517 research outputs found
DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL
To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response.
Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks.
The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments
Extraordinary variability and sharp transitions in a maximally frustrated dynamic network
Using Monte Carlo and analytic techniques, we study a minimal dynamic network
involving two populations of nodes, characterized by different preferred
degrees. Reminiscent of introverts and extroverts in a population, one set of
nodes, labeled \textit{introverts} (), prefers fewer contacts (a lower
degree) than the other, labeled \textit{extroverts} (). As a starting point,
we consider an \textit{extreme} case, in which an simply cuts one of its
links at random when chosen for updating, while an adds a link to a random
unconnected individual (node). The model has only two control parameters,
namely, the number of nodes in each group, and ). In the steady
state, only the number of crosslinks between the two groups fluctuates, with
remarkable properties: Its average () remains very close to 0 for all
or near its maximum () if
. At the transition (), the fraction
wanders across a substantial part of , much like a pure random walk.
Mapping this system to an Ising model with spin-flip dynamics and unusual
long-range interactions, we note that such fluctuations are far greater than
those displayed in either first or second order transitions of the latter.
Thus, we refer to the case here as an `extraordinary transition.' Thanks to the
restoration of detailed balance and the existence of a `Hamiltonian,' several
qualitative aspects of these remarkable phenomena can be understood
analytically.Comment: 6 pages, 3 figures, accepted for publication in EP
Epidemic spreading on preferred degree adaptive networks
We study the standard SIS model of epidemic spreading on networks where
individuals have a fluctuating number of connections around a preferred degree
. Using very simple rules for forming such preferred degree networks,
we find some unusual statistical properties not found in familiar
Erd\H{o}s-R\'{e}nyi or scale free networks. By letting depend on the
fraction of infected individuals, we model the behavioral changes in response
to how the extent of the epidemic is perceived. In our models, the behavioral
adaptations can be either `blind' or `selective' -- depending on whether a node
adapts by cutting or adding links to randomly chosen partners or selectively,
based on the state of the partner. For a frozen preferred network, we find that
the infection threshold follows the heterogeneous mean field result
and the phase diagram matches the predictions of
the annealed adjacency matrix (AAM) approach. With `blind' adaptations,
although the epidemic threshold remains unchanged, the infection level is
substantially affected, depending on the details of the adaptation. The
`selective' adaptive SIS models are most interesting. Both the threshold and
the level of infection changes, controlled not only by how the adaptations are
implemented but also how often the nodes cut/add links (compared to the time
scales of the epidemic spreading). A simple mean field theory is presented for
the selective adaptations which capture the qualitative and some of the
quantitative features of the infection phase diagram.Comment: 21 pages, 7 figure
Modeling interacting dynamic networks: I. Preferred degree networks and their characteristics
We study a simple model of dynamic networks, characterized by a set preferred
degree, . Each node with degree attempts to maintain its
and will add (cut) a link with probability (). As
a starting point, we consider a homogeneous population, where each node has the
same , and examine several forms of , inspired by
Fermi-Dirac functions. Using Monte Carlo simulations, we find the degree
distribution in steady state. In contrast to the well-known Erd\H{o}s-R\'{e}nyi
network, our degree distribution is not a Poisson distribution; yet its
behavior can be understood by an approximate theory. Next, we introduce a
second preferred degree network and couple it to the first by establishing a
controllable fraction of inter-group links. For this model, we find both
understandable and puzzling features. Generalizing the prediction for the
homogeneous population, we are able to explain the total degree distributions
well, but not the intra- or inter-group degree distributions. When monitoring
the total number of inter-group links, , we find very surprising behavior.
explores almost the full range between its maximum and minimum allowed
values, resulting in a flat steady-state distribution, reminiscent of a simple
random walk confined between two walls. Both simulation results and analytic
approaches will be discussed.Comment: Accepted by JSTA
Extreme Thouless effect in a minimal model of dynamic social networks
In common descriptions of phase transitions, first order transitions are
characterized by discontinuous jumps in the order parameter and normal
fluctuations, while second order transitions are associated with no jumps and
anomalous fluctuations. Outside this paradigm are systems exhibiting `mixed
order transitions' displaying a mixture of these characteristics. When the jump
is maximal and the fluctuations range over the entire range of allowed values,
the behavior has been coined an `extreme Thouless effect'. Here, we report
findings of such a phenomenon, in the context of dynamic, social networks.
Defined by minimal rules of evolution, it describes a population of extreme
introverts and extroverts, who prefer to have contacts with, respectively, no
one or everyone. From the dynamics, we derive an exact distribution of
microstates in the stationary state. With only two control parameters,
(the number of each subgroup), we study collective variables of
interest, e.g., , the total number of - links and the degree
distributions. Using simulations and mean-field theory, we provide evidence
that this system displays an extreme Thouless effect. Specifically, the
fraction jumps from to (in the
thermodynamic limit) when crosses , while all values appear with
equal probability at .Comment: arXiv admin note: substantial text overlap with arXiv:1408.542
A New Method of SHM for Steel Wire Rope and its Apparatus
Steel wire ropes often operate in a high‐speed swing status in practical engineering, and the reliable structural health monitoring (SHM) for them directly relates to human lives; however, they are usually beyond the capability of present portable magnet magnetic flux leakage (MFL) sensors based on yoke magnetic method due to its strong magnetic force and large weight. Unlike the yoke method, a new method of SHM for steel wire rope is proposed by theoretical analyses and also verified by finite element method (FEM) and experiments, which features much weaker magnetic interaction force and similar magnetization capability compared to the traditional yoke method. Meanwhile, the relevant detection apparatus or sensor is designed by simulation optimization. Furthermore, experimental comparisons between the new and yoke sensors for steel wire rope inspection are also conducted, which successfully confirm the characterization of smaller magnetic interaction force, less wear, and damage in contrast with traditional technologies. Finally, methods for SHM of steel wire rope and apparatus are discussed, which demonstrate the good practicability for SHM of steel wire rope under poor working conditions
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