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| 基于压缩感知和卷积神经网络的谐波检测方法研究 |
| Study on Harmonic Detection Method Based on Compressed Sensing and Convolutional Neural Network |
| 收稿日期：2021-07-26 |
| DOI：10.11980/j.issn.0254-508X.2021.12.009 |
| 关键词: 造纸工业 谐波检测 压缩感知 卷积神经网络 |
| Key Words:papermaking industry harmonic detection compression sensing convolutional neural network |
| 基金项目:国家自然科学基金（面上）项目（62073206）；陕西省自然科学基础研究计划项目（2019JQ-551）。 |
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| 摘要:本文提出一种基于压缩感知（CS）和卷积神经网络（CNN）的谐波检测新方法。在CS理论下建立了具有压缩采样和重构功能的谐波检测框架，并利用CNN理论设计了具有免变换字典的重构网络。研究结果表明，本文所提出的方法具有可行性，可为造纸工业中的谐波检测提供了一种新手段。 |
| Abstract:A new harmonic detection method based on compressed sensing （CS） and convolutional neural network （CNN） was proposed in this paper. A harmonic detection framework with compressed sampling and reconstruction function was established based on CS theory， and a reconstruction network with transform free dictionary was designed via CNN theory. The results showed that the method proposed in this paper was feasible and provided a new method for harmonic detection in papermaking industry. |
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