Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-88-101
A. Zaenchkovski, A. Lazarev, Victor Yu. Sinyavskiy
Currently information exchange methods and means of communication development are being done a significant impact on the level of all industrial and economic entities innovation potential, which is also the same for their group formations, such as regional complexes. It is necessary to note high degree of integration and interdependence of all such systems elements and processes closely interconnected by different kind of networks. Among them, it is possible to highlight the interaction between participants of scientific and industrial cluster within the framework of innovative activities, which should provide possibility to transfer and receive various kinds of data, which could be both open and confidential type. At the current stage, there is not many applied tools for ensuring confidentiality in the implementation of these processes. For example, they partially solve the problem of traffic tunnelling systems based on OpenVPN or WireGuard tunnels, and other software solutions provide the potential of an extensible cloud (Nextcloud). However, analysing the functionality of these solutions, it is possible to identify shortcomings that do not allow their implementation in the complex production and economic systems processes of innovative development. Thus, existing traffic tunnelling solutions are not adapted for deployment on a corporate scale with a flexible organisational structure. In solutions based on Nextcloud, the complexity disadvantages of the server configuration and the cost of the primary software configuration are highlighted. To solve the above problems, in article has been proposed an intelligent traffic tunneling system, which is based on using additional means of primary automated OpenVPN connection initialization at neural module expense. A dynamic digital fingerprint distribution system with two-way key exchange was used as an authorization server. The developed software solution was tested and then compared with existing analogues. This experiment may to conclusion that the developed software solution is not inferior in a number of aspects to existing methods, and can subsequently be used to ensure secure information and communication exchange between industrial and economic entities in clusters during innovative processes implementation.
{"title":"Development of a secure neural traffic tunneling system with post-performance evaluation","authors":"A. Zaenchkovski, A. Lazarev, Victor Yu. Sinyavskiy","doi":"10.37791/2687-0649-2022-17-5-88-101","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-88-101","url":null,"abstract":"Currently information exchange methods and means of communication development are being done a significant impact on the level of all industrial and economic entities innovation potential, which is also the same for their group formations, such as regional complexes. It is necessary to note high degree of integration and interdependence of all such systems elements and processes closely interconnected by different kind of networks. Among them, it is possible to highlight the interaction between participants of scientific and industrial cluster within the framework of innovative activities, which should provide possibility to transfer and receive various kinds of data, which could be both open and confidential type. At the current stage, there is not many applied tools for ensuring confidentiality in the implementation of these processes. For example, they partially solve the problem of traffic tunnelling systems based on OpenVPN or WireGuard tunnels, and other software solutions provide the potential of an extensible cloud (Nextcloud). However, analysing the functionality of these solutions, it is possible to identify shortcomings that do not allow their implementation in the complex production and economic systems processes of innovative development. Thus, existing traffic tunnelling solutions are not adapted for deployment on a corporate scale with a flexible organisational structure. In solutions based on Nextcloud, the complexity disadvantages of the server configuration and the cost of the primary software configuration are highlighted. To solve the above problems, in article has been proposed an intelligent traffic tunneling system, which is based on using additional means of primary automated OpenVPN connection initialization at neural module expense. A dynamic digital fingerprint distribution system with two-way key exchange was used as an authorization server. The developed software solution was tested and then compared with existing analogues. This experiment may to conclusion that the developed software solution is not inferior in a number of aspects to existing methods, and can subsequently be used to ensure secure information and communication exchange between industrial and economic entities in clusters during innovative processes implementation.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"150 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77373701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-34-40
O. Bulygina, Nikolay N. Prokimnov
The oil industry is the leading sector of the Russian economy, that makes the largest contribution to the country’s budget, creates a huge number of jobs and fully meets the domestic needs for oil and its products. In Russia, transportation of crude oil from fields to consumers (primarily refineries) is carried out by 5 modes of transport. Pipeline transport has received the greatest distribution. It provides transportation for 83% of crude oil and 30% of oil products. The most important element of the pipeline system is tank parks, which are used to collect and store oil at the junctions of technological pipeline sections and transshipment to other modes of transport. They are especially dangerous industrial objects. Therefore, they are subject to extremely stringent design and construction requirements. The most important stage in the construction of a tank park is the site selection, which is carried out on the basis of economic criteria and engineering requirements. In order to reduce the number of options for its location, where the survey party will travel, it is proposed to conduct a preliminary selection of the most promising territories by solving the task of multi-criteria optimization. The presence of a huge number of criteria leads to the need to use heuristic methods, among which swarm optimization algorithms based on modeling the collective behavior of various living organisms are widely used. To solve this problem, it is proposed to use bacterial optimization algorithms that allow taking into account both favorable and negative factors. Fuzzy logic elements can be added to the classical algorithm (it is proposed to set the initial positions of bacteria using fuzzy-logical inference systems, where the available statistics and expert assessments will be input parameters). In general, the proposed approach can be used to select sites for the construction of various hazardous industrial facilities, for which a large number of parameters must be taken into account.
