Wyniki wyszukiwania - BazTech

1

Smart non-destractive test of a concrete wall using a hammer

Ito Atsushi, Koike Masafumi, Hibino Katsuhiko

International Journal of Electronics and Telecommunications

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2024

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Vol. 70, No. 1

45--50

EN

Large concrete structures such as buildings, bridges, and tunnels are aging. In Japan and many other countries, those built during economic reconstruction after World War II are about 60 to 70 years old, and flacking and other problems are becoming more noticeable. Periodic inspections were made mandatory by government and ministerial ordinance during the 2013-2014 fiscal year, and inspections based on the new standards have just begun. There are various methods to check the soundness of concrete, but the hammering test is widely used because it does not require special equipment. However, long experience is required to master the hammering test. Therefore, mechanization is desired. Although the difference between the sound of a defective part and a normal part is very small, we have shown that neural network is useful in our research. To use this technology in the actual field, it is necessary to meet the forms of concrete structures in various conditions. For example, flacking in concrete exists at various depths, and it is impossible to learn about flacking in all cases. This paper presents the results of a study of the possibility of finding flacking at different depths with a single inspection learning model and an idea to increase the accuracy of a learning model when we use a rolling hammer.

2

Building Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru)

Chicchon Miguel, Malinverni Eva Savina, Sanità Marsia, Pierdicca Roberto, Colosi Francesca, Trujillo Francisco James León

Geomatics and Environmental Engineering

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2024

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Vol. 18, no. 3

25--43

EN

The amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approach that is based on the use of AI algorithms for building footprint segmentation and changedetection analysis by means of RS images is proposed. It involves a UNet convolutional network based on an EfficientNet B0 to B7 encoder. The network was trained on two public data sets from SpaceNet that were based on WV2 and WV3 satellite images: SpaceNet V1 (Rio), and SpaceNet V2 (Shanghai). In the pre-processing phase, the images from the two data sets have been equalized in order to improve their quality and avoid overfitting. The building segmentation has been performed on HRV images of the study area that were downloaded from Google Earth Pro. The value that was achieved in the IoU metric was around 70% in both experiments. The purpose of this proposed methodology is to assist scientists in drafting monitoring and conservation protocols based on already-recorded data in order to prevent future disasters and hazards.

3

Advancing Electrical Losses Assessment Methods in Power Systems

Rexhepi Vezir, Rexhepi Shaqir

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 3

326--335

EN

The operation of modern power systems requires a sophisticated technological infrastructure to effectively manage and evaluate their parameters and performance. This infrastructure includes the generation, transmission and distribution power system components. This paper provides an overview of the loss evaluation to a part of Kosovo’s power system, substation with wind and photovoltaic (PV) energy sources integrated (SS Mramori, SS Kitka, and SS Kamenica) and the analysis of the loss assessment methods. One the assessment method in the research encompass simulated loss scenarios and their corresponding values in network components, employing the simulation based on the respective software tools. In current trends, power systems are visualized through the Supervisory Control and Data Acquisition (SCADA) platform. However, in Kosovo, although losses are integral to the SCADA system, they are represented as a overall value in the online mode, not encompassed depict losses per-components in real-time. This limitation hinders effective online power system optimization regarding the losses. As consequence, the purpose of this study is proposal a logical method developed through neural networks. The methodology incorporates various parameters, including as inputs variables; voltages, currents, active and reactive powers, and their computed values for extracting losses (X(x1, x2, ..., xn)). These parameters undergo systematic processing through hidden layers (Y(x1, x2, ..., xn)), leading to the classification of components within the power system. Finally, at the output stage (A(x1, x2, ..., xn)), an assessment is conducted based on the level of losses observed in the components of the power system. This implementation method promises significant benefits for transmission systems, impacting not only reducing losses, power quality but also yielding economic advantages.

4

Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

Rashid Zaid Hikmat, Sarhan Riyadh Ahmed, Hassan Mohammed Salih

Diagnostyka

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2024

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Vol. 25, No. 1

art. no. 2024114

EN

Real-time motion control is basically dependent on robot kinematics analysis where there is no unique solution of the inverse kinematics of serial manipulators. The predictive artificial neural network is a powerful one for finding appropriate results based on training data. Therefore, an artificial neural network is proposed to implement on Arduino microcontroller for a 4-DOF robot manipulator where the inverse kinematics problem was partitioned to suit the low specification of the board CPU. With MATALB toolbox, multiple neural network configuration based were trained and experienced for the best fit of the dimensionless feedforward data that is obtained from the forward kinematics of reachable workspace with average MSE of 6.5e-7. The results showed the superior of the proposed networks reducing the joints error by 90 % at least with comparing to traditional one. An Arduino on-board program was developed to control the robot independly based on pre validated parameters of the network. The experimental results approved the proposed method to implement the robot in real time with maximum error of (± 0.15 mm) in end effector position. The presented approach can be applied to achieve a suitable solution of n-DOF self-depend robot implementation using micro stepping actuators.

