DOI: 10.26102/2310-6018/2024.44.1.009
The article proposes a method for solving the problem of adapting a discrete inventory management model to the problem of combat operations of two armies. The aim is to identify the control effect on the linear system of difference equations, which allows it to be transferred from the initial to the final state in the specified parameters provided that costs are minimized. Discrete controlled processes play an important role in the theory and practice of optimal control since many planning tasks are described precisely by systems of difference equations. A system of equations of this type is characterized by a discrete type of control of the number of combat units at the current stage. Deliveries are formed at fixed intervals. The effectiveness of management is controlled (verified) by a quadratic quality criterion, which characterizes the cost of conducting combat operations. The criterion shows the total cost of supplies and maintenance of combat units, the change in the number of which is determined by three factors: the rate of losses as a result of hostilities, natural losses and the rate of receipt of reinforcements. The construction of an optimal control effect is carried out by the feedback method. It is noted that the solving this task is complicated by the fact that it is necessary to find among all possible solutions those that will make it possible to achieve your goals with the least expenditure of human and material resources. These costs are presented as functions of several variables, the values of which are known at the initial time. The article proves that in order to solve the problem of optimal resource management in relation to the case of combat operations of two armies, the feedback method is the most preferable. Several examples have been analyzed. The implementation of the feedback method clearly shows that a longer period of confrontation significantly reduces losses. The materials of the article are of practical value for strategic planning in the context of military conflicts.
Keywords: optimal control, discrete system, feedback principle, combat operations, control influence
DOI: 10.26102/2310-6018/2024.44.1.012
The paper considers the methods of authorship identification for fanfiction texts based on popular works of literature and cinema. The data for the study include texts from 5 popular topics of Ficbook online library. The most common is the closed set attribution task. Regarding practical issues, it can be assumed that the true author of an anonymous text will not always be included in the candidates set. Therefore, the process of author identification was regarded as a more complex version of the typical classification problem – the open set of authors. The proposed methods are based on the machine learning methods: fastText and One-Class SVM with informative features selection and statistical approaches of vector representation similarity measures. Statistical methods have proven to be the least effective even for the simple cross-thematic case. In comparison with the method based on One-Class SVM, the difference in accuracy reaches 15 %. For cross-thematic attribution, the average accuracy of the method based on the combination of One-Class SVM with feature selection and fastText was 85 %, while for the more complex task – classification within a group – it ranged from 75 to 78 % depending on the thematic group.
Keywords: text authorship attribution, fastText, machine learning, text analysis, information security
DOI: 10.26102/2310-6018/2024.44.1.018
Currently, managing computing resources in modern distributed computing systems is the relevant problem. As a result of infrastructure capability evolution, distributed computing can be organized in dynamic, heterogeneous and geographically distributed computing environments, examples of which are “fog” and “edge” ones. The dynamics of both load and topology imply the need to change the system configuration, namely, assigning user tasks to computing devices with the allocation of the necessary resources. The latter raises the issue of increasing the efficiency of the scheduler (broker), which facilitates management of network resources within the allocated fragment. Algorithmic and software schedulers are based on models and methods of scheduling theory and implement either simple heuristics, mathematical programming methods or metaheuristics. However, an analysis of publicly available problem statements has shown that, firstly, they are special cases and implement certain situations of computing resource distribution, and secondly, they do not fully reflect the properties of heterogeneity, geographical distribution and dynamics of computing environments. As part of this study, a general model of computing resource allocation problem is proposed with consideration to the listed properties, and a solution method using the subject ontology of metaheuristic methods is proposed. The feasibility of constructing and applying an ontology is shown using the example of analyzing the effectiveness of genetic algorithms depending on the values of the computing resource allocation problem parameters which is being solved.
