Composite materials play important roles in multifunctional applications, and thus, the diagnosis of damage patterns in composite materials becomes crucial to avoid critical events" such as structural or functional failures. The impact of an individual damage in composite materials has been extensively studied, however, the interaction of defects/cracks, which leads to critical fracture paths, has not been understood well. In this paper, we develop a Bayesian estimation based statistical analysis technique that estimates the damage pattern of a composite material, in particular, the relative positions of defects in the material, by measuring its through-thickness dielectric properties. We first explain the fundamental dielectric principle that leads to the detection of defect patterns. A capacitance model is then built to measure the material permittivity, and the relationship between the dielectric permittivity and relative positions are found using COMSOL Multiphysics. The interaction effects between defects observed in the simulation are interpreted using the fundamental dielectric principle. A Bayesian estimation based statistical analysis model is then developed to estimate the relative positions of defects in composite materials from the measured global dielectric properties.
In heterogeneous battlefield teams, the balance between team and individual objectives forms the
basis for the internal topological structure of teams. The stability of team structure is studied by
presenting a graphical coalitional game (GCG) with Positional Advantage (PA). PA is Shapley
value strengthened by the Axioms of value. The notion of team and individual objectives is
studied by defining altruistic and competitive contribution made by an individual; altruistic and
competitive contributions made by an agent are components of its total or marginal contribution.
Moreover, the paper examines dynamic team effects by defining three online sequential decision
games based on marginal, competitive and altruistic contributions of the individuals towards
team. The stable graphs under these sequential decision games are studied and found to be
structurally connected, complete, or tree respectively.
The consensus problem in multi-agent systems often assumes that all agents are equally trustworthy to seek agreement.
But for multi-agent military applications - particularly those that deal with sensor fusion or multi-robot formation
control - this assumption may create the potential for compromised network security or poor cooperative performance.
As such, we present a trust-based solution for the discrete-time multi-agent consensus problem and prove its asymptotic
convergence in strongly connected digraphs. The novelty of the paper is a new trust algorithm called RoboTrust, which
is used to calculate trustworthiness in agents using observations and statistical inferences from various historical
perspectives. The performance of RoboTrust is evaluated within the trust-based consensus protocol under different
conditions of tolerance and confirmation.
We present a rigorous treatment of coalition formation based on trust interactions in multi-agent systems. Current
literature on trust in multi-agent systems primarily deals with trust models and protocols of interaction in noncooperative
scenarios. Here, we use cooperative game theory as the underlying mathematical framework to study the
trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We rigorously
justify the behaviors of agents for different classes of games, and discuss ways to exploit the formal properties of these
games for specific applications, such as unmanned cooperative control.
This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive.
Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and
so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation.
However, using that approach, players cannot change their objectives online in real time without calling for a
completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning
optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for
instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This
allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.
During mission execution in military applications, the TRADOC Pamphlet 525-66 Battle Command and Battle Space
Awareness capabilities prescribe expectations that networked teams will perform in a reliable manner under changing
mission requirements, varying resource availability and reliability, and resource faults. In this paper, a Command and
Control (C2) structure is presented that allows for computer-aided execution of the networked team decision-making
process, control of force resources, shared resource dispatching, and adaptability to change based on battlefield
conditions. A mathematically justified networked computing environment is provided called the Discrete Event Control
(DEC) Framework. DEC has the ability to provide the logical connectivity among all team participants including
mission planners, field commanders, war-fighters, and robotic platforms. The proposed data management tools are
developed and demonstrated on a simulation study and an implementation on a distributed wireless sensor network. The
results show that the tasks of multiple missions are correctly sequenced in real-time, and that shared resources are
suitably assigned to competing tasks under dynamically changing conditions without conflicts and bottlenecks.
A new matrix formulation is given that allows fast, direct design and reconfiguration of rule- based controllers for manufacturing systems. Given a bill of materials or assembly tree, Steward's sequencing matrix is constructed. Then, resources and agents are added through `resource matrices' such as those used by Kusiak, and extra inputs are added to resolve shared-resource conflicts. The result is a multiloop DE controller with outer loops for dispatching of shared resources. The matrix formulation allows a rigorous analysis of deadlock in terms of circular blockings, siphons, and the numbers of resources available; this allows efficient dispatching and routing with deadlock avoidance. An assembly task is used to illustrate the concepts introduced.
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