The relevance of the article is due to the importance of the study of the relationship between demand and prices in regional energy markets for the purposes of forming the long-term dynamics of energy demand and working out strategic decisions in the field of energy and economic security of the country and its regions. The need to develop new approaches to study the long-term state of regional energy markets is caused, among other things, by the latest in a series of energy transitions that is ongoing in the world. One of the main features of the transition is that consumers have the ability to control their energy consumption, to have energy production and storage capacity, and to interact with the power system. These new properties of consumers are changing the very system of energy supply and pricing in the energy sector.
This study describes the multi-stage methodological approach developed by the author. Each stage of the approach consists of the successive solving of an individual problem or several problems of varying importance and complexity, for each of which we have developed dedicated methods and models. The key defining feature of the approach is the possibility to return (if necessary) to the previous stage to adjust conditions or parameters. A unique feature of the approach is the joint interrelated treatment of the system of consumption and energy supply of the region and the iterative alignment of decisions made at the regional level with the energy supply system of the higher territorial level. The proposed multi-stage methodology makes it possible to determine the mutual influence of demand and prices in regional energy markets, taking into account consumer behavior and evaluates the regional price elasticity of demand for certain energy resources. This will enable one to adjust the long-term dynamics of demand for energy and will contribute to improving the validity of future options of development of the power industry and energy sector.

Currently, the direction associated with the study of integrated energy systems is actively developing. This direction considers various types of systems (electricity, heat, cold, gas supply, etc.) as subsystems of a single system and allows you to get advantages over traditional approaches to the study of energy systems, in which these systems are considered independently of each other. The article proposes a methodical approach to organizing a single information space for solving the problems of developing integrated energy systems in the form of a single software platform. This approach includes the following components: platform building principles; platform architecture; technologies and development tools for platform implementation; the basic structure of the library of software components, their interfaces and integration mechanisms into the platform. In the process of building a software system, it is proposed to use ontologies that are used to solve the following problems: automatic integration of software components into the AnyLogic software environment; automated construction of a software system for solving the problem of managing the development of integrated energy systems; application of the software platform in solving practical problems. In accordance with the proposed methodological approach, a software platform prototype has been implemented. The solution of the problem of development is demonstrated on the example of the scheme of an integrated energy system. The results of a computational experiment on a test circuit of an integrated energy system, which was carried out using the developed software prototype, are presented. As a result of the experiment performed on the developed multi-agent model, it was possible to form the optimal scheme of an integrated energy system for power supply to consumers, taking into account system conditions and limitations.

The purpose of this study is to develop an effective method for processing the results of thermophysical experiments based on solving two types of nonlinear mathematical programming problems. The article provides a description of the proposed method for identifying the coefficients of the mathematical model of a thermophysical experiment based on the results of measured experimental data. We also consider two mathematical models that interpret the results of the performed field experiments. The technique presented in the article is based on the maximum likelihood method and takes into account the relative errors of all sensors used to obtain the values of the measured parameters. Moreover, the technique involves a two-stage approach in solving the problem of identifying the  parameters of a mathematical model. At the first stage, the maximum relative error among the measured parameters is minimized, which makes it possible to identify and eliminate "bad" measurements. Further, at the second stage, the sum of the modules of relative errors of all measured parameters is minimized. Computational experiments have shown that this approach is more efficient than the  classical least squares method, which is sensitive to the presence of "bad" measurements and, under certain conditions, can become a ravine function. The last section of the article presents the results of computational experiments testing the proposed method. Calculations have shown that this approach is very effective and allows you to adjust the coefficients of mathematical models with high accuracy.

A new mathematical model of a system of cylindrical heat pipes with a composite wick is presented. The results of numerical modeling and experimental studies of a heat pipe system (SHP) to ensure the removal of a given thermal power from radio-electronic equipment for various purposes are presented, and its minimum mass is determined. The limiting values of the removed thermal power of a single heat pipe, a two-level CTT under the conditions of a gravitational field are established. According to experimental data, the STT is capable of removing 667 W/kg from a height of 0.11 m at a temperature of minus 5 °C, 910 W/kg at a temperature of plus 40 °C. The presented results of the work make it possible to optimally solve many engineering problems related to the transfer of thermal power with minimal losses, cooling and temperature control or thermal stabilization of various objects used both on Earth and in outer space.

