2023

Постійне посилання на фонд

https://ela.kpi.ua/handle/123456789/57991

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Зараз показуємо 1 - 20 з 42

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Uncertainties in data processing, forecasting and decision making
(КПІ ім. Ігоря Сікорського, 2023) Levenchuk, L. B.; Tymoshchuk, O. L.; Guskova, V. H.; Bidyuk, P. I.
Abstract. Forecasting, dynamic planning, and current statistical data processing are defined as the process of estimating an enterprise’s current state on the market compared to other competing enterprises and determining further goals as well as sequences of actions and resources necessary for reaching the goals stated. In order to perform high-quality forecasting, it is proposed to identify and consider possible uncertainties associated with data and expert estimates. This is one of the system analysis principles to be hired for achieving high-quality final results. A review of some uncertainties is given, and an illustrative example showing improvement of the final result after considering possible stochastic uncertainty is provided.
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Research of food security problems of the wartorn regions of ukraine using geomatics methods
(КПІ ім. Ігоря Сікорського, 2023) Zgurovsky, M.; Yefremov, K.; Gapon, S.; Pyshnograiev, I.
Every year, the world faces new difficult challenges in maintaining global security. Compliance with food security principles is an important component of the global context of world development. Recent military conflicts have had a strong impact on the development of regions that provide food for millions of people around the world. Ukraine plays a key role in providing agricultural products to the population of countries from different continents. The article is devoted to the study of the state of agricultural crops in a regional section during the period of active hostilities by means of geomatics, which allow one to assess the degree of transformation of sustainable farming quickly, determine the trend of the development of the industry, and calculate the likely scale of changes in the obtained products in the coming years. As a result, with the help of deep learning models integrated into geoinformation systems, the boundaries of agricultural fields in the Kherson and Zaporizhia regions were determined, the state of moisture and bioproductivity of agricultural crops was determined for three years, an analysis of changes has been made in the state of agricultural fields under the influence of new factors of conducting active hostilities during the first half of 2022, the next harvest productivity forecast was made in two southern regions of Ukraine. The study was carried out by the team of the World Data Center for Geoinformatics and Sustainable Development of the Igor Sikorsky Kyiv Polytechnic Institute. It was part of research on the analysis of the behavior of complex socio-economic systems and processes of sustainable development in the context of the quality and safety of people’s lives.
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Intelligent information system of the city's socio-economic infrastructure
(КПІ ім. Ігоря Сікорського, 2023) Lipianina-Honcharenko, Khrystyna; Bodyanskiy, Yevgeniy; Sachenko, Anatoliy
Abstract. Urban development is an important problem that can be solved with the help of intelligent information systems. Such systems ensure efficient management of the city’s diverse infrastructure. The researchers developed a concept of such an information system based on a conceptual model and using data flow for intelligent decision-making. The system was tested for 1460 days in the city of Ternopil. The modelling results showed that the city’s central area is stable, with 50% of enterprises in the “growing” state and 70% of people in the “satisfactory” state. People often move to the northeastern and western zones due to higher levels of comfort and more affordable housing. However, the total distance of car trips has increased by 249%, negatively impacting the environment. The condition of enterprises in other zones is less stable with lower “growth” indicators, but there are zones with “stable” and “satisfactory” conditions.
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The algorithm for predicting the cryptocurrency rate taking into account the influence of posts of a group of famous people in social networks
(КПІ ім. Ігоря Сікорського, 2023) Bidyuk, P.; Gavrilenko, O.; Myagkyi, M.
Abstract. This article presents an algorithm for predicting the rate of a selected cryptocurrency, taking into account the posts of a group of famous people in a par ticular social network. The celebrities chosen as experts, i.e., famous personalities whose posts on social networks were studied, are either familiar with the financial industry, particularly the cryptocurrency market, or some cryptocurrency. The data set used was the actual rates of the cryptocurrency in question for the selected period and the statistics of expert posts in the selected social network. The study used methods such as the full probability formula and the Bayesian formula. It was found that posts by famous people on social media differently affected cryptocurrency rates. The “main” expert was identified, and his posts were used to forecast the se lected cryptocurrency’s rate.
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Basic algorithm for approximation of the boundary trajectory of short-focus electron beam using the root-polynomial functions of the fourth and fifth order
(КПІ ім. Ігоря Сікорського, 2023) Melnyk, I.; Pochynok, A.
