Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Suspended particle characterization using convolution neural networks from acoustic backscatter data. |
| Συγγραφείς: |
Hartley, Joseph, Mortimer, Lee, Peakall, Jeffrey, Bourne, Richard, Dodds, Jonathan, Fairweather, Michael, Hunter, Timothy |
| Πηγή: |
AIP Conference Proceedings; 2026, Vol. 3489 Issue 1, p1-4, 4p |
| Θεματικοί όροι: |
Convolutional neural networks, Particle size determination, Signal processing, Data analysis, Sound wave scattering |
| Περίληψη: |
Artificial neural networks (ANNs) and convolution neural networks (CNNs) have been developed and demonstrated to simultaneously predict the particle mean diameter and concentration using ultrasonic backscatter data considering spherical silica glass beads suspended in a calibration tank. Training data was obtained across a range of concentrations (2.4 – 70.6 g.L−1) and particle sizes (35.2 – 208 휇푚) using the Metflow UVP-DUO instrumentation, measured at two transducer probe frequencies, 2 and 4 MHz. A preprocessor transforms the raw signal data into a normalized G-function for each measurement, which is used to train the ANN and CNN. The ANN performs well on the training dataset below around 250 epochs obtaining a mean square error of around 0.05, beyond which overfitting is observed. Upon switching to the CNN, a further improvement down to 0.04 is obtained and only 150 epochs are required to train the algorithm to this degree of accuracy. Overall, the CNN learns the features of the G-function data faster, and provides more accurate predictions of the particle properties demonstrating that patterns in the G-function play an important role in its relation to characterization. The presented results offer promise for the use of machine learning algorithms to process and analyze ultrasonic backscatter data, and serve as a foundation for more improved techniques in the future. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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