16,227 research outputs found
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
Stochastic Hysteresis and Resonance in a Kinetic Ising System
We study hysteresis for a two-dimensional, spin-1/2, nearest-neighbor,
kinetic Ising ferromagnet in an oscillating field, using Monte Carlo
simulations and analytical theory. Attention is focused on small systems and
weak field amplitudes at a temperature below . For these restricted
parameters, the magnetization switches through random nucleation of a single
droplet of spins aligned with the applied field. We analyze the stochastic
hysteresis observed in this parameter regime, using time-dependent nucleation
theory and the theory of variable-rate Markov processes. The theory enables us
to accurately predict the results of extensive Monte Carlo simulations, without
the use of any adjustable parameters. The stochastic response is qualitatively
different from what is observed, either in mean-field models or in simulations
of larger spatially extended systems. We consider the frequency dependence of
the probability density for the hysteresis-loop area and show that its average
slowly crosses over to a logarithmic decay with frequency and amplitude for
asymptotically low frequencies. Both the average loop area and the
residence-time distributions for the magnetization show evidence of stochastic
resonance. We also demonstrate a connection between the residence-time
distributions and the power spectral densities of the magnetization time
series. In addition to their significance for the interpretation of recent
experiments in condensed-matter physics, including studies of switching in
ferromagnetic and ferroelectric nanoparticles and ultrathin films, our results
are relevant to the general theory of periodically driven arrays of coupled,
bistable systems with stochastic noise.Comment: 35 pages. Submitted to Phys. Rev. E Minor revisions to the text and
updated reference
Resonant effects in a voltage-activated channel gating
The non-selective voltage activated cation channel from the human red cells,
which is activated at depolarizing potentials, has been shown to exhibit
counter-clockwise gating hysteresis. We have analyzed the phenomenon with the
simplest possible phenomenological models by assuming discrete
states, i.e. two normal open/closed states with two different states of ``gate
tension.'' Rates of transitions between the two branches of the hysteresis
curve have been modeled with single-barrier kinetics by introducing a
real-valued ``reaction coordinate'' parameterizing the protein's conformational
change. When described in terms of the effective potential with cyclic
variations of the control parameter (an activating voltage), this model
exhibits typical ``resonant effects'': synchronization, resonant activation and
stochastic resonance. Occurrence of the phenomena is investigated by running
the stochastic dynamics of the model and analyzing statistical properties of
gating trajectories.Comment: 12 pages, 9 figure
Stochastic modeling error reduction using Bayesian approach coupled with an adaptive Kriging based model
Purpose - Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution.
Design/methodology/approach - The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique.
Findings - The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage.
Originality/value - The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction
Simplified model for the non-linear behaviour representation of reinforced concrete columns under biaxial bending
In the present paper a simplified model is proposed for the force-deformation behaviour of reinforced concrete members under biaxial loading combined with axial force. The starting point for the model development was an existing fixed-length plastic hinge element model that accounts for the non-linear hysteretic behaviour at the element end-sections, characterized by trilinear moment-curvature laws. To take into account the section biaxial behaviour, the existing model was adopted for both orthogonal lateral directions and an interaction function was introduced to couple the hysteretic response of both directions.
To calibrate the interaction function it were used numerical results, obtained from fibre models, and experimental results. For the parameters identification, non-linear optimization approaches were adopted, namely: the gradient based methods followed by the genetic, evolutionary and nature-inspired algorithms.
Finally, the simplified non-linear model proposed is validated through the analytical simulation of biaxial test results carried out in full-scale reinforced concrete columns
A unified framework for approximation in inverse problems for distributed parameter systems
A theoretical framework is presented that can be used to treat approximation techniques for very general classes of parameter estimation problems involving distributed systems that are either first or second order in time. Using the approach developed, one can obtain both convergence and stability (continuous dependence of parameter estimates with respect to the observations) under very weak regularity and compactness assumptions on the set of admissible parameters. This unified theory can be used for many problems found in the recent literature and in many cases offers significant improvements to existing results
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