The problem of assessing the reliability and analyzing the functioning of an autonomous wind-diesel complex is considered. Its semi-Markov and hidden Markov models are constructed. A hidden Markov model is used to find performance estimates for an autonomous wind-diesel complex and predict its states based on the resulting signal vector. The results of the study are obtained in a general form and are invariant with respect to the laws of distribution of random variables describing the elements. They make it possible to predict the operating modes of an autonomous wind-diesel complex.

The article describes the joint assessment and prediction of the functional state of the human operator and the technical part of the system "operator-technical object". The technique is being developed in a generalized version with a human, and can be used for various types of technical systems, including aircraft (LA) and other transport systems, as an application to process control in symbiotic (man-machine) complexes. At the information level, the procedure for redistributing functions between the operator and automation is implemented according to the integral indicator of the safety of the predicted movement of the object, the value of which determines the final choice of the situational control mode (manual, combined or automatic modes).

The hydrodynamic tube is one of the most effective tools for studying the process of wing flow in aerodynamics and hydrodynamics. This makes it possible to study the flow characteristics under controlled conditions and simulate conditions that could not be studied in real flight. Flow visualization methods, such as colored jets or fine particles, allow us to obtain qualitative data on the behavior of the flow, being a valuable tool for understanding the flow features. But it is more interesting to have quantitative flow characteristics that allow us to predict the development of flow processes and develop recommendations for improving safety. The presented research is aimed at developing a system of contactless three-dimensional measurements in a hydrotube based on photogrammetric methods. An original visualization model is proposed that takes into account the effects of refraction in the case of several optical media (air-glass-liquid), which provides spatial accuracy of measurements. This model is implemented in an experimental photogrammetric system, an accuracy estimate is given both for the calibration of the system and for the measurement of the aerodynamic model. The developed method of calibration of the photogrammetric system has demonstrated its applicability to the problem of three-dimensional measurements in a hydrodynamic tube.

The article describes a technique for constructing an oriented hypergraph of chemical reactions for a reacting system. The analysis of such a graph makes it possible to evaluate the influence of certain reactions and reaction paths (total reactions) on the chemical composition of the final products. In this work, all possible combinations of elementary reactions of hydrogen combustion in oxygen were enumerated, while the criterion for the connectivity of the corresponding graph was the formation of all substances from a pre-specified list, while the probability of termination was calculated.

In this work, a mathematical model is built and the parameters of the spatial movement of a modified disk-shaped glider are determined when maneuvering in depth and course. The modification consisted in creating a profiled annular "groove" on the body of the glider. The introduction of this structural element reduces the value of the overturning moment and increases the damping moment, which, in turn, improves the stability parameters of the glider in the vertical plane. Maneuvering was carried out due to the operation of the mechanism for changing the buoyancy and the mechanism for fine trimming the glider. It is shown that the introduction of a fine trim mechanism into the design makes it possible to optimize the angle of attack of the glider. The schemes of operation of the mechanisms for changing buoyancy and fine trim were determined, which ensure stable maneuvering of a disk-shaped glider in depth and course.

The paper presents an algorithm for clustering the areas of predicted areas, implemented in a system for the early detection of forest fires. The system is based on a neural network model for searching for a fire object detection. At the output of the neural network model, an array of bounding boxes, the estimated location of objects of a given class, class labels, and estimates of the probability of belonging to a class are formed. The task of the algorithm considered in the article is to select the desired objects with a bounding box and calculate the average value of the probability of belonging to a class by combining and averaging the bounding boxes that have intersections with the desired object. The article provides a brief overview of existing algorithms for displaying the resulting bounding box on an image. The rationale for choosing a neural network model for an early fire detection system is given. The results of the comparison of the NMS, Soft-NMS algorithms and the area clustering algorithm for solving the problem of detecting a smoke cloud in an image are presented.

