Poster: The Concept of an Ultrasensitive Industrial Ultrasound Scanner Using Hilbert and Wavelet Transforms in a Machine Learning Model

The main goal of the research was to develop an effective, highresolution tomographic apparatus capable of non-invasively capturing real-time internal images of industrial tank reactors. For this purpose, a prototype of an ultrasonic tomograph (UST) was developed, which combines innovative design solutions and modern algorithmic techniques. A special feature of the presented solution is the use of a neural network with an unusual architecture. A deep, multi-branch neural network consisting of two inputs was used. The first input is a 120-element vector (sequence) of raw measurements. The third input consists of three sequences obtained as a result of the transformation of raw measurements: instantenous frequency (IF), approximation coefficients (Ca), and detail coefficients (Cd). The prototype was tested on a real model. The tomographic reconstructions obtained using the innovative neural architecture were compared with images obtained using a standard neural network. The results clearly confirm the high effectiveness of the presented approach.