5

Fault diagnosis of electrical faults of three-phasei nduction motors using acoustic analysis

Glowacz Adam, Sulowicz Maciej, Kozik Jarosław, Piech Krzysztof, Glowacz Witold, Li Zhixiong, Brumercik Frantisek, Gutten Miroslav, Korenciak Daniel, Kumar Anil, Lucas Guilherme Beraldi, Irfan Muhammad, Caesarendra Wahyu, Lui Hui

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 1

art. no. e148440

EN

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

6

Acoustic-Based Drone Detection Using Neural Networks – A Comprehensive Analysis

Paszkowski Waldemar, Gola Arkadiusz, Świć Antoni

Advances in Science and Technology. Research Journal

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2024

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Vol. 18, no 1

36--47

EN

The article presents and describes the implementation of research on the detection of a drone in an urban environment using of the sound features. The methods of drone detection were recognized on the basis of modeling and evaluation of the features of the audio and acoustic signal. The authors proposed the use of a neural network model for the needs of drone detection taking into account acoustic measurements in an anechoic chamber and in an urban environment. The final part presents the obtained results of the drone detection. For the purposes of detection, a neural network model was used in order to recognize the obtained images of the spectograms of sound sources.

7

Zastosowanie sieci neuronowych do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu

Betka Szymon, Blok Marek

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

220--223

PL

Detekcja impulsów w odebranym sygnale radiowym, zwłaszcza w obecności silnego szumu oraz trendu, jest trudnym zadaniem. Artykuł przedstawia propozycje rozwiązań wykorzystujących sieci neuronowe do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu. Na potrzeby realizacji tego zadania zaproponowano dwie architektury. W pracy przedstawiono wyniki badań wpływu kształtu impulsu, mocy zakłóceń szumowych oraz trendu obecnego w sygnałach wejściowych sieci, na skuteczność detekcji zaproponowanych rozwiązań.

EN

Detecting pulses in a received radio signal, especially in the presence of strong noise and trend, is a difficult task. The article presents proposed solutions based on neural networks for the detection of pulses of known shape in the presence of strong noise and trend. Two architectures are proposed for the purpose. The paper presents the results of the study of the influence of the pulse shape, the noise power, and the trend present in the input signals of the network on the detection performance of the proposed solutions.

8

Wskazywanie punktu startowego dla iteracyjnych algorytmów obliczania położenia z użyciem sieci neuronowej

Sadowski Jarosław, Stefański Jacek

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

244--247

PL

W referacie przedstawiono wyniki badań nad możliwością wskazywania punktu startowego do pierwszej iteracji dla algorytmu iteracyjnego obliczania położenia w systemie lokalizacji dwuwymiarowej. Do wskazywania punktu startowego użyto jednokierunkowej sieci neuronowej a celem badań było znalezienie jak najmniejszej struktury sieci, pozwalającej na zbieżność algorytmu estymacji położenia w całym obszarze badań.

EN

The paper presents the results of a study on the possibility of starting point selection for the first iteration for an iterative position calculation algorithm in a two dimensional location system. A feedforward neural network was used to indicate the starting point and the aim of the study was to find the smallest possible network structure, allowing the position estimation algorithm to converge over the entire study area.

9

Dekodowanie kodów iterowanych z użyciem sieci neuronowej

Fiedot Oskar, Blok Marek

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

190--193

PL

Nadmiarowe kody iterowane są jedną z prostych metod pozyskiwania długich kodów korekcyjnych zapewniających dużą ochronę przed błędami. Jednocześnie, chociaż ich podstawowy iteracyjny dekoder jest prosty koncepcyjnie oraz łatwy w implementacji, to nie jest on rozwiązaniem optymalnym. Poszukując alternatywnych rozwiązań zaproponowano, przedstawioną w pracy, strukturę dekodera tego typu kodów wspomaganą przez sieci neuronowe. Zaproponowane rozwiązanie pozwala na wykrywanie oraz korekcję błędów w odbieranych ciągach.

