36,129 research outputs found
Anomaly Detection Based on Indicators Aggregation
Automatic anomaly detection is a major issue in various areas. Beyond mere
detection, the identification of the source of the problem that produced the
anomaly is also essential. This is particularly the case in aircraft engine
health monitoring where detecting early signs of failure (anomalies) and
helping the engine owner to implement efficiently the adapted maintenance
operations (fixing the source of the anomaly) are of crucial importance to
reduce the costs attached to unscheduled maintenance. This paper introduces a
general methodology that aims at classifying monitoring signals into normal
ones and several classes of abnormal ones. The main idea is to leverage expert
knowledge by generating a very large number of binary indicators. Each
indicator corresponds to a fully parametrized anomaly detector built from
parametric anomaly scores designed by experts. A feature selection method is
used to keep only the most discriminant indicators which are used at inputs of
a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.Comment: International Joint Conference on Neural Networks (IJCNN 2014),
Beijing : China (2014). arXiv admin note: substantial text overlap with
arXiv:1407.088
Search method for long-duration gravitational-wave transients from neutron stars
We introduce a search method for a new class of gravitational-wave signals,
namely long-duration O(hours - weeks) transients from spinning neutron stars.
We discuss the astrophysical motivation from glitch relaxation models and we
derive a rough estimate for the maximal expected signal strength based on the
superfluid excess rotational energy. The transient signal model considered here
extends the traditional class of infinite-duration continuous-wave signals by a
finite start-time and duration. We derive a multi-detector Bayes factor for
these signals in Gaussian noise using \F-statistic amplitude priors, which
simplifies the detection statistic and allows for an efficient implementation.
We consider both a fully coherent statistic, which is computationally limited
to directed searches for known pulsars, and a cheaper semi-coherent variant,
suitable for wide parameter-space searches for transients from unknown neutron
stars. We have tested our method by Monte-Carlo simulation, and we find that it
outperforms orthodox maximum-likelihood approaches both in sensitivity and in
parameter-estimation quality.Comment: 20 pages, 9 figures; submitted to PR
An information-theoretic approach to the gravitational-wave burst detection problem
The observational era of gravitational-wave astronomy began in the Fall of
2015 with the detection of GW150914. One potential type of detectable
gravitational wave is short-duration gravitational-wave bursts, whose waveforms
can be difficult to predict. We present the framework for a new detection
algorithm for such burst events -- \textit{oLIB} -- that can be used in
low-latency to identify gravitational-wave transients independently of other
search algorithms. This algorithm consists of 1) an excess-power event
generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of
these events across a detector network, and 3) an analysis of the coincident
events using a Markov chain Monte Carlo Bayesian evidence calculator --
\textit{LALInferenceBurst}. These steps compress the full data streams into a
set of Bayes factors for each event; through this process, we use elements from
information theory to minimize the amount of information regarding the
signal-versus-noise hypothesis that is lost. We optimally extract this
information using a likelihood-ratio test to estimate a detection significance
for each event. Using representative archival LIGO data, we show that the
algorithm can detect gravitational-wave burst events of astrophysical strength
in realistic instrumental noise across different burst waveform morphologies.
We also demonstrate that the combination of Bayes factors by means of a
likelihood-ratio test can improve the detection efficiency of a
gravitational-wave burst search. Finally, we show that oLIB's performance is
robust against the choice of gravitational-wave populations used to model the
likelihood-ratio test likelihoods
Beliefs in Decision-Making Cascades
This work explores a social learning problem with agents having nonidentical
noise variances and mismatched beliefs. We consider an -agent binary
hypothesis test in which each agent sequentially makes a decision based not
only on a private observation, but also on preceding agents' decisions. In
addition, the agents have their own beliefs instead of the true prior, and have
nonidentical noise variances in the private signal. We focus on the Bayes risk
of the last agent, where preceding agents are selfish.
