http://scholars.ntou.edu.tw/handle/123456789/192 2026-04-24T19:05:25Z 2026-04-24T19:05:25Z 方志中 何達立 http://scholars.ntou.edu.tw/handle/123456789/26553 2026-03-17T01:40:38Z 2025-01-01T00:00:00Z

標題: 小水線面雙體離岸風電人員運輸船耐海性能評估 作者: 方志中; 何達立

2025-01-01T00:00:00Z Liao, Hung-Rui Yu, Hsing-Cheng Li, Szu-Ju http://scholars.ntou.edu.tw/handle/123456789/26520 2026-03-12T03:37:04Z 2025-01-01T00:00:00Z

標題: A Deep Learning-Based Plantar Pressure Measurement System for Accurate Foot Arch Index Estimation 作者: Liao, Hung-Rui; Yu, Hsing-Cheng; Li, Szu-Ju 摘要: The medial longitudinal arch is fundamental to weight distribution, balance, and lower limb biomechanics, and its evaluation is important for identifying abnormalities such as flatfoot or high arch. Traditional clinical methods for assessing the foot arch index (FAI) are often constrained by limited accessibility and inconsistent accuracy. To overcome these limitations, this study proposes a deep learning-based plantar pressure measurement system (DLPPMS) designed for accurate and affordable static foot arch evaluation. The system integrates two resistive pressure sensor arrays combined into a 24 x 24 matrix to acquire plantar pressure data in real time. To enhance spatial resolution and improve the fidelity of pressure distribution, Bessel interpolation is employed to generate smooth, high-resolution plantar pressure maps. Deep learning-based pose estimation and instance segmentation models are further applied to isolate the plantar region and identify anatomical keypoints relevant for FAI computation. The system was validated on participants with flatfoot, normal arch, and high arch conditions, demonstrating high segmentation accuracy, reliable keypoint localization, and consistent FAI estimation with minimal error compared to reference values. These results confirm that the DLPPMS provides accurate, repeatable, and low-cost assessment of the medial longitudinal arch under static conditions. Overall, this work highlights the potential of combining pressure sensing, interpolation algorithms, and deep learning into a portable and scalable system, offering promising applications not only for clinical diagnostics but also for biomechanical research, preventive healthcare, and rehabilitation monitoring.

2025-01-01T00:00:00Z Li, Kuo-Hao Wang, Chao-Nan Tang, Yao-Chi http://scholars.ntou.edu.tw/handle/123456789/26484 2026-03-12T03:36:54Z 2025-01-01T00:00:00Z

標題: Leveraging graph attention networks for enhanced latent defect detection in precision built-in spindle assembly lines 作者: Li, Kuo-Hao; Wang, Chao-Nan; Tang, Yao-Chi 摘要: This study introduces a novel approach employing Graph Attention Networks (GAT) to detect pre-installation defects in built-in spindles. Traditional quality control relies heavily on manual inspections and basic mechanical testing, which often miss subtle defects. Using vibration datasets from 13 spindles with identical specifications, this research applies a GAT-based diagnostic method, transforming vibration signals into graph representations. GAT's attention mechanism effectively extracts essential node and structural features, enabling early and accurate defect identification. Experimental results demonstrate that the proposed GAT model significantly outperforms conventional techniques such as k-nearest neighbors (KNN), Support Vector Machines (SVM), and Graph Convolutional Networks (GCN). By adding noise to simulate harsh operational conditions, the GAT model maintained superior clustering and classification performance, achieving 100% accuracy under noiseless conditions and exhibiting exceptional robustness even at a challenging -5 dB signal-to-noise ratio (SNR). Additionally, GAT effectively handles imbalanced datasets and displays strong generalization capabilities, underscoring its practical industrial potential. This research marks a notable advancement in spindle manufacturing quality control, highlighting promising future directions for deep learning in industrial diagnostics.

2025-01-01T00:00:00Z Tsai, Kun-Yuan Fang, Chih-Chung http://scholars.ntou.edu.tw/handle/123456789/26387 2026-03-12T03:36:25Z 2025-01-01T00:00:00Z

標題: Analysis of real-time ship manoeuvring simulation with a ship collision avoidance E-navigation aid system 作者: Tsai, Kun-Yuan; Fang, Chih-Chung 摘要: This study aims to develop a smart navigation database for ship manoeuvring based on real-time simulations in calm water. In the ship manoeuvring simulation, the entry of a large container ship into the Kaohsiung and Keelung harbours is executed by different marine mates by using the PC version of the real-time ship simulator. First, multiple real-time simulations are conducted for each harbour, and qualitative analyses are performed based on relevant statistics. Furthermore, the ship collision avoidance E-navigation-aid system developed by the authors is used to assist the five marine mates in entering the Kaohsiung and Keelung harbours, and the differences between the scenario in which the mates are assisted by the system and that in which they perform manual operation are discussed. Finally, based on simulation statistics, comparisons of voyage time, voyage distance, and rudder operation with and without the ship collision avoidance E-navigation-aid system are presented and discussed.

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