«INFORMATICS AND APPLICATIONS»
Scientific journal
Volume 15, Issue 4, 2021

Content | About  Authors

Abstract and Keywords

ALGORITHMS FOR AN APPROXIMATE SOLUTION OF THE TRACK POSSESSION PROBLEM ON THE RAILWAY NETWORK SEGMENT

• A. V. Bosov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation, Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
• A. N. Ignatov  Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
• A. V. Naumov  Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation

Abstract: Algorithmic and instrumental support for solving problems of railway transport control, based on the presentation of applied problems in the form of optimization statements in which linear programming tools are used, is being developed. Previously proposed models and applied statements are expanded with a new problem of finding a track possession - a time interval at which some sections of the railway network are closed for repair work. To solve it, a mathematical model and an optimization statement are proposed for the simultaneous search for a track possession and a train schedule for a certain segment of the railway network. The original setting is reduced to a mixed integer linear programming problem. To take into account possible computational difficulties in solving the problem, a method for finding an approximate solution is proposed which is based on the formation ofa basic schedule of movement and its subsequent correction taking into account the need for the track possession. To find an approximate solution, two algorithms have been implemented. In the first, a basic and adjusted train timetable is built in stages by groups oftrains united by the same departure and destination stations, and in the second, stages are carried out one train at a time according to the time of readiness for departure. The results of a numerical experiment are presented.

Keywords: multigraph; railway network; schedule; track possession; mixed integer linear programming

MINIMAX ESTIMATES OF THE LOSS FUNCTION BASED ON INTEGRAL ERROR PROBABILITIES DURING THRESHOLD PROCESSING OF WAVELET COEFFICIENTS

• A. A. Kudryavtsev  Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M. V Lomonosov Moscow State University, 1-52 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation, Moscow Center for Fundamental and Applied Mathematics, M. V. Lomonosov Moscow State University, 1 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation
• O. V. Shestakov  Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M. V Lomonosov Moscow State University, 1-52 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation, Moscow Center for Fundamental and Applied Mathematics, M. V. Lomonosov Moscow State University, 1 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation, Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: Noise reduction is one of the main tasks of signal processing. Wavelet transform-based methods for solving this problem have proven to be reliable and effective. Thresholding methods that use the idea of a sparse representation of a signal function in the space of wavelet coefficients have become especially popular. These methods use fast nonlinear algorithms that adapt to the local features of the signal being processed. The parameters of these algorithms are selected based on some quality criterion or minimization of a given loss function. Most often, the mean square risk is considered as a loss function. However, in some applications, minimizing the mean square risk does not always lead to satisfactory results. In the present paper, the authors consider the loss function based on the integral probabilities of errors in calculating the wavelet coefficients. For hard and soft thresholding methods, the boundaries for the optimal threshold values are calculated and the minimax order of the considered loss function in the class of Lipschitz-regular signals is estimated.

Keywords: wavelets; loss function; thresholding

ANALYSIS OF PEAK LOAD DISTRIBUTION IN THE MULTIUSER NETWORK

• Yu. E. Malashenko  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• I. A. Nazarova  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: Within the framework of a multicommodity network model, nondiscriminatory distribution of the tantamount flows of various types transmitted between all pairs of nodes simultaneously is analyzed. When setting and solving optimization problems, the resource required by a certain source-receiver pair is treated as the sum of the capacity values of all edges located on all routes of this source-receiver flow. The sum of the corresponding edge flows is interpreted as the total load on the network occurring during a transmission of this internode flow.
A nuclear-chain of lexicographically ordered problems of searching for routes with equal loads for source-receiver pairs is solved in computational experiments. At each iteration, a vector of peak values of jointly permissible internode flows is used for assessment of the system's functionality. The method allows for a finite number of steps to find the final nondiscriminating maximin distribution of resources providing the peak load of all network edges.

Keywords: multiuser network; equalizing maximum peak load distribution; network peak load; functional characteristics

STRUCTURED DEFINITIONS OF DISCOURSE RELATIONS IN THE SUPRACORPORA DATABASE OF CONNECTIVES

• O. Yu. Inkova  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• M. G. Kruzhkov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The paper presents initial outcomes resulting from development of structured definitions of discourse relations based on novel classification principles and describes how these definitions are captured in the Supracorpora Database of Connectives (SCDB). The authors provide an overview of existing approaches to definition of discourse relations and propose novel principles for capturing structured definitions of discourse relations in the SCDB based on several aspects which include (i) the basic semantic operation that the logical-semantic relation (LSR) is based on: implication, relative timeline positioning, comparison, and correlationbetween general and specific, between an element and a set; (ii) the linguistic level that the LSR is established on: propositional level, utterance (illocutionary) level, and metalinguistic level; (iii) thepolarity, i. e., whetherthe LSRis established directly between the provisions p and q featuring in the text or whether their negative correlates should also be considered in the relation interpretation; and (iv) the semantic and pragmatic features of the context. The paper provides some examples of such structured definitions. The structured definitions are captured within the SCDB by a set of interrelated tables. In addition, the "Family" table is introduced to offer information about conceptual closeness of some sets of classification features.
The proposed structure allows researchers to access similarities and distinctions between various LSRs - as of today, this functionality is not implemented in any of the existing corpora that include annotation of discourse relations.

