986 research outputs found
A hidden Markov model for matching spatial networks
Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm is robust in regards to data heterogeneities and imperfections (geometric discrepancies and differences in level of details) and adaptable to match any type of spatial networks. It also has the advantage of requiring no mandatory parameters, as proven by a sensitivity exploration, except a distance threshold that filters potential matching candidates in order to speed-up the process. Finally, a comparison with a commonly cited approach highlights good matching accuracy and completeness
Direct numerical simulation of the dynamics of sliding rough surfaces
The noise generated by the friction of two rough surfaces under weak contact
pressure is usually called roughness noise. The underlying vibration which
produces the noise stems from numerous instantaneous shocks (in the microsecond
range) between surface micro-asperities. The numerical simulation of this
problem using classical mechanics requires a fine discretization in both space
and time. This is why the finite element method takes much CPU time. In this
study, we propose an alternative numerical approach which is based on a
truncated modal decomposition of the vibration, a central difference
integration scheme and two algorithms for contact: The penalty algorithm and
the Lagrange multiplier algorithm. Not only does it reproduce the empirical
laws of vibration level versus roughness and sliding speed found experimentally
but it also provides the statistical properties of local events which are not
accessible by experiment. The CPU time reduction is typically a factor of 10.Comment: 16 pages, 16 figures, accepted versio
Historical collaborative geocoding
The latest developments in digital have provided large data sets that can
increasingly easily be accessed and used. These data sets often contain
indirect localisation information, such as historical addresses. Historical
geocoding is the process of transforming the indirect localisation information
to direct localisation that can be placed on a map, which enables spatial
analysis and cross-referencing. Many efficient geocoders exist for current
addresses, but they do not deal with the temporal aspect and are based on a
strict hierarchy (..., city, street, house number) that is hard or impossible
to use with historical data. Indeed historical data are full of uncertainties
(temporal aspect, semantic aspect, spatial precision, confidence in historical
source, ...) that can not be resolved, as there is no way to go back in time to
check. We propose an open source, open data, extensible solution for geocoding
that is based on the building of gazetteers composed of geohistorical objects
extracted from historical topographical maps. Once the gazetteers are
available, geocoding an historical address is a matter of finding the
geohistorical object in the gazetteers that is the best match to the historical
address. The matching criteriae are customisable and include several dimensions
(fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is
to facilitate historical work, we also propose web-based user interfaces that
help geocode (one address or batch mode) and display over current or historical
topographical maps, so that they can be checked and collaboratively edited. The
system is tested on Paris city for the 19-20th centuries, shows high returns
rate and is fast enough to be used interactively.Comment: WORKING PAPE
Statistics of the separation between sliding rigid rough surfaces: Simulations and extreme value theory approach
When a rigid rough solid slides on a rigid rough surface, it experiences a
random motion in the direction normal to the average contact plane. Here,
through simulations of the separation at single-point contact between
self-affine topographies, we characterize the statistical and spectral
properties of this normal motion. In particular, its rms amplitude is much
smaller than that of the equivalent roughness of the two topographies, and
depends on the ratio of the slider's lateral size over a characteristic
wavelength of the topography. In addition, due to the non-linearity of the
sliding contact process, the normal motion's spectrum contains wavelengths
smaller than the smallest wavelength present in the underlying topographies. We
show that the statistical properties of the normal motion's amplitude are well
captured by a simple analytic model based on the extreme value theory
framework, extending its applicability to sliding-contact-related topics
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