«Systems and Means of Informatics»
Scientific journal
Volume 36, Issue 1, 2026
Content | About Authors
Abstract and Keywords
- Yu. G. Diachenko
- N. V. Morozov
- D. Yu. Stepchenkov
- D. Yu. Diachenko
Abstract: The article focuses on the interface development for self-timed (ST) circuits interacting with both synchronous and ST environments, specifically on a FIFO (First Input First Output) data reception buffer register. The request-acknowledge nature of ST circuits interacting with their environment, the absence of a global clock signal, and their independence from the actual delays of logic cells leads to unspecified input data processing times. The environmental conditions and the data itself determine the time it takes for appearing valid result at the ST circuit's output. To improve the performance of a computing system incorporating both synchronous and ST circuits, it is advisable to use a FIFO for exchanging data and the results of their processing. A FIFO allows one to mask the discrepancy between the frequency of input data receipt and the time it takes to process it in a ST circuit. The article examines the design features of ST FIFO, proposes cases for their implementation, analyzes their consumer characteristics, and substantiates the conclusion that, based on the totality of consumer characteristics, the best choice is ST FIFO on hysteretic latches, which provides maximum performance with a slight deterioration in hardware complexity if the FIFO capacity exceeds four operands.
Keywords: self-timed circuit; FIFO; hardware costs; performance; computer- aided design; logic synthesis; conversion
- I. N. Sinitsyn
- V. I. Sinitsyn
- E. R. Korepanov
- T. D. Konashenkova
Abstract: New methodological tools of optimal synthesis of multidimensional linear stochastic system (StS) on Bayes criterion (BC) are based on quantitative estimate of output stochastic process (StP). Nonlinear StS is described by Pugachev equation for input and output StP. Input StP contains useful signal and random additive multidimensional normal noise with zero mathematical expectation and known matrix of covariance functions. Random noise does not depend upon vector of random parameters of useful signal. Distribution of random vector parameters is known. After a short survey, the model of BC optimal estimate of output StP is constructed on the basis of wavelet canonical expansion (CE) of random noise and wavelet CE of input StP. To find unknown parameters in optimal output StP, an estimate architecture of multilayer wavelet neural networks (WNN) is developed. The WNN training algorithm for inverse error prevalence by the method of steepest descent is used. Formulae for mathematical expectation, second initial probabilistic moment, and error covariance matrix of BC optimal estimate of output StP are obtained. Numerical experiments with cubic two-dimensional StS illustrate WNN preference with wavelet CE.
Keywords: Bayes criterion; canonical expansion; modeling; loss function; optimal estimate; stochastic process; stochastic system; wavelet; wavelet-neural network
- S. A. Bosov
- I. V. Uryupin
Abstract: The work is motivated by a specific class of navigation problems of autonomous underwater vehicles, for which the use of acoustic measurement means encounters their sensitivity to random delays in data arrival. At long distances, this effect may lead to a significant increase in estimation error even at moderate motion speeds. The existing formal mathematical formulation reduces to the problem of state estimation for stochastic dynamic systems with random observation time delays under conditions of incomplete prior information. The practical implementation problem, on which the paper is focused, reduces to the development and software implementation of computationally efficient stochastic filtering algorithms. The method of linear pseudomeasurements adapted to an observation model with random delay is used as the basic tool. In addition to previously considered formulations with complete prior information on the parameters of the motion and observation models, the paper analyzes cases of incomplete information typical in practice. For two of them - uncertainty of measurement accuracy characteristics at the stage of target detection and unknown error distributions under changing observation conditions, methods for solving practical problems are proposed. The algorithms are described within the framework of the general objective - to form a conceptual approach to the construction of conditionally optimal, adaptive, and robust filtering algorithms for the specified classes of models.
Keywords: autonomous underwater vehicles; navigation; positioning; target tracking; stochastic system with random observation time delays; stochastic filtering; linear pseudomeasurements; suboptimal filtering; extended Kalman filter; conditionally minimax filtering; conditionally optimal estimation; sonars
- S. P. Kovalyov
- O. V. Lukinova
Abstract: Key design decisions regarding the integration of asset digital twins as a unified core for domain digital transformation support platforms are presented. Based on the OSE/RM (Open Systems Environment Reference Model), thetech- nological aspects of digital platform design are demonstarted. The fundamental requirements for public services hosted on the platform are examined. A generic digital twin architecture is described as a set of interacting heterogeneous asset models. The model development process and deployment schemes across platform components and applications are specified. As a case study, models deployed according to the proposed scheme are employed to construct an integrated energy management cycle including the calculation of energy asset status indicators, automated determination, and execution of control actions. The proposed design decisions are formally justified using algebraic methods based on category theory.