{"title":"Application of bacterial optimization algorithms for selecting a site to construct a tank park on the main oil pipeline","authors":"O. Bulygina, Nikolay N. Prokimnov","doi":"10.37791/2687-0649-2022-17-5-34-40","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-34-40","url":null,"abstract":"The oil industry is the leading sector of the Russian economy, that makes the largest contribution to the country’s budget, creates a huge number of jobs and fully meets the domestic needs for oil and its products. In Russia, transportation of crude oil from fields to consumers (primarily refineries) is carried out by 5 modes of transport. Pipeline transport has received the greatest distribution. It provides transportation for 83% of crude oil and 30% of oil products. The most important element of the pipeline system is tank parks, which are used to collect and store oil at the junctions of technological pipeline sections and transshipment to other modes of transport. They are especially dangerous industrial objects. Therefore, they are subject to extremely stringent design and construction requirements. The most important stage in the construction of a tank park is the site selection, which is carried out on the basis of economic criteria and engineering requirements. In order to reduce the number of options for its location, where the survey party will travel, it is proposed to conduct a preliminary selection of the most promising territories by solving the task of multi-criteria optimization. The presence of a huge number of criteria leads to the need to use heuristic methods, among which swarm optimization algorithms based on modeling the collective behavior of various living organisms are widely used. To solve this problem, it is proposed to use bacterial optimization algorithms that allow taking into account both favorable and negative factors. Fuzzy logic elements can be added to the classical algorithm (it is proposed to set the initial positions of bacteria using fuzzy-logical inference systems, where the available statistics and expert assessments will be input parameters). In general, the proposed approach can be used to select sites for the construction of various hazardous industrial facilities, for which a large number of parameters must be taken into account.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"48 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85566204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-41-50
T. Chistyakova, O.E. Shashikhina
The article discusses issues related to the development of a flexible intelligent software package for solving the problem of optimal planning of multi-assortment production. These industries are characterized by a large range of products, many types and configurations of equipment, with an increase in the dimension of the problem, the number of options for production schedules grows exponentially, therefore, it is extremely important to develop a specialized complex for effective optimal planning and scheduling, insisting on the characteristics of various multi-assortment industries. The purpose of this work is to increase the productivity of multi-assortment enterprises and reduce the time of production of products by developing methods and algorithms for optimizing scheduling in the form of a problem-oriented software package. The article presents a mathematical formulation of the optimization problem and a set of mathematical models and algorithms for the formation of objective functions for optimal scheduling of reconfigurable productions. Conducting this study is based on the use of methods of scheduling theory, optimization and evolutionary calculations, tools for object-oriented development of complex software systems and databases. The proposed software package has various intelligent user interfaces, supplemented by databases of products, equipment and technological regulations, a library of objective functions and mathematical optimization methods, an expert system tuning module, as well as an interactive system for visualizing the resulting production plans in the form of a Gantt chart and decision tree of the optimization problem. Testing of the software package was carried out on the data of polymer and metallurgical enterprises in Russia and Germany and confirmed the effectiveness of solving planning problems. Implementation of the proposed software package makes it possible to ensure efficient loading of enterprise equipment, reduce production costs and simplify the process of making managerial decisions in the course of production planning.