EN

Redundant iterated codes are one of the simple methods of deriving long correction codes that provide high error protection. At the same time, although their basic iterative decoder is conceptually simple and easy to implement, it is not an optimal solution. Looking for alternative solutions, a neural network-assisted decoder structure for this type of codes was proposed. The solution presented in this paper allows the detection and correction of errors in the received sequences.

10

Zastosowanie sieci neuronowych do detekcji zwarć zwojowych silnika PMSM z wykorzystaniem modelowania matematycznego

Krzysztofiak mateusz, Tarchała Grzegorz

Przegląd Elektrotechniczny

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2023

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R. 99, nr 4

30--35

PL

W artykule zaprezentowano metodę diagnostyki zwarć zwojowych stojana silnika PMSM, wykorzystującą sieć neuronową. Przeanalizowano sygnały prądowe i napięciowe pochodzące ze struktury sterowania, a następnie poddano je analizie w celu ekstrakcji najlepszych symptomów uszkodzeń. Wybrane cechy podano na wejście sieci neuronowej podczas procesu treningu. Badania eksperymentalne prezentują potencjał zastosowania modelowania matematycznego jako generatora wzorców symptomów uszkodzeń.

EN

This paper presents a method for diagnosing the inter-turn short circuits of a PMSM, using a neural network. Current and voltage signals from the control structure were then analysed to extract the best fault symptoms. The selected features were given to the neural network input during the training process. The tests performed on a experimental setup demonstrate the potential of using mathematical modelling as a fault symptom pattern generator.

11

System detekcji i pozycjonowania bezzałogowych statków powietrznych

Szymański Sebastian, Górski Krzysztof, Grzesiak Jakub

Przegląd Elektrotechniczny

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2023

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R. 99, nr 9

201--204

PL

W pracy przedstawiono projekt systemu służącego do detekcji i pozycjonowania bezzałogowych statków powietrznych na podstawie analizy próbek dźwiękowych z wykorzystaniem sztucznej inteligencji. Zaprezentowano przykładowy prototyp systemu działającego na polu walki, obrazującego na mapie wykryte źródła dźwięku.

EN

This paper presents the design of a system for the detection and positioning of UAVs based on the analysis of sound samples using artificial intelligence. An example prototype of the system operating on the battlefield, depicting detected sound sources on a map, is presented.

12

Rozpoznawanie głosu i identyfikacja mówcy: przegląd wybranych metod rozpoznawania cech biometrycznych mowy

Śliwak-Orlicki Tomasz, Górski Krzysztof

Przegląd Elektrotechniczny

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2023

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R. 99, nr 9

225--229

PL

W niniejszej pracy przedstawiono ogólnie rozwój technologii rozpoznawania mowy, począwszy od pierwszych eksperymentów XIX wieku, aż po współczesne osiągnięcia w tej dziedzinie. Przeanalizowano przekształcenia technologiczne na przestrzeni ostatnich lat, omówiono kluczowe odkrycia oraz najważniejsze wydarzenia, które odegrały istotną rolę w rozwoju tej dziedziny, wskazując jednocześnie wybrane procesy wspomagające skuteczność rozpoznawania mowy pod kątem identyfikacji biometrycznej. Przedstawiono w zarysie charakterystyczne cechy wymowy dla języka polskiego.

EN

This paper presents a general overview of the development of speech recognition technology, from the first experiments of the 19th century to modern developments in this field. It analyses technological transformations over the past years, discusses key discoveries and key events that have played a significant role in the development of this field, while highlighting selected processes that support the effectiveness of speech recognition in terms of biometric identification. The characteristic features of pronunciation for the Polish language are outlined.

13

Comparison of CNN and LSTM algorithms for solving the EIT inverse problem

Kłosowski Grzegorz, Maj Michał, Oleszek Michał

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

230--233

EN

This article presents comparative research to verify the suitability of selected machine learning methods for the problem of solving the inverse problem in electrical impedance tomography. The research involved the use of a tomograph to image areas of moisture inside the walls. The measurement data collected by the tomograph was transformed into 3D spatial images using two types of artificial neural networks - convolutional neural network (CNN) and recurrent long short-term memory network (LSTM).