Abstract. The new iterative method of approximating the boundary trajectory of a short-focus electron beam propagating in a free drift mode in a low-pressure ionized gas under the condition of compensation of the space charge of electrons is considered and discussed in the article. To solve the given approximation task, the rootpolynomial functions of the fourth and fifth order were applied, the main features of which are the ravine character and the presence of one global minimum. As an initial approach to solving the approximation problem, the values of the polynomial coefficients are calculated by solving the interpolation problem. After this, the approximation task is solved iteratively. All necessary polynomial coefficients are calculated multiple times, taking into account the values of the function and its derivative at the reference points. The final values of polynomial coefficients of high-order root-polynomial functions are calculated using the dichotomy method. The article also provides examples of the applying fourth-order and fifth-order root-polynomial functions to approximate sets of numerical data that correspond to the description of ravine functions. The obtained theoretical results are interesting and important for the experts who study the physics of electron beams and design modern industrial electron beam technological equipment.
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Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography
(КПІ ім. Ігоря Сікорського, 2023) Dharmadhikari, Dipa D.; Tamane, Sharvari C.
Abstract. Technology for Cloud Computing (CC) has advanced, so Cloud Computing creates a variety of cloud services. Users may receive storage space from the provider as Cloud storage services are quite practical; many users and businesses save their data in cloud storage. Data confidentiality becomes a larger risk for service providers when more information is outsourced to Cloud storage. Hence in this work, a Ciphertext and Elliptic Curve Cryptography (ECC) with Identity-based encryption (CP-IBE) approaches are used in the cloud environment to ensure data security for a healthcare environment. The revocation problem becomes complicated since characteristics are used to create cipher texts and secret keys; therefore, a User revocation algorithm is introduced for which a secret token key is uniquely produced for each level ensuring security. The initial operation, including signature, public audits, and dynamic data, are sensible to Sybil attacks; hence, to overcome that, a Sybil Attack Check Algorithm is introduced, effectively securing the system. Moreover, the conditions for public auditing using shared data and providing typical strategies, including the analytical function, security, and performance conditions, are analyzed in terms of accuracy, sensitivity, and similarity.
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Decision-tree and ensemble-based mortality risk models for hospitalized patients with СOVID-19
(КПІ ім. Ігоря Сікорського, 2023) Vyklyuk, Ya.; Levytska, S.; Nevinskyi, D.; Hazdiuk, K.; Škoda, M.; Andrushko, S.
The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of the main indicators that lead to the patients’ mortality. Using the good-known parameters that are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible and understandable decision tree and ML ensemble of classifiers models that would allow the physician to determine the prognosis in a few minutes and, accordingly, to understand the need for treatment adjustment, transfer of the patient to the emergency department. The accuracy of the resulting ensemble of models fitted on actual hospital patient data was in the range of 0.88–0.91 for different metrics. Creating a data collection system with further training of classifiers will dynamically increase the forecast’s accuracy and automate the doctor’s decision-making process/
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Modified SEIRD model for describing the COVID-19 epidemic
(КПІ ім. Ігоря Сікорського, 2023) Klymenko, A.I.; Podkolzin, G.B.
This article is devoted to mathematical models in epidemiology, in particular SIR, SEIR, and SEIRD models. It explores the importance of these models in predicting the spread of infectious diseases and evaluating the effectiveness of control measures. These models allow for assessing important epidemic parameters such as the speed of infection transmission, the number of people infected, and the number of deaths. This data can help in making decisions regarding the imposition and lifting of quarantine restrictions, opening and closing of schools and other institutions, as well as in developing vaccination strategies and other control measures. In summary, mathematical models such as SIR, SEIR, and SEIRD are important tools in the fight against epidemics. They enable epidemiologists and medical professionals to predict and control the spread of diseases, thus preserving the health and lives of people.
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A concatenation approach-based disease prediction model for sustainable health care system
(КПІ ім. Ігоря Сікорського, 2023) Tharageswari, K.; Sundaram, N. Mohana; Santhosh, R.
Abstract. In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).
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Digital twins: stages of concept development, areas of use, prospects
(КПІ ім. Ігоря Сікорського, 2023) Pankratova, N.D.; Grishyn, K.D.; Barilko, V.E.
Abstract. The results of a review of the digital twin concept development, the areas of their use, and the prospects are highlighted. The history of the emergence and de velopment of the digital twin concept, its definition, and its classification are given. The relevance of the technology under consideration is reflected. The purpose of this review is to provide the most complete, up-to-date information on the current state of the digital twin technology, its application in various fields of human activity, and further prospects for the development of the industry. An extensive bibliography on the topic is provided, which may be helpful for researchers and representatives of various industries.
