To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.
In commercial flight trajectory prediction, the models based on deep learning have high versatility and accurate forecast. However, these prediction models have the problem of poor effect when using small data sets. This paper proposes a commercial flight trajectory prediction model based on representative trajectory. This model consists of two parts: Representative trajectory generation and Trajectory prediction. Firstly, our model clusters the commercial flight trajectories on the same route, then generate representative trajectories to represent the route pattern. Secondly, our model uses the Savitzky-Golay filter to eliminate the noise characteristics in these representative trajectories, then combines those with a deviation correction algorithm to predict flight trajectories. By comparing with several other prediction models, the experiment results support that our model performs better in a small data set of the commercial flight trajectory.
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