  • 2018年3月21日上午10点
  • 浙江大学智能系统与控制研究所304教室

Nikolaos Bekiaris-Liberis
Marie Sklodowska-Curie Fellow
Dep. of Production Eng. & Managem.
Technical University of Crete
Chania, Greece 73100
E-mail: nikos.bekiaris@dssl.tuc.gr
http://users.isc.tuc.gr/~nlimperis/

Abstract: Numerous physical processes are described by transport PDE-ODE interconnections. LTI systems with constant input delays is perhaps the most elementary class in this category, where the transport speed is constant and the boundary of the spatial domain is fixed, besides the plant being linear. For this class of systems, predictor feedback is now a well-known delay-compensating control design tool. The situation becomes dramatically more complex when, in addition to the ODE being nonlinear, the speed of propagation or the boundary of the domain is a nonlinear function of the overall infinite-dimensional state (i.e., of the PDE or the ODE state) of the system. For such interconnections, I will present predictor-feedback design ideas, which I will then illustrate with several application examples, including, traffic systems (where the transport speed is a nonlinear function of the PDE state), extruders for 3D printing (giving rise to a system with ODE state-dependent moving boundary), and metal rolling (where the transport speed is a nonlinear function of the ODE state).

—————————
Nikolaos Bekiaris-Liberis received the Ph.D. degree in Aerospace Engineering from the University of California, San Diego, in 2013. From 2013 to 2014 he was a postdoctoral researcher at the University of California, Berkeley and from 2014 to 2017 he was a research associate and adjunct professor at Technical University of Crete, Greece. Dr. Bekiaris-Liberis is currently a Marie Sklodowska-Curie Fellow at the Dynamic Systems & Simulation Laboratory, Technical University of Crete. He has coauthored the SIAM book Nonlinear Control under Nonconstant Delays. His interests are in delay systems, distributed parameter systems, nonlinear control, and their applications.

Dr. Bekiaris-Liberis was a finalist for the student best paper award at the 2010 ASME Dynamic Systems and Control Conference and at the 2013 IEEE Conference on Decision and Control. He received the Chancellor’s Dissertation Medal in Engineering from the University of California, San Diego, in 2014. Dr. Bekiaris-Liberis received the best paper award in the 2015 International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. He is the recipient of a 2017 Marie Sklodowska-Curie Individual Fellowship Grant.

普渡大学Steven T. Wereley教授的PIV短期课程

资料和参考教材:
Particle Image VelocimetryA Practical Guide
Authors: Raffel, M., Willert, C.E., Wereley, S., Kompenhans, J.
http://www.springer.com/gp/book/9783540723073?cm_mmc=sgw-_-ps-_-book-_-978-3-540-72307-3

时间:下午4点 – 6点,Nov. 9, 10, 13 and 14.
地点:TBA
联系人:许超

Thu – Lecture 1 (two hours)

  1. Introduction
  2. Tracer particles, illumination
  3. Particle imaging

Fri – Lecture 2 (two hours) Fri

  1. Statistics of PIV
  2. Recording techniques

Mon – Lecture 3 (two hours)

  1. Eval Techniques I, corr, peak fitting
  2. Eval Techniques II, corr tracking, padding
  3. Eval Techniques III, corr avg, CDIC

Tue – Lecture 4 (two hours)

  1. Image processing, particle ident, part tracking
  2. Data validation, correction, statistics
  3. Resolution, uncertainty
  4. Advanced Topics, stereo, holo, temp

Bio:

Professor Wereley completed his masters and doctoral research at Northwestern University. He joined the Purdue University faculty in August of 1999 after a two-year postdoctoral appointment at the University of California Santa Barbara. During his time at UCSB he worked with a group developing, patenting, and licensing to TSI, Inc., the micro-Particle Image Velocimetry technique. His current research interests include opto/electrokinetics, investigating microscopic biological flows, harnessing diffusion for sensing applications, and developing new ways of measuring flows at the smallest length scales. Professor Wereley is the co-author of Fundamentals and Applications of Microfluidics (Artech House, 2002 and 2006) and Particle Image Velocimetry: A Practical Guide (Springer, 2007). He is on the editorial board of Experiments in Fluids and is an Associate Editor of Springer’s Microfluidics and Nanofluidics. Professor Wereley has edited Springer’s recent Encyclopedia of Microfluidics and Nanofluidics and Kluwer’s BioMEMS and Biomedical Nanotechnology.

