Ship Trajectory Clustering Based on Trajectory Resampling and Enhanced BIRCH Algorithm

Zhuang, Y; Yang, Guanghao; He, Rong; Hui, Yang; Ci, Hui; Wang, Ran

doi:10.3390/jmse11020407

Cited by 14 publications

(4 citation statements)

References 34 publications

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“…BERT employs the Masked Language Model (MLM) approach, randomly masking certain words to predict the masked portions based on contextual information before and after the masked words (Yan et al, 2023). In contrast to traditional left-to-right single-direction language model fine-tuning methods, MLM integrates context and emphasizes word-level semantic relationships.…”

Section: Bert Modelmentioning

confidence: 99%

Intelligent Customer Service System Optimization Based on Artificial Intelligence

Wu,

She,

Zhou

2024

Journal of Organizational and End User Computing

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To elevate the intelligence of customer service dialogue systems, this article proposes an intelligent customer service system comprising chat dialogue subsystems, task-oriented multi-turn dialogue subsystems, single-turn dialogue subsystems, and an integration model. Firstly, to enhance diversity of responses and improve user experience, particularly in casual chat scenarios, this article presents a Seq2Seq-based approach for multi-answer responses, allowing for more expressive emotional expression in responses. Secondly, to address situations where customers cannot articulate their needs in a single sentence during multi-turn dialogues, this article designs a task-oriented multi-turn dialogue module. It employs intent recognition and slot filling to maintain contextual information throughout the conversation, aiding customers in problem resolution. Lastly, to overcome the current limitation of intelligent customer service models providing relatively one-dimensional answers in specific domains.

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Section: Bert Modelmentioning

confidence: 99%

Intelligent Customer Service System Optimization Based on Artificial Intelligence

Wu,

She,

Zhou

2024

Journal of Organizational and End User Computing

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“…Within an IoT environment, the technique described in this study focuses on the continuous learning of microservice models. In order to cluster the data, K-means-and BIRCH-based clustering techniques are used [8]. As a result of this particular scenario, the clustering algorithm considers cluster centers that fall within a range of three standard deviations to be part of the same cluster.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Securing IoT Infrastructure with Emphasis on Smart Vertical Networks

Kolhar,

Aldossary

2023

Designs

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As a result of the Internet of Things (IoT), smart city infrastructure has been able to advance, enhancing efficiency and enabling remote management. Despite this, this interconnectivity poses significant security and privacy concerns, as cyberthreats are rapidly adapting to exploit IoT vulnerabilities. In order to safeguard privacy and ensure secure IoT operations, robust security strategies are necessary. To detect anomalies effectively, intrusion detection systems (IDSs) must employ sophisticated algorithms capable of handling complex and voluminous datasets. A novel approach to IoT security is presented in this paper, which focuses on safeguarding smart vertical networks (SVNs) integral to sector-specific IoT implementations. It is proposed that a deep learning-based method employing a stacking deep ensemble model be used, selected for its superior performance in managing large datasets and its ability to learn intricate patterns indicative of cyberattacks. Experimental results indicate that the model is exceptionally accurate in identifying cyberthreats, exceeding other models, with a 99.8% detection rate for the ToN-IoT dataset and 99.6% for the InSDN dataset. The paper aims not only to introduce a robust algorithm for IoT security, but also to demonstrate its efficacy through comprehensive testing. We selected a deep learning ensemble model due to its proven track record in similar applications and its ability to maintain the integrity of IoT systems in smart cities.

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“…There have been studies on the location accuracy of some AIS. For instance, a method for detecting the two most dangerous abnormal behaviors based on AIS was conducted in [14], a study establishing a major ship navigation route between ports based on the extracted ship orbiter cluster was conducted in [15], and a study suggesting a data preparation process for managing actual kinematic data and detecting fishing boats was conducted in [16]. Most studies on V-pass terminals used in most fishing boats focus only on V-pass data utilization; thus, few studies on the location accuracy-a crucial variableexist [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Jung

Park

Choi

2023

JMSE

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Fishing boat laws in Korea require fishing boats to be equipped with a location transmitter. Approximately 91% of registered fishing boats have V-pass terminals, whereas those with a gross tonnage of 10 t or more have either V-pass or automatic identification system (AIS) terminals (or use both). Most navigators, including the fishing boat location transmitter, rely on the satellite signals provided by the global navigation satellite system (GNSS) without considering the accuracy of the navigation instruments that indicate ship location. Many scholars are conducting research to analyze the accuracy of GNSS locations through continuous experiments; however, few of them focus on fishing boat location transmitters. Particularly, the location accuracy of V-pass equipment is unknown. Notably, the V-pass and AIS terminals used in fishing boats are mainly designed to locate fishing boats in need of assistance following a marine accident rather than to provide information on approaching ships and preventing collisions. Therefore, this study aimed to compare GNSS location storage data extracted from the location transmitter (V-pass and AIS terminal) of the accident fishing boat with the DGPS location information from the Electronic Chart Display and Information System to check the position error and use it for accident analysis and investigation.

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Ship Trajectory Clustering Based on Trajectory Resampling and Enhanced BIRCH Algorithm

Cited by 14 publications

References 34 publications

Intelligent Customer Service System Optimization Based on Artificial Intelligence

Intelligent Customer Service System Optimization Based on Artificial Intelligence

A Deep Learning Approach for Securing IoT Infrastructure with Emphasis on Smart Vertical Networks

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

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