Volodymyr Lytvynenko

Work place: Kherson National Technical University, Ukraine

E-mail: immun56@gmail.com

Website: https://www.researchgate.net/profile/Volodymyr-Lytvynenko

Research Interests: Supervised Learning, Knowledge Discovery, Clustering, Classification, Data Mining, Pattern Recognition, Text Mining, Machine Learning


Volodymyr Lytvynenko currently works at the Department of Informatics and Computer Sciences, Kherson National Technical University. He provides research in Data Mining, Computing in Mathematics, Natural Science, Engineering and Medicine, and Artificial Neural networks. His current research project is “Development of methods and algorithms for reverse engineering of gene regulatory networks based on soft computing.”

Author Articles
Modeling and Simulating Mutual Testing in Complex Systems by Using Petri Nets

By Viktor Mashkov Volodymyr Lytvynenko Irina Lurie

DOI: https://doi.org/10.5815/ijigsp.2023.06.07, Pub. Date: 8 Dec. 2023

The paper tackles the problem of performing mutual testing in complex systems. It is assumed that units of complex systems can execute tests on each other. Tests among system units are part of system diagnosis that can be carried out both before and during system operation. The paper considers the case when tests are executed during system operation. Modelling and simulating mutual tests will allow evaluation of the efficiency of using joint testing in the system. In the paper, the models that use Petri Nets were considered. These models were used for simulating the execution of tests among system units. Two methods for performing such simulations were evaluated and compared. Recommendations for choosing a more appropriate way were made. Simulation results have revealed minor model deficiencies and possible implementation of mutual testing in complex systems. Improvement of the model was suggested and assessed. A recommendation for increasing the efficiency of system diagnosis based on joint testing was made.

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Digital Method of Automated Non-destructive Diagnostics for High-power Magnetron Resonator Blocks

By Serge Olszewski Yaroslav Tanasiichuk Viktor Mashkov Volodymyr Lytvynenko Irina Lurie

DOI: https://doi.org/10.5815/ijigsp.2022.01.04, Pub. Date: 8 Feb. 2022

The paper reveals the problem of the lack of standard non-destructive diagnostic methods for high-power microwave devices aimed at regeneration. The issue is understudied and requires further research. The conducted analysis of state of the art on the subject area exhibited that image processing was used to specify the examined object's target characteristics in a wide range of research. Having summarized the considered image comparison methods on the subject area of this work, the authors formulated several requirements for the selected image analysis method based on the automated non-destructive diagnosis of resonator units for high-power magnetrons. The primary requirement is using non-iterative algorithms; the second condition is a chosen method of image analysis, and the third option is the number of pixels for a processed image. It must significantly exceed the number of descriptors required for making a decision. Guided by the analysis results and based on the results of previous studies conducted by the authors, the algorithm for identifying a defect in the resonator unit of a microwave device based on the image of the frequency-azimuthal distribution for the probing field phase difference expressed by the Zernike moments is proposed. MATLAB R14a was used as a modeling environment. The descriptor vector was restricted to the Zernike moments, including the 7th order. The work is interdisciplinary and written at the intersection of technical diagnostics, microwave engineering, and digital image processing.

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Comparative Studies of Self-organizing Algorithms for Forecasting Economic Parameters

By Volodymyr Lytvynenko Olena Kryvoruchko Irina Lurie Nataliia Savina Oleksandr Naumov Mariia Voronenko

DOI: https://doi.org/10.5815/ijmecs.2020.06.01, Pub. Date: 8 Dec. 2020

This manuscript presents the economic research results based on their input-output characteristics and functional description with inductive modeling methods and tools. There are a wide plethora of methods to be used for solving this type of problem, including various neural network models, linear and nonlinear regressions, reference vectors’ methods, fuzzy models, etc. The main disadvantage of these methods is that the obtained models cannot always interpret and obtain a model of optimal complexity. Unlike the mentioned methods and tools, the group method of data handling (GMDH) allows building models directly from a data sample without the attraction of additional a priori information. This algorithm admits finding internal dependencies in the data and determining optimal model complexity. There is a broad range of iterative GMDH algorithms that have been developed and studied. Oversampling algorithms are applicable for solving the structural identification problems for a limited number of arguments. Iteration algorithms are suitable for solving tasks with many arguments, but they do not guarantee proper structure development. Multi-row GMDH iteration algorithms are the most popular ones. However, they have several sufficient defects, such as informative argument loss or non-informative argument inclusion, as well as a polynomial degree of exponential growth. In this context, the applicability of the GMDH-based iterative and combined architectures for solving the model's interrelation problems between a volume of capital investments and GDP by activity types in the transport branch is considered. The determination coefficient is utilized for the estimation of the obtained models based on a complicated evaluation procedure. The Kolmogorov-Smirnov criterion estimates the model’s adequacy. The F-criterion Fisher assesses the significance of polynomial models. The demonstrated results proved that the combined iterative and combinatorial algorithms turned out to be the most effective solution for all evaluation criteria.

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Method for Unit Self-Diagnosis at System Level

By Viktor Mashkov Volodymyr Lytvynenko

DOI: https://doi.org/10.5815/ijisa.2019.01.01, Pub. Date: 8 Jan. 2019

This paper suggests unconventional approach to system level self-diagnosis. Traditionally, system level self-diagnosis focuses on determining the state of the units which are tested by other system units. In contrast, the suggested approach utilizes the results of tests performed by a system unit to determine its own state. Such diagnosis is in many respects close to self-testing, since a unit evaluates its own state, which is inherent in self-testing. However, as distinct from self-testing, in the suggested approach a unit evaluates it on the basis of tests that it does not performs on itself, but on other system units. The paper considers diļ¬€erent diagnosis models with various testing assignments and diferent faulty assumptions including permanent and intermittent faults, and hybrid- fault situations. The diagnosis algorithm for identifying the unit’s state has been developed, and correctness of the algorithm has been verified by computer simulation experiments.

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Technology of Gene Expression Profiles Filtering Based on Wavelet Analysis

By Sergii Babichev Jiri Skvor Jiri Fiser Volodymyr Lytvynenko

DOI: https://doi.org/10.5815/ijisa.2018.04.01, Pub. Date: 8 Apr. 2018

The paper presents the technology of gene expression profiles filtering based on the wavelet analysis methods. A structural block-chart of the wavelet-filtering process, which involves concurrent calculation of Shannon entropy for both the filtered data and allocated noise component is proposed. Simulation of the wavelet-filtering process was performed with the use of orthogonal and biorthogonal wavelets on different levels of wavelet decomposition and with the use of various values of the thresholding coefficient. Result of the simulation has allowed us to propose the technology to determine the optimal parameters of the wavelet filter based on complex analysis of the filtered data and allocated noise component.

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