We first derive the optimal decision rule by recursive belief update and
conclude, counterintuitively, that beliefs deviating from the true prior could
be optimal in this setting. The effect of nonidentical noise levels in the
two-agent case is also considered and analytical properties of the optimal
belief curves are given. Next, we consider a predecessor selection problem
wherein the subsequent agent of a certain belief chooses a predecessor from a
set of candidates with varying beliefs. We characterize the decision region for
choosing such a predecessor and argue that a subsequent agent with beliefs
varying from the true prior often ends up selecting a suboptimal predecessor,
indicating the need for a social planner. Lastly, we discuss an augmented
intelligence design problem that uses a model of human behavior from cumulative
prospect theory and investigate its near-optimality and suboptimality.Comment: final version, to appear in IEEE Transactions on Signal Processin
Enabling high confidence detections of gravitational-wave bursts
With the advanced LIGO and Virgo detectors taking observations the detection
of gravitational waves is expected within the next few years. Extracting
astrophysical information from gravitational wave detections is a well-posed
problem and thoroughly studied when detailed models for the waveforms are
available. However, one motivation for the field of gravitational wave
astronomy is the potential for new discoveries. Recognizing and characterizing
unanticipated signals requires data analysis techniques which do not depend on
theoretical predictions for the gravitational waveform. Past searches for
short-duration un-modeled gravitational wave signals have been hampered by
transient noise artifacts, or "glitches," in the detectors. In some cases, even
high signal-to-noise simulated astrophysical signals have proven difficult to
distinguish from glitches, so that essentially any plausible signal could be
detected with at most 2-3 level confidence. We have put forth the
BayesWave algorithm to differentiate between generic gravitational wave
transients and glitches, and to provide robust waveform reconstruction and
characterization of the astrophysical signals. Here we study BayesWave's
capabilities for rejecting glitches while assigning high confidence to
detection candidates through analytic approximations to the Bayesian evidence.
Analytic results are tested with numerical experiments by adding simulated
gravitational wave transient signals to LIGO data collected between 2009 and
2010 and found to be in good agreement.Comment: 15 pages, 6 figures, submitted to PR
Template-based Gravitational-Wave Echoes Search Using Bayesian Model Selection
The ringdown of the gravitational-wave signal from a merger of two black
holes has been suggested as a probe of the structure of the remnant compact
object, which may be more exotic than a black hole. It has been pointed out
that there will be a train of echoes in the late-time ringdown stage for
different types of exotic compact objects. In this paper, we present a
template-based search methodology using Bayesian statistics to search for
echoes of gravitational waves. Evidence for the presence or absence of echoes
in gravitational-wave events can be established by performing Bayesian model
selection. The Occam factor in Bayesian model selection will automatically
penalize the more complicated model that echoes are present in
gravitational-wave strain data because of its higher degree of freedom to fit
the data. We find that the search methodology was able to identify
gravitational-wave echoes with Abedi et al.'s echoes waveform model about 82.3%
of the time in simulated Gaussian noise in the Advanced LIGO and Virgo network
and about 61.1% of the time in real noise in the first observing run of
Advanced LIGO with significance. Analyses using this method are
performed on the data of Advanced LIGO's first observing run, and we find no
statistical significant evidence for the detection of gravitational-wave
echoes. In particular, we find combined evidence of the three events
in Advanced LIGO's first observing run. The analysis technique developed in
this paper is independent of the waveform model used, and can be used with
different parametrized echoes waveform models to provide more realistic
evidence of the existence of echoes from exotic compact objects.Comment: 16 pages, 6 figure
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
We investigate the Bayesian framework for detection of continuous
gravitational waves (GWs) in the context of targeted searches, where the phase
evolution of the GW signal is assumed to be known, while the four amplitude
parameters are unknown. We show that the orthodox maximum-likelihood statistic
(known as F-statistic) can be rediscovered as a Bayes factor with an unphysical
prior in amplitude parameter space. We introduce an alternative detection
statistic ("B-statistic") using the Bayes factor with a more natural amplitude
prior, namely an isotropic probability distribution for the orientation of GW
sources. Monte-Carlo simulations of targeted searches show that the resulting
Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e. has a
higher expected detection probability at equal false-alarm probability) than
the frequentist F-statistic.Comment: 12 pages, presented at GWDAW13, to appear in CQ
Classification of chirp signals using hierarchical bayesian learning and MCMC methods
This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm
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