http://scholars.ntou.edu.tw/handle/123456789/209 2026-08-10T19:44:43Z 2026-08-10T19:44:43Z Jwo, Dah-Jing Abdi, Abdirisak Daud Chang, Yi http://scholars.ntou.edu.tw/handle/123456789/26805 2026-08-10T03:12:21Z 2026-01-01T00:00:00Z

標題: Robust Nonlinear GNSS Navigation Under Heavy-Tailed Measurement Noise Using a Cauchy-Kernel Correntropy Extended Kalman Filter 作者: Jwo, Dah-Jing; Abdi, Abdirisak Daud; Chang, Yi 摘要: In urban canyon environments, the performance of global navigation satellite systems (GNSS) is severely degraded by multipath propagation, signal occlusion, and non-Gaussian measurement noise. These effects reduce positioning accuracy and service reliability. Kalman filtering and its nonlinear extensions are widely used for GNSS positioning. However, these filters rely on Gaussian noise assumptions and are formulated using the minimum mean square error (MMSE) criterion. Consequently, their performance degrades in non-Gaussian environments. Recently, filtering methods based on the maximum correntropy criterion (MCC) have been investigated as robust alternatives to MMSE-based approaches. The performance of MCC filters is strongly influenced by the choice of the kernel function. In particular, Gaussian kernel-based MCC algorithms may suffer from numerical instability under large measurement noise and strong sensitivity to kernel bandwidth selection. These limitations compromise estimation robustness and convergence stability. To address these limitations, this study proposes a Cauchy kernel-based maximum correntropy extended Kalman filter (CKMCEKF). The effectiveness of the proposed filter was validated using both simulation and real GNSS datasets. The results confirm the superior accuracy, stability, and reduced sensitivity to kernel bandwidth selection of the proposed method.

2026-01-01T00:00:00Z Lee, Kimberly Hsiu-Chin Guo, Jiunn-Liang Chang, Wen-Jer http://scholars.ntou.edu.tw/handle/123456789/26723 2026-08-10T03:11:57Z 2026-01-01T00:00:00Z

標題: Shattering the Seafaring Stereotype: An Ethnographic Exploration of Gender Equality on Taiwan's Domestic Oil Tanker Fleet 作者: Lee, Kimberly Hsiu-Chin; Guo, Jiunn-Liang; Chang, Wen-Jer 摘要: Despite the global hegemonic masculinity of the maritime sector, this ethnographic study investigates a 'critical anomaly' within a Taiwanese product oil tanker fleet where women occupy a majority of key navigational roles, including Master. Grounded in Feminist Standpoint Theory and the Hegelian dialectical method, the research utilises longitudinal field observations across two separate voyages (totaling 15 days) and is enriched by five in-depth interviews and one focus group with female deck officers ranging from Cadets to Masters. By centering the lived experiences of these women, the study examines the structural and behavioural factors enabling this unique gendered configuration. Findings reveal that this high female representation is the 'synthesis' of two primary constraints: uncompetitive low wages and restrictive domestic labour regulations. These factors inadvertently created a structural labour vacuum - a rupture in the masculine regime - that allowed women to enter and excel. This opportunity is sustained by the women's 'compensatory professional labor,' where exceptional competence and commitment act as a recursive validation process to neutralise gendered skepticism. The study concludes that successful penetration into masculine domains requires the alignment of sustained structural vacancies with demonstrated excellence, suggesting that industrial obstacles can be dialectically converted into strategic gateways for career advancement.

2026-01-01T00:00:00Z Chang, Tsai-Hsin Kao, Sheng-Long Hu, Kuo-Jui http://scholars.ntou.edu.tw/handle/123456789/26702 2026-08-10T03:11:52Z 2026-01-01T00:00:00Z

標題: Enterprise system algorithm development for intelligent maritime safety systems using Fuzzy Chaotic C-Means logistic mapping 作者: Chang, Tsai-Hsin; Kao, Sheng-Long; Hu, Kuo-Jui 摘要: Port authorities require interpretable methods to identify sea areas with elevated abnormal ship behaviour risk in intelligent maritime safety systems. This study develops an enterprise system algorithm that integrates fuzzy inference, Fuzzy C-Means, chaos-based logistic mapping, and Marine Geographic Information System visualisation using Automatic Identification System data. The framework constructs grid-level indicators from Course Over Ground, Speed Over Ground, Rate Of Turn, heading, Cross-Track Distance, and drift angle. It produces the Manoeuvring Dynamics Index and Trajectory Deviation Index, then transforms grid scores into chaos-prone risk patterns. The results support hotspot identification, patrol planning, and traffic management.

2026-01-01T00:00:00Z Chang, Tsai-Hsin Kao, Sheng-Long http://scholars.ntou.edu.tw/handle/123456789/26664 2026-08-10T03:11:43Z 2026-01-01T00:00:00Z

標題: Chaos-based two-stage framework for abnormal ship behavior identification using AIS data 作者: Chang, Tsai-Hsin; Kao, Sheng-Long 摘要: Abnormal ship navigation behavior is a critical precursor to maritime incidents. Conventional detectors based on rules often fail to characterize the nonlinear and irregular dynamics that arise during maneuvering. This study proposes a ship behavior analytics framework with two stages informed by chaos theory that integrates multivariate chaos screening with Cross-Track Distance (XTD) control monitoring for robust and operationally interpretable abnormality assessment from Automatic Identification System (AIS) data. AIS time series are resampled to a uniform time step, and missing observations are handled using segmentation controlled by data gaps to preserve consistent sliding window evaluation while mitigating discontinuity artifacts. The first stage performs sliding window chaos screening for Speed Over Ground (SOG), Course Over Ground (COG), and Drift Angle (DRIFT) using the 0-1 test, Largest Lyapunov Exponent (LLE), and delay coordinate phase space reconstruction. Using multiple indicators improves robustness to noise and parameter sensitivity and provides complementary evidence of dynamical instability. The second stage analyzes XTD dynamics to reveal incipient control instability even when lateral deviation remains moderate. Derivative and statistical descriptors computed within each window quantify intensified lateral corrections and elevated activity at short horizon frequencies. Experimental results show that combining multivariate chaos metrics with control descriptors derived from XTD improves sensitivity to subtle yet operationally meaningful instabilities and supports interpretable abnormal ship behavior identification. The proposed framework provides an extensible basis for navigational safety monitoring and maritime surveillance and can support maritime traffic management through targeted screening and diagnostics.

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