48,692 research outputs found
Intelligent monitoring of the health and performance of distribution automation
With a move to 'smarter' distribution networks through an increase in distribution automation and active network management, the volume of monitoring data available to engineers also increases. It can be onerous to interpret such data to produce meaningful information about the health and performance of automation and control equipment. Moreover, indicators of incipient failure may have to be tracked over several hours or days. This paper discusses some of the data analysis challenges inherent in assessing the health and performance of distribution automation based on available monitoring data. A rule-based expert system approach is proposed to provide decision support for engineers regarding the condition of these components. Implementation of such a system using a complex event processing system shell, to remove the manual task of tracking alarms over a number of days, is discussed
Multi-Sensor Event Detection using Shape Histograms
Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterized by similar waveform patterns re-appearing within one or more
sensors. Further such patterns can be of variable duration. In this work, we
propose a method for detecting such events in time-series data using a novel
feature descriptor motivated by similar ideas in image processing. We define
the shape histogram: a constant dimension descriptor that nevertheless captures
patterns of variable duration. We demonstrate the efficacy of using shape
histograms as features to detect events in an SVM-based, multi-sensor,
supervised learning scenario, i.e., multiple time-series are used to detect an
event. We present results on real-life vehicular sensor data and show that our
technique performs better than available pattern detection implementations on
our data, and that it can also be used to combine features from multiple
sensors resulting in better accuracy than using any single sensor. Since
previous work on pattern detection in time-series has been in the single series
context, we also present results using our technique on multiple standard
time-series datasets and show that it is the most versatile in terms of how it
ranks compared to other published results
The first analytical expression to estimate photometric redshifts suggested by a machine
We report the first analytical expression purely constructed by a machine to
determine photometric redshifts () of galaxies. A simple and
reliable functional form is derived using galaxies from the Sloan
Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method
automatically dropped the and bands, relying only on , and
for the final solution. Applying this expression to other SDSS-DR10
galaxies, with measured spectroscopic redshifts (), we achieved a
mean and a scatter when averaged up to . The method was
also applied to the PHAT0 dataset, confirming the competitiveness of our
results when faced with other methods from the literature. This is the first
use of symbolic regression in cosmology, representing a leap forward in
astronomy-data-mining connection.Comment: 6 pages, 4 figures. Accepted for publication in MNRAS Letter
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