Keywords: ontology, resource allocation, distributed computing, distributed computing management, resource management, optimization
DOI: 10.26102/2310-6018/2024.44.1.013
The paper proposes two approaches to analyzing time series of bacterioplankton abundance in three different layers of the water column in Lake Baikal. In the first approach, the values of the seasonal component of the series are calculated using the moving average method, and additive and multiplicative models are constructed, from which the best models are selected on the basis of the calculated reliability coefficients. The seasonal component values in each of them are estimated. In the second one, correlation and regression analysis of joint changes in bacterioplankton abundance, temperature and lake water level is performed. Statistical hypotheses about the significance of correlation coefficients between the considered factors are put forward and tested. A mathematical model of multiple regression with inclusion of dummy variables describing the influence of seasonal fluctuations on changes in bacterioplankton abundance is constructed. Statistical assessment of the significance of the model and the factors included in the model is calculated. The results of correlation-regression analysis are interpreted in relation to the subject area under study. The findings can be used in predicting the amount of bacterioplankton in different periods of time, in making an ecological substantiation of the state of the lake, as well as in forecasting its microbiological state.
Keywords: time series, bacterioplankton, moving average method, seasonal component, correlation and regression analysis, multiple regression model, lake Baikal
DOI: 10.26102/2310-6018/2024.44.1.014
Planning is an important process for a project. The main planning processes include defining activities, planning resources, determining the duration of work, and developing a schedule. The paper examines projects with independent activities. The purpose of the study is to optimize project schedule by period. Three particular problems are considered. The first problem is to distribute activities over periods in order to achieve the maximum total effect of their implementation taking into account cost constraints in each period and the possibility of partial implementation of the activities in a given period. The solution algorithm is based on the Cost-Effect method. The validity of the proposed algorithm has been proved. The second problem deals with the distribution of work over periods with the prohibition of transferring part of the work to other periods and limitation of costs in each period. Based on the method of dichotomous programming, we propose an algorithm for solving the problem for two periods. For the number of periods greater than two, an approximate algorithm is suggested. For the case when information on unperformed activities in the course of project implementation changes, the problem of maximizing the total effect from the implementation of project activities in the current period is considered. Additionally, the effect from the implementation of a set of activities is visible after their completion and a certain effect manifests from the partial implementation of another set of activities. The effect obtained is proportional to the part of the amount of work performed. An algorithm for solving the problem based on obtaining parametric dependences of the total effect for each set of activities on the value of costs is proposed. The validity of the algorithm has been proved. Examples illustrating the application of the proposed algorithms are presented.
Keywords: project, work, period, effect, costs, resource, satchel problem, dichotomous programming method
DOI: 10.26102/2310-6018/2024.44.1.034
The paper considers the problem of signal propagation indoors. Several stages were considered in solving this problem. At the first stage, a model of electromagnetic wave propagation through the wall was built. An approach based on geometric optics was used. To calculate the degree of absorption, it is necessary to take into account the dielectric and magnetic permeability of the wall material. In order to automate the calculation process, a program was written in C++, which makes it possible to quickly determine the power values under given conditions. The attenuation of the radio signal depending on the angle of incidence on the wall is investigated. At the second stage, the tasks of determining the level of a propagating electromagnetic wave at various points inside the room are considered. At the third stage, the problem of optimizing the placement of the transmitting device inside the room is considered. A random search method was used with a sequential narrowing of the range of values. At the same time, the use of a local optimization method of the grid method was required. For each section of the grid, a local optimization method was used, which was the golden ratio method. As a result, after the implementation of several tens of thousands of iterations, the optimal placement of the transmitting device was determined. The scientific and practical significance of the work lies in the development of a complex algorithm for optimizing the placement of transmitting devices in the room based on a computational experiment.
Keywords: wireless communication, electromagnetic wave propagation, electromagnetic wave absorption, optimization, signal strength, signal attenuation
DOI: 10.26102/2310-6018/2024.44.1.031
The article evaluates in detail the capabilities of patch antennas application based on the analysis of their advantages and disadvantages. The new patch antenna design was subjected to modeling, including description of its structure and creation of a three-dimensional model. The field distribution in the patch antenna geometry was obtained, which gives a complete picture of the influence of its structural elements on the electromagnetic properties. The directional diagram of the patch antenna is obtained, which reveals the angular features of its radiation. Plots of the gain of a single patch antenna, the gain of an 8×8 uniform array, and the gain of an 8×8 microstrip patch antenna plotted in dB-scale are constructed. It is shown that the design of the rectangular microstrip patch antenna with V-shaped notches provides better polarization at the edges compared to the center in the proposed patch antenna model, which can be a critical factor in real-world applications, especially in areas where communication quality is subject to external influences. The frequency at which this antenna resonates is 1,403 GHz, this allows for a wider bandwidth and improved impedance matching. These results emphasize the promising potential of the investigated patch antenna design in modern communication technologies and wireless data transmission systems.
