Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 3
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Документ Відкритий доступ Generalized scenarios of transition to chaos in ideal dynamic systems(КПІ ім. Ігоря Сікорського, 2024) Horchakov, Oleksii; Shvets, AleksandrThe implementation of a new scenario of transition to chaos in the classi-cal Lorenz system has been discovered. Signs of the presence of an implementation of the generalized intermittency scenario for dynamic systems are described. Phase-parametric characteristics, Lyapunov characteristic exponents, distributions of in-variant measures, and Poincaré sections are constructed and analyzed in detail, which confirm the implementation of the generalized intermittency scenario in an ideal Lorenz systemДокумент Відкритий доступ Reducing risk for assistive reinforcement learning policies with diffusion models(КПІ ім. Ігоря Сікорського, 2024) Tytarenko, AndriiCare-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. It creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. While cost has been a barrier to accessibility, techno-logical progress can democratize these solutions. Safety remains a paramount con-cern, especially given the intricate interactions between assistive robots and humans. This study explores the application of reinforcement learning (RL) and imitation learning in improving policy design for assistive robots. The proposed approach makes the risky policies safer without additional environmental interactions. The enhancement of the conventional RL approaches in tasks related to assistive robotics is demonstrated through experimentation using simulated environments.Документ Відкритий доступ The role of generative artificial intelligence (gai) in scientific research(КПІ ім. Ігоря Сікорського, 2024) Petrenko, AnatoliiThe emergence and growing capabilities of Generative Artificial Intelli-gence (GAI) are profoundly transforming scientific research. Although AI extends human intelligence by automating certain tasks, it complements rather than replaces human creativity. This article discusses the implications of AI for the scientific process, including ethical considerations and the need for a balanced approach that combines the strengths of human and artificial intelligence in the process of discov-ering knowledge and solving complex problems. The discussion extends to the need for universities to adapt their curricula to prepare future researchers for the AI era, emphasizing scenario-based thinking and uncertainty management as important skills for the future.Документ Відкритий доступ Fairness of 2d corotational beam spline as compared with geometrically nonlinear elastic beam(КПІ ім. Ігоря Сікорського, 2024) Orynyak, Igor; Yablonskyi, Petro; Koltsov, Dmytro; Chertov, Oleg; Mazuryk, RomanThe goal of this paper is to further investigate the properties and advan-tages of corotational beam spline, CBS, as suggested recently. Emphasis is placed on the relatively simple task of drawing the spline between two endpoints with pre-scribed tangents. In the capacity of “goodness” of spline, the well-known notion of “fairness” is chosen, which presents itself as the integral from the squared curvature of spline over its length and originates from the elastic beam theory as the minimum of energy of deformation. The comparison is performed with possible variants of the cubic Bezier curve, BC, and geometrically nonlinear beam, GNB, with varying lengths. It was shown that CBS was much more effective than BC, where any at-tempt to provide better fairness of BC by varying the distances from endpoints to two intermediate points generally leads to lower fairness results than CBS. On the other hand, GNB, or in other words, the elastica curve, can give slightly better val-ues of fairness for optimal lengths of the inserted beam. It can be explained by the more sophisticated scientific background of GNB, which employs 6 degrees of free-dom in each section, compared with CBS, which operates only by 4 DoFДокумент Відкритий доступ Identification of nonlinear systems with periodic external actions (part I)(КПІ ім. Ігоря Сікорського, 2024) Gorodetsky, ViktorThe problem of identifying nonlinear systems with periodic external ac-tions is considered in the article. The number of such actions in the system is not limited, and these actions can be either additive or multiplicative. We use a time se-ries of observed system variables to calculate unknown equation coefficients. The proven theorem allows us to separate the unknown coefficients of the system into variables and constants. The proposed computational procedure allows us to avoid possible errors caused by the discrete nature of observable time series. Identification of zero coefficients is carried out in two ways, eliminating erroneous zeroing of the terms of the equations. The method is illustrated with a numerical example of identi-fying a chaotic system with periodic external actionsДокумент Відкритий доступ Comparison of methods for interpolation and extrapolation of boundary trajectories of short-focus electron beams using root-polynomial functions(КПІ ім. Ігоря Сікорського, 2024) Melnyk, Igor V.; Pochynok, Alina V.; Skrypka, Mykhailo Yu.The article considers and discusses the comparison