Modeling the Voltage Produced by Ultrasound in Seawater by Stochastic and Artificial Intelligence Methods.
Alina BărbulescuCristian Ștefan DumitriuPublished in: Sensors (Basel, Switzerland) (2022)
Experiments have proved that an electrical signal appears in the ultrasonic cavitation field; its properties are influenced by the ultrasound frequency, the liquid type, and liquid characteristics such as density, viscosity, and surface tension. Still, the features of the signals are not entirely known. Therefore, we present the results on modeling the voltage collected in seawater, in ultrasound cavitation produced by a 20 kHz frequency generator, working at 80 W. Comparisons of the Box-Jenkins approaches, with artificial intelligence methods (GRNN) and hybrid (Wavelet-ARIMA and Wavelet-ANN) are provided, using different goodness of fit indicators. It is shown that the last approach gave the best model.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- magnetic resonance imaging
- convolutional neural network
- ultrasound guided
- contrast enhanced ultrasound
- ionic liquid
- transcription factor
- high frequency
- molecularly imprinted
- neural network
- computed tomography
- mass spectrometry
- high resolution
- solid phase extraction
- tandem mass spectrometry