PL

W tym artykule przedstawiono badania porównawcze w celu weryfikacji przydatności wybranych metod uczenia maszynowego do zagadnienia polegającego na rozwiązaniu problemu odwrotnego w elektrycznej tomografii impedancyjnej. Badania polegały na wykorzystaniu tomografu do obrazowania obszarów zawilgocenia wewnątrz murów. Zgromadzone za pomocą tomografu dane pomiarowe zostały przekształcone na obrazy przestrzenne 3D za pomocą dwóch rodzajów sztucznych sieci neuronowych – konwolucyjne sieci neuronowej (CNN) oraz sieci rekurencyjnej typu long short-term memory (LSTM).

14

Using neural networks to examine trending keywords in Inventory Control

Sadowski Adam, Sadowski Michał, Engelseth Per, Galar Zbigniew, Skowron-Grabowska Beata

Production Engineering Archives

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2023

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Vol. 29, Iss. 4

474-489

EN

Inventory control is one of the key areas of research in logistics. Using the SCOPUS database, we have processed 9,829 articles on inventory control using triangulation of statistical methods and machine learning. We have proven the usefulness of the proposed statistical method and Graph Attention Network (GAT) architecture for determining trend-setting keywords in inventory control research. We have demonstrated the changes in the research conducted between 1950 and 2021 by presenting the evolution of keywords in articles. A novelty of our research is the applied approach to bibliometric analysis using unsupervised deep learning. It allows to identify the keywords that determined the high citation rate of the article. The theoretical framework for the intellectual structure of research proposed in the studies on inventory control is general and can be applied to any area of knowledge.

15

Active Current Sensor Fault-Tolerant Control of Induction Motor Drive with Online Neural Network-Based Rotor and Stator Resistance Estimation

Adamczyk Michal, Orlowska-Kowalska Teresa

Power Electronics and Drives

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2023

|

Vol. 8 (43)

235-251

EN

This article presents an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CS.

16

Analysis of false alarm causes in video fire detecion systems

Mazur Michał, Wiśnios Michał, Paś Jacek

Journal of KONBiN

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2023

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Vol. 53, iss. 2

187--208

EN

Video-based fire detection systems represent an innovative path in fire signalling. Thanks to a suitably designed algorithm, a system of this kind can enable the detection of a flame based on its characteristics such as colour or shape, which were not previously used in classical fire detection systems. Video-based detection systems, due to their early stage of development in the fire protection market, are not yet a certified, fully tested method for early fire detection. This paper focuses on the analysis of possible causes of false alarms occurring in video-based fire detection systems in relation to classical Fire Alarm Systems (FAS). For this purpose, a video-based flame detection algorithm is designed and implemented to further analyse the phenomena occurring in such systems.

PL

Systemy wizyjnej detekcji pożaru stanowią innowacyjną ścieżkę w zakresie sygnalizacji pożarowej. Dzięki odpowiednio zaprojektowanemu algorytmowi, system tego rodzaju może umożliwiać detekcję płomienia na podstawie takich jego cech jak barwa lub kształt, które do tej pory nie były wykorzystywane w klasycznych systemach wykrywania pożaru. Systemy wizyjnej detekcji ze względu na wczesny okres ich rozwoju na rynku systemów ochrony przeciwpożarowej, nie są jeszcze certyfikowaną, w pełni sprawdzoną metodą wczesnego wykrywania pożarów. Niniejszy artykuł skupia się na analizie możliwych przyczyn fałszywych alarmów występujących w wizyjnych systemach detekcji pożaru w odniesieniu do klasycznych Systemów Sygnalizacji Pożarowej (SSP). W tym celu zaprojektowany i zaimplementowany zostały algorytm wizyjnej detekcji płomienia, który pozwoli dokładniej przeanalizować zjawiska zachodzące w tego rodzaju systemach.

17

Estimation of Suspended Sediment Load Using Artificial Neural Network in Khour Al Zubair Port, Iraq

Hassan Ayman A., Ibrahim Husham T., Al-Aboodi Ali H.

Journal of Ecological Engineering

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2023

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Vol. 24, nr 6

54--64

EN

The port of Khour Al-Zubair is located 60.0 km south of the city centre of Basrah; it is also located 105.0 kilometres from the northern tip of the Arabian Gulf. The main goal of this paper is to estimate the concentration of suspended deposit (SSC) in “Khour Al-Zubair” port using a Multilayer Perceptron Neural Network (MLP) based on hydraulic and local boundary parameters while also studying the effect of these parameters on estimating the SSC. Five input parameters (channel width, water depth, discharge, cross-section area, and flow velocity) are used for estimating SSC. Different input hydraulic and local boundary parameter combinations in the three sections (port center, port south, and port north) were used for creating nine models. The use of both hydraulic and local boundary parameters for SSC estimation is very important in the port area for estimating sediment loads without the need for field measurements, which require effort and time.

