Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 4

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    Типові та узагальнені переходи до детермінованого хаосу нетипових атракторів неідеальних динамічних систем
    (КПІ ім. Ігоря Сікорського, 2022) Швець, О. Ю.
    Розглянуто деякі прикладні нелінійні неідеальні динамічні системи п’ятого порядку, які застосовуються для опису коливань сферичних маятників та у гідродинаміці. Побудовано максимальні атрактори, як регулярні, так і хаотичні, таких систем. Обговорено різноманітні біфуркації максимальних атракторів. Установлено перехід до детермінованого хаосу для максимальних атракторів за типовими сценаріями Фейгенбаума та Манневілля–Помо. Досліджено імплементацію сценарію узагальненої переміжності для хаотичних максимальних атракторів таких систем. Виявлено ознаку реалізації сценарію узагальненої переміжності.
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    Application of beam theory for the construction of twice differentiable closed contours based on discrete noisy points
    (КПІ ім. Ігоря Сікорського, 2022) Orynyak, I.; Koltsov, D.; Chertov, O.; Mazuryk, R.
    The smoothing of measured noisy positions of discrete points has considerable significance in various industries and computer graphic applications. The idea of work consists of the employment of the technique of beam with spring supports. The local coordinates systems are established for beam straight line segments, where the initial angles between them are accounted for in the conjugation equations, which provide the angular continuity. The notions of imaginary points are introduced, the purpose of which is to approach the real length of the smoothed contour to the length of the straight chord. Several examples of closed denoised curve reconstruction from an unstructured and highly noisy 2D point cloud are presented.
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    Dynamic certification and assessment of the buildings life cycle under regular explosive impacts
    (КПІ ім. Ігоря Сікорського, 2022) Trofymchuk, O. M.; Kaliukh, I. I.; Dunin, V. A.; Kyrash, S. Y.
    Today in Ukraine, there is no single legalized, generally accepted methodology (at the level of a Ukrainian building standard) for dynamic certification of buildings and structures. A unified approach is proposed as such a technique. It includes four components: visual inspection of buildings; experimental studies of the dynamic response of buildings or structures to explosive effects; mathematical modeling of the stress-strain state of the object under study; synthesis of the results of visual inspection; experimental studies and numerical simulation in order to generalize them systematically. As an approbation, the deterioration of the resource of reinforced concrete structures of residential buildings under the conditions of constant mass industrial explosions with a capacity of 500 to 700 tons in the quarry of Southern GZK (Mining and Processing Plant) in the city of Kryvyi Rih, Ukraine, has been studied. Based on the processing of numerous experimental data and the results of mathematical modeling, a probabilistic model for predicting the deterioration of the technical condition of reinforced concrete structures of the Center for Children and Youth Creativity “Mriya” has been obtained. Calculations of the risks of destruction of the building’s load-bearing elements for its vulnerable areas made it possible to clarify its service life. It decreased by 30 years compared to the standard in 2012.
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    On some methods for solving the problem of power distribution of data transmission channels taking into account fuzzy constraints on consumption volumes
    (КПІ ім. Ігоря Сікорського, 2022) Ivokhin, E. V.; Adzhubey, L. T.; Vavryk, P. R.; Makhno, M. F.
    The article deals with the mathematical formulation of the problem of optimal distribution of the power of data transmission channels in information and computer networks with a three-level architecture and fuzzy restrictions on consumption volumes. An efficient algorithm has been developed for solving the problem, the peculiarity of which is the inability to meet the end user’s needs at the expense of the resources of different suppliers. A standard solution method based on a fuzzy optimization problem of mathematical programming is considered. A constructive variant of finding a solution based on the backtracking method is proposed. Computational experiments have been carried out. The developed approach was used to determine the optimal configuration of a three-level information and computer network with a given number of communication servers.
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    Clusterization of vector and matrix data arrays using the combined evolutionary method of fish schools
    (КПІ ім. Ігоря Сікорського, 2022) Bodyanskiy, Ye.; Shafronenko, А.; Pliss, І.
    The problem of clustering data arrays described in both vector and matrix forms and based on the optimization of data distribution density functions in these arrays is considered. For the optimization of these functions, the algorithm that is a hybrid of Fish School Search, random search, and evolutionary optimization is proposed. This algorithm does not require calculating the optimized function’s derivatives and, in the general case, is designed to find optimums of multiextremal functions of the matrix argument (images). The proposed approach reduces the number of runs of the optimization procedure, finds extrema of complex functions with many extrema, and is simple in numerical implementation.
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    Data mining tools for complex socio-economic processes and systems
    (КПІ ім. Ігоря Сікорського, 2022) Obelets, T. V.
    The paper considers discovering new and potentially useful information from large amounts of data that actualizes the role of developing data mining tools for complex socio-economic processes and systems based on the principles of the digital economy and their processing using network applications. The stages of data mining for complex socio-economic processes and systems were outlined. The algorithm of data mining was considered. It is determined that the previously used stages of data mining, which were limited to the model-building process, can be extended through the use of more powerful computer technology and the emergence of free access to large amounts of multidimensional data. The available stages of data mining for complex socio-economic processes and systems include the processes of facilitating data preparation, evaluation, and visualization of models, as well as indepth learning. The data mining tools for complex socio-economic processes and systems in the context of technological progress and following the big data paradigm were identified. The data processing cycle has been investigated; this process consists of a series of steps starting with the input of raw data and ending with the output of useful information. The knowledge obtained at the data processing stage is the basis for creating models of complex socio-economic processes and systems. Two types of models (descriptive and predictive) that could be created in the data mining process were outlined. Algorithms for estimating and analyzing data for modeling complex socio-economic processes and systems in accordance with the pre-set task were determined. The efficiency of introducing neural networks and deep learning methods used in data mining was analyzed. It was determined that they would allow effective analysis and use of the existing large data sets for operational human resources management and strategic planning of complex socio-economic processes and systems.
