articles
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№ 5(107)
25 october 2023 year
Rubric: Algorithmic efficiency Authors: Golikov R., Meksheneva Z., Molchanov I., Rynkova A. |
Heuristic algorithms are often used as an alternative when solving problems of high computational complexity or lacking an exact solution, allowing to quickly obtain the desired result. Usually, they do not have a strict mathematical justification, but their application is justified in terms of practicality. Formally, algorithms that use approximate methods can be classified as heuristic. However, when applying them, the problem of determinism lack is often arises, which does not always allow one to evaluate the solution obtained accuracy. The paper considers a methodical approach to assessing the accuracy of heuristic algorithms designed to determine the useful signal shape and parameters on the strong noise component background. It is based on the method of analogy and consists in modeling an artificial signal with given parameters and a background noise interference similar in its characteristics to additive white Gaussian noise. In this case, the noise component is formed by software using a pseudo-random number sequence generator. Such generators are included in the packages of almost all high-level programming languages built-in functions. A comparative analysis of the real and artificial noise characteristics is presented, that shown the problem solving by numerical modeling possibility. The results of accuracy estimation in determining the artificial signal parameters, that is separated from the noise component using piecewise linear approximation and averaging heuristic algorithms, are obtained. The problem of empirical data smoothing with the discrete signal equivalent replacement by a quadratic functions whose parameters provide a piecewise parabolic approximation its shape is also considered. This procedure eliminates the residual signal bounce that inevitably occurs as a result of linearization and allows further recording at any sampling rate. Thus, the proposed approach allows us to quantify the accuracy of heuristic algorithms used in determining the expected signal parameters. Continue... |
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№ 5(107)
25 october 2023 year
Rubric: Algorithmic efficiency Authors: Vasilyev V., Panfilova I., Serikova A., Sulavko A. |
The aim of the work is to develop a system for verifying subjects by face based on a neural network model that is executed in a protected mode. Protected mode means that the identity verification system is highly resistant to destructive influences, such as competitive attacks, and allows storing and processing biometric data without compromising it. The system is based on a biometrics to code converter trained according to GOST R 52633.5, which allows you to associate the subject’s facial biometrics image with its cryptographic key or long password, which can later be used for authentication, and deep convolutional neural networks. For face detection in the image, the MTCNN artificial neural network architecture was used, and several neural network architectures were tested for feature extraction: InceptionResnet, Facenet512, VGG-Face and OpenFace. The best results were shown by the InceptionResnet neural network. When evaluating the effectiveness and testing the reliability of the proposed system on a special dataset of faces collected under different lighting conditions in a room, it was possible to achieve a relatively low value of equal probability of errors of the first and second kind (EER = 0.0146 with a key length of 278 bits), which confirms the effectiveness of the considered approach to building face verification systems. Continue... |
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№ 5(107)
25 october 2023 year
Rubric: Algorithmic efficiency Authors: Trubin A., Mastyaev F. A., Tokmakova E., Vershinina A. |
During the analysis of the most common architectures for building neural networks for image analysis – a direct convolutional layer, a recurrent neural network, a convolutional neural network – the authors determined that the last architecture option for the task is the most suitable. The solutions available on the market, although they allow us to recognize emotions, do not evaluate them, and this is an important result for a wide variety of tasks, because such an assessment allows us to more accurately predict the possible future behavior of both individuals and groups of people. The purpose of this article is to design an application software product (application) and develop a prototype that could perform the functions of analyzing emotions and evaluating them. The objectives of this study include the choice of the architecture of the designed application; the development of an algorithm for the operation of the application; the design of the user interface; a description of the neural network learning process and its results, the model of which was defined in the previous article; demonstration of the prototype (control example). The scientific novelty of the projected application lies in the formation of an assessment of the psycho-emotional state of those people whose images of their faces were evaluated. Such an assessment with recommendations can be widely demanded in various branches of human activity, since it is important to be able to express emotions not only to ordinary people, but also to those whose job is to transfer their emotional state to others: business coaches, journalists, actors, animators, dancers, etc. The presence of emotional intelligence is very important at the moment time. Continue... |
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№ 5(107)
25 october 2023 year
Rubric: Models and methods Authors: Dli M. I., Chernovalova M., Morgunova E., Sokolov A. M. |