Steve Wereley, Professor of Mechanical Engineering
Purdue University

时间:4 p.m., Nov. 6, 2017(周一)
地点:TBA
联系人:许超,cxu@zju.edu.cn

Abstract:
Fluid flow in the micro and nanoscale world of Lab-on-Chip devices can behave very unusually.  It is essential to have precise experimental tools to see what is happening at these very small length scales. Unfortunately these small length scales also prove challenging for experimental tools. This seminar will explore the tools available for viewing (microscale flow visualization) and measuring (micro-PIV) such small flows.  Numerous examples will be presented in which microscale flows are visualized and measured.  These examples range from flows in Lab-on-Chip devices, in and around mechanical devices, and around biological organisms. In many situations 2D planar measurements are sufficient but in some situations, 3D measurements are required. Several ways of measuring flows in 3D will be discussed.  Furthermore, beyond the fluid’s velocity field, other flow quantities of interest are often needed, such as temperature and pressure.  Methods for imaging or measuring these important scalar fields will also be discussed.

Bio:

Professor Wereley completed his masters and doctoral research at Northwestern University. He joined the Purdue University faculty in August of 1999 after a two-year postdoctoral appointment at the University of California Santa Barbara. During his time at UCSB he worked with a group developing, patenting, and licensing to TSI, Inc., the micro-Particle Image Velocimetry technique. His current research interests include opto/electrokinetics, investigating microscopic biological flows, harnessing diffusion for sensing applications, and developing new ways of measuring flows at the smallest length scales. Professor Wereley is the co-author of Fundamentals and Applications of Microfluidics (Artech House, 2002 and 2006) and Particle Image Velocimetry: A Practical Guide (Springer, 2007). He is on the editorial board of Experiments in Fluids and is an Associate Editor of Springer’s Microfluidics and Nanofluidics. Professor Wereley has edited Springer’s recent Encyclopedia of Microfluidics and Nanofluidics and Kluwer’s BioMEMS and Biomedical Nanotechnology.

  • The American Control Conference, Philadelphia, PA, USA | July 10-12, 2019
  • The American Control Conference, Denver, CO, USA | July 1-3, 2020
  • The American Control Conference, Milwaukee, WI, USA | June 27-29, 2018
  • AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Lousiana, USA |  February 2 – 7, 2018
  • The 10th International Conference on Machine Learning and Computing (ICMLC 2018), Macau, China | February 26 – 28, 2018
  • The 56th IEEE Conference on Decision and Control Melbourne, Australia | December 12-15, 2017
  • Conference on Neural Information Processing Systems (NIPS), Long Beach Convention Center, Long Beach | December 04 – 09, 2017
  • IEEE/RSJ International Conference onIntelligent Robots and Systems, Vancouver, BC, Canada | September 24–28, 2017
  • IEEE International Conference on Robotics and Automation, Brisbane, Australia | May 21 – 25, 2018

What can we do with a quantum liquid?

  • 时 间:2017年6月29日(星期四)14:30 – 15:30
  • 地 点:玉泉校区邵逸夫科学馆 117报告厅

Quantum liquids are physical systems which display the effects not only of quantum mechanics but also those of quantum statistics that is of the characteristic indistinguishability of elementary particles. The most spectacular manifestations of quantum statistics are the phenomenon of Bose-Einstein condensation and the closely related one of Cooper pairing; in both cases a finite fraction of all the particles in the system are forced to all do exactly the same thing at the same time, and as a result effects which would normally be obscured by thermal noise may become visible, sometimes spectacularly so.

安东尼·莱格特:英国物理学家,美国科学院院士、美国物理学会会士、英国皇家学会会士。1938年生于伦敦,相继于英国牛津百里奥学院和牛津默顿学院获得文学和物理学学士学位,1964年获牛津大学物理学博士学位,现为美国伊利诺伊大学物理系教授。莱格特教授是国际学术界公认的量子物理学领袖,由于他在超流体理论研究中做出的原创性工作被瑞典皇家科学院授予2003年诺贝尔物理学奖。

媒体报道

Multi-model Predictive Control: Controller formulation and Application to Medical Oxygen Concentrators

Speaker: Mayuresh V. Kothare, PhD, R. L. McCann Professor, Chairman | Department of Chemical and Biomolecular Engineering, Lehigh University

Lecture room: CSC-304 浙江大学玉泉校区智能系统与控制研究所(CSC)304教室

Time: 14:00, June 12 (Monday), 2017

Abstract:

A Multi-Model Predictive Control (M-MPC) algorithm is developed for a novel Rapid Pressure Swing Adsorption (RSPSA) oxygen concentrator system. The RPSA uses a zeolite material to purify oxygen from ambient feed air, and is designed to produce 90% oxygen for medicinal therapies. In previous work, a standard linear MPC algorithm was implemented on the RPSA which rejects disturbances and improves set point tracking in the 90% operating range, but a RPSA can produce a wide range of oxygen purities. The single linear model MPC algorithm is unable to  provide the ability to transition the purity between vastly different values. The M-MPC algorithm uses a collection of linear models each of which corresponds to a different oxygen purity range. The linear models are identified around different operating points, and use Pseudo-Random Binary Signal (PRBS) simulation data in the identification process. With this improved control algorithm, the RPSA can produce a range of oxygen purities, and reject typical RPSA process disturbances. M-MPC switching rules, operating region boundary considerations and set point tracking cases are presented and discussed. General concepts on M-MPC will be presented and ideas on stability analysis will be discussed.

