A Design of Network Attack Detection Using Causal and Non-causal Temporal Convolutional Network

He, Pengju; Zhang, Haibo; Feng, Yaokai; Sakurai, Kouichi

doi:10.1007/978-3-031-45933-7_30

Cited by 1 publication

(1 citation statement)

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“…TCN, a specialized neural network structure tailored for processing time series data, stands out for its distinctive approach compared to traditional recurrent neural networks (RNN). Instead of utilizing recurrent layers, the Temporal Convolutional Network employs one-dimensional convolutional layers, which results in enhanced efficiency and effectiveness, especially when dealing with extensive sequences (He et al, 2023). This design incorporates key features, such as causal convolution, ensuring that the model exclusively relies on past information for predictions, and dilated convolution, which widens the receptive field of the convolutional layer, enabling it to capture dependencies over extended intervals.…”

Section: Temporal Convolutional Network: Tcnmentioning

confidence: 99%

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

Song,

Gao

2024

Front. Energy Res.

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As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal’s marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN-Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. “ACO-TCN-Attention” is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy.

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Section: Temporal Convolutional Network: Tcnmentioning

confidence: 99%