Keywords: patch antenna, wireless communication, radiation pattern, gain factor, electromagnetic characteristics, antenna modeling, resonant frequency, size optimization, impedance matching
DOI: 10.26102/2310-6018/2024.44.1.010
The paper considers the applicability of a project approach to managing the activities of criminal investigation units. The problem of developing project management models for countering criminal threats is underscored. The disadvantages that do not make it possible to use the existing models of resource allocation in the subject area under study without significant adaptation are indicated. The criminal threat prevention project has been defined. The general parameters of such a project are given, which helps to determine the problem of renewable resource allocation. The approach to calculating the minimum amount of renewable resources required for the implementation of the k-th operation of the project. The problem of allocating renewable resources of the criminal threat prevention project, the purpose of which is to solve a crime, is defined. To solve this problem, an appropriate model of integer programming is proposed. A numerical example of the problem solution and a method of software implementation of the proposed model in a tabular processor are given. The features that the authors believe should be taken into account when applying the proposed model and methodology in practice and possible areas of future research are considered: the study of the project duration dependence type on the number of performers and other factors; the development of a knowledge base on the parameters of the conducted operations.
Keywords: criminal threat prevention project, allocation of renewable resources, project approach, critical path method, CPM, PERT
DOI: 10.26102/2310-6018/2024.44.1.008
The paper considers the relevant issues related to the problem of calculations and forecasting in the production of solar electricity as a renewable energy source. To detect problems, the initial data for modeling and their sources have been identified. Renewable energy sources are systematized and an example is given for each. An analysis of the state of the global energy market and the state of government policy in the field of energy in Russia has underscored the need to address solar energy issues and solve the problems of forecasting electricity generation. This is important not only due to the availability of resources, but also to environmental friendliness. The classification of existing models and methods for forecasting SES energy generation is examined. Existing methods allow calculations to predict the power generation capacity, but they give average figures for the year. New technological and innovative methods are required to solve the existing problem. The key factors and aspects of the introduction and operation of a solar power plant are presented. The main difficulty in forecasting is taking into account a variety of nonlinear characteristics. An attempt to solve this problem is proposed. An overview of the state of the problem and trends in the development of solar energy is made, among which the main problems are identified and solutions are outlined.
Keywords: solar energy, renewable energy sources, aspects and operation of a solar power plant implementation, forecasting solar energy generation, forecasting methods
DOI: 10.26102/2310-6018/2024.44.1.019
Predictive management with all its errors and difficulties is still an effective means of providing an organizational and technical system with time to increase its readiness for changes in the situation. To formulate and solve the problem of optimal control of this process, the Fokker-Planck-Kolmogorov equation was used, which is the first approximation in the probabilistic description of random processes. To formulate the optimal control problem, the Letov criterion was modified, a coordinate-parametric approach was applied, and the obvious fact of an increase in management costs with a decrease in the time to improve the readiness of the organizational and technical system was taken into account in the form of the square of change rate in the probability density. The Euler-Ostrogradsky-Poisson equations are applied to the final Lagrangian. The resulting nonlinear equations were solved using the small parameter method. The study of the resulting solution proves that even with optimal control, the magnitude of control actions increases in proportion to the target value and duration of control (increasing the planning horizon), the increase occurs according to the cube of the exponential, that is, very slowly at the beginning of control and very sharply at the end, and a similar pattern of increase demonstrates the dependence of the control influences from the demand for management results, but it is expressed through hyperbolic functions.