{"title":"Intelligent soft package for modeling the planning process of multi‑assortment industrial production","authors":"T. Chistyakova, O.E. Shashikhina","doi":"10.37791/2687-0649-2022-17-5-41-50","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-41-50","url":null,"abstract":"The article discusses issues related to the development of a flexible intelligent software package for solving the problem of optimal planning of multi-assortment production. These industries are characterized by a large range of products, many types and configurations of equipment, with an increase in the dimension of the problem, the number of options for production schedules grows exponentially, therefore, it is extremely important to develop a specialized complex for effective optimal planning and scheduling, insisting on the characteristics of various multi-assortment industries. The purpose of this work is to increase the productivity of multi-assortment enterprises and reduce the time of production of products by developing methods and algorithms for optimizing scheduling in the form of a problem-oriented software package. The article presents a mathematical formulation of the optimization problem and a set of mathematical models and algorithms for the formation of objective functions for optimal scheduling of reconfigurable productions. Conducting this study is based on the use of methods of scheduling theory, optimization and evolutionary calculations, tools for object-oriented development of complex software systems and databases. The proposed software package has various intelligent user interfaces, supplemented by databases of products, equipment and technological regulations, a library of objective functions and mathematical optimization methods, an expert system tuning module, as well as an interactive system for visualizing the resulting production plans in the form of a Gantt chart and decision tree of the optimization problem. Testing of the software package was carried out on the data of polymer and metallurgical enterprises in Russia and Germany and confirmed the effectiveness of solving planning problems. Implementation of the proposed software package makes it possible to ensure efficient loading of enterprise equipment, reduce production costs and simplify the process of making managerial decisions in the course of production planning.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"241 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89162995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-125-142
Sergey N. Karabtsev, Ivan P. Davzit, R. Kotov, Evgeny S. Gurov
There are a number of strategic tasks in the system of higher education, the solution of which by traditional methods is not possible or very difficult. One of these tasks is the management of the contingent of students. The complexity of this process is determined by the requirement that the university fulfill various key indicators while ensuring the quality of education. The aim of the study is to improve the process of students’ contingent management of the educational institution based on data management. Universities accumulate huge number of various information, the analysis of which is able to provide the decision-making based on data but not on intuition. The analysis of large information array is not possible without the usage of modern products and technologies related to Business Intelligence. This paper sets out the task of creating a decision support system (DSS) for contingent management, a range of questions is described, to which this system will quickly give answers and help an analyst or the head of a university in making decisions. As the research methods used, the methodology for creating a DSS with a description of the main results of each stage, as well as methods of statistical data analysis, is used. The DSS introduction to the daily activities of Higher education institution allows getting the rapid response to changes in academic achievement, forecasting contingent retention and potential budget losses, assessing the number of vacancies and qualitative performance. The system allows the rector of the university to monitor the dynamics of the main indicators on a weekly basis and gives an idea of the university from the founder’s point of view. Further research is aimed at developing the information system by adding advisory functions, as well as expanding the range of questions that the system is able to give a quick answer to – evaluating the activities of the teaching staff by key indicators, estimating the costs of implementing one or another area of training, and others.
