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- W4293518744 abstract "Satellite telemetry data is important strategic resource. We can monitor and predict the status of the satellite through analyzing the telemetry data. However, due to interference on the satellite and sensor failure, the telemetry data will jump and generate outliers. Therefore, it is necessary to identify and remove the outliers. This paper proposes an outlier removal method based on deconvolutional reconstruction network. The deconvolutional reconstruction network is composed of multiple convolution and deconvolution which is used to learn the internal laws from massive telemetry data. The learned network can make accurate predictions for normal data except for outliers. Our method use this difference to set the threshold and perform outlier removal. The deconvolutional reconstruction network proposed in this paper uses a very few parameters for rapid learning. The network can converge within 20 epochs for multiple sets of telemetry datasets which contains more than 60k discontinuous points. Numerical experiments show that the proposed method can achieve perfect removal effects." @default.
- W4293518744 created "2022-08-30" @default.
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- W4293518744 date "2022-08-03" @default.
- W4293518744 modified "2023-09-30" @default.
- W4293518744 title "Outlier Removal of Discontinuous Satellite Telemetry Data Based on Deconvolutional Reconstruction Network" @default.
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- W4293518744 doi "https://doi.org/10.1109/ddcls55054.2022.9858392" @default.
- W4293518744 hasPublicationYear "2022" @default.
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