###### Abstract  
Knowledge of the liquid-gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time-frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water-air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metaplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water-air flow regimes (plug, slug, bubble, and transitional plug-bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases. two-phase flow; gamma-ray absorption; statistical signal analysis; histogram; convolutional neural network +
Footnote †: journal: Medical  
applications of capacitance and conductance sensors in the analysis of liquid-gas flows. In [9], wavelet variance data, principal component analysis, linear discriminant analyses, and the fuzzy c-means clustering algorithm were used to classify the type of flow. Optical techniques used in the study of two-phase flows are described in [11; 12; 13]. In [11], a machine learning methodology based on support vector analysis was used to identify the flow regime using the map developed in this work. The article [13] describes the use of three types of convolutional neural networks to analyze oil-water flow images obtained with a camera. Other sensor techniques used in the analysis of two-phase flows include industrial tomography: electrical, optical [14; 15; 16; 17], radiography: using X-rays and neutrons [18; 19; 20; 21], accelerometers [22; 23], impedance meter [24], wire-mesh sensor [25; 26]. An important method that has been used for many years in the study of two-phase flows is gamma radiation techniques, in particular the gamma-absorption method. A number of applications based on gamma-ray attenuation and scattering, combined with artificial intelligence methods for recognizing the flow structure and determining the void fraction, are described in the literature, e.g., [27; 28; 29; 30; 31; 32]. Diverse methods of computational intelligence have been applied for flow type classification in combination with nuclear technique, e.g., expert systems and ANNs: MLP, RBF, PNN, SVM, GMDH, CNN, RNN [28; 29; 30; 31; 32; 33; 34; 35; 36]. Generally, machine learning techniques use various features of signals from scintillation detectors extracted in the value, time, frequency, or time-frequency domain [27; 30; 36; 37; 38]. The probability density function (PDF) and the cumulative probability density function (CPDF) are characteristics of signals in the value domain and can be applied as input parameters for recognizing the flow structure using machine learning methods. This has been described in works using various sensor techniques: high-speed camera [39; 40], differential pressure system [41], capacitance sensor [42], impedance meter [43], single-wire resistivity probe [44]. In this work, a convolutional neural network VGG-16 (Visual Geometry Group) was applied to analyze histogram images of signals obtained in water-air flow measurements using the gamma radiation absorption method.