Keywords: supracorpora database; logical-semantic relations; connectives; annotation; faceted classification

CREATION OF A STOCHASTIC DYNAMIC ONE-SECTOR ECONOMIC MODEL WITH DISCRETE TIME AND ANALYSIS OF THE CORRESPONDING OPTIMAL CONTROL PROBLE

• P. V. Shnurkov  National Research University Higher School of Economics, 34 Tallinskaya Str., Moscow 123458, Russian Federation

Abstract: The work is devoted to the creation of a stochastic dynamic model of optimal control with discrete time within the framework of a one-sector economic system. The basis is a classical deterministic dynamic model of the economic system in which one universal product is produced. This product is divided into investment and consumer components. System management consists in determining the relationship between these components.
In this work, it is assumed that the main parameters of the system depend on some random factor that characterizes the influence of the external environment. This factor is described by a homogeneous Markov chain with a finite set of states and a given transition probability matrix. In this work, a stochastic model of the evolution of the system under consideration is constructed which is a two-dimensional Markov process with discrete time. In terms of its economic content, the first component of this process is specific capital and the second is the state of an external random factor. The control parameter or decision at each moment of time represents the share of the specific product produced directed to investment. The recurrent setting of the cost additive indicator of management efficiency is described. The theoretical basis for solving the problem of optimal control is the method of dynamic programming. In this work, a system of Bellman functional equations is obtained, the solution of which is the optimal control strategy.

Keywords: optimal control problem with discrete time; stochastic dynamic one-sector economic model; controlled two-dimensional Markov chain; dynamic programming method for a discrete-time control problem; Bellman equations

CAPTURING EVOLUTION OF LEXICOGRAPHIC KNOWLEDGE IN DYNAMIC CLASSIFICATION SYSTEMS

• A. A. Goncharov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• I. M. Zatsman  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• M. G. Kruzhkov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• E. Yu. Loshchilova  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The paper examines two problems that have to be addressed in order to capture the evolution of lexicographic knowledge. The lexicographic knowledge discussed in the paper is presented in the form of classifications categories that are used to provide linguistic markup for textual data in information systems. Evolution of lexicographic data is considered based on the example of a supracorpora database (SCDB). The first problem deals with integration of the framework for capturing changes of semantic content of classification categories into the SCDB. The proposed solution to this problem involves integration of two new tables that capture information about temporal states of the classification categories and about change operations applied to those categories. The paper describes how these tables are integrated into the SCDB structure. The second problem deals with providing the user interface for application of changes to the classification categories. The interface implemented in the SCDB is described in detail. The proposed solutions can be scaled so that they would make it possible to capture evolution not only of lexicographic knowledge but of scientific knowledge in general if this knowledge can be represented in the form of a dynamic classification system.

Keywords: evolution of lexicographic knowledge; dynamic classification system; ontology versioning; linguistic annotation; reclassification of annotations

DISTRIBUTED INFORMATION SYSTEM FOR CALCULATING THE STRUCTURAL PROPERTIES OF COMPOSITE MATERIALS

• K. K. Abgaryan  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation, Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
• E. S. Gavrilov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation, Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation

Abstract: The use of composite materials has found wide application in various branches of engineering due to their advantages over metals with equal mechanical and operational properties. To solve the problems arising in the field of creating composite materials with a set of specified properties, new approaches to the development of mathematical models and information systems based on them are widely used today. The paper presents an original multiscale mathematical model that allows calculating the structural characteristics of composite materials and can be used to numerically study fatigue fracture of composite materials in case of accidental impact damage. On the basis of this multiscale model, a distributed information system was created for conducting large-scale research in the field of modeling composite materials with specified properties. The development of this approach in the future will help to ensure the formation of information for a reasonable choice of composite materials with desired properties for aerospace and other industries.

