99,369 research outputs found
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations
We describe work in progress by a collaboration of astronomers and
statisticians developing a suite of Bayesian data analysis tools for extrasolar
planet (exoplanet) detection, planetary orbit estimation, and adaptive
scheduling of observations. Our work addresses analysis of stellar reflex
motion data, where a planet is detected by observing the "wobble" of its host
star as it responds to the gravitational tug of the orbiting planet. Newtonian
mechanics specifies an analytical model for the resulting time series, but it
is strongly nonlinear, yielding complex, multimodal likelihood functions; it is
even more complex when multiple planets are present. The parameter spaces range
in size from few-dimensional to dozens of dimensions, depending on the number
of planets in the system, and the type of motion measured (line-of-sight
velocity, or position on the sky). Since orbits are periodic, Bayesian
generalizations of periodogram methods facilitate the analysis. This relies on
the model being linearly separable, enabling partial analytical
marginalization, reducing the dimension of the parameter space. Subsequent
analysis uses adaptive Markov chain Monte Carlo methods and adaptive importance
sampling to perform the integrals required for both inference (planet detection
and orbit measurement), and information-maximizing sequential design (for
adaptive scheduling of observations). We present an overview of our current
techniques and highlight directions being explored by ongoing research.Comment: 29 pages, 11 figures. An abridged version is accepted for publication
in Statistical Methodology for a special issue on astrostatistics, with
selected (refereed) papers presented at the Astronomical Data Analysis
Conference (ADA VI) held in Monastir, Tunisia, in May 2010. Update corrects
equation (3
Neural Feedback Scheduling of Real-Time Control Tasks
Many embedded real-time control systems suffer from resource constraints and
dynamic workload variations. Although optimal feedback scheduling schemes are
in principle capable of maximizing the overall control performance of
multitasking control systems, most of them induce excessively large
computational overheads associated with the mathematical optimization routines
involved and hence are not directly applicable to practical systems. To
optimize the overall control performance while minimizing the overhead of
feedback scheduling, this paper proposes an efficient feedback scheduling
scheme based on feedforward neural networks. Using the optimal solutions
obtained offline by mathematical optimization methods, a back-propagation (BP)
neural network is designed to adapt online the sampling periods of concurrent
control tasks with respect to changes in computing resource availability.
Numerical simulation results show that the proposed scheme can reduce the
computational overhead significantly while delivering almost the same overall
control performance as compared to optimal feedback scheduling.Comment: To appear in International Journal of Innovative Computing,
Information and Contro
On Multi-Step Sensor Scheduling via Convex Optimization
Effective sensor scheduling requires the consideration of long-term effects
and thus optimization over long time horizons. Determining the optimal sensor
schedule, however, is equivalent to solving a binary integer program, which is
computationally demanding for long time horizons and many sensors. For linear
Gaussian systems, two efficient multi-step sensor scheduling approaches are
proposed in this paper. The first approach determines approximate but close to
optimal sensor schedules via convex optimization. The second approach combines
convex optimization with a \BB search for efficiently determining the optimal
sensor schedule.Comment: 6 pages, appeared in the proceedings of the 2nd International
Workshop on Cognitive Information Processing (CIP), Elba, Italy, June 201
Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method for collecting data for which the least squares
estimator for the ranking problem has maximal Fisher information. Our approach,
based on experimental design, is to view data collection as a bi-level
optimization problem where the inner problem is the ranking problem and the
outer problem is to identify data which maximizes the informativeness of the
ranking. Under certain assumptions, the data collection problem decouples,
reducing to a problem of finding multigraphs with large algebraic connectivity.
This reduction of the data collection problem to graph-theoretic questions is
one of the primary contributions of this work. As an application, we study the
Yahoo! Movie user rating dataset and demonstrate that the addition of a small
number of well-chosen pairwise comparisons can significantly increase the
Fisher informativeness of the ranking. As another application, we study the
2011-12 NCAA football schedule and propose schedules with the same number of
games which are significantly more informative. Using spectral clustering
methods to identify highly-connected communities within the division, we argue
that the NCAA could improve its notoriously poor rankings by simply scheduling
more out-of-conference games.Comment: 31 pages, 10 figures, 3 table
A Modeling Framework for Schedulability Analysis of Distributed Avionics Systems
This paper presents a modeling framework for schedulability analysis of
distributed integrated modular avionics (DIMA) systems that consist of
spatially distributed ARINC-653 modules connected by a unified AFDX network. We
model a DIMA system as a set of stopwatch automata (SWA) in UPPAAL to analyze
its schedulability by classical model checking (MC) and statistical model
checking (SMC). The framework has been designed to enable three types of
analysis: global SMC, global MC, and compositional MC. This allows an effective
methodology including (1) quick schedulability falsification using global SMC
analysis, (2) direct schedulability proofs using global MC analysis in simple
cases, and (3) strict schedulability proofs using compositional MC analysis for
larger state space. The framework is applied to the analysis of a concrete DIMA
system.Comment: In Proceedings MARS/VPT 2018, arXiv:1803.0866
Query-Driven Sampling for Collective Entity Resolution
Probabilistic databases play a preeminent role in the processing and
management of uncertain data. Recently, many database research efforts have
integrated probabilistic models into databases to support tasks such as
information extraction and labeling. Many of these efforts are based on batch
oriented inference which inhibits a realtime workflow. One important task is
entity resolution (ER). ER is the process of determining records (mentions) in
a database that correspond to the same real-world entity. Traditional pairwise
ER methods can lead to inconsistencies and low accuracy due to localized
decisions. Leading ER systems solve this problem by collectively resolving all
records using a probabilistic graphical model and Markov chain Monte Carlo
(MCMC) inference. However, for large datasets this is an extremely expensive
process. One key observation is that, such exhaustive ER process incurs a huge
up-front cost, which is wasteful in practice because most users are interested
in only a small subset of entities. In this paper, we advocate pay-as-you-go
entity resolution by developing a number of query-driven collective ER
techniques. We introduce two classes of SQL queries that involve ER operators
--- selection-driven ER and join-driven ER. We implement novel variations of
the MCMC Metropolis Hastings algorithm to generate biased samples and
selectivity-based scheduling algorithms to support the two classes of ER
queries. Finally, we show that query-driven ER algorithms can converge and
return results within minutes over a database populated with the extraction
from a newswire dataset containing 71 million mentions
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