143 research outputs found
Traffic congestion in interconnected complex networks
Traffic congestion in isolated complex networks has been investigated
extensively over the last decade. Coupled network models have recently been
developed to facilitate further understanding of real complex systems. Analysis
of traffic congestion in coupled complex networks, however, is still relatively
unexplored. In this paper, we try to explore the effect of interconnections on
traffic congestion in interconnected BA scale-free networks. We find that
assortative coupling can alleviate traffic congestion more readily than
disassortative and random coupling when the node processing capacity is
allocated based on node usage probability. Furthermore, the optimal coupling
probability can be found for assortative coupling. However, three types of
coupling preferences achieve similar traffic performance if all nodes share the
same processing capacity. We analyze interconnected Internet AS-level graphs of
South Korea and Japan and obtain similar results. Some practical suggestions
are presented to optimize such real-world interconnected networks accordingly.Comment: 8 page
Dynamic Behavior of Interacting between Epidemics and Cascades on Heterogeneous Networks
Epidemic spreading and cascading failure are two important dynamical
processes over complex networks. They have been investigated separately for a
long history. But in the real world, these two dynamics sometimes may interact
with each other. In this paper, we explore a model combined with SIR epidemic
spreading model and local loads sharing cascading failure model. There exists a
critical value of tolerance parameter that whether the epidemic with high
infection probability can spread out and infect a fraction of the network in
this model. When the tolerance parameter is smaller than the critical value,
cascading failure cuts off abundant of paths and blocks the spreading of
epidemic locally. While the tolerance parameter is larger than the critical
value, epidemic spreads out and infects a fraction of the network. A method for
estimating the critical value is proposed. In simulation, we verify the
effectiveness of this method in Barab\'asi-Albert (BA) networks
A Scale-Free Topology Construction Model for Wireless Sensor Networks
A local-area and energy-efficient (LAEE) evolution model for wireless sensor
networks is proposed. The process of topology evolution is divided into two
phases. In the first phase, nodes are distributed randomly in a fixed region.
In the second phase, according to the spatial structure of wireless sensor
networks, topology evolution starts from the sink, grows with an
energy-efficient preferential attachment rule in the new node's local-area, and
stops until all nodes are connected into network. Both analysis and simulation
results show that the degree distribution of LAEE follows the power law. This
topology construction model has better tolerance against energy depletion or
random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure
Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks
In complex networks, the failure of one or very few nodes may cause cascading
failures. When this dynamical process stops in steady state, the size of the
giant component formed by remaining un-failed nodes can be used to measure the
severity of cascading failures, which is critically important for estimating
the robustness of networks. In this paper, we provide a cascade of overload
failure model with local load sharing mechanism, and then explore the threshold
of node capacity when the large-scale cascading failures happen and un-failed
nodes in steady state cannot connect to each other to form a large connected
sub-network. We get the theoretical derivation of this threshold in
degree-degree uncorrelated networks, and validate the effectiveness of this
method in simulation. This threshold provide us a guidance to improve the
network robustness under the premise of limited capacity resource when creating
a network and assigning load. Therefore, this threshold is useful and important
to analyze the robustness of networks.Comment: 11 pages, 4 figure
Propagation Characteristics of Explosive Waves in Layered Media Numerical Analysis
The layered media under one-dimensional strain with different wave-impedance materials have been studied. The three typical prototypes have been analysized, including steel plate, aluminum foam, and concrete as the middle layer, and the upper and lower layers are concrete material. The attenuation of the amplitude of stress at different positions, the peak stress and the duration at the dissimilar material interface, and the absorbing energy distribution in different layers for different models have been obtained by numerical simulation. The material of the middle layer with lower impedance can effectively reduce the amplitude of stress, increase the duration of explosive wave, and change the distribution of energy in different layers. But the influence of the middle layer with higher impedance material on layered media is contrary. The middle layer with soft material is the better matching of wave impedance to explosive wave propagation. The analytical conclusions are of great significance for the design of protective structures against the explosion-induced hazards and minesafety protection from outburst and explosion.Defence Science Journal, 2009, 59(5), pp.499-504, DOI:http://dx.doi.org/10.14429/dsj.59.155
Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and
foundational role in the field of Learning from Demonstration (LfD).
Nevertheless, they confront the formidable challenge of striking a delicate
balance between achieving precision in learning and ensuring the overall
stability of the system. In response to this substantial challenge, this paper
introduces a novel DS algorithm rooted in neural network technology. This
algorithm not only possesses the capability to extract critical insights from
demonstration data but also demonstrates the capacity to learn a candidate
Lyapunov energy function that is consistent with the provided data. The model
presented in this paper employs a straightforward neural network architecture
that excels in fulfilling a dual objective: optimizing accuracy while
simultaneously preserving global stability. To comprehensively evaluate the
effectiveness of the proposed algorithm, rigorous assessments are conducted
using the LASA dataset, further reinforced by empirical validation through a
robotic experiment
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