5,850 research outputs found
A note on modeling some classes of nonlinear systems from data
We study the modeling of bilinear and quadratic systems from measured data. The measurements are given by samples of higher order frequency response functions. These values can be identified from the corresponding Volterra series of the underlying nonlinear system. We test the method for examples from structural dynamics and chemistry
Weak Form of Stokes-Dirac Structures and Geometric Discretization of Port-Hamiltonian Systems
We present the mixed Galerkin discretization of distributed parameter
port-Hamiltonian systems. On the prototypical example of hyperbolic systems of
two conservation laws in arbitrary spatial dimension, we derive the main
contributions: (i) A weak formulation of the underlying geometric
(Stokes-Dirac) structure with a segmented boundary according to the causality
of the boundary ports. (ii) The geometric approximation of the Stokes-Dirac
structure by a finite-dimensional Dirac structure is realized using a mixed
Galerkin approach and power-preserving linear maps, which define minimal
discrete power variables. (iii) With a consistent approximation of the
Hamiltonian, we obtain finite-dimensional port-Hamiltonian state space models.
By the degrees of freedom in the power-preserving maps, the resulting family of
structure-preserving schemes allows for trade-offs between centered
approximations and upwinding. We illustrate the method on the example of
Whitney finite elements on a 2D simplicial triangulation and compare the
eigenvalue approximation in 1D with a related approach.Comment: Copyright 2018. This manuscript version is made available under the
CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0
Realization Theory for LPV State-Space Representations with Affine Dependence
In this paper we present a Kalman-style realization theory for linear
parameter-varying state-space representations whose matrices depend on the
scheduling variables in an affine way (abbreviated as LPV-SSA representations).
We deal both with the discrete-time and the continuous-time cases. We show that
such a LPV-SSA representation is a minimal (in the sense of having the least
number of state-variables) representation of its input-output function, if and
only if it is observable and span-reachable. We show that any two minimal
LPV-SSA representations of the same input-output function are related by a
linear isomorphism, and the isomorphism does not depend on the scheduling
variable.We show that an input-output function can be represented by a LPV-SSA
representation if and only if the Hankel-matrix of the input-output function
has a finite rank. In fact, the rank of the Hankel-matrix gives the dimension
of a minimal LPV-SSA representation. Moreover, we can formulate a counterpart
of partial realization theory for LPV-SSA representation and prove correctness
of the Kalman-Ho algorithm. These results thus represent the basis of systems
theory for LPV-SSA representation.Comment: The main difference with respect to the previous version is as
follows: typos have been fixe
Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
Re-identification is generally carried out by encoding the appearance of a
subject in terms of outfit, suggesting scenarios where people do not change
their attire. In this paper we overcome this restriction, by proposing a
framework based on a deep convolutional neural network, SOMAnet, that
additionally models other discriminative aspects, namely, structural attributes
of the human figure (e.g. height, obesity, gender). Our method is unique in
many respects. First, SOMAnet is based on the Inception architecture, departing
from the usual siamese framework. This spares expensive data preparation
(pairing images across cameras) and allows the understanding of what the
network learned. Second, and most notably, the training data consists of a
synthetic 100K instance dataset, SOMAset, created by photorealistic human body
generation software. Synthetic data represents a good compromise between
realistic imagery, usually not required in re-identification since surveillance
cameras capture low-resolution silhouettes, and complete control of the
samples, which is useful in order to customize the data w.r.t. the surveillance
scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on
recent re-identification benchmarks, outperforms all competitors, matching
subjects even with different apparel. The combination of synthetic data with
Inception architectures opens up new research avenues in re-identification.Comment: 14 page
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