Journal of Marine Science and Technology-Taiwan

Current Articles

    • Research Article1 January 2026

      Port and Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach

        Port and vessel networks increasingly operate on IP/Ethernet backbones with high‑noise, high‑dimensional traffic. We present a lightweight hybrid intrusion‑detection model that couples a variational autoencoder (VAE) with a multilayer perceptron (MLP) and augments training with a boundary‑oriented latent‑space mixup strategy. The VAE models the distribution of normal traffic and identifies anomalies through reconstruction errors. Subsequently, it generates robust latent vectors, enabling the MLP to perform highly accurate supervised classification. On the UNSW‑NB15 dataset, the proposed pipeline attains ≥97% accuracy and an outstanding recall of 99.56% in binary intrusion detection, and visualization of the latent space (PCA) together with reconstruction‑error analyses and the confusion matrix corroborates clear anomaly separation. The model uses a compact design. The VAE produces latent representations, and an MLP classifies them. With latent-space mixup, it detects new anomalies and reliably classifies known threats, making it suitable for smart-port monitoring and ship-communication security at the edge.
    • Research Article1 January 2026

      SAR Ship Detection Based on Shallow Feature Guidance

      Maritime ship detection is of great significance for both military security and civilian applications. Synthetic Aperture Radar (SAR), with its all-weather and all-day imaging capability, plays a vital role in maritime surveillance. Nevertheless, SAR ship targets typically appear small in scale, embedded in complex backgrounds, blurred at boundaries, and easily confused with near-shore features, which pose substantial challenges for accurate detection. To address these issues, we propose a SAR ship detection network that integrates dual enhancements of small-object representation and edge information. The network introduces two key components: the Small Target Refine Pyramid (STRP) to strengthen shallow feature representation for small targets, and the Edge Information Enhancement and Propagation Mechanism (EIEP) to refine and transmit boundary cues. Additionally, a customized bounding box regression loss, Focaler-MPDIoU, is designed to improve localization accuracy and robustness to complex object morphologies. Experiments on benchmark SAR ship detection datasets (HRSID, SSDD, and LS-SSDD) demonstrate that the proposed method achieves superior performance compared with both classical and state-of-the-art detection models.
    • Research Article1 January 2026

      Enhancing Shipboard Safety Management under the ISM Code: An Innovative Risk Assessment Framework with a Stern Tube Case Study

      The shipboard safety management system (SMS) is designed to enhance safe operations, risk management, and emergency response to improve overall ship safety and efficiency. This paper demonstrates the use of an engine room simulator (ERS) for collecting failure modes and applies it to a comprehensive failure analysis of the stern tube lubricating oil system. A new risk closeness coefficient method was developed, integrating expert background knowledge and weighted risk assessments. The analysis, based on multiple expert evaluations, covered five subsystems, eight main components, 23 failure modes, and 112 failure causes. This study presents 26 recommendations for maritime practitioners and onboard operators to prevent and mitigate risks. A comparative analysis highlights the differences between the AHP-EMM and traditional FMEA methods.

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