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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.
