462 research outputs found
Graph-based real-time fault diagnostics
A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components
Graphic simualtion test bed for robotics applications in a workstation environment
Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots
When Dialects Collide: How Socioeconomic Mixing Affects Language Use
The socioeconomic background of people and how they use standard forms of
language are not independent, as demonstrated in various sociolinguistic
studies. However, the extent to which these correlations may be influenced by
the mixing of people from different socioeconomic classes remains relatively
unexplored from a quantitative perspective. In this work we leverage geotagged
tweets and transferable computational methods to map deviations from standard
English on a large scale, in seven thousand administrative areas of England and
Wales. We combine these data with high-resolution income maps to assign a proxy
socioeconomic indicator to home-located users. Strikingly, across eight
metropolitan areas we find a consistent pattern suggesting that the more
different socioeconomic classes mix, the less interdependent the frequency of
their departures from standard grammar and their income become. Further, we
propose an agent-based model of linguistic variety adoption that sheds light on
the mechanisms that produce the observations seen in the data
A microfluidic chip based model for the study of full thickness human intestinal tissue using dual flow
© 2016 Author(s). The study of inflammatory bowel disease, including Ulcerative Colitis and Crohn's Disease, has relied largely upon the use of animal or cell culture models; neither of which can represent all aspects of the human pathophysiology. Presented herein is a dual flow microfluidic device which holds full thickness human intestinal tissue in a known orientation. The luminal and serosal sides are independently perfused ex vivo with nutrients with simultaneous waste removal for up to 72 h. The microfluidic device maintains the viability and integrity of the tissue as demonstrated through Haematoxylin & Eosin staining, immunohistochemistry and release of lactate dehydrogenase. In addition, the inflammatory state remains in the tissue after perfusion on the device as determined by measuring calprotectin levels. It is anticipated that this human model will be extremely useful for studying the biology and tes ting novel interventions in diseased tissue
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta
Rounding of first-order phase transitions and optimal cooperation in scale-free networks
We consider the ferromagnetic large- state Potts model in complex evolving
networks, which is equivalent to an optimal cooperation problem, in which the
agents try to optimize the total sum of pair cooperation benefits and the
supports of independent projects. The agents are found to be typically of two
kinds: a fraction of (being the magnetization of the Potts model) belongs
to a large cooperating cluster, whereas the others are isolated one man's
projects. It is shown rigorously that the homogeneous model has a strongly
first-order phase transition, which turns to second-order for random
interactions (benefits), the properties of which are studied numerically on the
Barab\'asi-Albert network. The distribution of finite-size transition points is
characterized by a shift exponent, , and by a different
width exponent, , whereas the magnetization at the transition
point scales with the size of the network, , as: , with
.Comment: 8 pages, 6 figure
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
Correlated dynamics in egocentric communication networks
We investigate the communication sequences of millions of people through two
different channels and analyze the fine grained temporal structure of
correlated event trains induced by single individuals. By focusing on
correlations between the heterogeneous dynamics and the topology of egocentric
networks we find that the bursty trains usually evolve for pairs of individuals
rather than for the ego and his/her several neighbors thus burstiness is a
property of the links rather than of the nodes. We compare the directional
balance of calls and short messages within bursty trains to the average on the
actual link and show that for the trains of voice calls the imbalance is
significantly enhanced, while for short messages the balance within the trains
increases. These effects can be partly traced back to the technological
constrains (for short messages) and partly to the human behavioral features
(voice calls). We define a model that is able to reproduce the empirical
results and may help us to understand better the mechanisms driving technology
mediated human communication dynamics.Comment: 7 pages, 6 figure
Density of critical clusters in strips of strongly disordered systems
We consider two models with disorder dominated critical points and study the
distribution of clusters which are confined in strips and touch one or both
boundaries. For the classical random bond Potts model in the large-q limit we
study optimal Fortuin-Kasteleyn clusters by combinatorial optimization
algorithm. For the random transverse-field Ising chain clusters are defined and
calculated through the strong disorder renormalization group method. The
numerically calculated density profiles close to the boundaries are shown to
follow scaling predictions. For the random bond Potts model we have obtained
accurate numerical estimates for the critical exponents and demonstrated that
the density profiles are well described by conformal formulae.Comment: 9 pages, 9 figure
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