Keywords: multiscale modeling; composite materials; integration platform; software package; distributed system

ON TRANSFER LEARNING METHODS IN BIOMEDICAL IMAGES CLASSIFICATION TASKS

• E. Yu. Shchetinin  Financial University under the Government of the Russian Federation, 49 Leningradsky Prospekt, Moscow 125993, Russian Federation
• L. A. Sevastianov  Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russian Federation, Joint Institute for Nuclear Research, 6 Joliot-Curie Str., Dubna, Moscow Region 141980, Russian Federation

Abstract: Computer studies of the effectiveness of deep transfer learning methods for solving the problem of human brain tumors recognition based on magtetic resonance imaging (MRI) are carried out. Various strategies of transfer learning and fine-tuning of the models are proposed and implemented. Deep convolutional networks VGG-16, ResNet-50, Xception, and MobileNetV2 were used as the baseline models, pre-trained on ImageNet. Also, a deep convolutional neural network 2D_CNN was built and trained from scratch. Computer analysis of their performance metrics showed that when using the strategy of fine-tuning models on augmented MRI-scans data set, Xception model demonstrated higher accuracy values compared to other deep learning models. For Xception model, the accuracy of classification of MRI-scans with brain tumors was 96%, precision 99.43%, recall 96.03%, f1-score 97.7%, and AUC 98.92%.

Keywords: MRI scans; brain tumor; deep learning transfer; convolutional neural networks

THE ELECTRONIC COMPONENT BASE OF FAILURE RESILIENCE DIGITAL CIRCUITS

• I. A. Sokolov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• Yu. A. Stepchenkov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• Yu. G. Diachenko  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• Yu. V. Rogdestvenski  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• A. N. Kamenskih  Perm National Research Polytechnic University, 29 Komsomol Prosp., Perm 614990, Russian Federation

Abstract: The article presents the research of self-timed and synchronous circuits in terms of resilience to soft errors which can cause disruptions in the control system's operation of complex technical device. The use of a fail-resilient self-timed code is proposed, which considers the antispacer state as the second spacer state. This approach increases the self-timed circuit's failure resilience level. In the first approximation, quantitative estimates show that the self-timed pipeline has a better failure resilience than the synchronous counterparts by 2.0-4.7 times. The use of modified C-element to implement the pipeline register bit increases this advantage to 2.2-5.4 times. Due to this, self-timed circuits are the preferred basis of failure resilient control systems implementation for complex technical equipment.

Keywords: synchronous circuits; self-timed circuits; soft error; failure resilience; pipeline; transition completion indication; probability evaluation

ON THE CHOICE OF PARTIAL ORDERS ON FEATURE VALUES SETS IN THE SUPERVISED CLASSIFICATION PROBLEM

• E. V. Djukova  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• G. O. Masliakov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The authors consider one of the central problems of machine learning - the supervised classification.
A scheme for the logical classification algorithms synthesis is described under the assumption that the features descriptions of precedents are the elements of the finite partial orders Cartesian product. A criterion for the correctness of the voting algorithm of representative elementary classifiers is formulated. The authors study the possibility of defining linear orders on sets of feature values that provide better classification, which is not necessarily correct, in assumption that the source data are not ordered (the precedents descriptions are the elements of the antichains product). A procedure is proposed for "correct" consistent ordering of the acceptable values of separate features, while the remaining features are antichains. The results of experiments on real data are presented demonstrating the effectiveness of the methods developed in the work.

Keywords: machine learning; logical classification algorithms; correct supervised classification algorithm; partially ordered set; Cartesian product of partial orders; linear order; dualization over product of partial orders

STATISTICS AND CLUSTERS FOR DETECTION OF ANOMALOUS INSERTIONS IN BIG DATA ENVIRONMENT

• A. A. Grusho  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• N. A. Grusho  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• M. I. Zabezhailo  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• D. V. Smirnov  Sberbank of Russia, 19 Vavilov Str., Moscow 117999, Russian Federation
• E. E. Timonina  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• S. Ya. Shorgin  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The paper builds algorithms for reducing the level of "false alarms" when searching for anomalies in complex heterogeneous sequences of objects (Big Data). Traditionally, in mathematical statistics, such a decrease is achieved by minimizing the error of "false alarms." However, in the problems of detecting anomalies (rare intrusions of anomalous data), this approach leads to an increase in the probability of losing the required anomalies. In this paper, in order not to lose the required anomalies, on the contrary, in criteria designed for the least complexity of calculations, it is proposed to make a large error of the appearance of "false alarms" but use the fact that the number of objects allocated by such criteria is much smaller than the number of original objects in Big Data. The selected objects can then be grouped into a single cluster and additional information related to the objects in the cluster can be used to identify the required anomalies. The sense of these actions is that more difficult-to-compute characteristics of objects for dropping out "false alarms" will not require large computational resources on a smaller cluster of objects relative to the original data. It is shown that when certain conditions are satisfied, the order of using additional information does not affect the result of its use when filtering "false alarms." The results of the filtering algorithm in the sequence of objects are generalized to filtering "false alarms" in the form of causal schemes in the initial data. Known schemes show how "false alarms" can be filtered identifying only fragments of schemes.

