Pub Date : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-111-128
V. Osipov, A. Tsypin, O. V. Ledneva
One of the key factors in the country’s GDP growth is reproducible capital, which lays the foundation for the production of products, works and services. Accordingly, the study of the state, structure and dynamics of the dominant component, fixed assets, is one of the priority tasks of statistics and econometrics. This implies the purpose of the study, which is to assess the predictive capabilities of econometric models. To achieve this goal, a pool of mathematical-statistical and econometric methods was used, in particular tabular and graphic, descriptive statistics, correlation-regression, adaptive modeling. The main results include: analysis of the structure of investments did not find new or hidden patterns, so investments are directed to the modernization or renewal of capital-intensive areas – these are buildings, structures and land (about 40% of the total investment), the main industries are industry and transport; visual analysis of the dynamics of the temporary series of investments in fixed assets showed the presence of a long-term, seasonal and situational component; the construction of 6 econometric models reflecting the complex dynamics of the macro indicator in question made it possible to distinguish two adaptive models belonging to the group; thus, the best forecast opportunities for complex dynamics of investments in Russian fixed assets are observed in the three-parameter exponential smoothing model and SARIMA (1,0,0)(1,1,0) [4]. The results obtained in the course of the study will be useful for scientists involved in modeling and predicting complex-structured time series
{"title":"Using econometric models to forecast fixed asset investments","authors":"V. Osipov, A. Tsypin, O. V. Ledneva","doi":"10.37791/2687-0649-2023-18-1-111-128","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-111-128","url":null,"abstract":"One of the key factors in the country’s GDP growth is reproducible capital, which lays the foundation for the production of products, works and services. Accordingly, the study of the state, structure and dynamics of the dominant component, fixed assets, is one of the priority tasks of statistics and econometrics. This implies the purpose of the study, which is to assess the predictive capabilities of econometric models. To achieve this goal, a pool of mathematical-statistical and econometric methods was used, in particular tabular and graphic, descriptive statistics, correlation-regression, adaptive modeling. The main results include: analysis of the structure of investments did not find new or hidden patterns, so investments are directed to the modernization or renewal of capital-intensive areas – these are buildings, structures and land (about 40% of the total investment), the main industries are industry and transport; visual analysis of the dynamics of the temporary series of investments in fixed assets showed the presence of a long-term, seasonal and situational component; the construction of 6 econometric models reflecting the complex dynamics of the macro indicator in question made it possible to distinguish two adaptive models belonging to the group; thus, the best forecast opportunities for complex dynamics of investments in Russian fixed assets are observed in the three-parameter exponential smoothing model and SARIMA (1,0,0)(1,1,0) [4]. The results obtained in the course of the study will be useful for scientists involved in modeling and predicting complex-structured time series","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"6 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75692585","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-82-95
V. Rozhkov, K. Krutikov, V. V. Fedotov, S. G. Butrimov
In the article, using MatLab dynamic simulation modeling, a study was made of the excitation systems of powerful synchronous generators of stationary diesel generator sets, which are the main sources of emergency power supply for nuclear power plants. The optimal structural complexity mathematical model of a synchronous machine in relative units and orthogonal synchronous coordinate system is used. A comprehensive simulation of diesel generator sets was carried out with the reproduction of both the dynamics of the automatic control system for excitation of a synchronous generator and the diesel engine control system. The simulation takes into account the features of starting a diesel generator to accelerate a synchronous machine, its initial excitation from a battery. Particular emphasis is placed on the study of self-excitation modes through a transformer connected to the stator circuit of the generator and a thyristor rectifier with an excitation winding as a load, as well as parallel operation with the power system. As a result, the processes of starting a diesel generator set in idle mode, effective self-excitation, autonomous operation of the generator at idle, and applying a load to the generator up to the values of permissible overload were simulated. The work of all channels of the control system is shown, including the signals of the regulators of the automatic control system and mechanical variables that are inaccessible in practice. The adequacy of the developed model is proved by comparison with a real physical experiment when testing a diesel generator at a nuclear power plant. The possibility of using the model developed in MatLab as a virtual test site for testing a diesel generator set and a computer simulator for specialized engineering personnel of a nuclear power plant is demonstrated.