{"title":"Support decision‑making in the management of the educational institution contingent based on Business Intelligence","authors":"Sergey N. Karabtsev, Ivan P. Davzit, R. Kotov, Evgeny S. Gurov","doi":"10.37791/2687-0649-2022-17-5-125-142","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-125-142","url":null,"abstract":"There are a number of strategic tasks in the system of higher education, the solution of which by traditional methods is not possible or very difficult. One of these tasks is the management of the contingent of students. The complexity of this process is determined by the requirement that the university fulfill various key indicators while ensuring the quality of education. The aim of the study is to improve the process of students’ contingent management of the educational institution based on data management. Universities accumulate huge number of various information, the analysis of which is able to provide the decision-making based on data but not on intuition. The analysis of large information array is not possible without the usage of modern products and technologies related to Business Intelligence. This paper sets out the task of creating a decision support system (DSS) for contingent management, a range of questions is described, to which this system will quickly give answers and help an analyst or the head of a university in making decisions. As the research methods used, the methodology for creating a DSS with a description of the main results of each stage, as well as methods of statistical data analysis, is used. The DSS introduction to the daily activities of Higher education institution allows getting the rapid response to changes in academic achievement, forecasting contingent retention and potential budget losses, assessing the number of vacancies and qualitative performance. The system allows the rector of the university to monitor the dynamics of the main indicators on a weekly basis and gives an idea of the university from the founder’s point of view. Further research is aimed at developing the information system by adding advisory functions, as well as expanding the range of questions that the system is able to give a quick answer to – evaluating the activities of the teaching staff by key indicators, estimating the costs of implementing one or another area of training, and others.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"141 5","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72444199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-102-115
E. V. Chumakova, D. Korneev, M. Gasparian
The paper discusses the issues of implementing an adaptive testing system based on the use of artificial neural network (INS) modules, which should solve the problem of intelligent choice of the next question, forming an individual testing trajectory. The aim of the work is to increase the accuracy of the INS to form the level of complexity of the next test question for two types of architectures – direct propagation (FNN – Feedforward Neural Network) and recurrent with long-term short-term memory (LSTM – Long-Short Term Memory). The data affecting the quality of training are analyzed, the architectures of the input layer of the direct propagation INS are considered, which have significantly improved the quality of neural networks. To solve the problem of choosing the thematic block of the question, a hybrid module structure is proposed, including the INS itself and a software module for algorithmic processing of the results obtained from the INS. A study of the feasibility of using direct propagation ANNs in comparison with the LSTM architecture was carried out, the input parameters of the network were identified, various architectures and parameters of the ANN training were compared (algorithms for updating weights, loss functions, the number of training epochs, packet sizes). The substantiation of the choice of a direct distribution network in the structure of the hybrid module for selecting a thematic block is given. The above results were obtained using the Keras high-level library, which allows you to quickly start at the initial stages of research and get the first results. Traditionally, learning has taken place over a large number of eras.
{"title":"An approach to the design of a neural network for the formation of an individual trajectory of knowledge testing","authors":"E. V. Chumakova, D. Korneev, M. Gasparian","doi":"10.37791/2687-0649-2022-17-5-102-115","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-102-115","url":null,"abstract":"The paper discusses the issues of implementing an adaptive testing system based on the use of artificial neural network (INS) modules, which should solve the problem of intelligent choice of the next question, forming an individual testing trajectory. The aim of the work is to increase the accuracy of the INS to form the level of complexity of the next test question for two types of architectures – direct propagation (FNN – Feedforward Neural Network) and recurrent with long-term short-term memory (LSTM – Long-Short Term Memory). The data affecting the quality of training are analyzed, the architectures of the input layer of the direct propagation INS are considered, which have significantly improved the quality of neural networks. To solve the problem of choosing the thematic block of the question, a hybrid module structure is proposed, including the INS itself and a software module for algorithmic processing of the results obtained from the INS. A study of the feasibility of using direct propagation ANNs in comparison with the LSTM architecture was carried out, the input parameters of the network were identified, various architectures and parameters of the ANN training were compared (algorithms for updating weights, loss functions, the number of training epochs, packet sizes). The substantiation of the choice of a direct distribution network in the structure of the hybrid module for selecting a thematic block is given. The above results were obtained using the Keras high-level library, which allows you to quickly start at the initial stages of research and get the first results. Traditionally, learning has taken place over a large number of eras.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88665550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-6-14
V. Meshalkin, O. Butusov, T. Chistyakova
An original adaptive-index-clustering algorithm is proposed: “Managed vegetation index”. An original adaptive-multi-index-cluster algorithm for comprehensive assessment of the impact of chemical pollution on forests using satellite photographs is proposed, which is distinguished by the use of an adaptive procedure for the formation of pixel clusters displaying a plurality of spectral channels of a photographic image of each type of vegetation state of a forest stand in the zones of chemical pollution of forest tracts, as well as using the procedure for calculating the weighted average values of complex vegetation indices for each zone of chemical pollution, which allows, based on the values of complex vegetation indices, to determine various biological, phytological and physico-chemical states of forest areas.It should be noted that in order to solve the complex problem of constructing complex indices linked to ecological zones, it is proposed to use the simple idea of increasing the quality of modeling and forecasting by expanding the amount of information. The proposed problem can be solved using a statistical analysis of data on the distribution of pixels whose belonging to ecological zones is known in advance. The development of the algorithm is based on the following prerequisites: (1) using a linear combination of individual classical vegetation indices of the state of forest areas, it is possible to create a new specialized complex vegetation index that makes it possible to identify ecological zones in forest areas according to the levels of impact on forests of chemical pollution of industrial enterprises; (2) the possibility of using specialized complex vegetation indices in the form of weighted average linear combinations of classical vegetation indices. Specialized complex vegetation indices of adaptive selection of weight coefficients are capable of displaying various biological, physicochemical and ecological characteristics of the state of forests based on clustering of satellite image pixels. The proposed algorithm makes it possible to calculate, as a result of clustering, more accurate estimates of the total areas of ecological zones of forest tracts, which can be used as a basis for assessing the degree of ecological degradation of forest tracts and environmental damage.
