Факультет електроніки та інформаційних технологій (ЕлІТ)
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Item Functional networks for ergonomics and reliability tasks on the 90th anniversary of A. Gubinsky and V. Evgrafov(Sigurnost, 2022) Лавров, Євгеній Анатолійович; Лавров, Евгений Анатольевич; Lavrov, Yevhenii Anatoliiovych; Siryk, O.E.The paper considers the tasks of discovering ergonomic reserves for increasing the reliability and efficiency of automated systems. The authors show the necessity of using the Activity Approach and the Theory of Functional Networks, developed by the scientific school of Professor Anatoly Ilyich Gubinsky. The authors describe the history of Functional Networks approach and the current state of development of the functional networks models and show the possibilities for automating the assessment of reliability of human-machine interactions. The research describes the common ergonomics issues and its effective solution by the introduction of automated procedures for the analysis and evaluation of functional networks. The authors further present the ways of using the developed models in decision support systems in the automated systems design and operation.Item Prediction of the vibration moment of mount Etna based on electromagnetic signal monitoring(MM Science Journal, 2022) Нагорний, Володимир В`ячеславович; Нагорный, Владимир Вячеславович; Nahornyi, Volodymyr Viacheslavovych; Straser, V.; Cataldi, D.The forecast of the start date of the Etna volcano eruption is considered. For the first time in the history of volcanology, the forecast was made by two independent methods. The first method analyzed the nature of changes in electromagnetic emission in the environment surrounding the volcano, while the second method predicted the date of Etna's eruption based on a trend composed of regularly measured electromagnetic emission parameters.Item Vibration forecast in Europe from the results of groundwater monitoring on the territory of Ukraine(MM Science Journal, 2022) Нагорний, Володимир В`ячеславович; Нагорный, Владимир Вячеславович; Nahornyi, Volodymyr Viacheslavovych; Pigulevskiy, P.The article provides theoretical and practical arguments regarding the possibility of predicting local and strong distant earthquakes around the world, based on the detection of anomalous behavior of the groundwater physical properties in the Earth's tectonically stressed zones before local and strong earthquakes. The authors suggest that many strong and major events can be predicted at least a few months before they occur. The theoretical positions of the authors are confirmed by the successful prediction of an earthquake that occurred in Ukraine on January 21, 2022.Item Educational trends 2022: essence and innovation potential(EDITORIAL PRIMMATE SAS, 2022) Gumennykova, T.; Ilchenko, P.; Базиль, Олена Олександрівна; Базиль, Елена Александровна; Bazyl, Olena Oleksandrivna; Ilchenko, A.; Vydrych, O.The paper aims to analyze the educational trends of the year 2022 and determine whether they are relevant in the future, whether they are a response to the challenges of the present. Also, attention is paid to the method of SWOT-analysis, with the help of which the strengths and vulnerabilities of distance learning are identified. The results analyze the future of distance education, in particular, special attention is paid to the experience of implementing hybrid education as a likely promising direction of further learning. Another aspect is the introduction of STEM education, media education (as a counteraction to intentional propaganda), and Education for Sustainable Development as important elements of the modern learning process in the United States and European countries. In conclusion, it is concluded that these areas of educational activities will be trends in the next decade in the recurrence of the crisis. The scientific novelty of the work consists in the fact that for the first time at the synthetic level modern educational trends were studied, their prospects and risks of use were characterized.Item Model and Training Method of the Resilient Image Classifier Considering Faults, Concept Drift, and Adversarial Attacks(MDPI, 2022) Москаленко, В`ячеслав Васильович; Москаленко, Вячеслав Васильевич; Moskalenko, Viacheslav Vasylovych; Kharchenko, V.; Москаленко, Альона Сергіївна; Москаленко, Алена Сергеевна; Moskalenko, Alona Serhiivna; Петров, Сергій Олександрович; Петров, Сергей Александрович; Petrov, Serhii OleksandrovychModern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations.Item Mathematical Model for Adaptive Technology in E-learning Systems(MECS Press, 2022) Барченко, Наталія Леонідівна; Барченко, Наталья Леонидовна; Barchenko, Nataliia Leonidivna; Толбатов, Володимир Аронович; Толбатов, Владимир Аронович; Tolbatov, Volodymyr Aronovych; Лаврик, Тетяна Володимирівна; Лаврик, Татьяна Владимировна; Lavryk, Tetiana Volodymyrivna; Ободяк, Віктор Корнелійович; Ободяк, Виктор Корнелиевич; Obodiak, Viktor