Keywords: information security; search for anomalies; algorithms for filtering "false alarms"

MODEL FOR ANALYZING PRIORITY ADMISSION CONTROL OF URLLC AND eMBB COMMUNICATIONS IN 5G NETWORKS AS A RESOURCE QUEUING SYSTEM

• I. A. Kochetkova  Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russian Federation, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• A. I. Kushchazli  Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russian Federation
• P. A. Kharin  Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russian Federation
• S. Ya. Shorgin  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: Ultrareliable and low-latency communication (URLLC) data transmission and enhanced mobile broadband (eMBB) are critical scenarios for the fifth generation (5G) networks. A 5G network resource model was formalized using a triple - radio frequency bandwidth, time slot duration, and the maximum possible power of the transmitted signal. For the scheme of resource occupation which assumes an adaptive change in the signal power and uniform distribution of the time slot between devices, the authors show a conditional distribution of the probabilities of receiving a request for traffic transmission. The model with priority access URLLC and interruption of eMBB service, considering the specified model of resource occupation, is built in the form of a resource queuing system.

Keywords: 5G; URLLC; eMBB; priority admission control; interruption; resource queuing system

THEORETICAL FOUNDATION OF FORMATION OF AIRCRAFT WEIGHT APPEARANCE

• L. L. Vyshinsky  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
• Yu. A. Flerov  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The article is devoted to automation of tasks related to formation of weight appearance at the initial stage of aircraft design. At this stage, the main structures of the information weight model of the product are created which is detailed, improved, and used throughout the life cycle, including the stages of production and operation.
The article presents not only the theoretical foundations of formation of aircraft weight appearance (design "from prototypes," weight formulas) but also describes the software product developed by the authors which is a tool intended for use at the initial stage of designing aircraft of various types and purposes.

Keywords: mathematical modeling; computer aided design; aircraft; appearance formation; weight design; weight model; structure tree; project generator

THE CONCEPTION OF CREATING WHO HUB FOR PANDEMIC AND EPIDEMIC INTELLIGENCE: KEYWORDS AND THEIR TERMINOLOGICAL ANALYSIS

• I. M. Zatsman  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The conception of the computerized system which is being created on the initiative of the World Health Organization (WHO) and called WHO Hub for Pandemic and Epidemic Intelligence is considered. In the description of the conception, words "data," "information," and "knowledge" are used (in keywords "knowledge sharing," "knowledge representation," "knowledge exchange," and "knowledge generation"). The understanding of this conception as the basis for the creation of the WHO Hub will largely be determined by their interpretation corresponding to its contextual meaning. The need to create such systems at the national, regional, and global levels was justified in May 2021 in the report of the International Commission of Experts established by WHO, which gives relevance to the analysis of the system of terms of the conception not only for the creation of the WHO Hub. The main aim of the paper is to analyze the principal conceptual statements of WHO Hub creation and to propose an interpretation of their keywords and their Russian-language translation equivalents corresponding to its contextual meaning. At the same time, it is shown that in order to understand this conception, it is also necessary to find out the meanings of such English-language terms as "intelligence," "context, " and "insight. " In the case of translation of the conception into Russian, it is also necessary to find their Russian-language translation equivalents according to the contexts of their use.

Keywords: intelligence; data; information; knowledge; concept; context; insight; terminological analysis; informatics media

ANTHROPOGENIC "THIRD" NATURE: THE RELATIVELY AUTONOMOUS STATUS OF ITS ARTIFICIAL INTELLECTUAL SUBJECTS

• S. N. Grinchenko  Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: From the standpoint of informatics-cybernetic modeling of the development process of the self- controlling hierarchical-network system of Humankind, an anthropogenic "second" nature is considered in which an important part began to form from the middle of the 20th century with elements of the part of the "first" (inanimate) nature that were able relatively independently to make decisions (hierarchical artificial intelligence) and actively act (intelligent robots), i.e., to function somewhat independently of the person who created them. For this part of the second nature, the term "third" nature is proposed. Its formation became possible with the emergence of basic information technologies - local computers since ~ 1946 and telecommunications/networks since ~ 1979. Typical spatial and temporal parameters of the hierarchical-network system of Humankind (the results of a model calculation) are given. It is noted that the activity of the actions of the third nature elements - essentially autonomous components of hierarchical artificial intelligence and intelligent robots - is an existential (humanitarian!) danger for Humankind.

Keywords: second nature; third nature; informatics-cybernetic model; self-controlling hierarchical-network system of Humankind; information technology; hierarchical artificial intelligence; intelligent robots