Biosketch:

Dr. M. V. Kothare is currently the Chairman of the Department of Chemical and Biomolecular Engineering and R. L. McCann Professor at Lehigh University. He holds an appointment in Bioengineering at Lehigh University and is a Visiting Professor of Biomedical Engineering at Johns Hopkins School of Medicine. He received his B.Tech. in Chemical Engineering from the Indian Institute of Technology (IIT), Bombay in 1991 and MS/PhD degrees in Chemical Engineering from the California Institute of  Technology in 1995 and 1997. He has held a postdoctoral position at Mobil Oil Corporation and various visiting positions at City College New York, Purdue University, ETH Zurich and East China U. of Science and Technology (ECUST). His interdisciplinary areas of interest span the problems of constrained and optimal predictive control theory, robustness analysis, MEMS and microchemical systems, control of microsystems, embedded control of biomedical systems, neuroengineering and closed-loop neuroprosthetic systems. Kothare is recipient of the Institute Silver Medal from IIT Bombay for ranking first in Chemical Engineering, the Ted Peterson Student Paper Award (2000) and the Outstanding Young Researcher Award (2007) (under 40 years) from the Computing and Systems Technology division of the AIChE for his contributions to the literature of computing in engineering. He has received the US National Science Foundation CAREER award (2002), the Robinson award and Rossin and Hook Professorships at Lehigh University. He was a selected attendee (one of 82 engineers, ages 30-45) at the 2008 Frontiers of Engineering symposium of the US National Academy of Engineering. His interdisciplinary service activities have involved associate editor appointments with AUTOMATICA and the IEEE Transactions on Automatic Control, as well as guest editorial appointments for Journal of Process Control and the IFAC sponsored conference DYCOPS in 2010. In 2011, he was appointed Deputy Editor-in-Chief of IFAC PapersOnLine, an on-line archive of all peer-reviewed conference proceedings sponsored by the International Federation of Automatic Control (IFAC). In 2012, Kothare served as Chair of the CAST division of the AIChE. In 2012, he was elected Fellow of IEEE.

What Machine Learning can do for Science and Engineering?

2 pm, Tuesday, May 23, 2017; Room 304, Institute for Cyber-systems & Control, Zhejiang University(浙江大学玉泉校区智能系统与控制研究所304教室,联系人:许超,13706711953)

Speaker: Guang Lin(林光), PhD

Associate Professor, Department of Mathematics & School of Mechanical Engineering Purdue University

Web: https://www.math.purdue.edu/~lin491/

Machine learning has attracted a lot attention recently. In this talk, I will use several case studies to demonstrate what machine learning can do for Science and Engineering.

It is observed that birds, bats, insects, and fish can routinely harness unsteady fluid phenomena to improve their propulsive efficiency, maximize thrust and lift, and increase maneuverability. In this talk, I will demonstrate how to use machine learning strategy to characterize the time-varying fluid flows with very limited sensor information in modern engineering, for instance, biological propulsion and bio-inspired engineering design.

In addition, I will also present how to use machine learning techniques to employ very limited satellite data or sensor information to improve the climate model predictive capability or identify the contaminant source locations. Particularly, an adaptive importance sampling technique will be introduced to utilize machine learning method to capture multimodal distribution using a mixture of Gaussian distribution.

Ebola disease has been taking thousands of people’s life. It is critical to develop effective strategy to prevent Ebola’s outbreak. I will present how we can use machine learning techniques to develop more accurate Ebola model using limited data. In addition, we employ this accurate Ebola model to develop and test several different strategies to studies its effectiveness in preventing Ebola’s outbreak.

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Guang Lin got his bachelor in Zhejiang university in 1997 and PhD from division of applied mathematics at Brown university in 2007. Now he is an associate professor in both department of mathematics and school of mechanical engineering at Purdue University.
Guang Lin received NSF faculty early career development award in recognition of his work on uncertainty quantification and big data analysis in smart grid and other complex interconnected systems. Guang Lin has developed advanced optimization algorithms to calibrate complex global and regional climate models. For this work, he received a Ronald L. Brodzinski Award for Early Career Exception Achievement in 2012. Guang Lin also received 2010ASCR Leadership Computing Challenge (ALCC) award in recognition of his work in analyzing big climate data using extreme-scale supercomputers. Guang Lin has also received Outstanding Performance Award at Pacific Northwest National Laboratory in 2010, and Ostrach Fellowship at Brown University in Fall 2005.