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The use of deep neural networks to detect alarms in mines

机译:使用深神经网络检测矿山的报警

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摘要

The article discusses the problem of detecting signals against a background of noise for alarms for miners in underground mine workings in case of emergency. The theoretical aspects of linear and non-linear filtering of alarms are given, the solution to the problem of constructing a non-linear filter based on deep neural network (DNN) is described. The simulation results are presented and a comparative analysis of the performance of an individual miner receiver using the methods of linear coherent reception and a DNN filter is made. Neural network training was carried out on model and experimental data obtained at an existing underground mine.
机译:本文讨论了在紧急情况下,在地下矿井运作中探测噪音背景下的噪音背景的问题。 给出了警报的线性和非线性滤波的理论方面,描述了基于深神经网络(DNN)构建非线性滤波器的问题的解决方案。 提出了模拟结果和使用线性相干接收方法和DNN滤波器的单独矿工接收器的性能的比较分析。 在现有地下矿井中获得的模型和实验数据进行神经网络培训。

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