http://scholars.ntou.edu.tw/handle/123456789/213 Mon, 17 Aug 2026 23:47:31 GMT 2026-08-17T23:47:31Z https://scholars.ntou.edu.tw:443/retrieve/95/運輸科學系.png http://scholars.ntou.edu.tw/handle/123456789/213 http://scholars.ntou.edu.tw/handle/123456789/26769 標題: EASIER: a blockchain-based artificial intelligence support system for small and medium enterprises 作者: Chou, Kuan Cheng; Lee, Chieh; Perdana, Shania Andea; Tu, Mengru 摘要: Small and medium enterprises (SMEs) are increasingly relying on digital technology to enhance production efficiency. For SMEs in the manufacturing industry, orders typically include the buyer's input information. As SMEs lack the capacity to enforce information consistency, input data are presented in buyer-specific input information, leading to duplicate records, data inconsistencies, and substantial manual intervention. To address this problem, this study adopts the Action Design Research (ADR) approach to design an artefact that embeds an artificial intelligence (AI)-based model in a blockchain, named Engineering-Aware and Standardisation-Integrated Entity Recognition (EASIER). Such an artefact is designed to automatically read and transform input data into the SME's own standardised format and generate data entries in their manufacturing system. We develop and evaluate the proposed artefact using a real SME's BOM dataset, and our results demonstrate that it achieves 99.77% in both accuracy and an F1-score on the final test set. For SMEs, the proposed method can significantly reduce manual intervention effort and improve input data consistency. This study contributes to both theory and practice by demonstrating how AI-enabled standardisation artefacts, supported by blockchain-based governance mechanisms, can be designed with SMEs to deliver practical, resource-efficient digital transformation solutions. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26769 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26702 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. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26702 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26682 標題: Building multi-objective multimodal AI system by bi-symmetrical weighted distance project management 作者: Yu, Shih-Chuan; Lin, Chih-Ping; Su, Wei-Hao; Kao, Sheng-Long; Yang, Ming-Feng; Chuang, Ya-Chen 摘要: This study proposes a Bi-symmetrical Weighted Distance (BWD) optimization framework for multimodal AI system development under uncertainty. By integrating fuzzy multi-objective linear programming with possibilistic programming, the approach simultaneously minimizes development costs, deployment time, and acceleration costs. The BWD method effectively handles imprecise parameters through distance-based defuzzification, enhancing decision transparency in intelligent hyperautomation contexts. An industrial case study validates the methodology, demonstrating practical capability to navigate trade-offs among time, cost, and resources where traditional methods struggle with uncertain parameter relationships. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26682 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26664 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. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26664 2026-01-01T00:00:00Z