{"title":"Adaptive‑multi‑index‑cluster algorithm for comprehensive assessment of the impact of chemical pollution on forests using satellite photographs","authors":"V. Meshalkin, O. Butusov, T. Chistyakova","doi":"10.37791/2687-0649-2022-17-5-6-14","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-6-14","url":null,"abstract":"An original adaptive-index-clustering algorithm is proposed: “Managed vegetation index”. An original adaptive-multi-index-cluster algorithm for comprehensive assessment of the impact of chemical pollution on forests using satellite photographs is proposed, which is distinguished by the use of an adaptive procedure for the formation of pixel clusters displaying a plurality of spectral channels of a photographic image of each type of vegetation state of a forest stand in the zones of chemical pollution of forest tracts, as well as using the procedure for calculating the weighted average values of complex vegetation indices for each zone of chemical pollution, which allows, based on the values of complex vegetation indices, to determine various biological, phytological and physico-chemical states of forest areas.It should be noted that in order to solve the complex problem of constructing complex indices linked to ecological zones, it is proposed to use the simple idea of increasing the quality of modeling and forecasting by expanding the amount of information. The proposed problem can be solved using a statistical analysis of data on the distribution of pixels whose belonging to ecological zones is known in advance. The development of the algorithm is based on the following prerequisites: (1) using a linear combination of individual classical vegetation indices of the state of forest areas, it is possible to create a new specialized complex vegetation index that makes it possible to identify ecological zones in forest areas according to the levels of impact on forests of chemical pollution of industrial enterprises; (2) the possibility of using specialized complex vegetation indices in the form of weighted average linear combinations of classical vegetation indices. Specialized complex vegetation indices of adaptive selection of weight coefficients are capable of displaying various biological, physicochemical and ecological characteristics of the state of forests based on clustering of satellite image pixels. The proposed algorithm makes it possible to calculate, as a result of clustering, more accurate estimates of the total areas of ecological zones of forest tracts, which can be used as a basis for assessing the degree of ecological degradation of forest tracts and environmental damage.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"302 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75890947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-51-61
M. Dli, Y. Sinyavsky, Ekaterina I. Rysina, M. Vasiľková
The paper presents the results of research aimed at developing a method and software tools for identifying the class of a mixing device by its resistance coefficient through experimental data processing. Currently, the main methods for studying mixing devices are finite element methods, as well as procedures of estimating turbulent transfer parameters using laser dopplerometry and chemical methods of sample analysis. These methods require expensive equipment and provide results only for certain types of equipment. This makes it difficult to extend the inferences to a wider class of devices with different designs of mixing impellers. The proposed method involves processing the results of an experiment in which a point light source forming a beam directed vertically upwards is located at the bottom of a container filled with a transparent liquid. A mixing device with variable rotation frequency is placed in the container. When performing experiments in real conditions, small deviations in the size and location of the mixing device lead to difficult-to-predict fluctuations of the funnel surface. Therefore, the image of one marker describes a trajectory that is difficult to predict. It, under certain conditions, can intersect with the trajectories of other markers or be interrupted at the moment when the marker is closed by a stirrer blade passing over it. The resulting image of the markers is associated with a change in the rotational speed of the blade by a rather complex relationship. To identify this dependence, it is proposed to use deep neural networks operating in parallel in two channels. Each channel analyzes the video signal from the surface of the stirred liquid and the time sequence characterizing the change in the speed of rotation of the blades of the device. It is proposed to use neural networks of various architectures in the channels - a convolutional neural network in one channel and a recurrent one in another. The results of the operation of each data processing channel are aggregated according to the majority rule. The computational novelty of the proposed algorithm lies in the expansion of the receptive field for each of the networks due to the mutual conversion of images and time sequences. As a result, each of the networks is trained on a larger amount of data in order to identify hidden regularities. The effectiveness of the method is confirmed by testing it with the use of a software application developed in the MatLab environment.