18

Development of methods for predicting hydrate formation in gas storage facilities and measures for their prevention and elimination

Volovetskyi V. B., Doroshenko Ya. V., Matkivskyi S. V., Raiter P. M., Shchyrba O. M., Stetsiuk S. M., Protsiuk H. Ya.

Journal of Achievements in Materials and Manufacturing Engineering

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2023

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Vol. 117, nr 1

25--41

EN

Purpose: The purpose of this work is to study the processes of hydrate formation during the operation of wells and underground gas storage facilities. Development of a set of measures aimed at the prediction and timely prevention of hydrate formation in wells and technological equipment of gas storage facilities under different geological and technological conditions. Design/methodology/approach: The prediction of hydrate formation processes was carried out using a neural network that is a software product with weight factors calculated in MATLAB environment and the ability to adapt parameters of the network specified to updated and supplemented input data during its operation. So, within the MATLAB software environment, a software module of a two-layer artificial neural network with a random set of weight factors is created at the first stage. In the second stage, the neural network is trained using experimental field input/output data set, output data. In the third stage, an artificial neural network is used as a means of predicting hydrate formation with the ability to refine weight factors during its operation subject to obtaining additional updated data, as an input set, for modifying the coefficients and, accordingly, improving the algorithm for predicting of an artificial neural network. In the absence of new data for the additional training of an artificial neural network, it is used as a computing tool that, on the basis of input data about the current above-mentioned selected technological parameters of fluid in the pipeline, ensures the output values in the range from 0 to 1 (or from 0 to 100%), that indicates the probability of hydrates formation in the controlled section of the pipeline. Application of such an approach makes it possible to teach; additionally that is, to improve the neural network; therefore this means of predicting hydrate formations objectively increases reliability of results obtained in the process of predicting and functioning of the system. The authors of the work recommend to carry out an integrated approach to ensure clear control over the operation mode of wells and gas collection points. Findings: According to the results of experimental studies, the places of the most likely deposition of hydrates in underground gas storage facilities were identified, in particular, in the inside space of the flowline in places of accumulation of liquid contaminants (lowered pipeline sections) and an adjustable choke of the gas collection point. The available methods used to prevent and eliminate hydrate formation both in wells and at gas field equipment were analyzed. Such an analysis made it possible to put together a list of methods that are most appropriate for the conditions of gas storage facilities in Ukraine. The method of predicting hydrate formation in certain sections of pipelines based on algorithms of artificial neural networks is proposed. The developed methodology based on data on values of temperatures and pressures in certain sections of pipelines allows us to predict the beginning of the hydrate formation process at certain points with high accuracy and take appropriate measures. Research limitations/implications: To increase the efficiency of solving the problem of hydrate formation in gas storage facilities, it is expedient to introduce new approaches to timely predict complications, in particular, the use of neural networks and diverse measures. Practical implications: Implementation of the developed predicting methodology and methods and measures to prevent and eliminate hydrate formation in wells and technological equipment in underground gas storage facilities will increase the operation efficiency of underground gas storage facilities. Originality/value: The use of artificial intelligence to predict hydrate formations in flowlines of wells and technological equipment of underground gas storage facilities is proposed. Using this approach to predict and function the system as a whole ensures high reliability of the results obtained due to adaptation of the system to the specified control conditions.

19

An Approach to License Plate Recognition in Real Time Using Multi-stage Computational Intelligence Classifier

Kekez Michał

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 2

275--280

EN

Automatic car license plate recognition (LPR) is widely used nowadays. It involves plate localization in the image, character segmentation and optical character recognition. In this paper, a set of descriptors of image segments (characters) was proposed as well as a technique of multi-stage classification of letters and digits using cascade of neural network and several parallel Random Forest or classification tree or rule list classifiers. The proposed solution was applied to automated recognition of number plates which are composed of capital Latin letters and Arabic numerals. The paper presents an analysis of the accuracy of the obtained classifiers. The time needed to build the classifier and the time needed to classify characters using it are also presented.

20

A Neural Network Model for Object Mask Detection in Medical Images

Tereikovskyi Igor, Korchenko Oleksander, Bushuyev Sergey, Tereikovskyi Oleh, Ziubina Ruslan, Veselska Olga

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 1

41--46

EN

In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.