7,440 research outputs found
Graph constrained label propagation on water supply networks
In many real-world applications we have at our disposal a limited number of inputs in a theoretical database with full information, and another part of experimental data with incomplete knowledge for some of their features. These are cases that can be addressed by a label propagation process. It is a widely studied approach that may acquire complexity if new constraints in the new unlabeled data that should be taken into account are found. This is the case of the membership to a group or community in graphs. The proposal is to add the Laplacian matrix as well as another different similarity measures (may be not found in the original database) in the label propagation. A kernel embedding process together with a simple label propagation algorithm will be the main tools to achieve this approach by the use of all types of available information. In order to test the functionality of this new proposal, this work introduces an experimental study of biofilm development in drinking water pipes. Then, a label propagation through pipes belonging to a complete water supply network is approached. These pipes have their own properties depending on their network location and environmental co-variables. As a result, the proposal is a suitable and efficient way to deal with practical data, based on previous theoretical studies by the constrained label propagation process introduced.Herrera Fernández, AM.; Ramos Martinez, E.; Izquierdo Sebastián, J.; PĂ©rez GarcĂa, R. (2015). Graph constrained label propagation on water supply networks. AI Communications. 28(1):47-53. doi:10.3233/AIC-140618S475328
Graph-based Semi-Supervised & Active Learning for Edge Flows
We present a graph-based semi-supervised learning (SSL) method for learning
edge flows defined on a graph. Specifically, given flow measurements on a
subset of edges, we want to predict the flows on the remaining edges. To this
end, we develop a computational framework that imposes certain constraints on
the overall flows, such as (approximate) flow conservation. These constraints
render our approach different from classical graph-based SSL for vertex labels,
which posits that tightly connected nodes share similar labels and leverages
the graph structure accordingly to extrapolate from a few vertex labels to the
unlabeled vertices. We derive bounds for our method's reconstruction error and
demonstrate its strong performance on synthetic and real-world flow networks
from transportation, physical infrastructure, and the Web. Furthermore, we
provide two active learning algorithms for selecting informative edges on which
to measure flow, which has applications for optimal sensor deployment. The
first strategy selects edges to minimize the reconstruction error bound and
works well on flows that are approximately divergence-free. The second approach
clusters the graph and selects bottleneck edges that cross cluster-boundaries,
which works well on flows with global trends
Thermal Transients in District Heating Systems
Heat fluxes in a district heating pipeline systems need to be controlled on
the scale from minutes to an hour to adjust to evolving demand. There are two
principal ways to control the heat flux - keep temperature fixed but adjust
velocity of the carrier (typically water) or keep the velocity flow steady but
then adjust temperature at the heat producing source (heat plant). We study the
latter scenario, commonly used for operations in Russia and Nordic countries,
and analyze dynamics of the heat front as it propagates through the system.
Steady velocity flows in the district heating pipelines are typically turbulent
and incompressible. Changes in the heat, on either consumption or production
sides, lead to slow transients which last from tens of minutes to hours. We
classify relevant physical phenomena in a single pipe, e.g. turbulent spread of
the turbulent front. We then explain how to describe dynamics of temperature
and heat flux evolution over a network efficiently and illustrate the network
solution on a simple example involving one producer and one consumer of heat
connected by "hot" and "cold" pipes. We conclude the manuscript motivating
future research directions.Comment: 31 pages, 7 figure
A maximum entropy network reconstruction of macroeconomic models
In this article the problem of reconstructing the pattern of connection
between agents from partial empirical data in a macro-economic model is
addressed, given a set of behavioral equations. This systemic point of view
puts the focus on distributional and network effects, rather than
time-dependence. Using the theory of complex networks we compare several models
to reconstruct both the topology and the flows of money of the different types
of monetary transactions, while imposing a series of constraints related to
national accounts, and to empirical network sparsity. Some properties of
reconstructed networks are compared with their empirical counterpart
Robot graphic simulation testbed
The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts
Characterising population variability in brain structure through models of whole-brain structural connectivity
Models of whole-brain connectivity are valuable for understanding neurological function. This thesis
seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically
acquired diffusion data. We propose new approaches for studying these models. The aim is to
develop techniques which can take models of brain connectivity and use them to identify biomarkers
or phenotypes of disease.
The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified
to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections
are traced between 77 regions of interest, automatically extracted by label propagation from
multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract
are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data.
These are compared in subsequent studies.
To date, most whole-brain connectivity studies have characterised population differences using graph
theory techniques. However these can be limited in their ability to pinpoint the locations of differences
in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include
a spectral clustering approach for comparing population differences in the clustering properties of
weighted brain networks. In addition, machine learning approaches are suggested for the first time.
These are particularly advantageous as they allow classification of subjects and extraction of features
which best represent the differences between groups.
One limitation of the proposed approach is that errors propagate from segmentation and registration
steps prior to tractography. This can cumulate in the assignment of false positive connections, where
the contribution of these factors may vary across populations, causing the appearance of population
differences where there are none. The final contribution of this thesis is therefore to develop a common
co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject
into a single probabilistic model of diffusion for the population. This allows tractography to be
performed only once, ensuring that there is one model of connectivity. Cross-subject differences can
then be identified by mapping individual subjects’ anisotropy data to this model. The approach is
used to compare populations separated by age and gender
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