Intersection of triangles in space based on cutting off segment

The article proposes a new method for finding the triangle-triangle intersection in 3D space, based on the use of computer graphics algorithms -- cutting off segments on the plane when moving and rotating the beginning of the coordinate axes of space. This method is obtained by synthesis of two methods of cutting off segments on the plane -- Cohen-Sutherland algorithm and FC-algorithm. In the proposed method, the problem of triangle-triangle intersection in 3D space is reduced to a simpler and less resource-intensive cut-off problem on the plane. The main feature of the method is the developed scheme of coding the points of the cut-off in relation to the triangle segment plane. This scheme allows you to get rid of a large number of costly calculations. In the article the cases of intersection of triangles at parallelism, intersection and coincidence of planes of triangles are considered. The proposed method can be used in solving the problem of tetrahedron intersection, using the finite element method, as well as in image processing.Comment: Convergent Cognitive Information Technologies. Convergent 2019. Communications in Computer and Information Science, in press, Springer, Cham. http://it-edu.oit.cmc.msu.ru/index.php/convergent/convergent2019 (14 pages, 11 figures

Разработка и исследование алгоритмов формирования правил для узлов сетевой безопасности в мультиоблачной платформе

As part of the study, existing solutions aimed at ensuring the security of the network perimeter of the multi-cloud platform were considered. It is established that the most acute problem is the effective formation of rules on firewalls. Existing approaches do not allow optimizing the list of rules on nodes that control access to the network. The aim of the study is to increase the effectiveness of firewall tools by conflict-free optimization of security rules and the use of a neural network approach in software-defined networks. The proposed solution is based on the sharing of intelligent mathematical approaches and modern technologies of virtualization of network functions. In the course of experimental studies, a comparative analysis of the traditional means of rule formation, the neural network approach, and the genetic algorithm was carried out. It is recommended to use the multilayer perceptron neural network classifier for automatic construction of network security rules since it gives the best results in terms of performance. It is also recommended to reduce the size of the firewall security rule list using the Kohonen network, as this tool shows the best performance. A conflict-free optimization algorithm was introduced into the designed architecture, which produces finite optimization by ranking and deriving the most common exceptions from large restrictive rules, which allows increasing protection against attacks that are aimed at identifying security rules at the bottom of the firewall list. On the basis of the proposed solution, the adaptive firewall module was implemented as part of the research.В рамках исследования рассмотрены существующие решения, направленные на обеспечение безопасности сетевого периметра мультиоблачной платформы. Установлено, что наиболее острой является проблема эффективного формирования правил на межсетевых экранах. Существующие подходы не позволяют оптимизировать список правил на узлах, контролирующих доступ к сети. Целью исследования является повышение эффективности средств межсетевого экрана путем бесконфликтной оптимизации правил безопасности и применения нейросетевого подхода в программно-определяемых сетях. Предлагаемое решение основано на совместном использовании интеллектуальных математических подходов и современных технологий виртуализации сетевых функций. В ходе экспериментальных исследований проведен сравнительный анализ традиционных средств формирования правил, нейросетевого подхода, а также генетического алгоритма. Для автоматического построения правил сетевой безопасности рекомендуется применять нейросетевой классификатор архитектуры «многослойный персептрон», поскольку он даёт лучшие результаты с точки зрения производительности, и уменьшать размерность списка правил безопасности межсетевого экрана при помощи сети Кохонена, поскольку это средство показывает лучшую производительность. В спроектированную архитектуру был внедрен алгоритм бесконфликтной оптимизации, который производит конечную оптимизацию путем ранжирования и выведения наиболее часто встречаемых исключений из больших запретительных правил, что позволяет увеличить защиту от атак, которые направлены на выявление правил безопасности, находящихся внизу списка межсетевого экрана. На базе предложенного решения в рамках исследования реализован модуль адаптивного межсетевого экрана

DECOMPOSITION METHODS OF SOLVING THE PROBLEMS OF THE COMPUTER-INTEGRATED PRODUCTION CONTROL

The object of investigation: the automated control systems of the computer-integrated production. The purpose of the work: the investigation and search for the ways of the efficiency increase of the computer-integrated productions functioning due to the improvement of the methods of solving the problems of planning and control with the character of structure of the parameters set in the course of the multi-level decomposition of these problems. For the first time, it has been proved, that the diagram of break of the ordered parameters set form the distributive grid. The quantitative evaluation of the diagrams of decomposition, and the algorithms of its generation have been developed. The program complex has been developed for solving the problems operative control CIM, allowing to reduce the time of solving or to increase the increase the dimension of the problems, to be solved, by the order. The program complex of solution of the problems of control CIM has been introduced in service. The problems of the production planning and optimization of the operative control, the problem of the debts intertest have been solved. The field of application: the computer-integrated production, minorseries production in machine construction, the study process in the Higher School.Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Investigation of Optimization Algorithms for Neural Network Solutions of Optimal Control Problems with Mixed Constraints