{"title":"Dynamic simulation modeling of the excitation system of synchronous generators of stationary diesel generator sets for emergency power supply of a nuclear power plant","authors":"V. Rozhkov, K. Krutikov, V. V. Fedotov, S. G. Butrimov","doi":"10.37791/2687-0649-2023-18-1-82-95","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-82-95","url":null,"abstract":"In the article, using MatLab dynamic simulation modeling, a study was made of the excitation systems of powerful synchronous generators of stationary diesel generator sets, which are the main sources of emergency power supply for nuclear power plants. The optimal structural complexity mathematical model of a synchronous machine in relative units and orthogonal synchronous coordinate system is used. A comprehensive simulation of diesel generator sets was carried out with the reproduction of both the dynamics of the automatic control system for excitation of a synchronous generator and the diesel engine control system. The simulation takes into account the features of starting a diesel generator to accelerate a synchronous machine, its initial excitation from a battery. Particular emphasis is placed on the study of self-excitation modes through a transformer connected to the stator circuit of the generator and a thyristor rectifier with an excitation winding as a load, as well as parallel operation with the power system. As a result, the processes of starting a diesel generator set in idle mode, effective self-excitation, autonomous operation of the generator at idle, and applying a load to the generator up to the values of permissible overload were simulated. The work of all channels of the control system is shown, including the signals of the regulators of the automatic control system and mechanical variables that are inaccessible in practice. The adequacy of the developed model is proved by comparison with a real physical experiment when testing a diesel generator at a nuclear power plant. The possibility of using the model developed in MatLab as a virtual test site for testing a diesel generator set and a computer simulator for specialized engineering personnel of a nuclear power plant is demonstrated.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"30 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83320541","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-65-81
Gennady V. Ross, V. Konyavskiy, V. V. Medvedev
The article deals with an urgent problem related to organization of control of a team of intelligent mobile robots and their interaction with each other for the most effective achievement of the goal. The research is aimed at development of interrelated models of intelligent planning of robot behavior, which is based on a market approach resting on a new risk equilibrium model. The substantial and formal formulations of the task of planning of autonomous mobile robots activities are proposed. The author’s model and a set of new simulation models for calculation of the overstatement, understatement costs, as well as their risks are developed. Various calculation algorithms are proposed for various variants of robot interaction: control under conditions of a restricted limit of the most scarce resource (for example, battery energy); interaction between robots using information products (messages); robot control from the center; purchase and sale of the information product; making a decision on subordination and support of communication between robots, etc. Examples of description of robot behavior options (speed of movement, equipment with photos, videos, sampling tools, energy limit), classification of events (fire, traffic accident, violation of law and order, emergency situations, suspicious object) are offered. Examples of calculation procedures are given: robot behavior options, if it is possible to maintain speed depending on energy consumption; adjustment factors to take into account increase of the probability to detect an event due to improvement of the photo quality (wide format, high definition, frame frequency).
{"title":"Model of intelligent planning of robot behavior in a team of robots","authors":"Gennady V. Ross, V. Konyavskiy, V. V. Medvedev","doi":"10.37791/2687-0649-2023-18-1-65-81","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-65-81","url":null,"abstract":"The article deals with an urgent problem related to organization of control of a team of intelligent mobile robots and their interaction with each other for the most effective achievement of the goal. The research is aimed at development of interrelated models of intelligent planning of robot behavior, which is based on a market approach resting on a new risk equilibrium model. The substantial and formal formulations of the task of planning of autonomous mobile robots activities are proposed. The author’s model and a set of new simulation models for calculation of the overstatement, understatement costs, as well as their risks are developed. Various calculation algorithms are proposed for various variants of robot interaction: control under conditions of a restricted limit of the most scarce resource (for example, battery energy); interaction between robots using information products (messages); robot control from the center; purchase and sale of the information product; making a decision on subordination and support of communication between robots, etc. Examples of description of robot behavior options (speed of movement, equipment with photos, videos, sampling tools, energy limit), classification of events (fire, traffic accident, violation of law and order, emergency situations, suspicious object) are offered. Examples of calculation procedures are given: robot behavior options, if it is possible to maintain speed depending on energy consumption; adjustment factors to take into account increase of the probability to detect an event due to improvement of the photo quality (wide format, high definition, frame frequency).","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"19 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87847356","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-4-21
Vladimir A. Kalashnikov, V. Soloviev
