40,807 research outputs found
Correlation regimes in fluctuations of fatigue crack growth
This paper investigates correlation properties of fluctuations in fatigue
crack growth of polycrystalline materials, such as ductile alloys, that are
commonly encountered in structures and machinery components of complex
electromechanical systems. The model of crack damage measure indicates that the
fluctuations of fatigue crack growth are characterized by strong correlation
patterns within short time scales and are uncorrelated for larger time scales.
The two correlation regimes suggest that the 7075-T6 aluminum alloy, analyzed
in this paper, is characterized by a micro-structure which is responsible for
an intermittent correlated dynamics of fatigue crack growth within a certain
scale. The constitutive equations of the damage measure are built upon the
physics of fracture mechanics and are substantiated by Karhunen-Lo\`{e}ve
decomposition of fatigue test data. Statistical orthogonality of the estimated
damage measure and the resulting estimation error is demonstrated in a Hilbert
space setting.Comment: 30 pages, 8 figures, to appear in Physica
Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm
Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly
relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant
profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation
scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has
prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of
contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved
in the model integration process, discuss modelling and software development approaches, and present preliminary
results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant
reaction and transport in bed-sediments
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Estimating the Distribution of Random Parameters in a Diffusion Equation Forward Model for a Transdermal Alcohol Biosensor
We estimate the distribution of random parameters in a distributed parameter
model with unbounded input and output for the transdermal transport of ethanol
in humans. The model takes the form of a diffusion equation with the input
being the blood alcohol concentration and the output being the transdermal
alcohol concentration. Our approach is based on the idea of reformulating the
underlying dynamical system in such a way that the random parameters are now
treated as additional space variables. When the distribution to be estimated is
assumed to be defined in terms of a joint density, estimating the distribution
is equivalent to estimating the diffusivity in a multi-dimensional diffusion
equation and thus well-established finite dimensional approximation schemes,
functional analytic based convergence arguments, optimization techniques, and
computational methods may all be employed. We use our technique to estimate a
bivariate normal distribution based on data for multiple drinking episodes from
a single subject.Comment: 10 page
A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
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