of interpolation and extrapolation methods of estimation of the boundary trajectory of electron beams propagated in ionized gas. All estimations have been computed using root-polynomial functions to numerically solve a differential-algebraic system of equa-tions that describe the boundary trajectory of the electron beam. By providing analy-sis, it is shown and proven that in the case of solving a self-connected interpolation-extrapolation task, the average error of the beam radius estimation is generally smaller. This approach was especially effective in estimating the focal beam radius. An algorithm for solving self-connected interpolation-extrapolation tasks is given, and its efficiency is explained. Corresponding graphic dependencies are also given and analyzedДокумент Відкритий доступ Data scrambler knight tour algorithm(КПІ ім. Ігоря Сікорського, 2024) Romanuke, V. V.; Yaremko, S. A.; Kuzmina, O. M.; Yehoshyna, H. A.Nowadays, data scrambling remains a vital technique to protect sensitive information by shuffling it in a way that makes it difficult to decipher or reverse-engineer while still maintaining its usability for legitimate purposes. As manipulat-ing the usability of the scrambled data remains a challenge on the background of risking losing data and getting them re-identified by attackers, scrambling and de-scrambling should be accomplished faster by not increasing data loss and re-identification risks. A scrambling algorithm must have a linear time complexity, still shuffling the data to minimize the risks further. A promising approach is based on the knight open tour problem, whose solutions appear like a random series of knight positions. Hence, a knight open tour algorithm is formalized, by which the knight seems to move chaotically across the chessboard. The formalization is presented as an indented pseudocode to implement it efficiently, whichever programming lan-guage is used. The output is a square matrix representing the knight open tour. Based on the knight tour matrix, data scrambler and descrambler algorithms are pre-sented in the same manner. The algorithms have a linear time complexity. The knight-tour scrambling has a sufficiently low guess probability if an appropriate depth of scrambling is used, where the data is re-scrambled repetitively. The scram-bling depth is determined by repetitive application of the chessboard matrix, whose size usually increases as the scrambling is deepened. Compared to the pseudoran-dom shuffling of the data along with storing the shuffled indices, the knight-tour de-scrambling key is stored and sent far simpler yet ensures proper data security.Документ Відкритий доступ Determination of the generalized optimality criteria for selecting civilian shelter facilities from attacks by ballistic (cruise) missiles and kamikaze drones in urbanized areas(КПІ ім. Ігоря Сікорського, 2024) Yakovenko, V.; Furmanova, N.; Flys, I.; MalyiI, O.; Farafonov, O.; Moroz, H.The object of the study is the planning of the selection of civilian shelter from attacks by ballistic (cruise) missiles and kamikaze drones in urbanized areas. A generalized model for assessing the choice of civilian shelter facilities has been developed by applying linear forms of factor linkage in combination with a general-ized optimality criterion in the form of a linear combination of local criteria. The multivariate regression analysis method was chosen to study the correlation between the generalized criterion and the observed feature. A generalized criterion for the op-timal choice of civilian shelter facilities from attacks by ballistic (cruise) missiles and kamikaze drones in urbanized areas is calculated in the form of regression coef-ficients. The criterion can facilitate a simplified determination of the generalized in-dicator of a linear model for planning the protection of civilians in cities outside the area of hostilities. The initial data is a set of physical (technical) states of shelters with a list of values and features sufficient to assess their resistance to high dynamic loadsДокумент Відкритий доступ Improving the accuracy of neural network exchange rate forecasting using evolutionary modeling method(КПІ ім. Ігоря Сікорського, 2024) Fedin, S. S.A set ofmodels of feedforward neural networks is created to obtain op-erational forecasts of the time series of the hryvnia/dollar exchange rate. It is shown that using an evolutionary algorithm for the total search of basic characteristics and a genetic algorithm for searching the values of the matrix of neural network weight coefficients allows optimizing the configuration and selecting the best neural net-work models according to various criteria of their training and testing quality. Based on the verification of forecasting results, it is established that the use of neural net-work models selected by the evolutionary modelling method increases the accuracy of forecasting the hryvnia/dollar exchange rate compared to neural network models created without the use of a genetic algorithm. The accuracy of the forecasting re-sults is confirmed by the method of inverse verification using data from different retrospective periods of the time series using the criterion of the average absolute percentage error of the forecast