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Aiming for Zero Tracking Error in Control System by Iterating in Hardware Instead of Software
 Speaker : Richard W. Longman
Columbia University
New York, NY USA
 Abstract
In engineering one usually develops mathematical models of the world and uses them to design and optimize system performance. Iterative learning control (ILC) is a relatively new field that aims to optimize control system tracking performance by iterating with the real world  instead of iterating on a computer with a model. Experiments demonstrate that such methods can reach particularly small error levels that could never be reached by use of a mathematical model. To make ILC effective it must do a delicate balance – it must use information from a model to help it to quickly decrease the error, but it must not rely so heavily on the model that model inaccuracy is allowed to interfere with the ability to decrease toward zero error in the world, decreasing beyond the accuracy of one’s model. Iterative learning control methods are presented for linear systems. Then techniques are presented to extend the use of such methods to apply to multiinput, multioutput nonlinear systems.
Robust Optimization Issues in Parameter Estimation and Optimum Experimental Design for DAE models
 Speaker : Ekaterina Kostina
 Abstract
Estimating model parameters from experimental data is crucial to reliably simulate dynamic processes.
The identification problem can be described as follows. Let the dynamics of the model be described by a system of differential algebraic equations where the righthand side depends on an unknown vector of parameters. It is assumed that there is a possibility to measure a signal of an output device that writes at given time points the output signal of the dynamic system with some errors. According to the common approach, in order to determine the unknown parameters the optimization problem is solved in which the special functional is minimized under constraints that describe the specifics of the model. Any norm of the measurement errors may be used as the functional in the optimization problem. The choice of an adequate norm depends on the statistical properties of the measurement errors. The traditional choice is weighted l2norm. In practical applications, however, it often appears that the data contains outliers. Thus, a reliable parameter estimation procedure (e.g. based on l1norm) is necessary that deliver parameter estimates less sensitive (robust) to errors in measurements.
Another difficulty that occurs in practical applications is that the experiments performed to obtain necessary measurements are expensive, but nevertheless do not guarantee sufficient identifiability. The optimization of one or more dynamic experiments in order to maximize the accuracy of the results of a parameter estimation subject to cost and other technical inequality constraints leads to very complex nonstandard optimal control problems. One of the difficulties is that the objective function is a function of a covariance matrix and therefore already depends on a generalized inverse of the Jacobian of the underlying parameter estimation problem. Another difficulty is that the optimization results depend strongly on the assumed values of parameters which are only known to lie in a  possibly large – confidence region. Hence, robust optimal experiments are required that solve worstcase (minmax) optimization problems.
The talk presents new effective algorithms for robust parameter estimation and design of robust optimal experiments in dynamic systems. Numerical results for reallife applications from chemistry and chemical engineering will be presented.
This talk is based on joint work with H. G. Bock, S. Koerkel and J. P. Schloeder.
2D and 3D nonphotorealsitic rendering for the humanities
 Speaker : Michael J. Winckler
 Abstract
The production of photorealistic images is a prosperous
area of research in computer graphics. The aim is to produce
pictures that are virually indistinguishable from photographic
imapes. In contrast, nonphotorealistic rendering (NPR) focuses on
image generation techniques to produce pictures resembling
artistic or graphic rendering styles which are clearely
discernible from photographies. Application areas of NPR are
architectural drawings, imitation of artistic drawings and
cartoon generation. While the use of NPR for artistic purposes
is widely recognized, applications to the humanities only lately
aim at the possibility to produce images that show possible results
of research through the execution of adapted rendering styles.
GREEN* : towards energy efficient solutions for next generation large scale distributed systems
 Speaker : Laurent Lefevre
 Abstract
With the emergence of large scale Grids and data cen! ters, the amount of energy
(watts) required for high performance distributed computing and networking
becomes a real challenge. Taking into account of an efficient energy usage will have an impact on how we
design architectures, services and protocols. Various "green" approaches (like "Green Grid", "Green500", "Green
Internet"..) are currently proposed by academics and industrial
consortiums. This talk will review current challenges and solutions
associated to power aware approaches in distributed systems.
Computational Fluid Dynamics and Its Applications Using PC Clusters
 Speaker : JangHyuk Kwon
Department of Aerospace Engineering, KAIST
3731 Guseongdong, Yuseonggu, Daejeon, 305701 South Korea
jhwkon@kaist.ac.kr
 Abstract
These days, the supercomputing is popular everywhere including computational fluid dynamics. Still, the cost for supercomputing is high in cost for researchers in academia, hence a PC cluster is one of the solutions for high performance computing.
In this presentation, some of tools and CFD algorithms developed in my lab are explained. Firstly, a grid generator, KGRID is introduced. This is a structured grid generation and visualization tool which can handle complex configurations with multiblock grid and Chimera grid generations. Secondly, the algorithms developed for Euler and NavierStokes equations are explained. The flow solver, KFLOW has all Mach number flow calculation capability with high order hybrid schemes and convergence acceleration techniques.
The application problems are; flow computation for a wingbody configuration and aircrafts, prediction of store separation with Chimera grid technique, hyperbolic flow calculation with HLLE+ scheme, and prediction of dynamic damping coefficients of missiles and rockets. Prediction of flutter speed in transonic flow of an aircraft is one of important but time consuming applications using CFD.
Another popular application of CFD these days is the aerodynamic shape optimization. The design optimizations using the adjoint method, genetic algorithms, and reliability based method are explained briefly with some applications, such as the drag minimizations for an aircraft wing, an aircraft and a ship. Also, the multidisciplinary design optimization (MDO) is introduced briefly with applications.