n the last decade, there has been an active digitalization of industrial production based on rapidly developing information technologies, including artificial intelligence technologies. This is largely due to the development of deep learning methods and their applications in computer vision. Since the mid 2010s convolutional neural networks demonstrate exceptional efficiency in solving problems such as the detection, classification and segmentation of various objects. As a result, computer vision methods are beginning to be actively used in the problems of quality control of raw materials and finished products. All this applies to the mining industry. However, in the Russian scientific literature there are practically no systematic reviews of computer vision applications in this area. The present study aims to fill this gap. The paper provides a systematic review of the history of development and the current state of the methods and technologies of machine vision used in the mining industry for the analysis of solid materials, demonstrates the latest achievements in this area and examples of their application in the mining industry. The authors have analyzed 29 research papers in the field of application of computer vision in the mining industry and classified the stages of technology development from the mid-1980s, when computer vision was used without the use of machine learning, and ending with modern research based on the use of deep convolutional neural networks for solving problems of classification and segmentation. The effectiveness of the methods used is compared, their advantages and disadvantages are discussed, and forecasts are made for the development of computer vision methods in the mining industry in the near future. Examples are given showing that the use of convolutional neural networks made it possible to move to a qualitatively higher level of quality in solving problems of classification and segmentation as applied to the analysis of output volume, particle size distribution, including flakiness, angularity and roughness, dust and clay content, bulk density and emptiness, etc.
{"title":"Applications of computer vision in the mining industry","authors":"Vladimir A. Kalashnikov, V. Soloviev","doi":"10.37791/2687-0649-2023-18-1-4-21","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-4-21","url":null,"abstract":"n the last decade, there has been an active digitalization of industrial production based on rapidly developing information technologies, including artificial intelligence technologies. This is largely due to the development of deep learning methods and their applications in computer vision. Since the mid 2010s convolutional neural networks demonstrate exceptional efficiency in solving problems such as the detection, classification and segmentation of various objects. As a result, computer vision methods are beginning to be actively used in the problems of quality control of raw materials and finished products. All this applies to the mining industry. However, in the Russian scientific literature there are practically no systematic reviews of computer vision applications in this area. The present study aims to fill this gap. The paper provides a systematic review of the history of development and the current state of the methods and technologies of machine vision used in the mining industry for the analysis of solid materials, demonstrates the latest achievements in this area and examples of their application in the mining industry. The authors have analyzed 29 research papers in the field of application of computer vision in the mining industry and classified the stages of technology development from the mid-1980s, when computer vision was used without the use of machine learning, and ending with modern research based on the use of deep convolutional neural networks for solving problems of classification and segmentation. The effectiveness of the methods used is compared, their advantages and disadvantages are discussed, and forecasts are made for the development of computer vision methods in the mining industry in the near future. Examples are given showing that the use of convolutional neural networks made it possible to move to a qualitatively higher level of quality in solving problems of classification and segmentation as applied to the analysis of output volume, particle size distribution, including flakiness, angularity and roughness, dust and clay content, bulk density and emptiness, etc.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"17 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80115563","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-96-110
Bulat R. Kamalov, M. Tumbinskaya
Currently, a new type of information security threat is spreading – hidden mining, which uses the computing resources of users through browsers. Malicious software based on WebAssembly files unauthorizedly uses the computing resources of users of computer systems. The existing methods for detecting “hidden miners” in the browser environment are based on: dynamic analysis algorithms, however, they have a number of limitations, for example, it is required that malicious software for hidden mining work for a certain period of time, they are characterized by a large number of false positives; algorithms of browser extensions that use blacklists to prevent unauthorized access to the user’s browser environment, however, attackers often change their domain names, etc. The relevance of using special protection tools against browser-based cryptominers is beyond doubt. The purpose of this study is to increase the level of security of the browser environment of users of computer systems. Achieving this goal is possible by solving the main task - the timely automated detection of “hidden miners” in the browser environment and the prevention of unauthorized mining. The article describes software that does not depend on the browser or operating system used, is resistant to attempts to circumvent protection by intruders, will allow users to reliably recognize “hidden miners”, and increase the level of information security of a computer system. The software is based on classification algorithms implemented on the basis of a convolutional neural network. The results of the study and experimental data showed that as a result of testing the software, the recognition accuracy of “hidden miners” in the browser environment is 91.37%.
