Mokpo National Maritime University

The criticality of collision-avoidance technology for ensuring safe navigation of autonomous ships necessitates diverse testing scenarios that reflect complex maritime environments. However, previous testing scenarios, often based on virtual trajectories or simplified encounters, have shown limitations in adequately representing real-world conditions. This study proposes a novel framework for developing collision-avoidance testing scenarios based on actual collision cases. The framework consists of three stages: collision case collection, trajectory extraction, and scenario development. Relevant data were extracted from selected cases, and the trajectories of ships influencing the collision were combined to reconstruct the circumstances at the time of the incident. Encounter situations were then diversified by altering the roles and positions of own and target ships, and finally systematically categorised into a structured testing set. Unlike previous testing scenarios, the developed scenarios exhibit distinctive characteristics derived from actual collision cases, including situations where navigation rules cannot be strictly applied, dynamic encounters, speed variations, and environmental conditions. By reflecting real maritime environments, these scenarios provide a solid basis for validating and improving collision-avoidance algorithms. The proposed framework is expected to contribute not only to the advancement of autonomous-ship technology but also to the enhancement of maritime safety.

Anchorage areas are essential for safe and efficient maritime operations. However, conventional forecasting models often underperform in dynamic port conditions, as they rely heavily on historical averages and static assumptions. To address these limitations, this study proposes a forecasting framework for anchorage occupancy. This framework uses stacked ensemble learning, integrating both statistical and machine learning models to enhance predictive accuracy and operational reliability. The proposed approach was applied to occupancy data from the E1 anchorage at Ulsan Port, with performance evaluated across various forecasting models and ensemble strategies. In addition, a hexagon-based occupancy estimation method was implemented to assess spatial efficiency and safety in comparison to the traditional anchor circle method. The results demonstrate that the stacking ensemble model effectively captures complex, nonlinear patterns in vessel traffic and delivers improved forecasting performance. These findings highlight the practical potential of stacking ensemble techniques and spatial modeling innovations in enabling proactive anchorage management, reducing congestion, and enhancing maritime safety in real-world port environments.

This letter investigates the relationship between window penetration loss (WinPL) and building entry loss (BEL) over the 3-40 GHz band using twelve traditional and thermally-efficient window types. WinPL was derived from measurement, simulation, and analytical modeling, showing consistent frequency-dependent trends. In traditional buildings, WinPL showed negligible correlation with BEL (r = 0.06), whereas a weak but significant correlation (r = 0.20, p < 0.001) was found in thermally-efficient buildings. The results suggest BEL models should consider window characteristics for modern buildings to improve prediction accuracy in high-frequency indoor scenarios.

Fiber-reinforced polymers (FRPs), which are widely used in ship structures, have numerous advantages. However, environmental concerns exist regarding the raw materials' characteristics and challenges associated with disposal. Natural fibers such as hemp and flax are promising alternatives already applied in the automotive industry; nonetheless, their application to marine structures remains limited. Fiber content (Gc) measurements are crucial to satisfy structural design regulations, for which several methods exist. Among others, the burn-off method recommended by ISO 12215-5 and classification societies can accurately measure the Gc and internal defects of fiber composites. However, natural fibers have low ignition points, which renders the conventional burn-off method unsuitable. This study analyzed existing measurement techniques designed to assess Gc and internal defects considering the unique characteristics of cellulose-based natural FRPs (NFRPs). A modified burnoff method for NFRPs, which incorporates a pre-heating stage and adjusted combustion temperatures, is proposed to address the high moisture absorption and ignition point that are lower than those of resins inherent to natural fibers, overcoming the limitations of standard burn-off procedures. Experimental hemp and flax fiber composites were fabricated and their Gc was measured using various conventional methods and two proposed techniques: pre-heating followed by hydrometer testing and the modified burn-off method. Accordingly, the hemp specimens exhibited an average moisture absorption of approximately 2.623 +/- 0.976 wt%, whereas that of the flax specimens was approximately 1.877 +/- 1.115 wt%, and both were found to contain higher levels of internal defects compared to conventional glass-fiber-reinforced polymers. Overall, the modified burn-off method enables accurate quantitative evaluation of both Gc and internal defects in NFRPs.

This study proposes an integrated framework to evaluate the competitiveness of ten container terminals in Haiphong Port by combining both traditional and modern performance factors. Using the Entropy-TOPSIS method, the study objectively ranks terminal competitiveness based on criteria such as throughput, infrastructure, digitalization, and sustainability. Fuzzy C-Means clustering is employed to classify terminals into strategic groups, offering tailored policy implications for each group. Results show that terminals such as HITC, Nam Dinh Vu, and Tan Vu lead in both traditional and modernization scores, while others lag behind due to limitations in infrastructure and technology adoption. The model demonstrates that integrating smart and green metrics into port performance assessment offers a more comprehensive understanding of competitiveness. This approach provides a data-driven tool for port authorities and policymakers to prioritize investments and plan differentiated development strategies, especially in emerging economies facing digital transformation and environmental pressures.