Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks
The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, signi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Insects |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4450/14/10/817 |
| _version_ | 1797573562757808128 |
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| author | Xiaolin Xu Juhu Li Huarong Zhang |
| author_facet | Xiaolin Xu Juhu Li Huarong Zhang |
| author_sort | Xiaolin Xu |
| collection | DOAJ |
| description | The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method’s accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals. |
| first_indexed | 2024-03-10T21:10:50Z |
| format | Article |
| id | doaj.art-71d58483ce474869a6e91f6be0e20ecd |
| institution | Directory Open Access Journal |
| issn | 2075-4450 |
| language | English |
| last_indexed | 2024-03-10T21:10:50Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj.art-71d58483ce474869a6e91f6be0e20ecd2023-11-19T16:49:58ZengMDPI AGInsects2075-44502023-10-01141081710.3390/insects14100817Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN NetworksXiaolin Xu0Juhu Li1Huarong Zhang2School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaThe larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method’s accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals.https://www.mdpi.com/2075-4450/14/10/817beamformingmulti-channelboring vibration signalself-attention mechanismdenoising |
| spellingShingle | Xiaolin Xu Juhu Li Huarong Zhang Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks Insects beamforming multi-channel boring vibration signal self-attention mechanism denoising |
| title | Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks |
| title_full | Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks |
| title_fullStr | Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks |
| title_full_unstemmed | Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks |
| title_short | Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks |
| title_sort | multi channel time domain boring vibration enhancement method using rnn networks |
| topic | beamforming multi-channel boring vibration signal self-attention mechanism denoising |
| url | https://www.mdpi.com/2075-4450/14/10/817 |
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