Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology

Lin, Chien-Ju; Lin, Chun-Cheng; K., W. Zhan, Y. Xu, A. H. Li, J. Zhang,; Yeh, Cheng‐Yu; Hwang, Shaw‐Hwa

doi:10.18494/sam4077

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The development of personalized teaching can be promoted, students' mathematical thinking and innovation abilities can be cultivated, and more intelligent and personalized solutions can be provided for future education. (23)(24)(25) Here, combined with the needs of GCN and mathematics disciplines, the construction and application of KGs of higher mathematics disciplines were carried out. Firstly, the relevant knowledge of higher mathematics in universities was collected and processed.…”

Section: Discussionmentioning

confidence: 99%

Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching

Yuan,

Li,

Sun

et al. 2023

Sensors and Materials

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In this study, we first perform data preprocessing using higher mathematics textbooks and network resources to complete the construction of the dataset extracted from the knowledge of this mathematical discipline in order to study the application of a graph neural network in the knowledge graph (KG) of higher mathematics. Then, the graph convolutional network (GCN) and attention mechanism are introduced, and the relationship extraction model based on attention-guided GCN and the text classification model based on GCN are established, whereby the automatic extraction of higher mathematical knowledge can be realized. Finally, sensors are used to collect data from students' classroom responses, the data layer construction of a subject KG is realized, and the database is used to realize the storage visualization of triples. The results show that on the basis of the text classification model of GCN, higher mathematical knowledge classification is set, providing a reference example for the application of GCN in higher education. The constructed higher mathematics atlas has a total of 2580 triples, which can be used on the higher mathematics visual query platform. The self-built recognizer recognition test set obtains a P-value of 89.16% in the higher mathematics theorem law entity and a P-value of 97.73% in the test question of higher mathematics. Therefore, the GCN model established here can be effective in the construction of higher mathematical KGs.

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Section: Discussionmentioning

confidence: 99%

Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching

Yuan,

Li,

Sun

et al. 2023

Sensors and Materials

View full text Add to dashboard Cite

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Small Fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model

Deng,

Liu,

et al. 2024

Preprint

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Efficient management of small vessels is crucial for maritime operations, and effective classification and identification algorithms for small fishing vessels are key to enhancing ship management. This paper proposes a classification and recognition method for small fishing boats based on GASF sequence diagram coding, addressing the complex and challenging recognition environment. The method focuses on four typical small fishing vessels, utilizing Gramian Summation Angular Field (GASF) time series images and the Efficiency MPViT (EMPViT) model. Unlike traditional approaches, this study initially employs a high-precision laser sensor to gather one-dimensional contour data of fishing boats. Subsequently, the polynomial fitting method is used to delineate the shape of the fishing boat contour, which is then encoded into a two-dimensional time series image using the GASF encoding method. The enhanced EMPViT model is then applied to classify and identify small fishing vessels, with the results verified through ablation experiments. These experiments demonstrate that the EMPViT model surpasses traditional neural network models such as CNN and ViT in both accuracy and performance, achieving a peak accuracy of 99.98%.

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Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology

Cited by 2 publications

References 8 publications

Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching

Application of Graph Convolutional Network in the Construction of Knowledge Graph for Higher Mathematics Teaching

Small Fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model

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