The Diagnostic of Two-Phase Separation Process Using Digital Image Segmentation Algorithms

Abstract

The project aimed to develop and implement algorithms to diagnose the phase separation process based on digital images. The image processing techniques and various numerical methods for interpolation and integration were used to identify the process state. The swirl’s volume and diameters at its three different levels can be determined on-line. A consistent diagnostic signal is produced and can be used by the control unit. The program was written in Python using the OpenCV library that allows the analysis of digital images. The article presents the developed procedure that provides reliable results despite the poor quality of the input source video stream. The complete procedure was described with the results’ presentation and discussion at each step.

Body surface potential mapping time series recognition using convolutional and recurrent neural networks

“…Both of compared algorithms accuracy ("ordinary" algorithm and continuous signal recognition algorithm) is computed by means of equation presented below: acc = * 9 * : (8)…”

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Implementation of a commercial PAN network in the tracking system using the techniques of radio tomographic imaging

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Novel Hybrid Algorithm Using Convolutional Autoencoder With SVM for Electrical Impedance Tomography and Ultrasound Computed Tomography

“…There are several numerical methods [5,14,17,19,22,23,24]. The authors have developed an innovative hybrid algorithm for image reconstruction of impedance and ultrasonic transmission tomography.…”