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Full-Text Articles in Ocean Engineering
Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu
Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu
Journal of Marine Science and Technology–Taiwan
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 …
Deep Learning-Based Model For Automated Prediction Of Coastal Changes: A Robust Approach To Environmental Forecasting, Tsair-Fwu Lee, Chu-Ho Chang, Chin-Shiuh Shieh, Chih-Hsien Wu, Jen-Chung Shao, Chien-Liang Chiu
Deep Learning-Based Model For Automated Prediction Of Coastal Changes: A Robust Approach To Environmental Forecasting, Tsair-Fwu Lee, Chu-Ho Chang, Chin-Shiuh Shieh, Chih-Hsien Wu, Jen-Chung Shao, Chien-Liang Chiu
Journal of Marine Science and Technology–Taiwan
The coastline stands as a critical domain encompassing industry and the environment. The escalating global warming, leading to elevated sea levels and intensified wave-current interactions, has given rise to substantial coastal erosion. This predicament, in conjunction with excessive human development and Taiwan's coastal areas' extreme climatic impact, accentuates the perceptibility of coastal beach alterations. As a result, coastal erosion has emerged as a pressing issue necessitating resolution. Traditional methodologies for assessing coastline changes have conventionally relied on manual measurements. However, owing to the fluctuating distance of coastlines, influenced by tidal patterns, extended measurement processes over several months are susceptible to …
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Doctoral Dissertations and Master's Theses
Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the …
Artificial Intelligence In Prediction Of The Remaining Useful Life Of Wind Turbine Shaft Bearings, Jinsiang Shaw, B.J. Wu
Artificial Intelligence In Prediction Of The Remaining Useful Life Of Wind Turbine Shaft Bearings, Jinsiang Shaw, B.J. Wu
Journal of Marine Science and Technology–Taiwan
Long-term periodic rotation and unstable load changes in wind turbines can cause unexpected damage to high-speed shaft bearings (HSSBs). In this study, after preprocessing of the HSSB vibration signal, four different models for predicting bearing degradation in terms of remaining useful life (RUL) in days were investigated: support vector regression (SVR), convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM. The experimental results revealed that the CNN achieved the best mean absolute error (MAE), at 0.44 days, based on frequency response plot using the fast Fourier transform (FFT), while that of the CNN-LSTM model predicted using the amplitude profile …