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Automatic detection of meteors in spectrograms using artificial neural networks

机译:使用人工神经网络自动检测频谱图中的流星

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Artificial neural networks are widely used in classification problems due to their adaptability. In this paper, an original multi-layer perceptron is used to automatically detect meteors within radio recordings. The approach presented can be divided into two stages: data preparation and meteor detection. In the data preparation stage, samples are built from a number of the spectrogram's vertical lines. During the meteor detection stage, neural networks are trained using the inputs previously extracted, and their meteor detection capabilities are tested. The rate of correctly detected meteor samples by the neural networks trained in this paper is over 80%.
机译:人工神经网络因其适应性而广泛用于分类问题。在本文中,原始的多层感知器用于自动检测无线电记录中的流星。提出的方法可以分为两个阶段:数据准备和流星探测。在数据准备阶段,从许多频谱图的垂直线构建样本。在流星探测阶段,使用先前提取的输入来训练神经网络,并测试其流星探测能力。本文训练的神经网络对流星样本的正确检测率超过80%。

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