1,367 research outputs found
Competing Interactions among Supramolecular Structures on Surfaces
A simple model was constructed to describe the polar ordering of
non-centrosymmetric supramolecular aggregates formed by self assembling
triblock rodcoil polymers. The aggregates are modeled as dipoles in a lattice
with an Ising-like penalty associated with reversing the orientation of nearest
neighbor dipoles. The choice of the potentials is based on experimental results
and structural features of the supramolecular objects. For films of finite
thickness, we find a periodic structure along an arbitrary direction
perpendicular to the substrate normal, where the repeat unit is composed of two
equal width domains with dipole up and dipole down configuration. When a short
range interaction between the surface and the dipoles is included the balance
between the up and down dipole domains is broken. Our results suggest that due
to surface effects, films of finite thickness have a none zero macroscopic
polarization, and that the polarization per unit volume appears to be a
function of film thickness.Comment: 3 pages, 3 eps figure
Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach
In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance
Asymptotic inference in system identification for the atom maser
System identification is an integrant part of control theory and plays an
increasing role in quantum engineering. In the quantum set-up, system
identification is usually equated to process tomography, i.e. estimating a
channel by probing it repeatedly with different input states. However for
quantum dynamical systems like quantum Markov processes, it is more natural to
consider the estimation based on continuous measurements of the output, with a
given input which may be stationary. We address this problem using asymptotic
statistics tools, for the specific example of estimating the Rabi frequency of
an atom maser. We compute the Fisher information of different measurement
processes as well as the quantum Fisher information of the atom maser, and
establish the local asymptotic normality of these statistical models. The
statistical notions can be expressed in terms of spectral properties of certain
deformed Markov generators and the connection to large deviations is briefly
discussed.Comment: 20pages, 3 figure
Aeroelastic Model Structure Computation for Envelope Expansion
Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data
Spectral Line Removal in the LIGO Data Analysis System (LDAS)
High power in narrow frequency bands, spectral lines, are a feature of an
interferometric gravitational wave detector's output. Some lines are coherent
between interferometers, in particular, the 2 km and 4 km LIGO Hanford
instruments. This is of concern to data analysis techniques, such as the
stochastic background search, that use correlations between instruments to
detect gravitational radiation. Several techniques of `line removal' have been
proposed. Where a line is attributable to a measurable environmental
disturbance, a simple linear model may be fitted to predict, and subsequently
subtract away, that line. This technique has been implemented (as the command
oelslr) in the LIGO Data Analysis System (LDAS). We demonstrate its application
to LIGO S1 data.Comment: 11 pages, 5 figures, to be published in CQG GWDAW02 proceeding
Extracting dynamical equations from experimental data is NP-hard
The behavior of any physical system is governed by its underlying dynamical
equations. Much of physics is concerned with discovering these dynamical
equations and understanding their consequences. In this work, we show that,
remarkably, identifying the underlying dynamical equation from any amount of
experimental data, however precise, is a provably computationally hard problem
(it is NP-hard), both for classical and quantum mechanical systems. As a
by-product of this work, we give complexity-theoretic answers to both the
quantum and classical embedding problems, two long-standing open problems in
mathematics (the classical problem, in particular, dating back over 70 years).Comment: For mathematical details, see arXiv:0908.2128[math-ph]. v2: final
version, accepted in Phys. Rev. Let
Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant
Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft
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