News & Events
The 1119417 th visit
Schedule
Beijing Time: 2017-09-26 05:18:03

Course

Prof. Ho at ISINS'04
Regular Course
Short Course

[2016.03.21] Cloud Computing

Course Instructor: Dr. Timothy Chou

Based on his Stanford cs309a.stanford.edu class, numerous keynote speeches and enterprise workshops, Dr. Timothy Chou has developed 24 TED-sized lectures to first introduce you to the basics of cloud computing, next how to use the technologies to achieve operational efficiency, and finally how cloud computing can transform business. These will be delivered over 2 weeks (MWF) – 2 hours of lecture per day.

[2015.05.20] Discrete Event, Hybrid Systems, and the Internet of Things

Course Instructor: Prof. Christos G. Cassandras

Syllabus:

1. Modeling frameworks for Discrete Event Systems

2. Modeling frameworks for Hybrid Systems

3. Discrete Event and Hybrid computer simulation

4. Intelligent simulation and data-driven Rapid Learning methods

5. Applications to Cyber Physical Systems and Internet of Things

Biography of Prof. Christos G. Cassandras:

Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received degrees from Yale University (B.S., 1977), Stanford University (M.S.E.E., 1978), and Harvard University (S.M., 1979; Ph.D., 1982). In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 350 refereed papers in these areas, and five books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with The MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents®.

Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001 and the IEEE Conference on Decision and Control in 2002, and has also been an IEEE Distinguished Lecturer.

He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a “Smart Parking” system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.

[2014.05.30] Approximate Dynamic Programming

Course Instructor: Prof. Dimitri P. Bertsekas

Course Description:

Approximation and simulation based large-scale dynamic programming is a heated topic in the past 20 years. It has different names like reinforcement learning, neuro-dynamic programming, etc. It learned a lot from many fields such as artificial intelligence, optimization and controlling. This course will talk about control problem of dynamic systems under uncertainties, which is widely used in discrete deterministic optimization problems. Using approximation and simulation to solve the two basic difficulties of dynamic programming will be introduced, one is curse of dimensionality and the other one is modelling.

Syllabus:

1. Infinite-stage precise dynamic programming

2. Computing method of large-scale precise dynamic programming

3. Methods of Approximation and simulation in large-scale problems

4. Approximation strategies based on temporal difference, projection and Galerkin approximation

5. Aggregation method

6. Stochastic approximation, Q-learning and other methods

Biography of Prof. Dimitri P. Bertsekas:

Dimitri Bertsekas studied Mechanical and Electrical Engineering at the National Technical University of Athens, Greece, and obtained his Ph.D. in system science from the Massachusetts Institute of Technology. He has held faculty positions with the Engineering-Economic Systems Dept., Stanford University, and the Electrical Engineering Dept. of the University of Illinois, Urbana. Since 1979 he has been teaching at the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (M.I.T.), where he is currently McAfee Professor of Engineering.

His research spans several fields, including optimization, control, large-scale computation, and data communication networks, and is closely tied to his teaching and book authoring activities. He has written numerous research papers, and fourteen books, several of which are used as textbooks in MIT classes. His involvement with dynamic programming started with his Ph.D. thesis research, and has continued through the present with many research papers, and several books and research monographs.

Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book “Neuro-Dynamic Programming” (co-authored with John Tsitsiklis), the 2000 Greek National Award for Operations Research, the 2001 ACC John R. Ragazzini Education Award, and the 2009 INFORMS Expository Writing Award. In 2001, he was elected to the United States National Academy of Engineering for “pioneering contributions to fundamental research, practice and education of optimization/control theory, and especially its application to data communication networks.”

[2014.01.06] Applications of Markov Chain Model

Course Instructor: Prof. Wai-Ki Ching, The University of Hong Kong

Course Description:

This course will introduce hidden markov chains, high dimensional markov chains and their applications in researches on the Internet, manufacturing system, queueing system, inventory management, DNA sequence, genetic network, credit risk model, etc. It is a good complement of courses about stochastic control and optimization.

Syllabus:

Contents:

Part I aims at providing the fundamental knowledge in probability theory, Poisson process and Markov chain theory. Some applications such as inventory systems and PageRank algorithm are discussed.

Part II discusses a continuous time stochastic process, the Birth and Death process and its relation to Markovian queueing systems. Applications of Markovian queueing systems will also be discussed.

In Part III, we introduce iterative methods (computational methods) for solving a system of linear equations. It is important for solving the steady-state distribution of a queueing network.

Part IV, we introduce four research topics related to Markov chain models.

[2013.06.19] Dr. Wei Ren's Short Course: Distributed Control of Multi-agent Systems

Course Instructor: Prof. Wei Ren, University of California, Riverside.

