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5 July 2024 Accelerometer signals denoising without clean training data based on Noise2Noise approach
Fengling Zheng, Wei Li, Xiaohui Cui, G. S. Rajaboevich
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318415 (2024) https://doi.org/10.1117/12.3032817
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
The accelerometer signal is capable of providing crucial information regarding object motion, posture, and vibration. Therefore, it is of great significance to investigate noise suppression techniques for accelerometer signals. However, in complex industrial environments and under various equipment conditions, the noise generated exhibits a diverse nature, leading to suboptimal noise reduction outcomes when employing conventional denoising algorithms. This paper proposes a neural network model (AgentUNet) for denoising accelerometer signals. The network consists of a combination of UNet and Agent Attention, enhancing its ability to learn contextual information and achieve superior denoising performance. Additionally, instead of using clean signals as labeled data, we train the neural network using pairs of noisy data, thereby alleviating the challenge of collecting clean signals in industrial environments. Finally, we conduct experiments on experiment dataset. The experimental results indicate that AgentUNet achieves superior denoising performance compared to other baseline models. Furthermore, the denoising effectiveness of the neural network trained without using clean signals as labeled data is similar to that of the network trained using supervised learning methods.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Fengling Zheng, Wei Li, Xiaohui Cui, and G. S. Rajaboevich "Accelerometer signals denoising without clean training data based on Noise2Noise approach", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318415 (5 July 2024); https://doi.org/10.1117/12.3032817
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KEYWORDS
Denoising
Accelerometers
Neural networks
Signal processing
Network architectures
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