preprints.org > engineering > transportation science and technology > doi: 10.20944/preprints202404.0153.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 April 2024 / Approved: 1 April 2024 / Online: 2 April 2024 (16:16:15 CEST)

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASS) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks like real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASS using bibliographic analysis to inform policy and future research directions in this evolving field. We analyze 3,363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. Findings reveal a rapidly growing field dominated by image recognition research. Keywords like "datum," "image," and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "Image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASS and informing future policy and research agendas for the integration of electric propulsion systems in the maritime industry.

Maritime Autonomous Surface Ships (MASS), Research trends, Text Mining, Latent Dirichlet allocation (LDA) topic modeling, Shipboard Electronics, Shipping industry

Engineering, Transportation Science and Technology

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