50,002 research outputs found
A Quality and Cost Approach for Comparison of Small-World Networks
We propose an approach based on analysis of cost-quality tradeoffs for
comparison of efficiency of various algorithms for small-world network
construction. A number of both known in the literature and original algorithms
for complex small-world networks construction are shortly reviewed and
compared. The networks constructed on the basis of these algorithms have basic
structure of 1D regular lattice with additional shortcuts providing the
small-world properties. It is shown that networks proposed in this work have
the best cost-quality ratio in the considered class.Comment: 27 pages, 16 figures, 1 tabl
Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation
State-of-the-art emergency navigation approaches are designed to evacuate
civilians during a disaster based on real-time decisions using a pre-defined
algorithm and live sensory data. Hence, casualties caused by the poor decisions
and guidance are only apparent at the end of the evacuation process and cannot
then be remedied. Previous research shows that the performance of routing
algorithms for evacuation purposes are sensitive to the initial distribution of
evacuees, the occupancy levels, the type of disaster and its as well its
locations. Thus an algorithm that performs well in one scenario may achieve bad
results in another scenario. This problem is especially serious in
heuristic-based routing algorithms for evacuees where results are affected by
the choice of certain parameters. Therefore, this paper proposes a
simulation-based evacuee routing algorithm that optimises evacuation by making
use of the high computational power of cloud servers. Rather than guiding
evacuees with a predetermined routing algorithm, a robust Cognitive Packet
Network based algorithm is first evaluated via a cloud-based simulator in a
faster-than-real-time manner, and any "simulated casualties" are then re-routed
using a variant of Dijkstra's algorithm to obtain new safe paths for them to
exits. This approach can be iterated as long as corrective action is still
possible.Comment: Submitted to PerNEM'15 for revie
Multi-scale analysis of the European airspace using network community detection
We show that the European airspace can be represented as a multi-scale
traffic network whose nodes are airports, sectors, or navigation points and
links are defined and weighted according to the traffic of flights between the
nodes. By using a unique database of the air traffic in the European airspace,
we investigate the architecture of these networks with a special emphasis on
their community structure. We propose that unsupervised network community
detection algorithms can be used to monitor the current use of the airspaces
and improve it by guiding the design of new ones. Specifically, we compare the
performance of three community detection algorithms, also by using a null model
which takes into account the spatial distance between nodes, and we discuss
their ability to find communities that could be used to define new control
units of the airspace.Comment: 22 pages, 14 figure
Neural Networks in Mobile Robot Motion
This paper deals with a path planning and intelligent control of an
autonomous robot which should move safely in partially structured environment.
This environment may involve any number of obstacles of arbitrary shape and
size; some of them are allowed to move. We describe our approach to solving the
motion-planning problem in mobile robot control using neural networks-based
technique. Our method of the construction of a collision-free path for moving
robot among obstacles is based on two neural networks. The first neural network
is used to determine the "free" space using ultrasound range finder data. The
second neural network "finds" a safe direction for the next robot section of
the path in the workspace while avoiding the nearest obstacles. Simulation
examples of generated path with proposed techniques will be presented.Comment: 9 Page
Routing Diverse Evacuees with Cognitive Packets
This paper explores the idea of smart building evacuation when evacuees can
belong to different categories with respect to their ability to move and their
health conditions. This leads to new algorithms that use the Cognitive Packet
Network concept to tailor different quality of service needs to different
evacuees. These ideas are implemented in a simulated environment and evaluated
with regard to their effectiveness.Comment: 7 pages, 7 figure
Comparison of Selection Methods in On-line Distributed Evolutionary Robotics
In this paper, we study the impact of selection methods in the context of
on-line on-board distributed evolutionary algorithms. We propose a variant of
the mEDEA algorithm in which we add a selection operator, and we apply it in a
taskdriven scenario. We evaluate four selection methods that induce different
intensity of selection pressure in a multi-robot navigation with obstacle
avoidance task and a collective foraging task. Experiments show that a small
intensity of selection pressure is sufficient to rapidly obtain good
performances on the tasks at hand. We introduce different measures to compare
the selection methods, and show that the higher the selection pressure, the
better the performances obtained, especially for the more challenging food
foraging task
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