Course Description:

This course will introduce a consensus approach for distributed multi-vehicle cooperative control. While autonomous vehicles that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous vehicles operating in a coordinated fashion. Potential applications for multiple autonomous vehicles include autonomous household appliances, hazardous material handling systems, distributed reconfigurable sensor networks, surveillance and reconnaissance, space-based interferometry, and future autonomous combat systems. To enable these applications, a variety of cooperative control capabilities need to be developed. These capabilities include formation control, rendezvous, attitude alignment, flocking, foraging, task and role assignment, payload transport, air traffic control, and cooperative search. Execution of these capabilities requires that individual vehicles share a consistent view of the objectives and the world. Information consensus guarantees that vehicles sharing information over a network topology have a consistent view of information that is critical to the coordination task. By necessity, consensus algorithms are designed to be distributed, assuming only neighbor-to-neighbor interaction between vehicles. Consensus algorithms have applications in rendezvous, formation control, flocking, attitude alignment, and sensor networks.

The purpose of this course is to overview emergent distributed consensus algorithms and their applications in multi-vehicle cooperative control. Theoretical results on distributed consensus algorithms where the dynamics of the information state evolve according to first- and second-order dynamics and according to rigid body attitude dynamics and Euler-Lagrange equations will be introduced. Application examples of the distributed consensus algorithms in multi-vehicle cooperative control including rendezvous and formation keeping for wheeled mobile robots and deep space spacecraft formation flying will also be introduced.

The detailed schedule of the course is as follows. First, we will overview recent research in distributed multi-vehicle cooperative control. Second, we will introduce consensus algorithms for single- and double-integrator dynamics. In particular, we will introduce fundamental consensus algorithms for single-integrator dynamics, consensus tracking of a dynamic leader, consensus algorithms for double-integrator dynamics, consensus under realistic constraints, and swarm tracking algorithms. Third, we will introduce consensus algorithms for rigid body attitude dynamics and Euler-Lagrange systems. In particular, we will introduce attitude consensus for multiple rigid bodies, reference attitude tracking, and leaderless consensus for networked Lagrangian systems. Fourth, we will introduce applications of distributed consensus algorithms in multi-vehicle cooperative control. Particular topics include consensus-based design methodologies for cooperative control, rendezvous and axial alignment with multiple wheeled mobile robots, distributed formation control of multiple wheeled mobile robots with a virtual leader, and deep space spacecraft formation flying. In this course, different concepts will be illustrated by examples.

Syllabus:

Objective:

The purpose of this course is to overview distributed control algorithms, in particular consensus algorithms and their applications in multi-vehicle cooperative control. Theoretical results on distributed consensus algorithms for multi-agent systems are first introduced. The dynamics of the agents evolve according to first- or second-order dynamics and can be governed by rigid body attitude dynamics and Euler-Lagrange equations. Application examples are then investigated where the distributed consensus algorithms are used for coordinating vehicle formations, such as the rendezvous and formation keeping tasks. The mobile agents under consideration include wheeled mobile robots and deep space spacecraft.

Contents:

1. Overview of recent research on distributed multi-vehicle cooperative control.

2. Consensus algorithms for single and double-integrator dynamics. To be more specific, we introduce the basic consensus algorithms for single-integrator dynamics, consensus tracking of a dynamic leader, consensus algorithms for double-integrator dynamics, consensus under realistic constraints, and swarm tracking algorithms.

3. Consensus algorithms for rigid body attitude dynamics and Euler-Lagrange systems. In particular, we discuss attitude consensus for multiple rigid bodies, reference attitude tracking, and leaderless consensus for networked Lagrangian systems.

4. Applications of distributed consensus algorithms in multi-vehicle cooperative control. Specific topics include consensus-based design methodologies for rendezvous and axial alignment with multiple wheeled mobile robots, distributed formation control of multiple wheeled mobile robots with a virtual leader, and deep space spacecraft formation flying.

Lecture Notes:

Will be distributed during the course.

Preliminaries:

Basic background in systems and control theory.

Homework Assignments:

Four homework sets will be distributed by the course website. Homework is graded on a scale from 1 to 10. Missing sets receive the grade 1. The final grade for the course is the average of the grades for the four homework sets.

[2009.11.30] Dr. Timothy Chou's Short Course

The short course "Cloud Computing and Software Business Models" offered by Dr. Timothy Chou (Board member of Blackbaud and teacher ) was invited by CFINS.

The objective of this class is for business and technology students to understand.

1.The fundamental business models for the software industry.

2.The current state of the art in cloud computing

3.How all of this technology can be used to create new businesses

The lecture series will contain numerous examples from my Stanford lecture series and some of the leading technology companies including Cisco, Webex, Concur, Salesforce, Zappos, Opentable, IBM, Oracle, Blackbaud, Microsoft, RightNow, Kenexa, Taleo, Netsuite, Google, eBay, Pandora, Amazon, Rackspace and Opsource.

Dr. Timothy Chou Short Course
Dr. Timothy Chou Short Course

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[2008.5.20] Prof. Meerkov and Prof. Jingshan Li's Short Course

The short course "Production Systems Engineering" offered by Prof. Meerkov and Prof. Jingshan Li has started. Prof. Meerkov (from University of Michigan, Ann Arbor) and Prof. Jingshan Li (from University of Kentucky) were invited by CFINS. Students please visit course link to download materials.