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11 pages, 4861 KB  
Article
Unsupervised Ethograms of a Vulnerable Bird Species: The Red-Footed Falcon in Northern Italy
by Alessandro Ferrarini and Marco Gustin
Ecologies 2022, 3(4), 435-445; https://doi.org/10.3390/ecologies3040031 - 23 Sep 2022
Cited by 1 | Viewed by 2467
Abstract
Behavioral and movement ecology have quickly advanced as a result of the development of biotelemetry devices and analytical techniques. Global positioning system (GPS) transmitters assist scientists in gathering location and movement data at detailed spatial and temporal resolutions. Machine-learning methods can then be [...] Read more.
Behavioral and movement ecology have quickly advanced as a result of the development of biotelemetry devices and analytical techniques. Global positioning system (GPS) transmitters assist scientists in gathering location and movement data at detailed spatial and temporal resolutions. Machine-learning methods can then be applied to GPS data to provide insights into the ecological mechanisms of animal behavior and movements. By means of accurate GPS data-loggers, in 2019, 2020, and 2021, we tracked 8 red-footed falcons at the two largest colonies in Italy. We collected 13,484 GPS points and used recently introduced machine-learning methodology Unsupervised Animal Behaviour Examiner (UABE) to deduce the regular, nested, and hourly ethograms of the tracked individuals. We found clear and significant patterns of the red-footed falcons’ behaviors on monthly, daily, and hourly bases. Our study is a step forward in advancing the knowledge of this threatened species, and provides a baseline assessment of the current behavioral patterns of this red-footed falcon population, with which results of future studies can be compared to detect potential behavioral changes that act as early warnings of increased human disturbance. Full article
(This article belongs to the Special Issue New Methods and Viewpoints in Avian Ecology and Conservation)
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13 pages, 4732 KB  
Article
Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances
by Jose R. Razo-Hernandez, Martin Valtierra-Rodriguez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Jose F. Gomez-Aguilar and Jose de J. Rangel-Magdaleno
Electronics 2018, 7(12), 433; https://doi.org/10.3390/electronics7120433 - 12 Dec 2018
Cited by 9 | Viewed by 3637
Abstract
Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new [...] Read more.
Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new method to detect and classify PQDs is proposed. The proposal takes advantage of the low computational resources of both a phasor measurement unit (PMU)-based signal processing scheme and the homogeneity approach. To classify the PQDs, if–then–else rules are used. To validate and test the proposal, synthetic and real signals of sags, swells, interruptions, notching, spikes, harmonics, and oscillatory transients are considered. For the generation of real signals, a PQD generator based on a power inverter is used. In the proposed method, the PMU information is directly used to classify sags, swells, and interruptions, whereas the homogeneity index is used to distinguish among the remaining PQDs. Results show that the proposal is an effective and suitable tool for PQ monitoring. Full article
(This article belongs to the Special Issue Power Quality in Smart Grids)
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