Keywords: optimal control, fokker-Planck-Kolmogorov equation, probabilistic quality criteria, intensity of application of control actions, small parameter method
DOI: 10.26102/2310-6018/2024.44.1.007
The article examines the development of a new approach to storing and organizing the results of laboratory experiments with consideration to the features of their subsequent processing. To solve this problem, laboratory experiments are considered as structured data with unstructured parts. During the development of the system, the features of storing and processing laboratory test data were analyzed, after which the basic requirements for the system were formulated. The main data models were defined as well as the database entities. A standard relational data model has been chosen for storing structured data, and the storage of unstructured information such as experiment results or experiment parameters is implemented through the BJSON field. To solve the problem of providing secure access and creating an API for the system, the asynchronous FastAPI framework was chosen. The implementation of storing additional experiment files, which are located in the object storage and are associated with the experiment in the relational model through an additional entity, is also considered. The presented approach is notable for its flexibility to the structure of stored laboratory experiments, takes into account the features of geological laboratory experiments and also provides opportunities for complex meta-analysis of large volume of data. The system was tested and implemented into the technological process of the geotechnical laboratory at JSC MOSTDORGEOTREST.
Keywords: storage of geological laboratory experiment data, unstructured data, experiment results storage system, geoinformation system, database, geological environment, information resource, engineering geology
DOI: 10.26102/2310-6018/2024.44.1.004
The article presents the results of the acoustic emission method application (AE) and machine learning algorithms in the problem of diagnosing defects in the stratification of a multilayer printed circuit board structure (MPB). A combination of physical and computational experiments is used to solve the problem. To conduct full-scale tests, the study uses a vibration stand to generate a load on the test object and receive acoustic emission signals. The computational experiment is carried out using mathematical modeling in a specialized ABAQUS environment. In order to obtain the best solution to the problem, an optimization problem is solved during the experiment to determine the frequency of the harmonic signal generated by the vibration stand with a view to receiving the maximum response of the MPB under review and unambiguous identification of the bundle defect. When conducting the numerical experiments, the effects and reactions (AE signals) of MPB were modeled at different frequencies of input vibration signals ranging from 100 to 2000 Hz. Full-scale experiments were conducted in the laboratory of control and testing of radioelectronic devices at the Department of KPRES of RTU MIREA. The results of the study have shown that the vibration frequency most effective for detecting a delamination defect equals 1500 Hz (a defect of almost rectangular shape with a size of 30×37 mm). Subsequently, this was confirmed by correlation analysis, which made it possible to identify the maximum differences between the acoustic emission signals of a suitable MPB sample and a sample with a delamination defect for the input vibration of a given frequency. The second part of the study deals with processing of the physical and computational experiment results, establishing the degree of adequacy of the obtained mathematical models to real samples of MPB and the processes occurring in them, as well as the use of machine learning algorithms for more reliable diagnosis of MPB defects. In the presented study, the random forest and the support vector machine learning (SVM) methods were employed as machine learning algorithms. Based on the results of their execution, the accuracy of the two algorithms was evaluated.
Keywords: acoustic emission, multilayer printed circuit board, hidden defects, structure stratification, modeling, physical experiment, machine learning algorithm, support vector machine method, random forest method, non-destructive testing
DOI: 10.26102/2310-6018/2024.44.1.001
This paper considers methods for authorship attribution of natural-language and artificially generated texts, which are important in the context of cybersecurity and intellectual property protection to prevent misinformation and fraud. The use of authorship methods is justified by the findings on the fastText and support vector method (SVM) effectiveness discussed in past studies. The feature selection algorithm is chosen based on the comparison of five different methods: genetic algorithm, forward and backward sequential methods, regularization selection and Shapley's method. The considered selection algorithms include heuristic methods, game theory elements and iterative algorithms. The regularisation-based algorithm is found to be the most efficient method, while methods based on complete brute-force selection are found to be inefficient for any set of authors. The regularization-based and SVM-based selection accuracy averaged 77 %, outperforming the other methods by between 3 and 10 % for an identical number of features. For the same tasks, the average accuracy of fastText is 84 %. A study was conducted to examine the robustness of the developed approach to generative samples. SVM proved to be more robust to model confounding. The maximum loss of accuracy for fastText was 16 % and for SVM was 12 %.
