953 research outputs found
COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses XV. Assessing the achievability and precision of time-delay measurements
COSMOGRAIL is a long-term photometric monitoring of gravitationally lensed
QSOs aimed at implementing Refsdal's time-delay method to measure cosmological
parameters, in particular H0. Given long and well sampled light curves of
strongly lensed QSOs, time-delay measurements require numerical techniques
whose quality must be assessed. To this end, and also in view of future
monitoring programs or surveys such as the LSST, a blind signal processing
competition named Time Delay Challenge 1 (TDC1) was held in 2014. The aim of
the present paper, which is based on the simulated light curves from the TDC1,
is double. First, we test the performance of the time-delay measurement
techniques currently used in COSMOGRAIL. Second, we analyse the quantity and
quality of the harvest of time delays obtained from the TDC1 simulations. To
achieve these goals, we first discover time delays through a careful inspection
of the light curves via a dedicated visual interface. Our measurement
algorithms can then be applied to the data in an automated way. We show that
our techniques have no significant biases, and yield adequate uncertainty
estimates resulting in reduced chi2 values between 0.5 and 1.0. We provide
estimates for the number and precision of time-delay measurements that can be
expected from future time-delay monitoring campaigns as a function of the
photometric signal-to-noise ratio and of the true time delay. We make our blind
measurements on the TDC1 data publicly availableComment: 11 pages, 8 figures, published in Astronomy & Astrophysic
Batch process optimization via run-to-run constraints adaptation
© 2007 EUCA.In the batch process industry, the available models carry a large amount of uncertainty and can seldom be used to directly optimize real processes. Several measurement-based optimization methods have been proposed to deal with model mismatch and process disturbances. Constraints often play a dominant role in the dynamic optimization of batch processes. In their presence, the optimal input profiles are characterized by a set of arcs, switching times and active path and terminal constraints. This paper presents a novel method tailored to those problems where the potential of optimization arises mainly from the correct set of path and terminal constraints being active. The input profiles are computed between successive runs by dynamic optimization of a fixed nominal model, and the constraints in the optimization problem are adapted using measured information from previous batches. Note that, unlike many existing optimization schemes, the measurements are not used to update the process model. Moreover, the proposed approach has the potential to uncover the optimal input structure. This is demonstrated on a simple semi-batch reactor example
Correlation-Based Tuning of a Restricted-Complexity Controller for an Active Suspension System
A correlation-based controller tuning method is proposed for the ``Design and optimization of restricted-complexity controllers'' benchmark problem. The approach originally proposed for model following is extended to solve the disturbance rejection problem. The idea is to tune the controller parameters such that the closed-loop output be uncorrelated with the disturbance signal. Since perfect decorrelation between the closed-loop output and the disturbance signal is not attainable in the restricted-complexity controller design, the cross correlation between these two signals is minimized iteratively using the stochastic approximation method. Since control specifications can normally be expressed in terms of constraints on the sensitivity functions, a frequency-domain analysis of the criterion is performed. Straightforward implementation of the proposed approach on the active suspension system of the Automatic Control Laboratory of Grenoble (LAG) provides a 2nd-order controller that meets the control specifications very well
On Improving the Efficiency of Modifier Adaptation via Directional Information
In real-time optimization, the solution quality depends on the model ability to predict the plant Karush–Kuhn–Tucker (KKT) conditions. In the case of non-parametric plant-model mismatch, one can add input-affine modifiers to the model cost and constraints as is done in modifier adaptation (MA). These modifiers require estimating the plant cost and constraint gradients. This paper discusses two ways of reducing the number of input directions, thereby improving the efficiency of MA in practice. The first approach capitalizes on the knowledge of the active set to reduce the number of KKT conditions. The second approach determines the dominant gradients using sensitivity analysis. This way, MA reaches near plant optimality efficiently by adapting the first-order modifiers only along the dominant input directions. These approaches allow generating several alternative MA schemes, which are analyzed in terms of the number of degrees of freedom and compared in a simulated study of the Williams–Otto plant.Fil: Rodrigues, D.. Instituto Superior Tecnico; PortugalFil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la InformaciĂłn y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la InformaciĂłn y de Sistemas; ArgentinaFil: Bonvin, D.. Ecole Polytechnique Federale de Lausanne; Franci
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