Traditional single-layer complex networks models show unsatisfactory practicality in modeling multi-functional, collaboratively operating aviation systems. This paper proposes a method for constructing a directed weighted super network model for aviation transport systems. Firstly, four functional sub-networks are constructed, including airline company sub-network, air route sub-network, airports-airline sub-network and the sector sub-network. On basis of the sub-networks, a super network model for aviation transport is introduced. Characteristics of the super network are then analyzed by using metrics such as functional-structural correlation coefficient, and node hyperdegree ratio. Results show that the super network structure is unstable, with low levels of association between most sub-networks, but stronger associations between air route-the Sector and air company networks-airports-airline. This work sets good bases for further vulnerability analysis of complex network.
Air combat target tactical intent refers to the analysis and inference of the enemy's combat intentions in real-time, adversarial environments by extracting battlefield environmental information, static attributes, and real-time dynamic information of air combat targets, combined with knowledge from the military domain. To achieve this goal, many machine learning-based methods have been proposed to infer aircraft intentions. However, these methods are only applicable to individual aircraft and cannot predict the intentions of the entire formation. Therefore, we propose an attention-based multi-level LSTM model that incorporates multiple levels and attention mechanisms to enhance the focus on key information and improve prediction efficiency, resulting in promising experimental results.
KEYWORDS: Neural networks, Machine learning, Design and modelling, Matrices, Deep learning, Data modeling, Lithium, Vector spaces, Time series analysis, Sun
As a hot research direction in current academic studies, knowledge graph reasoning is aimed at solving the many challenges and pain points of knowledge graphs. This paper centers around temporal data prediction and presents a multi-level framework that leverages causal knowledge graphs. Our framework seamlessly integrates causal knowledge graphs with temporal data to enhance prediction accuracy. The framework is composed of two key components: causal knowledge graph construction and multi-level gated graph neural network prediction. By representing facts and relationships within the domain using causal knowledge graphs, the framework enhances the capability of the temporal data prediction model. The proposed framework design can provide better knowledge understanding for researchers in the field and achieve accurate prediction of temporal data.
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