14,396 research outputs found
Minimum requirements for feedback enhanced force sensing
The problem of estimating an unknown force driving a linear oscillator is
revisited. When using linear measurement, feedback is often cited as a
mechanism to enhance bandwidth or sensitivity. We show that as long as the
oscillator dynamics are known, there exists a real-time estimation strategy
that reproduces the same measurement record as any arbitrary feedback protocol.
Consequently some form of nonlinearity is required to gain any advantage beyond
estimation alone. This result holds true in both quantum and classical systems,
with non-stationary forces and feedback, and in the general case of
non-Gaussian and correlated noise. Recently, feedback enhanced incoherent force
sensing has been demonstrated [Nat. Nano. \textbf{7}, 509 (2012)], with the
enhancement attributed to a feedback induced modification of the mechanical
susceptibility. As a proof-of-principle we experimentally reproduce this result
through straightforward filtering.Comment: 5 pages + 2 pages of Supplementary Informatio
Techniques for the Fast Simulation of Models of Highly dependable Systems
With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system
The Multi-Location Transshipment Problem with Positive Replenishment Lead Times
Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm.Transshipment;Infinitesimal Perturbation Analysis (IPA);Simulation Optimization
Optimization and sensitivity analysis of computer simulation models by the score function method
Experimental Design;Simulation;Optimization;Queueing Theory
Hierarchical relational models for document networks
We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, that is, discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them. We derive efficient inference and estimation
algorithms based on variational methods that take advantage of sparsity and
scale with the number of links. We evaluate the predictive performance of the
RTM for large networks of scientific abstracts, web documents, and
geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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