Korneliiovych; Шелехов, Ігор Володимирович; Шелехов, Игорь Владимирович; Shelekhov, Ihor Volodymyrovych; Толбатов, Андрій Володимирович; Толбатов, Андрей Владимирович; Tolbatov, Andrii Volodymyrovych; Gnatyuk, S.; Tolbatova, O.The emergence of a large number of e-learning platforms and courses does not solve the problem of improving the quality of education. This is primarily due to insufficient implementation or lack of mechanisms for adaptation to the individual parameters of the student. The level of adaptation in modern e-learning systems to the individual characteristics of the student makes the organization of human-computer interaction relevant. As the solution of the problem, a mathematical model of the organization of human-computer interaction was proposed in this work. It is based on the principle of two-level adaptation that determines the choice of the most comfortable module for studying at the first level. The formation of an individual learning path is performed at the second level. The problem of choosing an e-module is solved using a fuzzy logic. The problem of forming a learning path is reduced to the problem of linear programming. The input data are the characteristics of the quality of student activity in the education system. Based on the proposed model the computer technology to support student activities in modular e-learning systems is developed. This technology allows increasing the level of student’s cognitive comfort and optimizing the learning time. The most important benefit of the proposed approach is to increase the average score and increase student satisfaction with learning.Item Advanced “Green” Prebiotic Composite of Bacterial Cellulose/Pullulan Based on Synthetic Biology-Powered Microbial Coculture Strategy(MDPI, 2022) Zhantlessova, S.; Savitskaya, I.; Kistaubayeva, A.; Ignatova, L.; Talipova, A.; Погребняк, Олександр Дмитрович; Погребняк, Александр Дмитриевич; Pohrebniak, Oleksandr Dmytrovych; Digel, I.Bacterial cellulose (BC) is a biopolymer produced by different microorganisms, but in biotechnological practice, Komagataeibacter xylinus is used. The micro- and nanofibrillar structure of BC, which forms many different-sized pores, creates prerequisites for the introduction of other polymers into it, including those synthesized by other microorganisms. The study aims to develop a cocultivation system of BC and prebiotic producers to obtain BC-based composite material with prebiotic activity. In this study, pullulan (PUL) was found to stimulate the growth of the probiotic strain Lactobacillus rhamnosus GG better than the other microbial polysaccharides gellan and xanthan. BC/PUL biocomposite with prebiotic properties was obtained by cocultivation of Komagataeibacter xylinus and Aureobasidium pullulans, BC and PUL producers respectively, on molasses medium. The inclusion of PUL in BC is proved gravimetrically by scanning electron microscopy and by Fourier transformed infrared spectroscopy. Cocultivation demonstrated a composite effect on the aggregation and binding of BC fibers, which led to a significant improvement in mechanical properties. The developed approach for “grafting” of prebiotic activity on BC allows preparation of environmentally friendly composites of better quality.Item Effect of Different Selenium Precursors on Structural Characteristics and Chemical Composition of Cu2ZnSnSe4 Nanocrystals(Institute of Physics Polish Academy of Sciences, 2022) Кахерський, Станіслав Ігорович; Кахерський, Станислав Игоревич; Kakherskyi, Stanislav Ihorovych; Pshenychnyi, R.; Доброжан, Олександр Анатолійович; Доброжан, Александр Анатольевич; Dobrozhan, Oleksandr Anatoliiovych; Vaziev, J.; Опанасюк, Анатолій Сергійович; Опанасюк, Анатолий Сергеевич; Opanasiuk, Anatolii Serhiiovych; Gnatenko, Y.In this work, Cu2ZnSnSe4 nanocrystals were synthesized by the polyol method. The chemical composition and morphological, structural, and microstructural properties of Cu2ZnSnSe4 nanocrystals, depending on the synthesis temperature and time, as well as the composition of the precursor, have been thoroughly investigated using scanning and transmission electron microscopy, energy dispersive X-ray analysis, X-ray diffraction, FTIR spectroscopy, Raman spectrometry, and low-temperature photoluminescence. We compared the properties of nanocrystals synthesized from different precursors containing selenourea or amorphous selenium as a source of selenium and then determined the optimal conditions for the synthesis of nanocrystals. It was found that the optimal synthesis time for nanocrystals obtained in the first approach is τ = 30–45 min, and in the second approach — τ = 120 min. It was also found that the optimal composition for the synthesis of single-phase Cu2ZnSnSe4 nanocrystals in the second approach is the molar ratio of precursors 2:1.5:1:4, and the synthesis temperature T = 280◦C. Thus, Cu2ZnSnSe4 nanocrystals synthesized under optimal conditions are used to develop nanoinks for printing solar cell