|
二维码（扫一下试试看！） |
| Study on Harmonic Detection Method Based on Compressed Sensing and Convolutional Neural Network |
| Received:July 26, 2021 |
| DOI：10.11980/j.issn.0254-508X.2021.12.009 |
| Key Words:papermaking industry harmonic detection compression sensing convolutional neural network |
| Fund Project: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）。 |
| Author Name | Affiliation | Postcode | | TANG Wei* | School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi Province, 710021 | 710021 | | LUAN Yiduo | School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi Province, 710021 | 710021 | | LIU Yan | School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi Province, 710021 | 710021 |
|
| Hits: 5727 |
| Download times: 3214 |
| 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. |
| View Full Text HTML View/Add Comment Download reader |
|
|
|