The article discusses the features of applying the precedent approach when managing complex energy-intensive systems in the context of the need to take into account various energy, technical, environmental and operational indicators, as well as the uncertainty of many internal and external factors influence. This leads to the presence of a large amount of semi-structured information that can be presented using various scales, which determines the prospects of using the precedent approach. The proposed fuzzy ontological model for supporting decision support based on precedents is described, characterized by the use of dynamic concepts, as well as concepts in the form of different scale numerical and linguistic variables. An algorithm for assessing the proximity of precedents based on an ontological model is proposed, which differs by taking into account the dynamic aspects of changes in the state of controlled systems. The developed algorithms for fuzzy inference for decision support based on precedents are presented, which allow the use of both linguistic and numerical variables as input characteristics of the fuzzy production model, as well as using various logical connections between the rules pre-requisites. The software that implements the developed model and algorithms is described. Particular attention is paid to the modified fuzzy inference component, implemented using Python 3.8.7 language tools. To implement the user interface of the specified component, the cross-platform graphic library Tkinter was used. The results of computational experiments using real data obtained during the operation of an energy-intensive system for processing fine ore raw materials, including a conveyor-type roasting machine, are presented. Minimization of specific total costs for thermal and electrical energy was considered as a criterion for the effectiveness of management decisions. The outcome obtained showed that the proposed model and software make it possible to obtain a result comparable to the one of using complex analytical dependencies, while ensuring a reduction in time and financial costs. Continue... |
| The work is aimed at improving the efficiency of cybersecurity management (CS) of economic entities (SED) by organizing effective CB monitoring, taking into account such features of its process as the heterogeneity of sources of initial CB monitoring data, their presentation in different data formats, their inaccuracy, and largely uncertainty and noisiness, as well as a large number of KB events processed by heterogeneous components of the ERMS KB monitoring system. In this paper, in contrast to existing methods, a complex two-stage method for fuzzy clustering of SI events is proposed, taking into account the assessment of the criticality of SI events and the functionality of the ES IS monitoring system. At the first stage, the KB event clustering model based on the fuzzy c-means method is used. This model allows splitting the set of CI events into several fuzzy clusters according to the a priori probability that the CI event is an incident. At the second stage, to refine the results of the clustering of SI events obtained at the first stage, the model of clustering of SI incidents based on the method of extracting α-kernels of fuzzy clusters is used. This model allows you to manually select the thresholds for the degree of belonging of SI incidents to fuzzy clusters, taking into account additional information and features of processing SI incidents in the SI monitoring system of a particular EDMS. The paper evaluates the effectiveness of the two-stage method of fuzzy clustering of KB incidents in the EDMS KB monitoring system. The proposed approach makes it possible to increase the efficiency of ERMS CM monitoring and reduce the period of time required to make a decision on the ERMS CM management due to the complex consideration of the features of CM event processing in the ERMS CM monitoring system. Continue... | |
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№ 5(107)
25 october 2023 year
Rubric: Researching of processes and systems Authors: Puchkov A., Prokimnov N., Rysina (Lobaneva) E., Shutova D. |
The study is devoted to improving the management system of a complex technological system for processing ore waste. Such waste accumulates in large volumes in the territories adjacent to the mining and processing plants, posing a great environmental threat to both the population and the environment due to dust formation and the penetration of harmful compounds into the soil and groundwater. Therefore, the task of improving the management systems for the processing of ore waste, as one of the priorities, is on the current agenda of the management of mining and processing plants. The complexity of the technological system is manifested in the presence of two processing lines that differ in the set of units, and the choice of line depends on the granulometric composition of ore waste. The scientific novelty of the research results is the proposed structure of the neural network controller based on the reference model for the technological system, which is used as deep recurrent neural networks. The general structure of the neuroregulator includes several local neurocontrollers for each of the units of the technological system. Recurrent neural networks make it possible to create high-precision digital copies of individual units of two processing lines and use them to simulate the response of control objects when setting up controllers. Approbation of the proposed structure of the neuroregulator was carried out in the MatLab-Simulik environment, neural networks were designed using the Deep Network Designer tool. The results of testing showed that the speed of the control system is increased compared to other architectures of neuroregulators available in the Simulik environment, which can positively affect the operation of the entire technological system in transient conditions, in particular, reduce technological losses. Continue... |
