Medium identification method and device

The method of any of preceding claims, wherein the one or more wave-based signals are acoustic signals, and the extracting step further includes performing a fast Fourier transform on the fused signal, extracting a frequency domain amplitude at each frequency, and obtaining an acoustic absorption and transmission curve as the wave propagation characteristics of the medium by normalising the extracted frequency domain amplitude by amplitudes of originally emitted wave-based signals.

The method of any of preceding claims, further comprising using a container compensation model to correct and compensate for the wave propagation characteristics of the medium accommodated in a container.

The method of any of preceding claims, wherein the computational analysis is conducted through a deep learning model including conditional generative adversarial network, or autoencoders or Transformer-based networks.

The method of any of preceding claims, wherein the trained analysis system includes a fully connected neural network comprising an architecture with an input layer, three hidden layers, and an output layer, and wherein the input layer receives a spectrum vector representing the intensity of a specific mass-to-charge ratio, the three hidden layers consist of 512, 256, and 128 neurons, each using the ReLU activation function to introduce nonlinearity, and the output layer uses a Softmax activation function to output a probability distribution over the identification.