Keywords: feature selection, authorship attribution, machine learning, neural networks, text analysis, information security
DOI: 10.26102/2310-6018/2024.44.1.011
Today, the X-ray analysis procedure makes it possible to detect osteoarthritis (OA) in the early stages of the disorder. The presence or absence of the disorder is detected only when it has already manifested, and X-ray diagnostics have been carried out. The use of automated procedures for analyzing X-ray images and the availability of archives of such information with a long history can improve the results of predicting complications in patients. The article describes the experience of developing an application for computer analysis of radiographs, which, based on deep learning methods, allows us to identify the risks of developing osteoarthritis of the hip joint. The archive of a specialized medical institute is used as a training sample. In order to increase the size of the training set of radiographs, a data augmentation method is used, which increases the variability of the original data and, in some cases, increases the recognition efficiency. The research uses a convolutional network (U-net) designed for image segmentation, which is trained on X-ray images of a specific medical institution. As part of a project to segment and analyze the geometric characteristics of X-ray images of the hip joints, the software to automate the recognition of the joint space size was developed, which helps to clarify the patient’s diagnosis and prognosis for the development of the pathology.
Keywords: convolutional neural network, image segmentation, machine learning, osteoarthritis, hip joint
DOI: 10.26102/2310-6018/2024.44.1.021
The current stage of unmanned aircraft system development is characterized by the widespread introduction of automated and intelligent electronic systems. One of the most difficult and critical stages in the development of unmanned aerial vehicles is determining the optimal locations for placing on-board equipment in the fuselage space. To solve this problem, the approach for determining the optimal installation locations for on-board equipment in the fuselage space of an unmanned aerial vehicle is proposed. The approach is based on the use of a genetic algorithm. A meaningful and mathematical formulation of the problem of determining the optimal installation locations for on-board equipment in the fuselage space of an unmanned aerial vehicle is given. Criteria and restrictions have been developed. As optimization criteria, first of all, electromagnetic compatibility criteria are considered, which are characterized by minimizing the sensitivity of on-board equipment above the level of electromagnetic field strength at the installation sites of on-board equipment, as well as limiting the excess of the threshold level of susceptibility of on-board equipment over the electromagnetic environment resulting from electromagnetic influences or interactions. Additionally, criteria for minimizing the total weighted length of cable connections are considered, and the maximum load-carrying capacity of the fuselage compartments of an unmanned aerial vehicle is limited. The plan has been developed for the installation of on-board equipment in the fuselage space using a developed program that implements a genetic algorithm.
Keywords: placement, optimization, on-board equipment, genetic algorithm, unmanned aerial vehicle
DOI: 10.26102/2310-6018/2024.44.1.017
An analytical study was carried out on the problem of preventing emergency situations and predictive diagnostics of equipment during hydrocarbon production in oil and gas fields as well as the ways to solve this problem by means of artificial intelligence based on deep neural networks. One of the key factors hindering the development of predictive equipment diagnostic systems is the lack of data describing pre-emergency situations, which is necessary for high-quality training of neural network models. An analysis of recent publications and research on the subject of telemetry data analysis and emergency recognition is provided. Neural network models are considered that can be used to predict the failure of pumping and compressor equipment and other units. Cases of the use of neural network models specially trained to solve this problem, as well as neural network models used in other tasks but analyzing similar data structures, were studied. The issue of transfer learning is raised to adapt neural network models originally developed and trained for other areas to use in the area under consideration in order to reduce the sample size when training industrial artificial intelligence. A comparison of the achieved results was carried out, and the advantages and disadvantages of existing technical solutions were identified.
Keywords: artificial neural networks, predictive diagnostics, machine learning, time series, telemetry, maintenance, data sets
DOI: 10.26102/2310-6018/2024.44.1.002
The relevance of the paper is due to the difficulties of oral interaction between people with speech disorders and normotypic interlocutors as well as the low quality of abnormal speech recognition by standard speech recognition systems and the inability to create a system capable of processing any speech disorders. In this regard, this article is aimed at developing a method for automatic recognition of dysarthric speech using a pre-trained neural network for recognizing phonemes and hidden Markov models for converting phonemes into text and subsequent correction of recognition results using a search in the space of acceptable words of the nearest Levenshtein word and a dynamic algorithm for splitting the output of the model into separate words. The main advantage of using hidden Markov models in comparison with neural networks is the small size of the training data set collected individually for each user, as well as the ease of training the model further in case of progressive speech disorders. The data set for model training is described, and recommendations for collecting and marking data for model training are given. The effectiveness of the proposed method is tested on an individual data set recorded by a person with dysarthria; the recognition quality is compared with neural network models trained on the data set used. The materials of the article are of practical value for creating an augmented communication system for people with speech disorders.
