articles
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№ 6(108)
25 december 2023 year
Rubric: Models and methods Authors: Protasov V., Mirakhmedov R., Potapova Z. |
A computer model and test results of a decentralized self-controlling UAV system using the molecular dynamics method and various forms of interaction potential are presented. In this model, each UAV is replaced by a “quasi-molecule” with point dimensions and a mass equal to the mass of the UAV. The motion of these “quasi-molecules” is calculated using Newtonian mechanics, taking into account the potential force field from other “quasi-molecules”. The system consists of a group of UAVs performing various ordered formations in accordance with a given mission, as well as obstacles and “hunters” trying to collide with the UAV. “Hunters” are also represented by point-like “quasi-molecules”. This allows us to simulate the presence in the system of both active and passive interference with UAV movement. When modeling passive interference, “hunters” are located on the surface of an obstacle at the nodes of a surface mesh superimposed on it. When modeling the movement of UAVs, taking into account their interaction with each other, active and passive interference, specially designed interaction potentials were used. Numerous tests have been carried out on the system under these conditions. No collisions of the UAVs with each other, with obstacles or with “hunters” were observed. This confirms the reliability of the developed principles for controlling the movement of a group of UAVs under conditions of intense active and passive interference. The main difference between the presented work and existing ones is that various missions are carried out by groups of UAVs without external control according to data from a technical 3D vision system within the framework of a fairly simple molecular dynamics model by changing the shape of interaction potentials. Continue... |
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№ 6(108)
25 december 2023 year
Rubric: Software engineering Authors: Stepanychev N., Filimonova E., Ratanova O., Trubin A. |
One of the important social tasks is the task of improving the efficiency of transport service systems. The paper proposes approaches to solving the problem of increasing the efficiency of contactless fare payment on passenger road transport, which are based on geolocation technology. Based on the consideration of fare payment schemes for closed and open cycles, scenarios for the implementation of fare payment using a flat fare system, a zonal fare system and electronic tickets with QR codes are presented. Several payment implementation work schemes have been developed based on the application of the geolocation concept and using various technologies to implement this concept, namely 1) Bluetooth Low Energy, 2) obtaining coordinates from GPS/GLONASS navigation systems, 3) data received through Wi-Fi access points and 4) location data provided by basic stations of GSM networks. A comparative analysis was carried out for each proposed option, a basic set of technical and software support tools was determined, and recommendations were given on the conditions that determine the feasibility of its application. For experimental confirmation of the operability of each of the options for obtaining the estimated values of the main characteristics, operating samples were constructed on which the corresponding measurements were carried out. Continue... |
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№ 6(108)
25 december 2023 year
Rubric: Defense software Authors: Zainchkovsky A., Lazarev A., Ledneva O., Prokimnov N. |
One of the most important components of the global Internet are traffic control and management systems. In order to achieve uninterrupted information and communication interaction, the organization of the process is constantly changing, covering not only individual subnets, but also p2p network architectures. The dominant areas for improving the network structure include 5G, IoT and SDN technologies, but their implementation in practice leaves the issue of ensuring the information security of networks built on their basis without a satisfactory solution. Current virtual tunnel deployment topologies and intelligent traffic distribution components provide only partial solutions, particularly in the form of access control based on user traffic and security through dedicated user certificates. The deployment of a tunnel is of particular importance in cases where it is necessary to ensure consistency and coordination of the work of complex socio-economic systems, an example of which is the information and communication exchange between participants in scientific and industrial clusters formed to implement projects for the creation of innovative products. However, existing solutions have disadvantages such as the need to purchase a license for full-featured access to the software product and specialized configuration of client-server authentication that provides secure access to a remote network route. The approach proposed by the authors, based on neural network distribution of traffic between clients of a private dedicated network, allows us to eliminate the noted shortcomings. Based on this principle, a multi-module system for intelligent packet routing was created and tested through unit testing. An analysis of the effectiveness of using a trained network address distribution model is presented in comparison with the use of a DHCP server based on the isc-dhcp-server package, distributed as the dhcpd service. Continue... |
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№ 6(108)
25 december 2023 year
Rubric: Researching of processes and systems Authors: Yasnitsky L., Cherepanov F., Goldobin M., Neverov A. |
