APPLYING A DESIGN MINDSET TO DEVELOP A PROTOTYPE OF AN ELECTRONIC SERVICE FOR ASSESSING THE IMPACT ON THE ENVIRONMENT

opinions in line with the concordation method. The estimated concordation coefficient was 0.83 and was checked for adequacy according to the Pearson criterion (20.75), which confirmed the reliability of the calculations at a significance level of 0.05. The scope of application may be environmental impact assessment of transport construction objects and gas stations. The conditions for practical use are the availability of access to the Internet and the availability of sufficient user qualifications for performing calculations. The object of research considered in this paper is the design mindset process in the development of a prototype of electronic service for assessing the environmental impact of the planned activities of business entities. The problems of applying the design mindset approach to the process of automating environmental impact assessment have been investigated. As a result of the application of the design mindset in combination with the stakeholder approach, a logical-structural scheme and architecture of the electronic service interface for different categories of users have been developed. The peculiarities of the prototype of the electronic service of environmental impact assessment (EIA ES) VE, together with the results of ECG and echo-ECG studies, data on the influence of additional factors that play a role in the occurrence of VE were used, such as the index of oxygen saturation of erythrocytes in the blood, changes in the thickness of the intima-media layer of the aortic artery and the amount of lipid fractions of blood plasma. when modeling spatial data. The results obtained based on calculations carried out by add-ing a new layer to the existing model showed higher accuracy than calculations carried out on the existing model. The paper presents the two-stage alignment and extending methods of parallel corpora for the Kazakh language. The Kazakh language is agglutinative with rich morphology and related to the Turkic language group. So, the traditional alignment methods for similar languages do not work for the Kazakh language. The alignment is used primarily to ensure that the fragment corresponding to the original is found in the translation. After that, identical fragments of parallel texts are compared with each other. At the initial stage, the question is what needs to be leveled. It is possible to align word by word, but this often becomes almost impossible for several reasons: sets of lexemes and expressions do not match in different languages. Considering the linguistic peculiarities of languages, the developed technologies and ways of universal alignment of parallel text may not work in languages with agglutination. It means that the form of the word is formed by additional affixes and auxiliary words that carry semantic and morphological information. The approach presented in this paper is to use a two-stage alignment, which uses a bilingual dictionary of synonyms. The evaluation with the use of the English-Kazakh corpus verifies that our method shows an average of 89 % correct alignment. The second method is designed to expand the parallel corpus due to the lack of natural parallel corpora of the Kazakh-English language pair with good quality. The developed method uses a combinatorial method taking into account the semantic and grammatical features of the Kazakh language. Different tenses of the Kazakh language are used for sentence generation, and different endings for parts of speech are also considered. Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartogra-phy, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area. Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and formation of features that describe texture differences, the selection and segmentation of texture areas, the classification of texture areas, and the identification of an object by texture. Depending on the features of the texture areas of the images used, segmentation methods based on area analysis can be divided into statistical, structural, fractal, spectral, and combined methods. The article discusses textural features for the analysis of texture images, and defines informative textural features to identify negative factors for crop growth. To solve the tasks, textural features are used. Much attention is paid to the development of software tools that al-low to highlight the features that describe the differences in textures for the segmentation of texture areas. This approach is universal and has great potential on the studied aerospace image to identify objects and boundaries of regions with different properties using clustering based on images of the same surface area taken in different vegetation periods. That is, the question of the applicability of sets of texture features and other parameters for the analysis of experimental data is being investigated. and their validity period, establishing and recognizing ROI (region of interest), formulating a verification decision. To separate the face in the client’s photograph and compare faces, methods based on deep learning, as well as the quick HoG method (Histogram of oriented gradients), were considered and implemented. Verification of these methods on a test dataset, which includes images of documents of two thousand clients, showed that the recognition accuracy was 91 % according to Jaccard’s metric. The average time of face separation using the HoG method was 0.2 seconds and when using trained models – 3.3 seconds. Using a combination of ROI and ORC separation methods makes it possible to significantly improve the accuracy of verification. The proposed client verification algorithm is implemented as an API on an ML server and integrated into the car sharing system. electrical energy increases by 15–40 % compared to the traditional magnetoelectric system. The findings of the current study are recommended for practical use in autonomous power plants based on wind turbines with genera-tors with permanent magnets. This paper reports the new design of an experimental bench to study the effectiveness of the positional drive control system of shut-off fittings. For research, the operating modes of the disk flap and ball valve, based on proportional elements with feedback (4–20 mA), were programmatically formed and described. The mathematical model of control analytically described on the example of a disk rotary valve with the possibility of further analysis of individual stages in accor-dance with the accepted assumptions. The operating control signal is justified with a serial asynchronous interface with an offset operating range of permissible values of mA. Separate stages in the operation of the synthesized shut-off based on accepted assumptions been described. Measurements were performed for disc damper angles of 30°, 60°, value of the pressure control bar) of measuring for the control system in of numerical modeling regarding the study of the rotary inter-flange mathematical efficiency of controlled shut-off fittings (V-shaped ball valve, disc rotary inter-flange valve) at a sugar factory. The average angle of rotation for a ball is 17.61 degrees; the average value of the vapor temperature after cooling is 130.91 °C (subject to a given value of 130.0 °C). The deviation of the set value is 0.7 %. The average value for the angle of rotation of the disc damper at 43.0 degrees showed the largest deviation of technological parameters of 1.45 %.

