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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)。
作者单位邮编
汤伟 陕西科技大学电气与控制工程学院陕西西安710021 710021
栾一多 陕西科技大学电气与控制工程学院陕西西安710021 710021
刘嫣 陕西科技大学电气与控制工程学院陕西西安710021 710021
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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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