The proposed technology is based on methods, models, software tools, and automation tools for solving problems of qualitative research of binary dynamic systems based on the Boolean constraint method in microservice computing infrastructure. The essence of the method is to reduce the problems under consideration to solving SAT or 2QBF problems by constructing a Boolean model of a dynamic property. The software includes tools for building Boolean models and checking their feasibility. We implemented these software tools as a package of applied microservices as part of our technology. The launch of the microservice is controlled by software agents installed in the nodes of the computing infrastructure, created based on the previously developed HPCSOMAS-MSC instrumental platform. This study aims to extend HPCSOMAS-MSC with tools for automation of computing nodes preparation in a distributed environment for deploying a package of applied microservices and specialized agents focused on the structure of a Boolean model to provide two-level control of parallel computing when checking its feasibility. At the upper level, the subtasks obtained by splitting the Boolean model are distributed over the nodes of the lower level, subjected to deep splitting, and solved in parallel as independent ones. Computational experiments for a qualitative study of binary dynamic systems with a large state vector dimension have shown the effectiveness of the proposed technology. The wide use of such systems as object models in conducting research in various subject areas determines the practical significance of the proposed technology.

The presented research is devoted to the consideration of the creation of a Regional Geoinformation System (RGIS) in the Altai Krai, which meets the requirements of the digitalization policy of the Russian Federation. The creation of the RGIS of the Altai Krai is an actual and promising practice-oriented direction that meets the task of geocyphrovization and geoinformation support of the subjects of the Russian Federation. The purpose of the study is to substantiate approaches and develop conceptual provisions for the creation of a GIS of the Altai Krai, which is based on the analysis of existing experience in the development of regional GIS in other subjects of the Russian Federation, as well as on the creation of a primary geoinformation database for the region within the framework of special local content: natural resource, agricultural, environmental. The main research method is geoinformation and cartographic. The main results are the development of a conceptual model of the RGIS, its structure and the structure of the database. Priority directions of the RGIS prototype have been identified, both for the territory of the region as a whole and for model administrative districts: agrarian, natural resource, environmental. The format of the GIS of the Altai Krai in the form of a GIS atlas and a geoportal is proposed, taking into account the GIS of transport and housing and communal services already developed and partially implemented for the territory of the region. The issues of the relationship of the RGIS with the spatial data infrastructure (SDI) and the geoportal are considered. An idea of the structure and composition of the regional GIS of the subjects of the Russian Federation is given, in particular, such characteristics as: the type and subject of GIS, the data model used, the customer and the GIS developer are indicated. The scientific novelty of the study lies in the originality of approaches to the creation of the RGIS of the Altai Krai, taking into account the experience of the implementation of the RGIS in other subjects of the Russian Federation

The article is devoted to the development of an otological data model for assessing the frequency of failures of electrical network elements. The model includes hierarchically ordered classes that characterize emergency shutdowns in detail, as well as various parameters of monitored objects; log of emergency shutdowns; substations; voltage; mathematical models for probabilistic estimation and forecasting, etc. The resulting ontology makes it possible to analyze the causes and duration of emergency shutdowns, power losses, identify in-row relationships, determine regression dependencies, and predict failures using various models. The created ontological data model is focused on the networks of the city of Irkutsk, but it can also be used for networks of other settlements. Based on the developed ontological model, an infological model was built, which is implemented in the database of the information system for assessing the repeatability of failures of electrical network elements. With the help of the implemented information system, it is possible to carry out a probabilistic assessment and prediction of the number of failures of electrical network elements using various models.

This work is focused on the development of methodological and algorithmic means of generating operational printed report forms based on the model-oriented approach. The paper shows the relevance of modern approach to software development without programming and justifies the use of model-driven architecture as the technological basis of "no code" platforms. Meta-metamodel description of "no code" data consolidation platform, that provides dynamic interpretation of the metamodel and data consolidation forms generation, is presented. Suggested the development of existing meta-metamodel by adding the entities "template" and "procedure", allowing for the efficient generation of printed report forms with dynamic content. The choice of Jinja2 templating engine for software implementation of report generation module is justified and a diagram of module sequence showing the interaction of the interface part of the platform with templating engine and metadata is presented.

It is shown that one of the ways to objectify productive knowledge bases in knowledge-based systems can serve as an inductive inference based on the combined method of similarity and difference and tables of joint occurrence of phenomena. An approach to the use of such a conclusion is proposed in conditions of possible low reliability and inconsistency of information sources forming tables. The approach is based on the concept of nonstrict probability, which, in turn, is based on the theory of logics with vector semantics in the VTF-logic variant. The tables themselves can be obtained from big data, primarily relational databases. It is assumed that such an approach will weaken subjectivism in the construction of production knowledge bases.