{"title":"A method for classifying mixing devices using deep neural networks with an expanded receptive field","authors":"M. Dli, Y. Sinyavsky, Ekaterina I. Rysina, M. Vasiľková","doi":"10.37791/2687-0649-2022-17-5-51-61","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-51-61","url":null,"abstract":"The paper presents the results of research aimed at developing a method and software tools for identifying the class of a mixing device by its resistance coefficient through experimental data processing. Currently, the main methods for studying mixing devices are finite element methods, as well as procedures of estimating turbulent transfer parameters using laser dopplerometry and chemical methods of sample analysis. These methods require expensive equipment and provide results only for certain types of equipment. This makes it difficult to extend the inferences to a wider class of devices with different designs of mixing impellers. The proposed method involves processing the results of an experiment in which a point light source forming a beam directed vertically upwards is located at the bottom of a container filled with a transparent liquid. A mixing device with variable rotation frequency is placed in the container. When performing experiments in real conditions, small deviations in the size and location of the mixing device lead to difficult-to-predict fluctuations of the funnel surface. Therefore, the image of one marker describes a trajectory that is difficult to predict. It, under certain conditions, can intersect with the trajectories of other markers or be interrupted at the moment when the marker is closed by a stirrer blade passing over it. The resulting image of the markers is associated with a change in the rotational speed of the blade by a rather complex relationship. To identify this dependence, it is proposed to use deep neural networks operating in parallel in two channels. Each channel analyzes the video signal from the surface of the stirred liquid and the time sequence characterizing the change in the speed of rotation of the blades of the device. It is proposed to use neural networks of various architectures in the channels - a convolutional neural network in one channel and a recurrent one in another. The results of the operation of each data processing channel are aggregated according to the majority rule. The computational novelty of the proposed algorithm lies in the expansion of the receptive field for each of the networks due to the mutual conversion of images and time sequences. As a result, each of the networks is trained on a larger amount of data in order to identify hidden regularities. The effectiveness of the method is confirmed by testing it with the use of a software application developed in the MatLab environment.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"78 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75404500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.37791/2687-0649-2022-17-4-97-112
A. Shorikov
The solution of the problem of forecasting the state of complex socio-economic systems is possible only on the basis of appropriate dynamic economic and mathematical models that describe their main parameters, the presence of control actions and risks. In this paper, it is proposed to use a deterministic minimax approach for modeling and solving the problem of estimating the predicted states of a production system in the presence of risks. To make managerial decisions at a manufacturing enterprise aimed at improving the efficiency of its functioning, it is necessary to have high-quality information support, the basis of which is the solution of the corresponding problem of predicting the states of its basic parameters. In this article, to describe the functioning of a production system, it is proposed to use a discrete-time controlled dynamical system in the presence of risks. It is assumed that the values of the control action (admissible control scenarios) are realized from a finite set of admissible elements of the corresponding finite-dimensional vector space, and the realizations of the values of the phase vector of the model and the risk vector are limited by the given compact polyhedrons in the corresponding finite-dimensional vector spaces. Application of the developed discrete-time controlled dynamical model that describes the output products of an enterprise in the presence of risks, and the developed methodology for the formation and minimax estimation of the predictive set of its phase states in a given period of time, allow us to develop appropriate numerical algorithms that can be used in the development and creation of computer intelligent information systems that provide support for making effective management decisions at manufacturing enterprises. The main results of this work is the development of a new economic-mathematical model that describes the dynamics of the output products of an enterprise in the presence of risks and the creation on its basis of a methodology for constructing and minimax estimation of the predictive set of its phase states in the form of implementing a finite number of one-step operations that allow their algorithmization. The results obtained in this work can serve as a basis for developing methods for optimizing the management of enterprise production processes and creating computer intelligent information systems to support managerial decision-making.
