在基于软件的仪器系统中应用人工智能对甚高频信号进行分类和监测

2020 Joint Conference of the IEEE International Frequency Control Symposium and International Symposium on Applications of Ferroelectrics (IFCS-ISAF) Pub Date : 2020-07-01 DOI:10.1109/IFCS-ISAF41089.2020.9234858

R. Ciocan

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引用次数: 0

摘要

设计了一种基于软件的测量系统，用于测量50 MHz信号的瞬态频率响应，精度优于0.3 ppm。长短期记忆(LSTM)是一种人工递归神经网络(RNN)架构，用于对该系统产生的信号进行特征检测和分类。检测和表征信号中的dropout，准确率优于78%。采用基于PXI的仪器仪表系统实现了基于软件的仪器仪表概念。软件解决方案在LabVIEW、Matlab和LabWindows/CVI中实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Use of Artificial Intelligence in Classification and Monitoring of VHF Signals in a Software Based Instrumentation System

A software based instrumentation system was designed to measure the transient frequency response for a 50 MHz signal with a precision better than 0.3 ppm. Long short-term memory (LSTM), an artificial recurrent neural network (RNN) architecture was used to detect and classify features on signals generated by this system. Dropouts in signal were detected and characterized with an accuracy better than 78%. The concept of software based instrumentation was implemented using a PXI based instrumentation system. The software solution was implemented in LabVIEW, Matlab and LabWindows/CVI.

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2020 Joint Conference of the IEEE International Frequency Control Symposium and International Symposium on Applications of Ferroelectrics (IFCS-ISAF)

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