http://scholars.ntou.edu.tw/handle/123456789/1 2026-08-17T23:47:20Z http://scholars.ntou.edu.tw/handle/123456789/26805 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 http://scholars.ntou.edu.tw/handle/123456789/26802 標題: Modelling the information sharing risks in circular supply chain: An artificial intelligence data-driven text mining hybrid method 作者: Tsai, Feng Ming; Kumpimpa, Tanawan; Chen, Chih-Cheng; Sethanan, Kanchana; Tseng, Ming-Lang 摘要: An artificial intelligence (AI) data-driven text mining hybrid method is proposed to extract risk attributes, and strategic roadmaps are developed using a fuzzy synthetic evaluation method integrated with the decision-making trial and evaluation laboratory. Information sharing is crucial for enhancing transparency and coordination in the circular supply chain (CSC); however, it is also associated with various risks. Many studies have increasingly emphasized information sharing but neglected the perspective of sociotechnical systems and the role of information asymmetry in the CSC system. This study conceptualizes risks within the information-sharing framework as sociotechnical misalignments among people, processes, technology, and organizational structures in the machinery manufacturing industry in Thailand. This study reveals that information behavior risks, digital competency risks, and organizational structure risks are primary determinants in managing the risks of business information sharing in the CSC. The findings indicate that addressing agility capability misalignment, business model opacity constraints, environmental impact regulatory pressure, and miscoordination constitutes a set of implementable practice for improving practical risk management. This study offers novel insights into the risks of information sharing (RISs) in the CSC by highlighting sociotechnical misalignments in risk emergence and information symmetry. 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26784 標題: Fin Stabilizer-Based Finite-Time H<sub>??/sub> Interval Type-2 Fuzzy Synthesis for Ship Rolling Motion With Bouc-Wen Hysteresis Under Imperfect Premise Matching 作者: Arumugam, Arunkumar; Chang, Wen-Jer; Lee, Yi-Chen; Lin, Yann-Horng; Li, Li; Aslam, Muhammad Shamrooz 摘要: When a ship operates at sea, it undergoes noticeable motions caused by waves and winds, with rolling being the most pronounced. Nowadays, fin stabilizers remain the most widely adopted devices for roll stabilization. However, they often exhibit hysteresis due to variations in hydrodynamic forces. To better address the control problem of ship rolling motion under nonlinearities, uncertainties, and hysteresis, this article proposes an Interval Type-2 (IT2) fuzzy controller design method that incorporates a novel Bouc-Wen (B-W) hysteresis estimator within the Takagi-Sugeno fuzzy model (TSFM) framework. The main contribution of this research lies in developing a B-W hysteresis state estimator within the IT2 TSFM framework to accurately capture the nonlinear hysteresis behavior. An IT2 fuzzy controller corresponding to the IT2 TSFM is developed using the imperfect premise matching method to improve the flexibility of controller design in practical applications. By combining with finite-time control theory, the IT2 fuzzy controller guarantees finite-time boundedness (FTB) and achieves performance index under the finite-time H-infinity criterion. According to Lyapunov theory, a new set of sufficient conditions is formulated using LMIs to guarantee the FTB for the ship rolling system. Finally, the simulation results of the nonlinear ship rolling system using the fin stabilizers are presented to verify the performance of the proposed IT2 fuzzy control approach. 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26777 標題: Low-carbon transition cold chain logistics model: a sustainable strategic roadmap for biomedicine practices 作者: Bui, Tat-Dat; Le, Nguyen Bao; Lim, Ming K.; Sethanan, Kanchana; Tseng, Ming-Lang 摘要: This study focuses on how a low-carbon transition reshapes the development of sustainable cold chain logistics. Unlike conventional supply chains, cold chain logistics consumes high amounts of energy and generate substantial carbon emissions because of refrigeration demands, while fragmented operations, inefficient infrastructure, poor collaboration, and limited research hinder the development of sustainable low-carbon strategies. This study aims to construct a causal hierarchical roadmap for low-carbon transformation in sustainable cold chain logistics through a multilevel perspective on sociotechnical transitions that integrates data-driven techniques. The results reveal that regulatory and carbon control mechanisms, digital infrastructure and intelligent logistics systems, and sustainable technologies and energy-efficient innovations constitute the causal aspects influencing operational cold chain logistics. This study provides a theoretically grounded and empirically validated pathway for fostering low-carbon, sustainable cold chain logistics and offers practical insights for policymakers and logistics practitioners seeking to accelerate innovation within temperature-sensitive supply systems. 2026-01-01T00:00:00Z