{"title":"Prediction and minimax estimation of the production system in the presence of risks","authors":"A. Shorikov","doi":"10.37791/2687-0649-2022-17-4-97-112","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-4-97-112","url":null,"abstract":"The solution of the problem of forecasting the state of complex socio-economic systems is possible only on the basis of appropriate dynamic economic and mathematical models that describe their main parameters, the presence of control actions and risks. In this paper, it is proposed to use a deterministic minimax approach for modeling and solving the problem of estimating the predicted states of a production system in the presence of risks. To make managerial decisions at a manufacturing enterprise aimed at improving the efficiency of its functioning, it is necessary to have high-quality information support, the basis of which is the solution of the corresponding problem of predicting the states of its basic parameters. In this article, to describe the functioning of a production system, it is proposed to use a discrete-time controlled dynamical system in the presence of risks. It is assumed that the values of the control action (admissible control scenarios) are realized from a finite set of admissible elements of the corresponding finite-dimensional vector space, and the realizations of the values of the phase vector of the model and the risk vector are limited by the given compact polyhedrons in the corresponding finite-dimensional vector spaces. Application of the developed discrete-time controlled dynamical model that describes the output products of an enterprise in the presence of risks, and the developed methodology for the formation and minimax estimation of the predictive set of its phase states in a given period of time, allow us to develop appropriate numerical algorithms that can be used in the development and creation of computer intelligent information systems that provide support for making effective management decisions at manufacturing enterprises. The main results of this work is the development of a new economic-mathematical model that describes the dynamics of the output products of an enterprise in the presence of risks and the creation on its basis of a methodology for constructing and minimax estimation of the predictive set of its phase states in the form of implementing a finite number of one-step operations that allow their algorithmization. The results obtained in this work can serve as a basis for developing methods for optimizing the management of enterprise production processes and creating computer intelligent information systems to support managerial decision-making.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"63 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80184803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.37791/2687-0649-2022-17-4-127-142
A. Polbin, Margarita A. Kropocheva
The article examines the dependence between the Russian ruble exchange rate and oil prices with the use of neural network modeling. The relevance of the study can be confirmed by the interest of the monetary authorities in modeling the dynamics of the exchange rate for developing monetary policy measures. The research objective of the article is the estimation of the relationship between the Russian ruble exchange rate and oil prices using multilayer perceptron and recurrent neural network models. Moreover, the influence of additional factors, including foreign exchange interventions and geopolitical risks, is estimated. The results show that neural networks provide sufficient accuracy in estimation of the target variable. Furthermore, during the periods with foreign exchange interventions and high geopolitical instability there was confirmed a decoupling of the examined variables. The modeled time series preserve non-linear nature of exchange rate data generating process, as well as the asymmetry in the reaction of the ruble exchange rate to oil price shocks. The hyperparameters selection, use of bootstrap and ensembles of neural networks provide more robust estimates and confidence intervals for the oil price elasticity of the ruble exchange rate. Therefore, the combination of the aforementioned methods makes it possible to draw meaningful economic conclusions based on the trained neural networks, avoiding the problem of neural network weights non-interpretability.