Keywords: hidden Markov models, dysarthria, automatic speech recognition, phonemes recognition, phoneme correction
DOI: 10.26102/2310-6018/2024.44.1.032
Underwater optical wireless communications are promising and future-oriented wireless carriers to support underwater activities focused on 5G and beyond (5GB) wireless systems. The main challenges for the deployment of underwater applications are the physicochemical properties and strong turbulence in the transmission channel. Therefore, this paper analyzes the end-to-end performance of a hybrid free space optics (FSO) and underwater wireless visible light communication (UVLC) system under intensity modulation or direct detection (IM/DD) in a method considering a pulse amplitude modulation (PAM) scheme. In this study, a fading model with Gamma-Gamma (GG) distribution is used to deal with channel conditions with moderate and strong turbulence, and the links are designed by combining plane wave modeling in the corresponding links, respectively. The proposed performance methods excel in higher achievable data rates with minimal delay response and improves network connectivity in real-time monitoring scenarios compared to conventional underwater wireless communication techniques. The simulation results provide reliable estimates of system performance metrics such as average bit error rate (ABER) and probability of failure (Pout) in the presence of pointing errors. Finally, this paper uses a Monte Carlo approach for best curve fitting and validate the numerical expression with simulation results.
Keywords: 5G and 5GB networks, cooperative communication, optical communication, underwater communication, underwater sensor networks (USNs), VLC light communication
DOI: 10.26102/2310-6018/2024.44.1.005
Sensor devices and biomedical imaging technologies used in clinical application scenarios are essential for providing a comprehensive portrait of patients’ state, but these technologies, despite their outstanding advantages, have their inherent disadvantages. Beginning with the principle of complementary images of medical imaging techniques, this review examines the functional near- infrared spectroscopy (fNIRS) technique and its use as a hybrid system. The fNIRS technology delivers impressive results in terms of the biological signal classification accuracy, but its use as a hybrid system with electroencephalography (EEG) and electromyography (EMG) achieved better results because it has become a complementary tool to fill the deficit of the common technology with it, and this has been highlighted in this review. The results show that the superiority in the biological signal classification accuracy provided by hybrid systems from fNIRS with EEG and EMG would provide a comprehensive and objective assessment of the patients’ state from the stage of illness to healing. In conclusion, we have no indication from the scientific studies of the previous four years (2020–2023) that demonstrate which of the hybrid systems is better than others when used in clinical practice, and this encourages further in-depth studies to validate the combination of methods to prove their success and preference.
Keywords: HBCIs, fNIRS, fMRI, EEG, EMG, MEG
DOI: 10.26102/2310-6018/2024.44.1.006
The paper considers the best-known models of a porous body used to simplify the performance of thermohydraulic calculations by the finite element method. The main approaches and dependencies when using the porous body model in calculations are shown. The results of thermohydraulic calculations using the Darcy porous body model are presented. The calculation of a heat exchanger with spirally wound tubes was performed, the calculation of a complex technological system consisting of mechanical filters of different configurations was performed. The discrepancies between the calculated and actual parameters of the equipment are determined. The use of a porous body model as a hydraulic analogue of equipment using the example of mechanical filters and a heat exchanger showed acceptable results (deviations from the design values range from 0,1 % to 10 %). These discrepancies are related to the accuracy/correctness of the selection of porous body resistance laws (dependencies). The use of the porous body approach in modeling the operating modes of technological systems including equipment with a complex design is explained, first of all, when it is required to predict the operating modes of the system as a whole from the result of computational modeling, but local processes occurring inside the equipment are not. Secondly, when it is necessary to reduce the time for performing calculations with low available power capabilities of computers. However, the proposed approach has disadvantages, in particular, the procedure for determining the degree of porosity of the simulated object and the laws of hydraulic resistance selected from empirical dependencies is quite complex.