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Identification of lung disease types using convolutional neural network and VGG-16 architecture
(КПІ ім. Ігоря Сікорського, 2023) Bukhori, S.; Verdy, B. Y. N.; Eka, Y. R. Windi; Januar, A. P.
Abstract. Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%.
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Research on hybrid transformer-based autoencoders for user biometric verification
(КПІ ім. Ігоря Сікорського, 2023) Havrylovych, M. P.; Danylov, V. Y.
Abstract. Our current study extends previous work on motion-based biometric verification using sensory data by exploring new architectures and more complex input from various sensors. Biometric verification offers advantages like uniqueness and protection against fraud. The state-of-the-art transformer architecture in AI is known for its attention block and applications in various fields, including NLP and CV. We investigated its potential value for applications involving sensory data. The research proposes a hybrid architecture, integrating transformer attention blocks with different autoencoders, to evaluate its efficacy for biometric verification and user authentication. Various configurations were compared, including LSTM autoencoder, transformer autoencoder, LSTM VAE, and transformer VAE. Results showed that combining transformer blocks with an undercomplete deterministic autoencoder yields the best performance, but model performance is significantly influenced by data preprocessing and configuration parameters. The application of transformers for biometric verification and sensory data appears promising, performing on par with or surpassing LSTM-based models but with lower inference and training time.
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Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: research and implementation
(КПІ ім. Ігоря Сікорського, 2023) Kulik, A.S.; Chukhray, A.G.; Havrylenko, O.V.
Abstract. Information technologies, particularly artificial intelligence methods, involve more and more deeply into all spheres of human activity: science, technology, art, and education. Ukraine also has sufficient potential and needs to develop educational support, which is the subject of this paper. The work aims to demonstrate the components of information technology for the creation of Intelligent Tutoring Systems (ITS), which are involved in studying various engineering disciplines. The work includes methods of system analysis, mathematical and simulation modeling, technical diagnostics, and artificial intelligence. The proposed models and methods are implemented in ITS prototypes for teaching mathematics, programming, and the automatic control theory. The Intelligent Tutoring Systems were implemented in the educational process of KhAI University and other institutions in Ukraine, Great Britain, Austria, and China. Experimental studies have shown increased student learning success rates using ITS compared to traditional methods. Improved and adapted for computer training methods of technical diagnostics, Bayesian networks, and developed models of algorithmic tasks, the learning process and the learner are valuable from a scientific point of view. In a practical sense, the obtained results can be used to create new specialized ITSs and build an expandable common learning platform integrating the basic disciplines of a specific educational field.
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Guaranteed root-mean-square estimates of the forecast of matrix observations under conditions of statistical uncertainty
(КПІ ім. Ігоря Сікорського, 2023) Nakonechnyi, O.G.; Kudin, G.I.; Zinko, P.M.; Zinko, T.P.
Abstract. We investigate the problem of linear estimation of unknown mathematical expectations based on observations of realizations of random matrix sequences. Constructive mathematical methods have been developed for finding linear guaranteed RMS estimates of unknown non-stationary parameters of average values based on observations of realizations of random matrix sequences. It is shown that such guaranteed estimates are obtained either as solutions to boundary value problems for systems of linear differential equations or as solutions to the corresponding Cauchy problems. We establish the form and look for errors for the guaranteed RMS quasiminimax estimates of the special forecast vector and parameters of unknown average values. In the presence of small perturbations of known matrices in the model of matrix observations, quasi-minimax RMS estimates are found, and their guaranteed RMS errors are obtained in the first approximation of the small parameter method. Two test examples for calculating the guaranteed root mean square estimates and their errors are given.
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Algorithms of statistical anomalies clearing for data science applications
(КПІ ім. Ігоря Сікорського, 2023) Pysarchuk, O.; Baran, D.; Mironov, Yu.; Pysarchuk, I.
The paper considers the nature of input data used by Data Science algorithms of modern-day application domains. It then proposes three algorithms designed to remove statistical anomalies from datasets as a part of the Data Science pipeline. The main advantages of given algorithms are their relative simplicity and a small number of configurable parameters. Parameters are determined by machine learning with respect to the properties of input data. These algorithms are flexible and have no strict dependency on the nature and origin of data. The efficiency of the proposed approaches is verified with a modeling experiment conducted using algorithms implemented in Python. The results are illustrated with plots built using raw and processed datasets. The algorithms application is analyzed, and results are compared.