{"title":"Modeling the relationship between the Russian ruble exchange rate and oil prices using artificial neural networks","authors":"A. Polbin, Margarita A. Kropocheva","doi":"10.37791/2687-0649-2022-17-4-127-142","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-4-127-142","url":null,"abstract":"The article examines the dependence between the Russian ruble exchange rate and oil prices with the use of neural network modeling. The relevance of the study can be confirmed by the interest of the monetary authorities in modeling the dynamics of the exchange rate for developing monetary policy measures. The research objective of the article is the estimation of the relationship between the Russian ruble exchange rate and oil prices using multilayer perceptron and recurrent neural network models. Moreover, the influence of additional factors, including foreign exchange interventions and geopolitical risks, is estimated. The results show that neural networks provide sufficient accuracy in estimation of the target variable. Furthermore, during the periods with foreign exchange interventions and high geopolitical instability there was confirmed a decoupling of the examined variables. The modeled time series preserve non-linear nature of exchange rate data generating process, as well as the asymmetry in the reaction of the ruble exchange rate to oil price shocks. The hyperparameters selection, use of bootstrap and ensembles of neural networks provide more robust estimates and confidence intervals for the oil price elasticity of the ruble exchange rate. Therefore, the combination of the aforementioned methods makes it possible to draw meaningful economic conclusions based on the trained neural networks, avoiding the problem of neural network weights non-interpretability.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89385330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.37791/2687-0649-2022-17-4-37-46
G. Rybina, Vladimir Y. Stepankov
The possibilities of a multi-agent approach for managing urban parking space are considered, which allows you to adequately represent the parking space and effectively solve the following tasks: monitoring congestion, searching and booking available parking spaces; building routes and navigation to selected places; parking; payment for parking services; monitoring compliance with parking rules; control and access control in closed parking lots (equipped with entrance and exit terminals and barriers); forecasting the main parameters, such as workload, income, turnover; informing users. The necessity of intellectualization of urban parking space management processes based on the use of methods and technologies of multi- agent systems (MAS), the main objectives of which are to: reduce the search time for parking spaces; increase the speed of traffic in paid parking areas; increase the turnover of parking spaces; reduce traffic congestion, fuel costs; reduce the number of parking violations on the road network; reducing the flow of personal vehicles entering the toll zone and stimulating the use of urban public transport; reducing environmental pollution. The greatest difficulty is the tasks of organizing the interaction of agents of various typologies in the collective solution of tasks, since each agent solving a specific task has only a partial idea of the overall task and must constantly interact with other agents. The features of prototyping MAS with an emphasis on modeling the interaction of certain types of intelligent agents in the problem area under study are presented. The obtained simulation results are the basis for the continuation and further development of research and development to create the final prototype of a MAS for urban parking space management.
{"title":"The simulation of intelligent agents communication in the multi-agent management system for urban parking space","authors":"G. Rybina, Vladimir Y. Stepankov","doi":"10.37791/2687-0649-2022-17-4-37-46","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-4-37-46","url":null,"abstract":"The possibilities of a multi-agent approach for managing urban parking space are considered, which allows you to adequately represent the parking space and effectively solve the following tasks: monitoring congestion, searching and booking available parking spaces; building routes and navigation to selected places; parking; payment for parking services; monitoring compliance with parking rules; control and access control in closed parking lots (equipped with entrance and exit terminals and barriers); forecasting the main parameters, such as workload, income, turnover; informing users. The necessity of intellectualization of urban parking space management processes based on the use of methods and technologies of multi- agent systems (MAS), the main objectives of which are to: reduce the search time for parking spaces; increase the speed of traffic in paid parking areas; increase the turnover of parking spaces; reduce traffic congestion, fuel costs; reduce the number of parking violations on the road network; reducing the flow of personal vehicles entering the toll zone and stimulating the use of urban public transport; reducing environmental pollution. The greatest difficulty is the tasks of organizing the interaction of agents of various typologies in the collective solution of tasks, since each agent solving a specific task has only a partial idea of the overall task and must constantly interact with other agents. The features of prototyping MAS with an emphasis on modeling the interaction of certain types of intelligent agents in the problem area under study are presented. The obtained simulation results are the basis for the continuation and further development of research and development to create the final prototype of a MAS for urban parking space management.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"100 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74345172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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