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“Journal of Applied Informatics” is a peer-reviewed science journal with international representation of editorial board and authors, covering a significant part of Russian IT-area. The topics of the publications are connected to the aspects of theory and application of computer modeling and information technologies in various professional areas. The journal is indexed by Russian Science Citation Index on Web of Science platform.

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In accordance with the decision of the Higher Attestation Commission of the Ministry of Education and Science of Russian Federation, journal is included in the «List of Leading Peer-Reviewed Scientific Journals and Publications authorized to publish main dissertation results»

Comparison of mathematical models of the dynamics of electrically charged gas suspensions for various concentrations of the dispersed component

The author: Tukmakov D.
This work is devoted to mathematical modeling of the dynamics of inhomogeneous electrically charged media. A dusty environment - solid particles suspended in a gas – was considered as an inhomogeneous medium. The mathematical model implemented a continuous approach to modeling the dynamics of inhomogeneous media. The complete hydrodynamic system of equations was solved for each component. The system of equations for the dynamics of each component included the equations of mass continuity, momentum components, and the energy conservation equation for the mixture component. Intercomponent interaction took into account momentum exchange and intercomponent heat transfer. The carrier medium was described as a viscous compressible heat-conducting gas. The flow was described as a flow with a two- dimensional geometry. The equations of the mathematical model were supplemented with initial and boundary conditions. The mathematical model took into account the wall viscosity in the channel. The system of equations of the mathematical model was integrated by McCormack's explicit finite-difference method. To obtain a monotonic grid function, a nonlinear scheme for correcting the numerical solution was used. The mathematical model was supplemented by the Poisson equation describing the electric field formed by charged dispersed particles. Poisson's equation was integrated by finite-difference methods on a gas-dynamic grid. Such a choice of the computational grid was necessary to calculate the concentration of particles required both for solving the electric field equation and for calculating the physical fields of the dynamics of inhomogeneous media. The reciprocal motion of a gas suspension caused by the movement of dispersed particles under the action of the Coulomb force was numerically investigated. The values of the surface and mass densities are determined, at which the models of the surface and mass densities of charges in the simulation of such a process are the same. It is revealed that the surface and mass models of charges are identical with respect to the volumetric content.

Data mining in the management of the Russian higher school

For a comprehensive assessment of the management decisions quality, it is necessary to take into account heterogeneous information presented both in numerical form and in natural language expressions. The effective occurs the use of data mining including neural network clustering and fuzzy set theory. The article presents our approach to the use of these methods for evaluating risks and the management decisions quality in Russian higher education on the example of the implementation of the most ambitious Project 5-100 for it. On the example, the expediency of the neural network clustering to assess the possibility of achieving the goals of any such large-scale project has been proved. Clustering the information database used for the analysis, makes it possible to carry out an objective selection of candidate universities-candidates for the right to receive state subsidies, as well as to adjust the composition of the Project participants. Another methods of intellectual analysis – the construction of a complex of fuzzy inference systems, – confirmed the possibility of a quantitative fi evaluating of the project based on the expert verbal estimates of the project. At the same time, the neural network clustering initially illustrated the unattainability of the Project 5-100 goals. The use of a complex of fuzzy inference systems confirmed this statement by the very low quantitative final assessment of the project on the basis of verbal expert opinions.

Methods of forming orthogonal polyhedra for cutting and packing objects of complex geometry

The article deals with the problem of packing objects of arbitrary geometry. Modern methods of designing irregular packing schemes use a mathematical model based on phi-functions and a hodograph vector function of dense placement. These methods make it possible to obtain exact solutions, but they are time-consuming and very sensitive to the dimension of the problem being solved and the degree of detail of the geometry of vector objects. The use of a discrete representation of placed objects in the form of orthogonal polyhedra can signifi increase the speed of construction a packing, which makes the problem of adequately transforming the shape of placed objects (vector models in the two-dimensional case and polygonal models in the three- dimensional case) relevant. The aim of the study is to systematize methods that provide the formation of orthogonal polyhedra of various dimensions for describing objects and containers of arbitrary geometry. Methods for creating orthogonal polyhedra based on set-theoretic operations (addition, subtraction and intersection), analytical modeling using a set of functions and relational operators, as well as voxelization of fl and volumetric object models are considered. The use of set-theoretic operations is best suited for the manual creation of orthogonal polyhedra with relatively simple geometry. The method of analytical modeling is intended for the formation of voxelized objects based on geometric fi es described by a set of analytically specifi functions. The application of various relational operators to obtain orthogonal polyhedra that describe the contour, internal and external regions of analytical given objects is shown. An algorithm for creating a container in the form of an orthogonal polyhedron based on a given vector model is proposed, which makes it possible to solve problems of irregular packing of objects inside containers of arbitrary shape. All the methods presented in the article are programmatically implemented with a generalization in terms of dimension and are applicable to solving any types of cutting and packing problems.

Models and methods of reinforcement learning in the structure of adaptive web-based information systems

The author: Shpolyanskaya I. Y.
Widespread use of web-based systems in business, marketing, e-learning, etc. makes it necessary to take into account and analyze the information needs of the user in order to optimize interaction with him. One of the main problems of creating adaptive web-based systems is the task of classifying information resources (pages) of the portal describing the offered product or service, for the subsequent formation of the user profile and personalized recommendations of services. Data mining and machine learning methods can be used to solve this problem. The article presents a new approach to creating adaptive web-based information systems using the reinforcement learning algorithms to classify information resources and to form personalized recommendations to users based on their preferences. An adaptive approach is proposed and justified, based on the use of Reinforcement Learning procedures, which allows you to automatically find the most effective strategies for the correct classification of the site's resources and the formation of user groups with the same type of requests and preferences. The proposed scheme allows you to create procedures for evaluating and ranking information resources of the system based on the analysis of user behavior on the site online. The reinforcement learning algorithms used make it possible to evaluate the relevance of each page of the site to the requests and preferences of the users from different categories in order to optimize the structure and content of the site, as well as to build an effective system of recommendations in accordance with the user's interests to be able to choose the most suitable products or services.

