161,222 research outputs found
Distributed Coupled Multi-Agent Stochastic Optimization
This work develops effective distributed strategies for the solution of
constrained multi-agent stochastic optimization problems with coupled
parameters across the agents. In this formulation, each agent is influenced by
only a subset of the entries of a global parameter vector or model, and is
subject to convex constraints that are only known locally. Problems of this
type arise in several applications, most notably in disease propagation models,
minimum-cost flow problems, distributed control formulations, and distributed
power system monitoring. This work focuses on stochastic settings, where a
stochastic risk function is associated with each agent and the objective is to
seek the minimizer of the aggregate sum of all risks subject to a set of
constraints. Agents are not aware of the statistical distribution of the data
and, therefore, can only rely on stochastic approximations in their learning
strategies. We derive an effective distributed learning strategy that is able
to track drifts in the underlying parameter model. A detailed performance and
stability analysis is carried out showing that the resulting coupled diffusion
strategy converges at a linear rate to an neighborhood of the true
penalized optimizer
A mathematical treatment of bank monitoring incentives
In this paper, we take up the analysis of a principal/agent model with moral
hazard introduced in [17], with optimal contracting between competitive
investors and an impatient bank monitoring a pool of long-term loans subject to
Markovian contagion. We provide here a comprehensive mathematical formulation
of the model and show using martingale arguments in the spirit of Sannikov [18]
how the maximization problem with implicit constraints faced by investors can
be reduced to a classical stochastic control problem. The approach has the
advantage of avoiding the more general techniques based on forward-backward
stochastic differential equations described in [6] and leads to a simple
recursive system of Hamilton-Jacobi-Bellman equations. We provide a solution to
our problem by a verification argument and give an explicit description of both
the value function and the optimal contract. Finally, we study the limit case
where the bank is no longer impatient
Periodic Monitoring and Recovery of Resources in Information Systems
This section deals with the issues of business continuity and recovery after disasters. The authors analyzed standards, laws, and regulations pertaining to the parameters of periodic monitoring and recovery in information systems. This section includes mathematical models of resources and environment periodic monitoring as well as periodic backup and recovery after interruptions or disasters. The work demonstrates that the well-known deterministic periodic monitoring and backup models do not take into account stochastic peculiarities of ergatic systems to the full extent. The authors developed new stochastic models of restricted monitoring and backup that allow taking into consideration resources constrains and random factors of information systems operation. The notion of Bernoulli stream has been introduced. This section suggests the criteria for selecting deterministic or stochastic monitoring and backup models and their combinations. A solution of direct and reverse task of the calculation of control and monitoring procedures frequency is offered. This section also provides a methodology for information system stability management, considering periodic monitoring, rollback, and recovery in case of interruption
Detection of weak stochastic force in a parametrically stabilized micro opto-mechanical system
Measuring a weak force is an important task for micro-mechanical systems,
both when using devices as sensitive detectors and, particularly, in
experiments of quantum mechanics. The optimal strategy for resolving a weak
stochastic signal force on a huge background (typically given by thermal noise)
is a crucial and debated topic, and the stability of the mechanical resonance
is a further, related critical issue. We introduce and analyze the parametric
control of the optical spring, that allows to stabilize the resonance and
provides a phase reference for the oscillator motion, yet conserving a free
evolution in one quadrature of the phase space. We also study quantitatively
the characteristics of our micro opto-mechanical system as detector of
stochastic force for short measurement times (for quick, high resolution
monitoring) as well as for the longer term observations that optimize the
sensitivity. We compare a simple, naive strategy based on the evaluation of the
variance of the displacement (that is a widely used technique) with an optimal
Wiener-Kolmogorov data analysis. We show that, thanks to the parametric
stabilization of the effective susceptibility, we can more efficiently
implement Wiener filtering, and we investigate how this strategy improves the
performance of our system. We finally demonstrate the possibility to resolve
stochastic force variations well below 1% of the thermal noise
Quantum Flywheel
A quantum flywheel is studied with the purpose of storing useful work in
quantum levels, while additional power is extracted continuously from the
device. The flywheel gains its energy form a quantum heat engine. Generally,
when a work repository is quantized the work exchange with the engine is
accompanied with heat exchange, which may degrade the charging efficiency. In
the particular realization of a quantum harmonic oscillator work repository,
quantum and thermal fluctuations dominates the dynamics. Quantum monitoring and
feedback control are applied to the flywheel, as it is shown to be an essential
part of stabilizing and regulating its state of operation, and bringing the
system to a steady state. A particular balance between information gained by
measuring the system and the information fed back to the system is found to
maximize the charging efficiency. The dynamics of the flywheel are described by
a stochastic master equation that accounts for the engine, the external
driving, the measurement, and the feedback operations
Bayesian feedback versus Markovian feedback in a two-level atom
We compare two different approaches to the control of the dynamics of a
continuously monitored open quantum system. The first is Markovian feedback as
introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. {\bf 70},
548 (1993)]. The second is feedback based on an estimate of the system state,
developed recently by Doherty {\em et al.} [Phys. Rev. A {\bf 62}, 012105
(2000)]. Here we choose to call it, for brevity, {\em Bayesian feedback}. For
systems with nonlinear dynamics, we expect these two methods of feedback
control to give markedly different results. The simplest possible nonlinear
system is a driven and damped two-level atom, so we choose this as our model
system. The monitoring is taken to be homodyne detection of the atomic
fluorescence, and the control is by modulating the driving. The aim of the
feedback in both cases is to stabilize the internal state of the atom as close
as possible to an arbitrarily chosen pure state, in the presence of inefficient
detection and other forms of decoherence. Our results (obtain without recourse
to stochastic simulations) prove that Bayesian feedback is never inferior, and
is usually superior, to Markovian feedback. However it would be far more
difficult to implement than Markovian feedback and it loses its superiority
when obvious simplifying approximations are made. It is thus not clear which
form of feedback would be better in the face of inevitable experimental
imperfections.Comment: 10 pages, including 3 figure
Modeling of Reliability and Performance Assessment of a Dissimilar Redundancy Actuation System With Failure Monitoring
Actuation system is a vital system in an aircraft, providing the force necessary to move flight control surfaces. The system has a significant influence on the overall aircraft performance and its safety. In order to further increase already high reliability and safety, Airbus has implemented a dissimilar redundancy actuation system (DRAS) in its aircraft. The DRAS consists of a hydraulic actuation system (HAS) and an electro-hydrostatic actuation system (EHAS), in which the HAS utilizes a hydraulic source (HS) to move the control surface and the EHAS utilizes an electrical supply (ES) to provide the motion force. This paper focuses on the performance degradation processes and fault monitoring strategies of the DRAS, establishes its reliability model based on the generalized stochastic Petri nets (GSPN), and carries out a reliability assessment considering the fault monitoring coverage rate and the false alarm rate. The results indicate that the proposed reliability model of the DRAS, considering the fault monitoring, can express its fault logical relation and redundancy degradation process and identify potential safety hazards
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