Kim, J.; Han, S.; Lee, H.; Koo, B.; Nam, M.; Jang, K.; Lee, J.; Chung, M. Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis. Electronics2024, 13, 1902.
Kim, J.; Han, S.; Lee, H.; Koo, B.; Nam, M.; Jang, K.; Lee, J.; Chung, M. Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis. Electronics 2024, 13, 1902.
Kim, J.; Han, S.; Lee, H.; Koo, B.; Nam, M.; Jang, K.; Lee, J.; Chung, M. Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis. Electronics2024, 13, 1902.
Kim, J.; Han, S.; Lee, H.; Koo, B.; Nam, M.; Jang, K.; Lee, J.; Chung, M. Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis. Electronics 2024, 13, 1902.
Abstract
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.
Keywords
Maritime Autonomous Surface Ships (MASS), Research trends, Text Mining, Latent Dirichlet allocation (LDA) topic modeling, Shipboard Electronics, Shipping industry
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.