10,570 research outputs found
Detection of an anomalous cluster in a network
We consider the problem of detecting whether or not, in a given sensor
network, there is a cluster of sensors which exhibit an "unusual behavior."
Formally, suppose we are given a set of nodes and attach a random variable to
each node. We observe a realization of this process and want to decide between
the following two hypotheses: under the null, the variables are i.i.d. standard
normal; under the alternative, there is a cluster of variables that are i.i.d.
normal with positive mean and unit variance, while the rest are i.i.d. standard
normal. We also address surveillance settings where each sensor in the network
collects information over time. The resulting model is similar, now with a time
series attached to each node. We again observe the process over time and want
to decide between the null, where all the variables are i.i.d. standard normal,
and the alternative, where there is an emerging cluster of i.i.d. normal
variables with positive mean and unit variance. The growth models used to
represent the emerging cluster are quite general and, in particular, include
cellular automata used in modeling epidemics. In both settings, we consider
classes of clusters that are quite general, for which we obtain a lower bound
on their respective minimax detection rate and show that some form of scan
statistic, by far the most popular method in practice, achieves that same rate
to within a logarithmic factor. Our results are not limited to the normal
location model, but generalize to any one-parameter exponential family when the
anomalous clusters are large enough.Comment: Published in at http://dx.doi.org/10.1214/10-AOS839 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
Robot introspection, as opposed to anomaly detection typical in process
monitoring, helps a robot understand what it is doing at all times. A robot
should be able to identify its actions not only when failure or novelty occurs,
but also as it executes any number of sub-tasks. As robots continue their quest
of functioning in unstructured environments, it is imperative they understand
what is it that they are actually doing to render them more robust. This work
investigates the modeling ability of Bayesian nonparametric techniques on
Markov Switching Process to learn complex dynamics typical in robot contact
tasks. We study whether the Markov switching process, together with Bayesian
priors can outperform the modeling ability of its counterparts: an HMM with
Bayesian priors and without. The work was tested in a snap assembly task
characterized by high elastic forces. The task consists of an insertion subtask
with very complex dynamics. Our approach showed a stronger ability to
generalize and was able to better model the subtask with complex dynamics in a
computationally efficient way. The modeling technique is also used to learn a
growing library of robot skills, one that when integrated with low-level
control allows for robot online decision making.Comment: final version submitted to humanoids 201
A system for monitoring NO2 emissions from biomass burning by using GOME and ATSR-2 data
In this paper, we propose a system for monitoring abnormal NO2 emissions in troposphere by using remote-sensing sensors. In particular, the system aims at estimating the amount of NO2 resulting from biomass burning by exploiting the synergies between the GOME and the ATSR-2 sensors mounted on board of the ERS-2 satellite. Two different approaches to the estimation of NO2 are proposed: the former, which is the simplest one, assumes a linear relationship between the GOME and ATSR-2 measurements and the NO2 concentration. The latter exploits a nonlinear and nonparametric method based on a radial basis function (RBF) neural network. The architecture of such a network is defined in order to retrieve the values of NO2 concentration on the basis of the GOME and ATSR-2 measurements, as well as of other ancillary input parameters. Experimental results, obtained on a real data set, confirm the effectiveness of the proposed system, which represents a promising tool for operational applications
Nonparametric Edge Detection in Speckled Imagery
We address the issue of edge detection in Synthetic Aperture Radar imagery.
In particular, we propose nonparametric methods for edge detection, and
numerically compare them to an alternative method that has been recently
proposed in the literature. Our results show that some of the proposed methods
display superior results and are computationally simpler than the existing
method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
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Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile
Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide
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