인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Aids to navigation (ATON) are essential facilities installed to support the safe navigation of vessels, providing critical navigational information. The electrical systems of ATON require a stable power supply, and any voltage anomalies must be promptly detected to ensure operational reliability. This study proposes an AI-based algorithm to address anomaly detection issues using voltage data collected from actual ATON systems between January 2018 and September 2021.
In this study, unlabeled data without explicit fault labels were used, and anomalies were labeled using Isolation Forest, IQR-based labeling, and extreme value-based labeling methods. Based on these labeled datasets, the performance of anomaly detection was evaluated using AutoEncoder and Gaussian Mixture Model (GMM). Experimental results demonstrated that the AutoEncoder achieved an average F1 Score of over 83% with IQR and Isolation Forest-based labeling, showing superior performance. In contrast, GMM showed limited anomaly detection performance and struggled to effectively capture the complex patterns in the data.
These findings highlight the potential for enhancing maritime safety and contributing to anomaly detection and fault prevention in various marine and industrial facilities.
In this study, unlabeled data without explicit fault labels were used, and anomalies were labeled using Isolation Forest, IQR-based labeling, and extreme value-based labeling methods. Based on these labeled datasets, the performance of anomaly detection was evaluated using AutoEncoder and Gaussian Mixture Model (GMM). Experimental results demonstrated that the AutoEncoder achieved an average F1 Score of over 83% with IQR and Isolation Forest-based labeling, showing superior performance. In contrast, GMM showed limited anomaly detection performance and struggled to effectively capture the complex patterns in the data.
These findings highlight the potential for enhancing maritime safety and contributing to anomaly detection and fault prevention in various marine and industrial facilities.
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