http://scholars.ntou.edu.tw/handle/123456789/215 2026-04-28T11:43:29Z 2026-04-28T11:43:29Z Xu, Wen-Qi Wang, Sheng-Ping Lin, Chih-Yu Chiang, Wei-Chuan Kitakado, Toshihide http://scholars.ntou.edu.tw/handle/123456789/26534 2026-03-12T03:49:04Z 2026-01-01T00:00:00Z

標題: Spatio-temporal dynamics for dolphinfish (Coryphaena hippurus) in the waters around Taiwan: Implications for stock assessment and fisheries management 作者: Xu, Wen-Qi; Wang, Sheng-Ping; Lin, Chih-Yu; Chiang, Wei-Chuan; Kitakado, Toshihide 摘要: Understanding the spatio-temporal dynamics of dolphinfish (Coryphaena hippurus) is essential for effective stock assessment and fisheries management. Owing to its migratory nature, rapid growth, and short lifespan, the species requires management approaches that account for spatial and temporal variability. In this study, two spatio-temporal modelling frameworks, VAST and sdmTMB, were applied to longline fishery logbook data collected in the waters around Taiwan from 2011 to 2023. Standardised indices of relative abundance were estimated using an area-weighted approach that accounted for both fished and unfished regions. Cluster analysis was conducted to classify fishing strategies based on species composition, revealing a dominant dolphinfishtargeted cluster concentrated in the eastern offshore region. Both spatio-temporal models detected consistent seasonal patterns, with peak abundance in the second quarter and a decline in the third, aligning with known migratory behaviour and oceanographic influences. Spatial hotspots were identified primarily in the northeastern waters influenced by the Kuroshio Current. While general agreement was observed between the models, sdmTMB produced higher spatial contrast and slightly greater abundance estimates in the early years. Differences in model structure, spatial resolution, and data coverage were found to influence predictive outcomes. Limitations regarding logbook data quality, spatial bias, and the absence of environmental covariates were acknowledged. Nevertheless, the results demonstrate the utility of spatio-temporal modelling in deriving robust abundance indices and identifying biologically and operationally significant fishing grounds that represent key habitats and high-effort zones for dolphinfish fisheries in the northwestern Pacific. These findings provide a strong basis for integrating spatial dynamics into stock assessments and support the development of adaptive, spatially explicit management strategies in the western Pacific.

2026-01-01T00:00:00Z Wei, Ming-Hao Chao, Chih-Min Lin, Chih-Yu Yeh, Chun-Chao http://scholars.ntou.edu.tw/handle/123456789/26477 2026-03-12T03:36:52Z 2025-01-01T00:00:00Z

標題: Fake Path Co-Construction Source Location Privacy Protection Scheme Design for UWSNs 作者: Wei, Ming-Hao; Chao, Chih-Min; Lin, Chih-Yu; Yeh, Chun-Chao 摘要: The openness of underwater wireless sensor networks (UWSNs) exposes them to potential eavesdropping attacks, enabling attackers to trace back and identify the source nodes of packet flows. This poses a significant threat to the confidentiality of sensitive applications, known as the Source Location Privacy (SLP) problem. Conventional packet encryption methods are ineffective in defending against SLP attacks since attackers do not need to know the content of the packets. A commonly used method to address the SLP problem is to establish fake transmission paths, making attackers follow fake paths and thus extending the time required to trace back to the source node. Existing SLP solutions that use fake transmission paths only consider individual source nodes, where the fake paths constructed for different source nodes are independent and cannot cooperate to resist attacks. In this paper, a Fake Path Co-Construction source location privacy protection protocol (FPCC) suitable for UWSNs is proposed. FPCC combines the existing transmission paths and creates co-constructed fake paths to simultaneously protect two source nodes. Simulation results confirm that FPCC, when compared with existing well-performed SLP protection protocols, extends safety time without increasing the number of nodes involved in transmitting fake packets.

2025-01-01T00:00:00Z Su, Yu-Sheng Wang, Yi-Wen Wu, Yun-Chin Xiao, Zheng-Yun Ding, Ting-Jou http://scholars.ntou.edu.tw/handle/123456789/26478 2026-03-12T03:36:52Z 2025-01-01T00:00:00Z

標題: A Deep Learning-Based Cloud Groundwater Level Prediction System 作者: Su, Yu-Sheng; Wang, Yi-Wen; Wu, Yun-Chin; Xiao, Zheng-Yun; Ding, Ting-Jou 摘要: In the context of global change, understanding changes in water resources requires close monitoring of groundwater levels. A mismatch between water supply and demand could lead to severe consequences such as land subsidence. To ensure a sustainable water supply and to minimize the environmental effects of land subsidence, groundwater must be effectively monitored and managed. Despite significant global progress in groundwater management, the swift advancements in technology and artificial intelligence (AI) have spurred extensive studies aimed at enhancing the accuracy of groundwater predictions. This study proposes an AI-based method that combines deep learning with a cloud-supported data processing workflow. The method utilizes river level data from the Zhuoshui River alluvial fan area in Taiwan to forecast groundwater level fluctuations. A hybrid imputation scheme is applied to reduce data errors and improve input continuity, including Z-score anomaly detection, sliding window segmentation, and STL-SARIMA-based imputation. The prediction model employs the BiLSTM model combined with the Bayesian optimization algorithm, achieving an R2 of 0.9932 and consistently lower MSE values than those of the LSTM and RNN models across all experiments. Specifically, BiLSTM reduces MSE by 62.9% compared to LSTM and 72.6% compared to RNN, while also achieving the lowest MAE and MAPE scores, demonstrating its superior accuracy and robustness in groundwater level forecasting. This predictive advantage stems from the integration of a hybrid statistical imputation process with a BiLSTM model optimized through Bayesian search. These components collectively enable a reliable and integrated forecasting system that effectively models groundwater level variations, thereby providing a practical solution for groundwater monitoring and sustainable water resource management.

2025-01-01T00:00:00Z Tsai, Tung-Tso Lin, Han-Yu Huang, Wei-Ning Kumar, Sachin Agarwal, Kadambri Chen, Chien-Ming http://scholars.ntou.edu.tw/handle/123456789/26473 2026-03-12T03:36:50Z 2025-01-01T00:00:00Z

標題: Anomaly Detection Through Outsourced Revocable Identity-Based Signcryption With Equality Test for Sensitive Data in Consumer IoT Environments 作者: Tsai, Tung-Tso; Lin, Han-Yu; Huang, Wei-Ning; Kumar, Sachin; Agarwal, Kadambri; Chen, Chien-Ming 摘要: In the realm of consumer Internet of Things environments, data related to personal health and medical records can be collected. However, as this healthcare data falls under personal privacy, it must undergo encryption procedures before being uploaded to the cloud to ensure data confidentiality. Additionally, there is a desire for the encrypted data uploaded to the cloud to be compared, enabling the timely detection of anomalous data. If there are issues with healthcare data, the cloud system can issue alerts. Indeed, there already exists a mechanism, namely identity-based signcryption with equality test (IBSCET), which can accomplish the desire by comparing whether two encrypted data contain the same message. However, IBSCET does not address the issue of user revocation, which is crucial in any system. To address this problem, we enhance the existing IBSCET to propose the first outsourced revocable IBSCET (OR-IBSCET) scheme. Under the bilinear Diffie-Hellman and the computational Diffie-Hellman assumptions, we also demonstrate that the proposed scheme possesses security of the indistinguishability under chosen ciphertext attacks, the existential unforgeability under chosen message attacks, and one-wayness under chosen ciphertext attacks.

2025-01-01T00:00:00Z