19,117 research outputs found
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
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Particle swarm optimization with composite particles in dynamic environments
This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1
Rough set theory applied to pattern recognition of partial discharge in noise affected cable data
This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data
An Experimental Study of Adaptive Control for Evolutionary Algorithms
The balance of exploration versus exploitation (EvE) is a key issue on
evolutionary computation. In this paper we will investigate how an adaptive
controller aimed to perform Operator Selection can be used to dynamically
manage the EvE balance required by the search, showing that the search
strategies determined by this control paradigm lead to an improvement of
solution quality found by the evolutionary algorithm
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