In this paper we propose a method for detecting an object in an image based on a global feature description. An information model is described and implementation options for each of the stages of the image object detection task are proposed. For the image preprocessing stage, the available options, including normalization, calculation of brightness function and application of Gaussian filter are given. The stage of forming a global feature description of an object is based on active perception theory (U-transformation). Object localization is performed based on the minimum Euclidean distance from the detected object to the reference objects from the database. Images from the Russian Traffic Sign Dataset database and their modified copies (images with superimposed noise, images with rotation of the objects searched for) were used for testing. When analyzing the test results, the parameters that give the highest accuracy for the proposed method of object detection in the image have been selected. In the presence of noise in the image, the localization accuracy of the proposed method was more than 70%. The proposed image object detection method is robust to rotation of the objects being searched. The resulting accuracy of about 94-96%, when compared with the accuracy of existing methods, showed that under normal conditions the developed method works similarly to existing methods.

Currently, many modern problems of applied mathematics and computer science are solved using graph theory. Various complex systems, such as neural networks or knowledge bases, can be represented and described as graphs. The most common problems using graph theory are: finding the shortest path, determining the maximum flow in the network, finding the minimum spanning trees and others. At the same time, there are quite a lot of unresolved problems. The relevance of the work is due to increased interest in the fields of artificial intelligence and knowledge engineering, the methods of which are the possibility of transforming the obtained subject models into logical and mathematical, in the form of programs for computers that carry out computer or simulation modeling of the systems under study. In the presented work the process of development of the special mathematical and algorithmic software for system of the analysis and processing of the expert information for the purpose of the automated search and modeling of decisions of problems for identification of dynamic systems is described. The problem of search and logical inference of synthesized solutions of problems on knowledge graphs is formulated. The model of knowledge base of the selected subject area in the form of knowledge multigraph is presented, as well as the method of search and logical inference of solutions of problems, with their software implementation in the programming language Python. The presented method of search of weakly connected subgraphs and synthesis of problem solutions is implemented by using theoreticalmultiple analysis, as well as elements of graph theory. To demonstrate the performance of the method, its implementation in the form of an algorithm in the Python programming language and the results of computational experiments are given. The novelty and practical significance of the work lies in the fact that the proposed method and algorithms can be used in the practical implementation of knowledge bases, logical inference mechanisms and processing of expert information in a variety of expert, calculation-logical and hybrid intelligent systems, replacing their foreign analogues.

The paper considers the issues of creating mechanisms that allow a collaborative robot to be considered a moral agent. It is shown that in the conditions of fundamentally irremovable contradictions and incompleteness, the use of logical schemes and various non-classical logics does not solve the problem of formalizing moral teachings and ethics of behavior. The example shows that the task of moral schemes is not only and not so much to determine or evaluate the agent action (behavior), but to implement the explanatory behavior component. Utilitarianism in the understanding of I. Bentham and J. S. Mill in combination with the axiological system of hedonism rules are considered as ethical theories of the robot behavior. Cognitive maps are used for qualitative analysis. Three fundamental statements of the moral behavior of a robot are formulate in the article

Currently, the methodology of artificial intelligence is gaining more and more applications. Neural networks (NN) attract a lot of attention. One of the main reasons for this is that the NN technology is, in a sense, universal. On the other hand, some other computer technologies, having the nature of artificial intelligence or others, also have the properties of universality. In the context of this article, these are technologies of fuzzy cognitive maps (FCM) and special systems of partial differential equations, so called “systems of conservation laws” (CL). All the approaches just mentioned are used to develop models of complex systems (CS), i.e. aggregates of a large number of elements with connections between them of various types. The specificity of the CL theory, which is able to model joint balances of various quantities on the level of physical processes, lies in the presence of nonlinear effects, such as the formation of singularities in solutions. The FCM technology allows for an extension of the original concept by B. Kosko with a wider range of interpretation. The article provides an illustration of how these technologies can be combined into a single methodology for creating a modeling medium for the CS based on the NN approach.

Recently, the direction defined by the term “Resilience” has been of great interest abroad. Research of Russian scientists in this area is conducted mainly in the field of technical sustainability as one of resilience aspect, while Western Europe scientists consider this area more broadly and include environmental, psychological, social and economic resilience. Energy and environmental security issues are of great importance in resilience studies. The article considers an approach to assessing the resilience of energy systems within the framework of the situational management concept. The justification of the need for the use of machine learning methods is given and an example of the use of these methods for the quantitative assessment of resilience is also given