In this paper, we consider the problem of selecting the most efficient optimization algorithm for neural network approximation—solving optimal control problems with mixed constraints. The original optimal control problem is reduced to a finite-dimensional optimization problem by applying the necessary optimality conditions, the Lagrange multiplier method and the least squares method. Neural network approximation models are presented for the desired control functions, trajectory and conjugate factors. The selection of the optimal weight coefficients of the neural network approximation was carried out using the gravitational search algorithm and the basic particle swarm algorithm and the genetic algorithm. Computational experiments showed that evolutionary optimization algorithms required the smallest number of iterations for a given accuracy in comparison with the classical gradient optimization method; however, the multi-agent optimization methods were performed later for each operation. As a result, the genetic algorithm showed a faster convergence rate relative to the total execution time

The Optimal Control of Cellular Communication Enterprise Development in Competitive Activity

International audienceThe work is devoted to the construction and justification of the mathematical model of the competitive behaviour of cellular communication in the form of a system of nonlinear differential equations with delay time describing the dynamics of changes in the subscriber base of cellular operators competing for shared resources

Approach to the Effective Use of Limited Computing Resources in Educational Institutions for Providing Multimedia Services

Existing approaches to the use of computing resources is too wasteful for educational institutions. Modern multimedia services require significant computing power, which are not always available. In this paper, we introduce an approach that allows more efficient use of limited resources by dynamically scheduling the distribution of data flows at several levels: between the physical computing nodes, virtual machines, and multimedia applications with use cloud computing

Request Stream Control for the Access to Broadband Multimedia Educational Resources in the Distance Learning System

This article presents a model of queuing system for broadband multimedia educational resources, as well as a model of access to a hybrid cloud system storage. These models are used to enhance the efficiency of computing resources in a distance learning system. An additional OpenStack control module has been developed to achieve the distribution of request streams and balance the load between cloud nodes

Development and Study of Algorithms for the Formation of Rules for Network Security Nodes in the Multi-Cloud Platform

As part of the study, existing solutions aimed at ensuring the security of the network perimeter of the multi-cloud platform were considered. It is established that the most acute problem is the effective formation of rules on firewalls. Existing approaches do not allow optimizing the list of rules on nodes that control access to the network. The aim of the study is to increase the effectiveness of firewall tools by conflict-free optimization of security rules and the use of a neural network approach in software-defined networks. The proposed solution is based on the sharing of intelligent mathematical approaches and modern technologies of virtualization of network functions. In the course of experimental studies, a comparative analysis of the traditional means of rule formation, the neural network approach, and the genetic algorithm was carried out. It is recommended to use the multilayer perceptron neural network classifier for automatic construction of network security rules since it gives the best results in terms of performance. It is also recommended to reduce the size of the firewall security rule list using the Kohonen network, as this tool shows the best performance. A conflict-free optimization algorithm was introduced into the designed architecture, which produces finite optimization by ranking and deriving the most common exceptions from large restrictive rules, which allows increasing protection against attacks that are aimed at identifying security rules at the bottom of the firewall list. On the basis of the proposed solution, the adaptive firewall module was implemented as part of the research

Intelligent Drone-assisted Anonymous Authentication and Key Agreement for 5G/B5G Vehicular Ad-Hoc Networks

Drones (or unmanned aerial vehicles) can play many assistant roles in the complex communication network, and can be used as an air relay node to support ground communications. It is expected to solve the sustainable communication problem of 5G/ beyond 5G (B5G) vehicular ad-hoc networks by using drones in rural or mountainous areas where communication is limited. In this paper, considering the emergency of vehicular ad-hoc networks, we design an assistant communication scheme based on the intelligent drone to help vehicles securely communicate with each other under adversary but actual conditions. Besides, the real identity of the vehicle must also be protected to prevent illegal elements from obtaining, and using them for crimes. To effectively assist vehicle communication, and ensure that vehicle privacy is not compromised, we propose an intelligent drone-assisted anonymous authentication, and key agreement for 5G/B5G vehicular ad-hoc networks. Utilizing the widely-used Real-Or-Random (ROR) model, and the formal security analysis, the proposed scheme is proven to be resistant to several attacks. Moreover, the proposed scheme has better performance in terms of computation overhead, and communication overhead through performance evaluation

Investigation of Transfer Learning Efficiency in Adversarial Attacks

Deep neural networks are becoming an increasingly effective tool for solving a wide range of complex applied tasks, because they are able to establish patterns in unstructured data, such as images, video and audio information. Despite the fact that the probability of error of modern neural network models decreases, ML models remain vulnerable to adversarial attacks. In this regard, the issues of ensuring the security of machine learning models from adversarial attacks are among the most urgent. Within the framework of this work, a study was conducted on the effectiveness of the transfer learning technology to increase the stability of convolutional neural networks from adversarial attacks. The study was conducted on the example of the task of recognizing road signs in images, which is key in the field of automotive unmanned transport networks. During the experiment, a basic neural network model was built to solve the problem of classifying road signs and attacks based on PGD and FGSM algorithms were implemented, which reduced the classification accuracy by about 2.8 times. To protect against the considered white box attacks on the neural network, competitive training was conducted, which increased the accuracy of the model by 12.11%. In addition, the technology of transfer training was considered, which made it possible to achieve an increase in reliability after competitive training by 42.38% compared to the original data set. The obtained research results can be used in the development of a secure intelligent decision support system for detecting an area containing a road sign on a DVR frame and subsequent classification of the selected image segment