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Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network
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| Zeitschriftentitel: | Shock and Vibration |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Shock and Vibration, 2017, 2017, S. 1-12 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Hindawi Limited
|
| Schlagwörter: |
| author_facet |
Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng |
|---|---|
| author |
Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng |
| spellingShingle |
Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng Shock and Vibration Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network Mechanical Engineering Mechanics of Materials Geotechnical Engineering and Engineering Geology Condensed Matter Physics Civil and Structural Engineering |
| author_sort |
li, mingzhu |
| spelling |
Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng 1070-9622 1875-9203 Hindawi Limited Mechanical Engineering Mechanics of Materials Geotechnical Engineering and Engineering Geology Condensed Matter Physics Civil and Structural Engineering http://dx.doi.org/10.1155/2017/7962828 <jats:p>Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI). A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN). According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model. The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE. The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise.</jats:p> Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network Shock and Vibration |
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10.1155/2017/7962828 |
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Hindawi Limited, 2017 |
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Hindawi Limited, 2017 |
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| title |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_unstemmed |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_fullStr |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full_unstemmed |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_short |
Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_sort |
wavelet denoising of vehicle platform vibration signal based on threshold neural network |
| topic |
Mechanical Engineering Mechanics of Materials Geotechnical Engineering and Engineering Geology Condensed Matter Physics Civil and Structural Engineering |
| url |
http://dx.doi.org/10.1155/2017/7962828 |
| publishDate |
2017 |
| physical |
1-12 |
| description |
<jats:p>Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI). A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN). According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model. The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE. The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise.</jats:p> |
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| author | Li, Mingzhu, Wang, Zhiqian, Luo, Jun, Liu, Yusheng, Cai, Sheng |
| author_facet | Li, Mingzhu, Wang, Zhiqian, Luo, Jun, Liu, Yusheng, Cai, Sheng, Li, Mingzhu, Wang, Zhiqian, Luo, Jun, Liu, Yusheng, Cai, Sheng |
| author_sort | li, mingzhu |
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| container_title | Shock and Vibration |
| container_volume | 2017 |
| description | <jats:p>Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI). A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN). According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model. The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE. The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise.</jats:p> |
| doi_str_mv | 10.1155/2017/7962828 |
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| imprint | Hindawi Limited, 2017 |
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| physical | 1-12 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | Hindawi Limited |
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| series | Shock and Vibration |
| source_id | 49 |
| spelling | Li, Mingzhu Wang, Zhiqian Luo, Jun Liu, Yusheng Cai, Sheng 1070-9622 1875-9203 Hindawi Limited Mechanical Engineering Mechanics of Materials Geotechnical Engineering and Engineering Geology Condensed Matter Physics Civil and Structural Engineering http://dx.doi.org/10.1155/2017/7962828 <jats:p>Vehicle Platform Vibration Signal (VPVS) denoising is essential to achieve high measurement accuracy of precise optical measuring instrument (POMI). A method to denoise the VPVS is proposed based on the wavelet coefficients thresholding and threshold neural network (TNN). According to the characteristics of VPVS, a novel thresholding function is constructed, and then its optimized threshold is selected through unsupervised learning of TNN. The original VPVS mixed in trend and random noise is constructed as VPVS model. A VPVS denoising flow is proposed based on the power spectral and energy distribution of the VPVS model. The simulation shows that the proposed denoising method achieves better results, compared to the previous denoising methods using the indexes of SNR and RMSE. The experiment demonstrates that it is efficient for denoising VPVS polluted by the trend and random noise.</jats:p> Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network Shock and Vibration |
| spellingShingle | Li, Mingzhu, Wang, Zhiqian, Luo, Jun, Liu, Yusheng, Cai, Sheng, Shock and Vibration, Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network, Mechanical Engineering, Mechanics of Materials, Geotechnical Engineering and Engineering Geology, Condensed Matter Physics, Civil and Structural Engineering |
| title | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_fullStr | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_full_unstemmed | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_short | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| title_sort | wavelet denoising of vehicle platform vibration signal based on threshold neural network |
| title_unstemmed | Wavelet Denoising of Vehicle Platform Vibration Signal Based on Threshold Neural Network |
| topic | Mechanical Engineering, Mechanics of Materials, Geotechnical Engineering and Engineering Geology, Condensed Matter Physics, Civil and Structural Engineering |
| url | http://dx.doi.org/10.1155/2017/7962828 |