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Assessment of the economical dimension of sustainable development of the ukraine’s regions based on the brightness of night lights
(КПІ ім. Ігоря Сікорського, 2023) Zgurovsky, M.; Yefremov, K.; Gapon, S.; Pyshnograiev, I.
Abstract. When assessing the level of development of territories, the problem of finding objective qualitative data that will characterize it arises. One of the possible sources of such data is the remote sensing of the Earth (RSE). The article is devoted to the analysis of the possibility of using the product of RSE – the map of night lights, for modeling the economical dimension of the sustainable development of the regions of Ukraine. Using the regression and correlation analysis and neural networks, appropriate models for assessing the level of economic development of the Kherson region, Donetsk region, and the AR of Crimea were obtained. The study was carried out by the team of the World Data Center for Geoinformatics and Sustainable Development of the Igor Sikorsky Kyiv Polytechnic Institute. It was part of research on the analysis of the behavior of complex socio-economic systems and processes of sustainable development in the context of the quality and safety of people’s lives.
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Researches and applications of the combinatorial configurations for innovative devices and process engineering
(КПІ ім. Ігоря Сікорського, 2023) Riznyk, V.V.
This paper is devoted to the memory of Solomon Wolf Golomb (1932– 2016) — a famous American mathematician, engineer, and professor of electrical engineering. He was interested in developing techniques for improving the quality indices of engineering devices and systems with respect to performance reliability, transmission speed, positioning precision, and resolving ability based on novel combinatorial configurations. In 1996 S. Golomb have supported the project “Researches and Applications of the Combinatorial Configurations for Innovative Devices and Process Engineering” as a scientific collaboration with the Former Soviet Union (FSU) research team from Lviv Polytechnic State University (Ukraine) under the Cooperative Grant Program by CRDF (U.S.). The underlying project to be edited by S. Golomb is presented, and short information on the development of the researches and applications of optimized systems with ring structure given.
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Нейронні мережі: дослідження правил прийняття ними рішень
(КПІ ім. Ігоря Сікорського, 2023) Петренко, А.І.; Вохранов, І.А.
Анотація. Питання отримання більшої зрозумілості поведінки нейронних мереж є досить актуальним, особливо у галузях із високим рівнем ризиків. Для вирішення цієї задачі досліджено можливості нового алгоритму декомпозиції DeepRED, здатного витягувати правила прийняття рішень глибинними нейронними мережами з декількома прихованими шарами DNN (Deep Neural Networks). Дослідження алгоритму DeepRED проводилося на прикладі вилучення правил експериментальної нейронної мережі за виконання класифікації зображень бази даних MNIST рукописних цифр, що дозволило виявити ряд обмежень алгоритму DeepRED.
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Study of the underground tunnel planning. Cognitive modelling
(КПІ ім. Ігоря Сікорського, 2023) Pankratova, N.D.; Musiienko, D.I.
A study of the underground tunnel planning reliability for megacities is proposed based on the use of foresight and cognitive modeling methodologies. Using the foresight methodology allows, with the help of expert estimation procedures, to identify critical technologies and build alternatives of scenarios with quantitative characteristics. For the justified implementation of a particular scenario, cognitive modeling is used, which allows to build causal relationships based on knowledge and experience, understand and analyze the behavior of a complex system for a strategic perspective with a large number of interconnections and interdependencies. The suggested study allows the reliability planning of underground tunnels on the basis of reasonable scenarios selection and justification of their creation priority.
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Algorithm for simulating melting polar ice, Earth internal movement and volcano eruption with 3-dimensional inertia tensor
(КПІ ім. Ігоря Сікорського, 2023) Matsuki, Yoshio; Bidyuk, Petro
Abstract. This paper reports the result of an investigation of a hypothesis that the melting polar ice of Earth flowing down to the equatorial region causes volcano eruptions. We assumed a cube inside the spherical body of Earth, formulated a 3-dimensional inertia tensor of the cube, and then simulated the redistribution of the mass that is to be caused by the movement of melted ice on the Earth’s surface. Such mass distribution changes the inertia tensor of the cube. Then, the cube’s rotation inside Earth was simulated by multiplying the Euler angle matrix by the inertia tensor. Then, changes in the energy intensity and the angular momentum of the cube were calculated as coefficients of Hamiltonian equations of motion, which are made of the inertia tensor and sine and cosine curves of the rotation angles. The calculations show that the melted ice increases Earth’s internal energy intensity and angular momentum, possibly increasing volcano eruptions.

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