Keywords: digital transformation; digital twin; digital platform; interoperability; energy management; category theory
- V. Yu. Korolev
- N. R. Romanyuk
Abstract: The paper addresses the problem of describing the stationary distribution for a multivariate stochastic difference equation, specifically, a first-order multivariate autoregressive scheme with random coefficients. It is demonstrated that any scalar covariance-mean mixture of multivariate normal distributions can serve as a stationary distribution within this framework. Such mixtures are characterized by a scalar mixing parameter that simultaneously scales both the vector
of expectations and the covariance matrix, thereby establishing an affine dependence between them. These mixtures have proven effective for modeling statistical regularities observed in various disciplines. It is proved that for any scalar covariance-mean mixture of multivariate normal distributions, it is possible to define a stochastic difference equation with appropriate coefficients for which the given mixture is a stationary distribution. The correspondence between the resulting mixture and the behavior of the coefficients that generate the corresponding stationary distribution is discussed. Also, a problem is considered that is in some sense inverse to that was mentioned above: does there exist a stationary distribution for a stochastic difference equation with given coefficients and if yes, then what does it look like. A version of sufficient conditions for the existence of such a stationary distribution for the stochastic difference equation is presented.
Keywords: stochastic difference equation; stationary distribution
Abstract: The article presents the final result of a study on the application of generative adversarial neural networks (GANs) for the creation of educational content. Wasserstein GANs (WGANs) are selected as an alternative to standard deep convolutional networks, which are characterized by high training complexity and impose restrictions on the kinds of generated content. A review of practical applications highlights several advantages of this type of network. In the present study, a WGAN with gradient penalty is applied to the problem of generating a vector of parameters for examination tickets. The neural network was trained and evaluated using a set of mathematical problems for the final exams in the course \Theory of functions of a complex variables" previously developed and labeled by experts. In practice, this generation technique demonstrated advantages in training stability and achieved higher output quality. Furthermore, quality metrics for the generated educational content based on the Kullback{Leibler divergence are proposed.
Keywords: educational content; machine learning; discrete distributions; generative models; generative adversarial networks; Wasserstein networks
- K. A. Zhukova
- E. V. Morozov
Abstract: The busy period of a stationary single-server retrial system with a constant retrial rate is studied. The analysis is based on a comparison of the original retrial system with a buffered M/G/l-type model featuring exceptional first service. It is shown that the busy period distribution satisfies a functional equation similar to the well-known equation for the conventional M/G/l system.
The main result is expressed in terms of Laplace-Stieltjes transforms for the continuous busy period duration and in terms of generating functions for the integer-valued busy period length. Several analytical examples are provided.
Keywords: retrial queue; busy period; regeneration; stationarity; Laplace- Stieltjes transform; constant retrial rate; first exceptional service
- V. D. Artemev
- G. M. Konovalov
- P. Y. Chaika
Abstract: The article is devoted to one of the most significant problems in the creation of the international thermonuclear experimental reactor (ITER) - ensuring reliability. In particular, it is important to assess the reliability of elements that belong to various subsystems of the reactor. Using the example of a real subsystem, the influence of various architectural modifications of redundancy schemes on the reliability of the interlock function in the fast energy output system of the ITER thermonuclear reactor is considered. The article analyzes the variants of structures with different levels of redundancy and logical coupling. The simulation was performed using the "k-out-of-n" method (k is the minimum number of elements ensuring the functioning of a system of n elements) with the calculation of reliability. The results obtained make it possible to reasonably choose circuit solutions to increase the fault tolerance of key protective functions.
Keywords: fast energy output system; redundancy; reliability; ITER
- N. A. Kalinin
- N. A. Skvortsov
- S. A. Stupnikov
Abstract: As interdisciplinary research infrastructures evolve, the necessity to adapt them to the specific needs of particular domains increases. Universal solutions aimed at providing researchers with generalized types of research data cannot be equally effective for different research domains. In particular, common infrastructures do not sufficiently account for the features of artifacts such as software code, machine learning models, and computational experiments, which complicates the use of research infrastructures for solving problems in computer science. An approach to adapting a basic infrastructure to the specifics of a particular research domain is proposed. This approach includes analyzing needs of the domain, identifying the features of the research artifacts used, formulating requirements for the infrastructure, and designing a research infrastructure architecture that takes into account the domain-specific requirements. The practical applicability of the approach is demonstrated through a computer science case study. An architecture for a research infrastructure is proposed, including basic universal subsystems and extensible with specialized subsystems.
To meet the needs of researchers in computer science, the necessity of supporting multiversion software artifacts and integrating software development and code execution tools is substantiated. The results obtained can be used in the design of research infrastructures in other domains.
Keywords: FAIR principles; domain analysis; research infrastructures; computer science
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