Predictive models integration with an environmental monitoring IoT platform

The research focuses on the development of applied software systems for automated environmental monitoring. The task of developing and integrating applied software, in particular calculation and analytical models based on machine learning (ML) methods, with an IoT platform of digital eco-monitoring for industrial enterprises is considered. Such a platform is used to create software and hardware systems of CEMS – Continuous Emissions Monitoring System class, designed for continuous monitoring of pollutant emissions into the atmospheric air at production facilities. Use of ML tools integrated with the platform allows to expand significantly the functionality of the existing CEMS, in particular to quickly build new SaaS services for forecasting the dynamics of pollution distribution. Given the high requirements for industrial systems, there is a need to create a specialized software product – an analytical server that implements the management of connected predictive analytical ML models with the required level of service quality, including automatic initialization of new analytical scripts as classes, isolation of individual components, automatic recovery after failures, data security and safety. The paper proposes a scheme of functional and algorithmic interaction between the IoT platform of digital eco- monitoring and the analytical server. The proposed implementation of the analytical server has a hierarchical structure, at the top of which is an application capable of accepting high-level REST requests to initialize calculations in real time. This approach minimizes the impact of one analytical script (class) on another, as well as extending the functionality of the platform in "hot" mode, that is, without stopping or reloading. Results demonstrating automatic initialization and connection of basic ML models for predicting pollutant concentrations are presented.

Simulation model of wireless ad-hoc network to study algorithms of traffiс routing

Communication network simulators are software designed to model, explore, test and debug network technologies, including wireless decentralized self-organizing networks or ad-hoc networks. They greatly simplify the research, development and optimization of routing protocols in these networks. However, the well-known simulators have a number of disadvantages, including the difficulty of adding custom extensions to ad-hoc network routing protocols, the lack of the necessary network stack, the lack of routing algorithm visualization modes, low performance, and difficulty in debugging communication protocols. The purpose of this work is to create a simulation model of a wireless network that would allow us to explore, debug and evaluate the developed algorithms and routing protocols for ad-hoc networks. At the same time, the requirements for interface ergonomics and the ability to visualize the operation of algorithms, ensure the collection of statistics, and create various scenarios for the operation of the network come to the fore. The article proposes the structure of the simulation model, which includes the modules of the network subscriber, application software, network layer of the OSI data transmission model, radio module, radio transmission environment, statistics collection, visualization and scenario management. To solve the tasks set, the approach of discrete-event modeling was used. To create a simulator of wireless decentralized networks and routing algorithms, a set of classes was developed that implement the modules of the simulation model. Based on the proposed structure, module classes and discrete event simulation algorithm, a software implementation of the simulation model was created using the C++ programming language and the Qt framework. The developed simulation model was used in the course of an experimental study of the effectiveness of the network routing algorithm. The proposed software will simplify the development and debugging of algorithms and routing protocols for ad-hoc networks.

Solving the inverse kinematics problem for sequential robot manipulators based on fuzzy numerical methods

Nowadays the introduction of robotic systems is one of the most common forms of the technological operations automation in various spheres of human activity. Among the robotic systems a special place is occupied by sequential multi-link robotic manipulators (SRM). SRM have become widespread due to relatively small dimensions and high maneuverability, which makes their use indispensable to solve various tasks. In practice, the effectiveness of the functioning of the SRM can be influenced by various types of external environment fuzzy factors. Among the external factors there is a group affecting the ability to determine the exact target position. Such factors often affect technical vision systems. This problem is especially relevant for special purpose mobile robots operating in aggressive environmental conditions. A situation similar to the described one also occurs when a medical robot manipulator is used for minimally invasive surgery, when the role of the control and monitoring system is assumed by an operator. In this regard, the organization of effective control taking into account influence of the external fuzzy factors, that prevent the correct recognition of the target position of the SRM instrument, is an urgent problem. The authors consider the solution of the inverse kinematics problem for SRM based on the use of fuzzy numerical methods, taking into account the possible occurrence of singular configurations in the process of solving.

Text sentiment analysis in banking

The paper presents the author's approach to solving the problem of sentiment analysis of online Russian-language messages about the activities of banks. The study data are customer reviews about banks in general and their products, services and quality of service posted on the Banki.ru portal. In this paper, the problem of text sentiment analysis is considered as a binary classification task based on a set of positive and negative reviews. A vector model with a tf-idf weighting scheme was used to represent the collected and preprocessed texts. The following algorithms with the selection of optimal parameters on the grid were used for binary classification task: naive Bayesian classifier, support vector machine, logistic regression, random forest and gradient boosting. Standard statistical metrics, such as accuracy, completeness, and F-measure, were used to evaluate the quality of solving the classification problem. For the indicated metrics, the best results were obtained on the classification model developed with the use of Support Vector Machine. Thematic text modeling was also carried out using the Dirichlet latent placement method to define the most typical topics of customer messages. As a result, it was concluded that the most popular message topics are "cards" and "quality of service". The obtained results can be used in the activities of banks to automate its reputation monitoring in the media and when routing client requests to solve various problems. When solving problems, the features of the Python programming language were actively used, namely, libraries for web scraping, machine learning, and natural language processing.