85,024 research outputs found
Crack identification in structures using optimal sensor locations
A Bayesian system identification methodology is presented for estimating the crack
location, size and orientation using strain measurements. The Bayesian statistical
approach combines information from measured data and analytical or computational
models of structural behavior to predict estimates of the crack characteristics along with
the associated uncertainties, taking into account modeling and measurement errors. An
optimal sensor location methodology is proposed to maximize the information that is
contained in the measured data for crack identification problems. For this, the most
informative, about the condition of the structure, data are obtained by minimizing the
information entropy measure of the uncertainty in the model parameter estimates
provided by the above statistical system identification method. Both crack identification
and optimal sensor location formulations lead to highly non-convex optimization
problems in which multiple local and global optima may exist. A hybrid optimization
method based on evolutionary strategies and gradient based techniques is used to
determine the global minimum. The effectiveness of the proposed methodologies is
illustrated using simulated data from a single crack in a thin plate subjected to known and
unknown static loading. The effects of modeling and measurement error on the
effectiveness of the crack detection method, as well as the methodology’s limitations are
investigated
Traffic matrix estimation on a large IP backbone: a comparison on real data
This paper considers the problem of estimating the point-to-point
traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks
Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"
This special issue collects contributions from the participants of the
"Information in Dynamical Systems and Complex Systems" workshop, which cover a
wide range of important problems and new approaches that lie in the
intersection of information theory and dynamical systems. The contributions
include theoretical characterization and understanding of the different types
of information flow and causality in general stochastic processes, inference
and identification of coupling structure and parameters of system dynamics,
rigorous coarse-grain modeling of network dynamical systems, and exact
statistical testing of fundamental information-theoretic quantities such as the
mutual information. The collective efforts reported herein reflect a modern
perspective of the intimate connection between dynamical systems and
information flow, leading to the promise of better understanding and modeling
of natural complex systems and better/optimal design of engineering systems
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued
conditional logic for quantitative plausible reasoning in the presence of uncertainty due to incomplete information. The
fundamental probability models that represent the structure’s uncertain behavior are specified by the choice of a stochastic
system model class: a set of input-output probability models for the structure and a prior probability distribution over this set
that quantifies the relative plausibility of each model. A model class can be constructed from a parameterized deterministic
structural model by stochastic embedding utilizing Jaynes’ Principle of Maximum Information Entropy. Robust predictive
analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if
structural response data is available, by its posterior probability from Bayes’ Theorem for the model class. Additional robustness
to modeling uncertainty comes from combining the robust predictions of each model class in a set of competing candidates
weighted by the prior or posterior probability of the model class, the latter being computed from Bayes’ Theorem. This higherlevel application of Bayes’ Theorem automatically applies a quantitative Ockham razor that penalizes the data-fit of more
complex model classes that extract more information from the data. Robust predictive analyses involve integrals over highdimensional spaces that usually must be evaluated numerically. Published applications have used Laplace's method of
asymptotic approximation or Markov Chain Monte Carlo algorithms
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