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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Stefenon, S. F., Seman, L. O., Klaar, A. C. R., García Ovejero, R. & Leithardt, V. R. Q. (2024). Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM. Ain Shams Engineering Journal. 15 (6)
Exportar Referência (IEEE) 
S. S. Frizzo et al.,  "Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM", in Ain Shams Engineering Journal, vol. 15, no. 6, 2024
Exportar BibTeX 
@article{frizzo2024_1775394447666,
	author = "Stefenon, S. F. and Seman, L. O. and Klaar, A. C. R. and García Ovejero, R. and Leithardt, V. R. Q.",
	title = "Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM",
	journal = "Ain Shams Engineering Journal",
	year = "2024",
	volume = "15",
	number = "6",
	doi = "10.1016/j.asej.2024.102722",
	url = "https://www.sciencedirect.com/journal/ain-shams-engineering-journal"
}
Exportar RIS 
TY  - JOUR
TI  - Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM
T2  - Ain Shams Engineering Journal
VL  - 15
IS  - 6
AU  - Stefenon, S. F.
AU  - Seman, L. O.
AU  - Klaar, A. C. R.
AU  - García Ovejero, R.
AU  - Leithardt, V. R. Q.
PY  - 2024
SN  - 2090-4479
DO  - 10.1016/j.asej.2024.102722
UR  - https://www.sciencedirect.com/journal/ain-shams-engineering-journal
AB  - Ensuring the reliability of electrical distribution networks is a pressing concern, especially given the power outages due to surface contamination on insulating components. Surface contamination can elevate surface conductivity, thereby resulting in failures that can lead to power shutdowns. Addressing this challenge, this paper proposes an approach for real-time monitoring of electrical distribution grids to prevent such incidents. A hypertuned version of the you only look once (YOLO) model is tailored for this application. We refine the model's hyperparameters by integrating a genetic algorithm to maximize its detection performance. The EigenCAM technique enhances the visual interpretability of the model's outcomes, providing operators with actionable insights for maintenance and monitoring tasks. Benchmark tests reveal that the proposed Hypertuned-YOLO outperforms Detectron (Masked R-CNN), YOLOv5, and YOLOv7 models. The Hypertuned-YOLO achieves an F1-score of 0.867 and a mAP@0.5 of 0.922, validating its robustness and efficacy.
ER  -