The object of research considered in this paper is the design mindset process in the development of a prototype of electronic service for assessing the environmental impact of the planned activities of business entities. The problems of applying the design mindset approach to the process of automating environmental impact assessment have been investigated. As a result of the application of the design mindset in combination with the stakeholder approach, a logical-structural scheme and architecture of the electronic service interface for different categories of users have been developed. The peculiarities of the prototype of the electronic service of environmental impact assessment (EIA ES) are to provide a common understanding of the requirements and results obtained, and convenient access to the execution of calculations. Distinctive features for the implementation of calculations in EIA ES are the quantitative obtaining of the values of indicators, which are represented by a report in the form of qualitative parameters through the use of a new algorithm. This allows business entities to perform calculations of indicators using online access at the stage of preparing the report.
The developed interface design and functionality of the EIA prototype were evaluated by the expert method on a 10-point scale when making calculations for a real project. The effectiveness of the electronic service has been confirmed by the consistency of expert  Ventricular extrasystoles (VE) are considered the most dangerous type of heart rhythm disorders for human life, their timely detection, diagnosis and prevention are urgent issues of cardiology. In order to ensure the objectivity of diagnosis of VE, it is necessary to process a large amount of information related to the results of various medical studies, tests, anamnesis, accompanying diseases, etc., along with a long-term Holter ECG monitor. In order to process such a large amount of information and make a correct diagnosis, the issue of applying medical expert systems (MES) to doctors is currently relevant. ESs using probabilistic models based on Bayes' theorem are currently preferred because there are uncertainties in medical diagnosis issues that the same symptoms may be related to different diseases. The object of this study is the development and construction of a Bayesian belief network (BBN) for the purpose of diagnosing VEs. The choice of BBN is justified by the fact that they have the ability to combine several types of information, as well as the ability to manage uncertainties and work with incomplete information. The result of the application of the developed BBN is a probabilistic assessment of the diagnosis of VE. This network was built in the NETICA system from Norsys Software Corp. A distinctive feature of this work is that when compiling the table of conditional probabilities of BBN for the diagnosis of The paper presents the two-stage alignment and extending methods of parallel corpora for the Kazakh language. The Kazakh language is agglutinative with rich morphology and related to the Turkic language group. So, the traditional alignment methods for similar languages do not work for the Kazakh language. The alignment is used primarily to ensure that the fragment corresponding to the original is found in the translation. After that, identical fragments of parallel texts are compared with each other. At the initial stage, the question is what needs to be leveled. It is possible to align word by word, but this often becomes almost impossible for several reasons: sets of lexemes and expressions do not match in different languages. Considering the linguistic peculiarities of languages, the developed technologies and ways of universal alignment of parallel text may not work in languages with agglutination. It means that the form of the word is formed by additional affixes and auxiliary words that carry semantic and morphological information. The approach presented in this paper is to use a two-stage alignment, which uses a bilingual dictionary of synonyms. The evaluation with the use of the English-Kazakh corpus verifies that our method shows an average of 89 % correct alignment. The second method is designed to expand the parallel corpus due to the lack of natural parallel corpora of the Kazakh-English language pair with good quality. The developed method uses a combinatorial method taking into account the semantic and grammatical features of the Kazakh language. Different tenses of the Kazakh language are used for sentence generation, and different endings for parts of speech are also considered.
