48,692 research outputs found

    Intelligent monitoring of the health and performance of distribution automation

    Get PDF
    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

    Full text link
    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

    Get PDF
    We report the first analytical expression purely constructed by a machine to determine photometric redshifts (zphotz_{\rm phot}) of galaxies. A simple and reliable functional form is derived using 41,21441,214 galaxies from the Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method automatically dropped the uu and zz bands, relying only on gg, rr and ii for the final solution. Applying this expression to other 1,417,1811,417,181 SDSS-DR10 galaxies, with measured spectroscopic redshifts (zspecz_{\rm spec}), we achieved a mean (zphotzspec)/(1+zspec)0.0086\langle (z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})\rangle\lesssim 0.0086 and a scatter σ(zphotzspec)/(1+zspec)0.045\sigma_{(z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})}\lesssim 0.045 when averaged up to z1.0z \lesssim 1.0. 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
    corecore