43,542 research outputs found
Towards Efficient Maximum Likelihood Estimation of LPV-SS Models
How to efficiently identify multiple-input multiple-output (MIMO) linear
parameter-varying (LPV) discrete-time state-space (SS) models with affine
dependence on the scheduling variable still remains an open question, as
identification methods proposed in the literature suffer heavily from the curse
of dimensionality and/or depend on over-restrictive approximations of the
measured signal behaviors. However, obtaining an SS model of the targeted
system is crucial for many LPV control synthesis methods, as these synthesis
tools are almost exclusively formulated for the aforementioned representation
of the system dynamics. Therefore, in this paper, we tackle the problem by
combining state-of-the-art LPV input-output (IO) identification methods with an
LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step.
The resulting modular LPV-SS identification approach achieves statical
efficiency with a relatively low computational load. The method contains the
following three steps: 1) estimation of the Markov coefficient sequence of the
underlying system using correlation analysis or Bayesian impulse response
estimation, then 2) LPV-SS realization of the estimated coefficients by using a
basis reduced Ho-Kalman method, and 3) refinement of the LPV-SS model estimate
from a maximum-likelihood point of view by a gradient-based or an
expectation-maximization optimization methodology. The effectiveness of the
full identification scheme is demonstrated by a Monte Carlo study where our
proposed method is compared to existing schemes for identifying a MIMO LPV
system
Multiscale Representations for Manifold-Valued Data
We describe multiscale representations for data observed on equispaced grids and taking values in manifolds such as the sphere , the special orthogonal group , the positive definite matrices , and the Grassmann manifolds . The representations are based on the deployment of Deslauriers--Dubuc and average-interpolating pyramids "in the tangent plane" of such manifolds, using the and maps of those manifolds. The representations provide "wavelet coefficients" which can be thresholded, quantized, and scaled in much the same way as traditional wavelet coefficients. Tasks such as compression, noise removal, contrast enhancement, and stochastic simulation are facilitated by this representation. The approach applies to general manifolds but is particularly suited to the manifolds we consider, i.e., Riemannian symmetric spaces, such as , , , where the and maps are effectively computable. Applications to manifold-valued data sources of a geometric nature (motion, orientation, diffusion) seem particularly immediate. A software toolbox, SymmLab, can reproduce the results discussed in this paper
Gradient-Based Estimation of Uncertain Parameters for Elliptic Partial Differential Equations
This paper addresses the estimation of uncertain distributed diffusion
coefficients in elliptic systems based on noisy measurements of the model
output. We formulate the parameter identification problem as an infinite
dimensional constrained optimization problem for which we establish existence
of minimizers as well as first order necessary conditions. A spectral
approximation of the uncertain observations allows us to estimate the infinite
dimensional problem by a smooth, albeit high dimensional, deterministic
optimization problem, the so-called finite noise problem in the space of
functions with bounded mixed derivatives. We prove convergence of finite noise
minimizers to the appropriate infinite dimensional ones, and devise a
stochastic augmented Lagrangian method for locating these numerically. Lastly,
we illustrate our method with three numerical examples
A non-equilibrium Monte Carlo approach to potential refinement in inverse problems
The inverse problem for a disordered system involves determining the
interparticle interaction parameters consistent with a given set of
experimental data. Recently, Rutledge has shown (Phys. Rev. E63, 021111 (2001))
that such problems can be generally expressed in terms of a grand canonical
ensemble of polydisperse particles. Within this framework, one identifies a
polydisperse attribute (`pseudo-species') corresponding to some
appropriate generalized coordinate of the system to hand. Associated with this
attribute is a composition distribution measuring the number
of particles of each species. Its form is controlled by a conjugate chemical
potential distribution which plays the role of the requisite
interparticle interaction potential. Simulation approaches to the inverse
problem involve determining the form of for which
matches the available experimental data. The difficulty in
doing so is that is (in general) an unknown {\em functional} of
and must therefore be found by iteration. At high particle
densities and for high degrees of polydispersity, strong cross coupling between
and renders this process computationally
problematic and laborious. Here we describe an efficient and robust {\em
non-equilibrium} simulation scheme for finding the equilibrium form of
. The utility of the method is demonstrated by
calculating the chemical potential distribution conjugate to a specific
log-normal distribution of particle sizes in a polydisperse fluid.Comment: 6 pages, 3 figure
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Understanding protein-protein interactions is central to our understanding of
almost all complex biological processes. Computational tools exploiting rapidly
growing genomic databases to characterize protein-protein interactions are
urgently needed. Such methods should connect multiple scales from evolutionary
conserved interactions between families of homologous proteins, over the
identification of specifically interacting proteins in the case of multiple
paralogs inside a species, down to the prediction of residues being in physical
contact across interaction interfaces. Statistical inference methods detecting
residue-residue coevolution have recently triggered considerable progress in
using sequence data for quaternary protein structure prediction; they require,
however, large joint alignments of homologous protein pairs known to interact.
The generation of such alignments is a complex computational task on its own;
application of coevolutionary modeling has in turn been restricted to proteins
without paralogs, or to bacterial systems with the corresponding coding genes
being co-localized in operons. Here we show that the Direct-Coupling Analysis
of residue coevolution can be extended to connect the different scales, and
simultaneously to match interacting paralogs, to identify inter-protein
residue-residue contacts and to discriminate interacting from noninteracting
families in a multiprotein system. Our results extend the potential
applications of coevolutionary analysis far beyond cases treatable so far.Comment: Main Text 19 pages Supp. Inf. 16 page
Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft
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