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    The problem of automatic classification of pictures using an intelligent decision-making system based on the knowledge graph and fine-grained image analysis
    (КПІ ім. Ігоря Сікорського, 2022) Martynenko, A. A.; Tevyashev, A. D.; Kulishova, N. Ye.; Moroz, B. I.
    In order to prevent the illegal export of paintings abroad, a museum examination using various methods for studying a work of art is carried out. At the same time, an analysis is also made of historical, art history, financial and other information and documents confirming the painting’s authenticity — provenance. Automation of such examination is hampered by the need to take into account numerical values of visual features, quality indicators, and verbal descriptions from provenance. In this paper, we consider the problem of automatic multi-task classification of paintings for museum expertise. A system architecture is proposed that checks provenance, implements a fine-grained image analysis (FGIA) of visual image features, and automatically classifies a painting by authorship, genre, and time of creation. Provenance is contained in a knowledge graph; for its vectorization, it is proposed to use a graph2vec type encoder with an attention mechanism. Fine-grained image analysis is proposed to be performed using searching discriminative regions (SDR) and learning discriminative regions (LDR) allocated by convolutional neural networks. To train the classifier, a generalized loss function is proposed. A data set is also proposed, including provenance and images of paintings by European and Ukrainian artists.
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    Methodological aspects of operative control system intellectualization for dynamic objects
    (КПІ ім. Ігоря Сікорського, 2022) Melnykov, S. V.; Malezhyk, P. M.; Gasanov, A. S.; Bidyuk, P. I.
    The problems of intelligent control system organization are considered: determining the number of intellectualization levels, the sequence of actions required for analysis of the control process, adding to the control system new elements providing for enhancement degree of its intellectualization, special features of its structural organization, estimating the possibilities of intellectualization, providing examples of practical intellectualization. The primary purpose of the study is to determine the purposeful organization of intelligent control systems as well as the necessity and usefulness of systemic consideration that takes into consideration the following: requirements of the problem statement, characteristics of the environment, means for acquiring and processing necessary information, working control mechanisms, functional characteristics and experience of user-operator. As a result of the analysis performed, characteristic levels of the intellectual development of a system were determined, the stages of performing intellectualization of a control system were proposed, and the effectiveness of proposed solutions for practical problems was shown.
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    Comparative analysis of modified semi-supervised learning algorithms on a small amount of labeled data
    (КПІ ім. Ігоря Сікорського, 2022) Lyubchyk, L. M.; Yamkovyi, K. S.
    The paper is devoted to improving semi-supervised clustering methods and comparing their accuracy and robustness. The proposed approach is based on expanding a clustering algorithm for using an available set of labels by replacing the distance function. Using the distance function considers not only spatial data but also available labels. Moreover, the proposed distance function could be adopted for working with ordinal variables as labels. An extended approach is also considered, based on a combination of unsupervised k-medoids methods, modified for using only labeled data during the medoids calculation step, supervised method of k nearest neighbor, and unsupervised k-means. The learning algorithm uses information about the nearest points and classes’ centers of mass. The results demonstrate that even a small amount of labeled data allows us to use semi-supervised learning, and proposed modifications improve accuracy and algorithm performance, which was found during experiments.
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    Аналіз та прогнозування рівня сталого розвитку в європейському контексті
    (КПІ ім. Ігоря Сікорського, 2022) Пишнограєв, І. О.; Ткаченко, І. О.
    Висвітлено результати проведеного дослідження із прогнозування рівня сталого розвитку в європейському контексті. На підставі аналізу науко- вих здобутків вітчизняних та зарубіжних науковців визначено, що наявні ме- тодології мають ряд проблем, зумовлених використанням великої кількості показників, що унеможливлює швидке приблизне оцінювання нового об’єкта чи періоду. З огляду на це, дослідження спрямовано на побудову моделі роз- рахунку рівня сталого розвитку на основі обмеженого набору відкритих даних, що значно полегшить процес як його оцінювання, так і прогнозування. Базою дослідження є дані Світового центру даних з геоінформатики та сталого роз- витку і проекту «Sustainable development index». Моделювання та аналіз вико- нано у застосунках MS Excel і RStudio. Отримані результати демонструють, що прогнозувати рівень сталого розвитку можна на основі моделі апроксима- ції, використовуючи обмежений набір індикаторів розвитку територій, що призведе до втрати мінімальної кількості інформації.
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    1D CNN model for ECG diagnosis based on several classifiers
    (КПІ ім. Ігоря Сікорського, 2022) Mahmoud M. Bassiouni; Islam Hegazy; Nouhad Rizk; El-Sayed A. El-Dahshan; Abdelbadeeh M. Salem
    One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.