Studies of the influence of the chemical composition of ore raw materials on the properties of foundry products, as a rule, do not consider the features of production processes. The main reasons for this are that, firstly, the results of such research are usually limited to laboratory studies, secondly, the models do not take into account the specifics of the structure of ore raw materials, since these data can be obtained only after the completion of all thermophysical and chemical-energy-technological processes of heat treatment, thirdly, the use of models is difficult due to the need to promptly account for changes in the composition and values of systemic and external factors during heat treatment. The purpose of the paper is to design neural network models and tools that provide the possibility of adaptive structural and parametric adjustment to changes in the parameters of the analyzed processes of heat treatment of ore raw materials. Data on the chemical composition of ore raw materials and information on heat treatment processes are used as parameters of the projected neural network models. As a result of the research, the hypothesis about the possibility of indirect accounting for the influence of structural features of ore raw materials on the quality of products has been confirmed. Confirmation of this hypothesis will allow us to offer effective tools for operational management of thermophysical and chemical-energy-technological processes of heat treatment of ore raw materials. Continue... |
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№ 6(108)
25 december 2023 year
Rubric: Algorithmic efficiency Authors: Puchkov A., Dli M. I., Tindova M. |
A method for solving the inverse kinematics problem for a three-link robotic manipulator is proposed based on one of the types of machine learning - reinforcement learning. In the general case, this task consists of finding the laws of change in the generalized coordinates of the manipulator’s gripping device that provide the specified kinematic parameters. When solving the problem analytically, the basis for calculating inverse kinematics is the Denavit – Hartenberg parameters with further implementation of numerical matrix calculations. However, taking into account the kinematic redundancy of multi-link manipulators, this approach is labor-intensive and does not allow automated consideration of changes in the external environment in real time, as well as the features of the robot’s field of application. Therefore, an urgent research task is to develop a solution whose structure contains a self-learning block that provides a solution to the inverse kinematics problem under conditions of a changing external environment, the behavior of which is unknown in advance. The proposed method is based on simulating the process of achieving the goal of robot control (positioning the gripping device of the manipulator) at a given point in space using the trial and error method. For approaching the goal at each learning step, a reward function is calculated, which is used when controlling the robot. In the proposed method, the agent is a recurrent artificial neural network, and the environment, the state of which is observed and assessed, is a robotic manipulator. The use of a recurrent neural network made it possible to take into account the history of the movement of the manipulator and overcome the difficulties associated with the fact that different combinations of angles between links can lead to the same point in the workspace. Testing of the proposed method was carried out on a virtual model of the robot, made using the MatLAB Robotics System Toolbox and the Simscape environment, which showed high efficiency in terms of the “time – accuracy” criterion of the proposed method for solving the inverse kinematics problem. Continue... |
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№ 1(109)
31 january 2024 year
Rubric: Performance management Authors: Puchkov A., Dli M. I., Prokimnov N., Vasilkova M. |
The results of research are presented, the purpose of which was to develop a method for predicting the outflow of clients of a commercial bank based on the use of machine learning models (including deep artificial neural networks) for processing client data, as well as the creation of software tools that implement this method. The object of the study is a commercial bank, and the subject of the study is its activities in the B2C segment, which includes commercial interaction between businesses and individuals. The relevance of the chosen area of research is determined by the increased activity of banks in the field of introducing digital services to reduce non-operating costs associated, in particular, with retaining clients, since the costs of attracting new ones are much higher than maintaining existing clients. The scientific novelty of the research results is the developed method for predicting the outflow of commercial bank clients, as well as the algorithm underlying the software that implements the proposed method. The proposed ensemble forecasting model is based on three classification algorithms: k-means, random forest and multilayer perceptron. To aggregate the outputs of individual models, it is proposed to use a learning tree of fuzzy inference systems of the Mamdani type. Training of the ensemble model is carried out in two stages: first, the listed three classifiers are trained, and then, based on the data obtained from their outputs, a tree of fuzzy inference systems is trained. The ensemble model in the proposed method implements a static version of the forecast, the results of which are used in a dynamic forecast performed in two versions – based on the recurrent least squares method and based on a convolutional neural network. Model experiments carried out on a synthetic dataset taken from the Kaggle website showed that the ensemble model has a higher quality of binary classification than each model individually. Continue... |
