1,190 research outputs found
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
Prediction and Simulator Verification of Roll/Lateral Adverse Aeroservoelastic Rotorcraft–Pilot Couplings
The involuntary interaction of a pilot with an aircraft can be described as pilot-assisted oscillations. Such
phenomena are usually only addressed late in the design process when they manifest themselves during ground/flight
testing. Methods to be able to predict such phenomena as early as possible are therefore useful. This work describes a
technique to predict the adverse aeroservoelastic rotorcraft–pilot couplings, specifically between a rotorcraft’s roll
motion and the resultant involuntary pilot lateral cyclic motion. By coupling linear vehicle aeroservoelastic models
and experimentally identified pilot biodynamic models, pilot-assisted oscillations and no-pilot-assisted oscillation
conditions have been numerically predicted for a soft-in-plane hingeless helicopter with a lightly damped regressive
lead–lag mode that strongly interacts with the roll modeat a frequency within the biodynamic band of the pilots. These
predictions have then been verified using real-time flight-simulation experiments. The absence of any similar adverse
couplings experienced while using only rigid-body models in the flight simulator verified that the observed
phenomena were indeed aeroelastic in nature. The excellent agreement between the numerical predictions and the
observed experimental results indicates that the techniques developed in this paper can be used to highlight the
proneness of new or existing designs to pilot-assisted oscillation
Neural Modeling and Control of Diesel Engine with Pollution Constraints
The paper describes a neural approach for modelling and control of a
turbocharged Diesel engine. A neural model, whose structure is mainly based on
some physical equations describing the engine behaviour, is built for the
rotation speed and the exhaust gas opacity. The model is composed of three
interconnected neural submodels, each of them constituting a nonlinear
multi-input single-output error model. The structural identification and the
parameter estimation from data gathered on a real engine are described. The
neural direct model is then used to determine a neural controller of the
engine, in a specialized training scheme minimising a multivariable criterion.
Simulations show the effect of the pollution constraint weighting on a
trajectory tracking of the engine speed. Neural networks, which are flexible
and parsimonious nonlinear black-box models, with universal approximation
capabilities, can accurately describe or control complex nonlinear systems,
with little a priori theoretical knowledge. The presented work extends optimal
neuro-control to the multivariable case and shows the flexibility of neural
optimisers. Considering the preliminary results, it appears that neural
networks can be used as embedded models for engine control, to satisfy the more
and more restricting pollutant emission legislation. Particularly, they are
able to model nonlinear dynamics and outperform during transients the control
schemes based on static mappings.Comment: 15 page
Experimental Results of Concurrent Learning Adaptive Controllers
Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie
Thermal diagnostic of the Optical Window on board LISA Pathfinder
Vacuum conditions inside the LTP Gravitational Reference Sensor must comply
with rather demanding requirements. The Optical Window (OW) is an interface
which seals the vacuum enclosure and, at the same time, lets the laser beam go
through for interferometric Metrology with the test masses. The OW is a
plane-parallel plate clamped in a Titanium flange, and is considerably
sensitive to thermal and stress fluctuations. It is critical for the required
precision measurements, hence its temperature will be carefully monitored in
flight. This paper reports on the results of a series of OW characterisation
laboratory runs, intended to study its response to selected thermal signals, as
well as their fit to numerical models, and the meaning of the latter. We find
that a single pole ARMA transfer function provides a consistent approximation
to the OW response to thermal excitations, and derive a relationship with the
physical processes taking place in the OW. We also show how system noise
reduction can be accomplished by means of that transfer function.Comment: 20 pages, 14 figures; accepted for publication in Class. Quantum Gra
Generating and transferring grassroots innovations in a multi-actor participatory process
Peer reviewe
Global parameter identification of stochastic reaction networks from single trajectories
We consider the problem of inferring the unknown parameters of a stochastic
biochemical network model from a single measured time-course of the
concentration of some of the involved species. Such measurements are available,
e.g., from live-cell fluorescence microscopy in image-based systems biology. In
addition, fluctuation time-courses from, e.g., fluorescence correlation
spectroscopy provide additional information about the system dynamics that can
be used to more robustly infer parameters than when considering only mean
concentrations. Estimating model parameters from a single experimental
trajectory enables single-cell measurements and quantification of cell--cell
variability. We propose a novel combination of an adaptive Monte Carlo sampler,
called Gaussian Adaptation, and efficient exact stochastic simulation
algorithms that allows parameter identification from single stochastic
trajectories. We benchmark the proposed method on a linear and a non-linear
reaction network at steady state and during transient phases. In addition, we
demonstrate that the present method also provides an ellipsoidal volume
estimate of the viable part of parameter space and is able to estimate the
physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems
Biology
Aperture synthesis for gravitational-wave data analysis: Deterministic Sources
Gravitational wave detectors now under construction are sensitive to the
phase of the incident gravitational waves. Correspondingly, the signals from
the different detectors can be combined, in the analysis, to simulate a single
detector of greater amplitude and directional sensitivity: in short, aperture
synthesis. Here we consider the problem of aperture synthesis in the special
case of a search for a source whose waveform is known in detail: \textit{e.g.,}
compact binary inspiral. We derive the likelihood function for joint output of
several detectors as a function of the parameters that describe the signal and
find the optimal matched filter for the detection of the known signal. Our
results allow for the presence of noise that is correlated between the several
detectors. While their derivation is specialized to the case of Gaussian noise
we show that the results obtained are, in fact, appropriate in a well-defined,
information-theoretic sense even when the noise is non-Gaussian in character.
The analysis described here stands in distinction to ``coincidence
analyses'', wherein the data from each of several detectors is studied in
isolation to produce a list of candidate events, which are then compared to
search for coincidences that might indicate common origin in a gravitational
wave signal. We compare these two analyses --- optimal filtering and
coincidence --- in a series of numerical examples, showing that the optimal
filtering analysis always yields a greater detection efficiency for given false
alarm rate, even when the detector noise is strongly non-Gaussian.Comment: 39 pages, 4 figures, submitted to Phys. Rev.
A 2D Hopfield Neural Network approach to mechanical beam damage detection
The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions
The chemical composition of nearby young associations: s-process element abundances in AB Doradus, Carina-Near, and Ursa Major
Recently, several studies have shown that young, open clusters are
characterised by a considerable over-abundance in their barium content. In
particular, D'Orazi et al. (2009) reported that in some younger clusters
[Ba/Fe] can reach values as high as ~0.6 dex. The work also identified the
presence of an anti-correlation between [Ba/Fe] and cluster age. For clusters
in the age range ~4.5 Gyr-500 Myr, this is best explained by assuming a higher
contribution from low-mass asymptotic giant branch stars to the Galactic
chemical enrichment. The purpose of this work is to investigate the ubiquity of
the barium over-abundance in young stellar clusters. We analysed
high-resolution spectroscopic data, focusing on the s-process elemental
abundance for three nearby young associations, i.e. AB Doradus, Carina-Near,
and Ursa Major. The clusters have been chosen such that their age spread would
complement the D'Orazi et al. (2009) study. We find that while the s-process
elements Y, Zr, La, and Ce exhibit solar ratios in all three associations, Ba
is over-abundant by ~0.2 dex. Current theoretical models can not reproduce this
abundance pattern, thus we investigate whether this unusually large Ba content
might be related to chromospheric effects. Although no correlation between
[Ba/Fe] and several activity indicators seems to be present, we conclude that
different effects could be at work which may (directly or indirectly) be
related to the presence of hot stellar chromospheres.Comment: Accepted for publication in MNRA
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