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Enhancing Estimation Performance in Distributed Sensing Through Autoencoder-Based Sensor Array Feature Extraction
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DOI：10.1109/JSEN.2024.3444813.png)
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Abstract
En 中文
In the distributed sensing method, various sensor arrays encompass both sensor readings and potential spatial information. While increasing the number of sensors can enhance accuracy, an alternative approach to achieve this lies in extracting spatial knowledge from combinations of horizontal and vertical sensor arrays. By adopting a scaffold-based conceptualization of sensor distribution, this study introduces a novel input preprocessing approach termed the scaffold-based preprocessing autoencoder (SPA) to augment distributed sensing methods. This approach leverages autoencoders to extract informative features from diverse combinations of sensor arrays arranged in vertical columns and horizontal planes. The effectiveness of the proposed approach is empirically validated in the posture sensing of a vacuum-powered bellow-shaped fluidic elastomer actuator (FEA), employing nine distributed flexible bending sensors. The results demonstrate that the neural network incorporating features extracted from sensor combinations surpasses the performance of conventional long short-term memory (LSTM) neural networks. Comparative investigations validate the distinct advantages of these arrangements, with horizontal combinations yielding improved estimation accuracy in the X- and Y-axes, and vertical combinations enhancing accuracy in the Z-axis. Collectively, these combinations yield reductions in root mean square error (RMSE) of 9.51-6.93 mm in the X-axis, 6.77-5.43 mm in the Y-axis, and 7.72-4.38 mm in the Z-axis. Besides, the application of SPA to the gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) models also demonstrated significant improvements. Both models exhibited a substantial reduction in the sum of RMSE in each axis, with decreases of 20% and 27%, respectively.
Keywords:
Sensors
Sensor arrays
Feature extraction
Sensor phenomena and characterization
Long short term memory
Estimation
Accuracy
Autoencoder
deep learning
distributed sensing
soft robotics
soft sensor
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W