absorbers by two- and three-dimensional printers.Item Human-centered management in polyergatic information systems. Multi-criteria distribution of functions between operators(IOP Publishing Ltd, 2022) Лавров, Євгеній Анатолійович; Лавров, Евгений Анатольевич; Lavrov, Yevhenii Anatoliiovych; Siryk, O.E.; Чибіряк, Яна Іванівна; Чибиряк, Яна Ивановна; Chybiriak, Yana Ivanivna; Zolkin, A.L.; Sedova, N.A.The article considers the problem of human factor in complex polyergatic systems with a flow of applications for functions (problem-solving) arising at random moments of time. The structure of a decision support system for the operator-manager, including subsystems of monitoring, forecasting and decision-making, is justified. The system of criteria relevant to solving the tasks of functions distribution was substantiated and its multi-criteria nature was shown. The technology of multi-criteria evaluation and choice of alternatives based on the methodology of hierarchical system analysis of problems and the method of analysis of hierarchies, Thomas Saaty has been proposed. The decision-making system, which has been tested in the operation of control systems of various complex technical and production objects, has been implemented. The proposed method differs from the known approaches in that this method is aimed at prompt decision-making, as well as in that it uses a multi-criteria approach and both pragmatic and ergonomic criteria are used as criteria.Item Information-extreme machine training system of functional diagnosis system with hierarchical data structure(National University "Zaporizhzhia Polytechnic", 2022) Shelehov, I.V.; Барченко, Наталія Леонідівна; Барченко, Наталья Леонидовна; Barchenko, Nataliia Leonidivna; Прилепа, Дмитро Вікторович; Прилепа, Дмитрий Викторович; Prylepa, Dmytro Viktorovych; Бібик, Мирослав Віталійович; Бибик, Мирослав Витальевич; Bibyk, Myroslav VitaliiovychContext. The problem of information-extreme machine learning of the functional diagnosis system is considered by the example of recognizing the technical state of a laser printer by typical defects of the printed material. The object of the research is the process of hierarchical machine learning of the functional diagnosis system of an electromechanical device. Objective. The main objective is to improve the functional efficiency of machine learning during functional diagnostics system retraining using automatically forming a new hierarchical data structure for an expanded alphabet of recognition classes. Method. A method of information-extreme hierarchical machine learning of the system of functional diagnosis of a laser printer based on typical defects of the printed material is proposed. The method was developed with functional approach of modeling the cognitive processes of natural intelligence, which makes it possible to give the diagnostic system the properties of adaptability under arbitrary initial conditions for the formation of images of printing defects and flexibility during retraining of the system due to an increase in the power of the alphabet of recognition classes. The method is based on the principle of maximizing the amount of information in the process of machine learning. The process of information-extreme machine learning is considered as an iterative procedure for optimizing the parameters of the functioning of the functional diagnostics system according to the information criterion. As a criterion for optimizing machine learning parameters, a modified Kullback’s information measure is considered, which is a functional of the exact characteristics of classification solutions. According to the proposed categorical functional model, an information-extreme machine learning algorithm has been developed based on a hierarchical data structure in the form of a binary decomposition tree. The use of such a data structure makes it possible to split a large number of recognition classes into pairs of nearest neighbors, for which the optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. Results. Information, algorithmic software for the system of functional diagnostics of a laser printer based on images of typical defects in printed material has been developed. The influence of machine learning parameters on the functional efficiency of the system of functional diagnostics of a laser printer based on images of defects in printed material has been investigated. Conclusions. The results of physical modeling have confirmed the efficiency of the proposed method of information-extreme machine learning of the system of functional diagnosis of a laser printer based on typical defects in printed material and can be recommended for practical use. The prospect of increasing the functional efficiency of information-extremal learning of the functional diagnostics system is to increase the depth of machine learning by optimizing additional parameters of the system’s functions, including the parameters of the formation of the input training matrix.