Keywords: parallel corpora, aligning, Kazakh, English, sentence generation, extending technology.  Research in the field of semantic text analysis begins with the study of the structure of natural language. The Kazakh language is unique in that it belongs to agglutinative languages and requires careful study. The object of this study is the text in the Kazakh language. Existing approaches to the study of the semantic analysis of text in the Kazakh language do not consider text analysis using the methods of thematic modeling and learning of neural networks. The purpose of this study is to determine the quality of a topic model based on the LDA (Latent Dirichlet Allocation) method with Gibbs sampling, through neural network learning. The LDA model can determine the semantic probability of the keywords of a single document and give them a rating score. To build a neural network, one of the widely used LSTM architectures was used, which has proven itself well in working with NLP (Natural Language Processing). As a result of learning, it is possible to see to what extent the text was trained and how the semantic analysis of the text in the Kazakh language went. The system, developed on the basis of the LDA model and neural network learning, combines the detected keywords into separate topics. In general, the experimental results showed that the use of deep neural networks gives the expected results of the quality of the LDA model in the processing of the Kazakh language. The developed model of the neural network contributes to the assessment of the accuracy of the semantics of the used text in the Kazakh language. The results obtained can be applied in systems for processing text data, for example, when Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartography, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area.
Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and formation of features that describe texture differences, the selection and segmentation of texture areas, the classification of texture areas, and the identification of an object by texture. Depending on the features of the texture areas of the images used, segmentation methods based on area analysis can be divided into statistical, structural, fractal, spectral, and combined methods.
The article discusses textural features for the analysis of texture images, and defines informative textural features to identify negative factors for crop growth. To solve the tasks, textural features are used. Much attention is paid to the development of software tools that allow to highlight the features that describe the differences in textures for the segmentation of texture areas. This approach is universal and has great potential on the studied aerospace image to identify objects and boundaries of regions with different properties using clustering based on images of the same surface area taken in different vegetation periods. That is, the question of the applicability of sets of texture features and other parameters for the analysis of experimental data is being investigated.
Keywords: textural features, agricultural crops, image processing, space images.

Ihor Korol
Uzhhorod National University, Uzhhorod, Ukraine ORCID: https://orcid.org/0000-0001-7826-0249 Convenient and accurate verification of the user of a car sharing system is one of the key components of the successful functioning of the car sharing system as a whole. The machine learning-based KYC (Know your customer) process algorithm makes it possible to improve the accuracy of customer data validation and verification. This makes it possible to eliminate possible losses and reputational losses of the company in case of unforeseen situations while using the client's car sharing services. The object of this study is to find a solution to the problem of user verification in a car sharing system based on the KYC process using deep learning methods with a combination of OCR (Optical Character Recognition) methods.
The statement of the user verification problem in the car sharing system was formalized and the key parameters for the KYC process have been determined. The algorithm of the KYC process was constructed. The algorithm includes six successive stages: separating a face in the photograph, comparing faces, checking documents and their validity period, establishing and recognizing ROI (region of interest), formulating a verification decision. To separate the face in the client's photograph and compare faces, methods based on deep learning, as well as the quick HoG method (Histogram of oriented gradients), were considered and implemented. Verification of these methods on a test dataset, which includes images of documents of two thousand clients, showed that the recognition accuracy was 91 % according to Jaccard's metric. The average time of face separation using the HoG method was 0.2 seconds and when using trained models -3.3 seconds. Using a combination of ROI and ORC separation methods makes it possible to significantly improve the accuracy of verification. The proposed client verification algorithm is implemented as an API on an ML server and integrated into the car sharing system.

Iryna Kovalenko
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine ORCID: https://orcid.org/0000-0003-1097-2041 The object of this study is electromechanical processes in an autonomous wind power plant with a magnetoelectrical generator.
Under actual conditions, the wind speed is constantly changing. The wind turbine works as efficiently as possible only at the rated value of wind speed. When the wind speed changes, the efficiency of converting mechanical wind energy into electrical energy decreases. Controlling the power of the electric generator when the wind speed changes is a relevant scientific and technical task.
A maximum power selection control system based on the parameters of an experimental sample of a synchronous magnetoelectric generator has been designed and investigated. A feature of the synthesized control system is that it was developed on the basis of the concept of inverse dynamics problems in combination with minimization of local functionals of instantaneous energy values. The control law provides weak sensitivity to parametric perturbations of the object and carries out dynamic decomposition of the interdependent nonlinear system, which predetermines its practical implementation. Transient processes of the power, voltage, and current of the stator, as well as the voltage and excitation current were established when the wind speed changes from 3 to 8 m/s, as well as when the active electrical resistance of the load changes.
The results of this study confirm the effectiveness of the maximum power take-off control system when wind speed and load change. When the wind speed changes within 3-8 m/s and the load by 50 %, the efficiency of converting mechanical wind energy into electrical energy increases by 15-40 % compared to the traditional magnetoelectric system.
The findings of the current study are recommended for practical use in autonomous power plants based on wind turbines with generators with permanent magnets.