9,934 research outputs found
Divergence and convergence of inertial particles in high Reynolds number turbulence
Inertial particle data from three-dimensional direct numerical simulations of
particle-laden homogeneous isotropic turbulence at high Reynolds number are
analyzed using Voronoi tessellation of the particle positions, considering
different Stokes numbers. A finite-time measure to quantify the divergence of
the particle velocity by determining the volume change rate of the Voronoi
cells is proposed. For inertial particles the probability distribution function
(PDF) of the divergence deviates from that for fluid particles. Joint PDFs of
the divergence and the Voronoi volume illustrate that the divergence is most
prominent in cluster regions and less pronounced in void regions. For larger
volumes the results show negative divergence values which represent cluster
formation (i.e. particle convergence) and for small volumes the results show
positive divergence values which represents cluster destruction/void formation
(i.e. particle divergence). Moreover, when the Stokes number increases the
divergence takes larger values, which gives some evidence why fine clusters are
less observed for large Stokes numbers. Theoretical analyses further show that
the divergence for random particles in random flow satisfies a PDF
corresponding to the ratio of two independent variables following normal and
gamma distributions in one dimension. Extending this model to three dimensions,
the predicted PDF agrees reasonably well with Monte-Carlo simulations and DNS
data of fluid particles.Comment: 23 pages, 9 figure
Expectation-Maximization Binary Clustering for Behavioural Annotation
We present a variant of the well sounded Expectation-Maximization Clustering
algorithm that is constrained to generate partitions of the input space into
high and low values. The motivation of splitting input variables into high and
low values is to favour the semantic interpretation of the final clustering.
The Expectation-Maximization binary Clustering is specially useful when a
bimodal conditional distribution of the variables is expected or at least when
a binary discretization of the input space is deemed meaningful. Furthermore,
the algorithm deals with the reliability of the input data such that the larger
their uncertainty the less their role in the final clustering. We show here its
suitability for behavioural annotation of movement trajectories. However, it
can be considered as a general purpose algorithm for the clustering or
segmentation of multivariate data or temporal series.Comment: 34 pages main text including 11 (full page) figure
Prediction of remaining life of power transformers based on left truncated and right censored lifetime data
Prediction of the remaining life of high-voltage power transformers is an
important issue for energy companies because of the need for planning
maintenance and capital expenditures. Lifetime data for such transformers are
complicated because transformer lifetimes can extend over many decades and
transformer designs and manufacturing practices have evolved. We were asked to
develop statistically-based predictions for the lifetimes of an energy
company's fleet of high-voltage transmission and distribution transformers. The
company's data records begin in 1980, providing information on installation and
failure dates of transformers. Although the dataset contains many units that
were installed before 1980, there is no information about units that were
installed and failed before 1980. Thus, the data are left truncated and right
censored. We use a parametric lifetime model to describe the lifetime
distribution of individual transformers. We develop a statistical procedure,
based on age-adjusted life distributions, for computing a prediction interval
for remaining life for individual transformers now in service. We then extend
these ideas to provide predictions and prediction intervals for the cumulative
number of failures, over a range of time, for the overall fleet of
transformers.Comment: Published in at http://dx.doi.org/10.1214/00-AOAS231 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Information transmission in oscillatory neural activity
Periodic neural activity not locked to the stimulus or to motor responses is
usually ignored. Here, we present new tools for modeling and quantifying the
information transmission based on periodic neural activity that occurs with
quasi-random phase relative to the stimulus. We propose a model to reproduce
characteristic features of oscillatory spike trains, such as histograms of
inter-spike intervals and phase locking of spikes to an oscillatory influence.
The proposed model is based on an inhomogeneous Gamma process governed by a
density function that is a product of the usual stimulus-dependent rate and a
quasi-periodic function. Further, we present an analysis method generalizing
the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the
information content in such data. We demonstrate these tools on recordings from
relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic
- …