Viktor Mashkov

Work place: University of J.E. Purkyne in Usti nad Labem, Czech Republic



Research Interests: Mutual Testing Procedure, System Modeling, Simulation, Computer systems and computational processes, Software Engineering, Petri Nets, System Level Self-diagnosis


Viktor Mashkov is a Doctor of Science in Engineering, Docent in the IT Department at the University of J.E. Purkyne in Usti nad Labem, the Czech Republic. His primary research focuses on the dependability of computer systems, software fault tolerance, system-level self-diagnosis, and multi-agent systems.

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

By Viktor Mashkov Volodymyr Lytvynenko Irina Lurie

DOI:, 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:, 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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Method for Unit Self-Diagnosis at System Level

By Viktor Mashkov Volodymyr Lytvynenko

DOI:, 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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