177,795 research outputs found
Identification of weakly coupled multiphysics problems. Application to the inverse problem of electrocardiography
This work addresses the inverse problem of electrocardiography from a new
perspective, by combining electrical and mechanical measurements. Our strategy
relies on the defini-tion of a model of the electromechanical contraction which
is registered on ECG data but also on measured mechanical displacements of the
heart tissue typically extracted from medical images. In this respect, we
establish in this work the convergence of a sequential estimator which combines
for such coupled problems various state of the art sequential data assimilation
methods in a unified consistent and efficient framework. Indeed we ag-gregate a
Luenberger observer for the mechanical state and a Reduced Order Unscented
Kalman Filter applied on the parameters to be identified and a POD projection
of the electrical state. Then using synthetic data we show the benefits of our
approach for the estimation of the electrical state of the ventricles along the
heart beat compared with more classical strategies which only consider an
electrophysiological model with ECG measurements. Our numerical results
actually show that the mechanical measurements improve the identifiability of
the electrical problem allowing to reconstruct the electrical state of the
coupled system more precisely. Therefore, this work is intended to be a first
proof of concept, with theoretical justifications and numerical investigations,
of the ad-vantage of using available multi-modal observations for the
estimation and identification of an electromechanical model of the heart
Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making
We describe regularized methods for image reconstruction and focus on the
question of hyperparameter and instrument parameter estimation, i.e.
unsupervised and myopic problems. We developed a Bayesian framework that is
based on the \post density for all unknown quantities, given the observations.
This density is explored by a Markov Chain Monte-Carlo sampling technique based
on a Gibbs loop and including a Metropolis-Hastings step. The numerical
evaluation relies on the SPIRE instrument of the Herschel observatory. Using
simulated and real observations, we show that the hyperparameters and
instrument parameters are correctly estimated, which opens up many perspectives
for imaging in astrophysics
Sparse Hopfield network reconstruction with regularization
We propose an efficient strategy to infer sparse Hopfield network based on
magnetizations and pairwise correlations measured through Glauber samplings.
This strategy incorporates the regularization into the Bethe
approximation by a quadratic approximation to the log-likelihood, and is able
to further reduce the inference error of the Bethe approximation without the
regularization. The optimal regularization parameter is observed to be of the
order of where is the number of independent samples. The value
of the scaling exponent depends on the performance measure.
for root mean squared error measure while for
misclassification rate measure. The efficiency of this strategy is demonstrated
for the sparse Hopfield model, but the method is generally applicable to other
diluted mean field models. In particular, it is simple in implementation
without heavy computational cost.Comment: 9 pages, 3 figures, Eur. Phys. J. B (in press
Imprecise dynamic walking with time-projection control
We present a new walking foot-placement controller based on 3LP, a 3D model
of bipedal walking that is composed of three pendulums to simulate falling,
swing and torso dynamics. Taking advantage of linear equations and closed-form
solutions of the 3LP model, our proposed controller projects intermediate
states of the biped back to the beginning of the phase for which a discrete LQR
controller is designed. After the projection, a proper control policy is
generated by this LQR controller and used at the intermediate time. This
control paradigm reacts to disturbances immediately and includes rules to
account for swing dynamics and leg-retraction. We apply it to a simulated Atlas
robot in position-control, always commanded to perform in-place walking. The
stance hip joint in our robot keeps the torso upright to let the robot
naturally fall, and the swing hip joint tracks the desired footstep location.
Combined with simple Center of Pressure (CoP) damping rules in the low-level
controller, our foot-placement enables the robot to recover from strong pushes
and produce periodic walking gaits when subject to persistent sources of
disturbance, externally or internally. These gaits are imprecise, i.e.,
emergent from asymmetry sources rather than precisely imposing a desired
velocity to the robot. Also in extreme conditions, restricting linearity
assumptions of the 3LP model are often violated, but the system remains robust
in our simulations. An extensive analysis of closed-loop eigenvalues, viable
regions and sensitivity to push timings further demonstrate the strengths of
our simple controller
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