Keywords: porous body model, complex technological systems, heat exchanger, finite element method, hydraulic resistance, mechanical filters
DOI: 10.26102/2310-6018/2024.44.1.003
The article examines the problem of developing an integration platform to facilitate end-to-end business processes supporting the life cycle of heterogeneous information objects. The platform topology is chosen according to the functionality of the integrated systems and the structure of the information object. To create a unified enterprise information environment, various topologies are considered, including peer-to-peer, message broker, centralized, and hybrid topologies. The basis for the description of an object is a complete data model, including defining attributes and transformation rules corresponding to each of the integrated systems. Using the object model of the information support system for digital products and special templates, a methodology for forming policies, methods and documents (PMD) and organizing a unified digital environment of the enterprise is proposed. However, to solve this problem, the development of a specialized integration platform is required which is capable of processing data from production facilities on a centralized basis and facilitating their interaction in a unified information environment. Such a platform must take into account the characteristics of each system component and ensure the security of information exchange; it also should be able to scale and adapt to the changing needs of the enterprise. In addition, this article discusses in detail various topologies for creating a unified enterprise information space. Peer-to-peer, message brokered, centralized, and hybrid topologies are included. Each of these topologies has its own characteristics and advantages, and the choice of the optimal one depends on the requirements and characteristics of a particular enterprise. To successfully implement integration and create a unified digital environment of the enterprise, it is suggested to use an object model of an information support system for digital products. This model helps to structure information and determine the relationships between various components of the system. Furthermore, the article proposes a methodology for the formation of PMD, which is the basis for organizing a unified digital environment of the enterprise. This methodology takes into account the requirements for security, consistency and efficiency of the system and also ensures standardization and consistency of processes within the enterprise.
Keywords: information production facilities, integration, digital environment, full data model, process automation
DOI: 10.26102/2310-6018/2024.44.1.015
The article examines the optimization of investment management in the formation and implementation of multi-object information system development program. The stage connected with the transition from the development program executed for a certain time period to a new development program with a given planning horizon is considered. It is shown that the investments are balanced at the moment of transition and the need to rebalance them arises in the process of implementation. For the first problem, a multilevel system of balance conditions is formed, which is the basis for the construction of optimization models of the balancing process. Since the lower level of balance conditions is associated with the requirement to increase the value of organizational system development indicators of objects up to a certain value set by the managing center, the optimization problems are based on predictive assessments. These estimates are calculated either using the results of neural network modeling or expert evaluation. When forming optimization models of the investment rebalancing process, two ways of detecting the deviation of the development indicators value from the planned growth trajectory are considered: at a given point in time; when the threshold value is exceeded. In these cases, the point in time is determined, at which the optimal strategy of investment allocation between time transitions is adjusted in order to reach a given level of development indicators at the end point. Thus, the proposed transition makes it possible to optimize the distribution of investments as part of the development program both in the process of their balancing and rebalancing.
Keywords: multi-object organization system, development program, investment, optimization, neural-network modeling, expert assessment
DOI: 10.26102/2310-6018/2024.44.1.030
The increasing scope of application of mobile technologies and devices as elements of distributed systems to enhance the efficiency and convenience of access to various information systems and digital services has made it necessary to improve methods and mechanisms for information protection and information security. One of the main security mechanisms is access control. Features of traditional (discretionary and mandatory) access control model application in distributed information systems (IS) when using mobile systems (MS) as elements are analyzed. Thematically, hierarchical model is proposed as the most effective model that meets the required security policy. For this access control model, an ontological method for forming trust rights to access objects is proposed based on the use of semantic proximity metrics. When using traditional thematic hierarchical access control models, the logical information architecture of IS resources forms a thematic hierarchical classifier (categorizer). The Hasse diagram introduces order relations in the thematic classifier on the security grid to form trust-thematic powers of IS users. Constructing Hasse diagrams on a security grid that includes several security levels is a rather complex algorithmic task. When constructing trust-thematic powers of users in order to avoid uncertainty due to the incompleteness of the constructed Hasse diagram and overestimation of the granted powers when forming access rights, it is proposed to use the semantic proximity of the user access request and the thematic heading of the hierarchical classifier. An analysis of existing approaches to the formation of semantic proximity metrics has shown that proximity measures based on the hierarchy of concepts can be used as the best metric for setting the user’s trust authority.
Keywords: mobile station, access control, hierarchical thematic classification, semantic proximity, semantic distance