해양 통신 시스템에서의 결측 데이터 문제와 진보된 데이터 대체 방법

Challenges of Missing Data in Maritime Communication Systems and Advanced Data Imputation Methods

  • 신하 쉬르티카 (국민대학교 금융정보보안학과) ;
  • 염선호 (국민대학교 금융정보보안학과) ;
  • 박수현 (국민대학교 컴퓨터공학과)
  • Shrutika Sinha (Dept. of Financial Information Security, Kookmin University) ;
  • Sun-Ho Yum (Dept. of Financial Information Security, Kookmin University) ;
  • Soo-Hyun Park (School of Computer Science, Kookmin University)
  • 발행 : 2024.10.31

초록

A robust and efficient communication network is crucial for ensuring the smooth operation, efficiency, and utmost safety of various maritime activities. Throughout the vast expanse of history, the highly significant maritime industry has heavily relied upon an extensive range of diverse communication methods. Accurate network performance is critical in maritime environments, where data loss due to signal interruptions, equipment failures, and other domain-specific factors frequently occur. This paper evaluates traditional and advanced data imputation techniques to assess their impact on the predictive accuracy of machine learning models used for network switching decisions in maritime settings. Results show that advanced deep learning techniques, like Autoencoder-based imputation can improve performance over traditional methods.

키워드

과제정보

이 논문은 2024 년도 해양경찰청 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구임(RS-2021-KS211488, 해양사고 신속대응 군집수색 자율수중로봇시스템 개발)

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