{"title":"Software for detecting “hidden miners” in a browser environment","authors":"Bulat R. Kamalov, M. Tumbinskaya","doi":"10.37791/2687-0649-2023-18-1-96-110","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-96-110","url":null,"abstract":"Currently, a new type of information security threat is spreading – hidden mining, which uses the computing resources of users through browsers. Malicious software based on WebAssembly files unauthorizedly uses the computing resources of users of computer systems. The existing methods for detecting “hidden miners” in the browser environment are based on: dynamic analysis algorithms, however, they have a number of limitations, for example, it is required that malicious software for hidden mining work for a certain period of time, they are characterized by a large number of false positives; algorithms of browser extensions that use blacklists to prevent unauthorized access to the user’s browser environment, however, attackers often change their domain names, etc. The relevance of using special protection tools against browser-based cryptominers is beyond doubt. The purpose of this study is to increase the level of security of the browser environment of users of computer systems. Achieving this goal is possible by solving the main task - the timely automated detection of “hidden miners” in the browser environment and the prevention of unauthorized mining. The article describes software that does not depend on the browser or operating system used, is resistant to attempts to circumvent protection by intruders, will allow users to reliably recognize “hidden miners”, and increase the level of information security of a computer system. The software is based on classification algorithms implemented on the basis of a convolutional neural network. The results of the study and experimental data showed that as a result of testing the software, the recognition accuracy of “hidden miners” in the browser environment is 91.37%.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"23 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85010877","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-129-143
N. Yandybaeva
An approach to assessing and forecasting indicators of the quality of life of the population in the region based on the concept of system dynamics is presented. A mathematical model has been developed, which is a system of non-linear, non-homogeneous, different-tempo differential equations, which include system variables and external factors. A digraph of causal relationships between system variables and external factors is constructed. As system variables, the model uses indicators of socio-economic development of the region: gross regional product, life expectancy at birth, population size, per capita per capita income, registered unemployment rate, birth rate, share of the population with income below the subsistence level, the weight of organizations using personal computers. The choice of external factors and functional dependencies in the developed model is substantiated. The adequacy of the developed mathematical model was checked using retrospective data and the calculation of the relative error. The interface of the author’s software application “Prognoz_2”, developed in the GUIDE MatLab environment, used to conduct computational experiments, is presented. An example of the practical implementation of the developed approach to assessing the quality of life in the Saratov and Samara regions is considered. The results of the computational experiment on the analysis and prediction of the quality of life on the time interval [2022;2026] years within the framework of the implementation of three scenarios are shown. The values of system variables in 2021 normalized relative to 2010 were used as initial conditions for the calculations. The developed software can be used to form scenarios for the socio-economic development of the region. Models and algorithms can be used as part of an information-advising system for making decisions at various levels of management.
{"title":"Dynamic model for predicting quality of life indicators in the region","authors":"N. Yandybaeva","doi":"10.37791/2687-0649-2023-18-1-129-143","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-129-143","url":null,"abstract":"An approach to assessing and forecasting indicators of the quality of life of the population in the region based on the concept of system dynamics is presented. A mathematical model has been developed, which is a system of non-linear, non-homogeneous, different-tempo differential equations, which include system variables and external factors. A digraph of causal relationships between system variables and external factors is constructed. As system variables, the model uses indicators of socio-economic development of the region: gross regional product, life expectancy at birth, population size, per capita per capita income, registered unemployment rate, birth rate, share of the population with income below the subsistence level, the weight of organizations using personal computers. The choice of external factors and functional dependencies in the developed model is substantiated. The adequacy of the developed mathematical model was checked using retrospective data and the calculation of the relative error. The interface of the author’s software application “Prognoz_2”, developed in the GUIDE MatLab environment, used to conduct computational experiments, is presented. An example of the practical implementation of the developed approach to assessing the quality of life in the Saratov and Samara regions is considered. The results of the computational experiment on the analysis and prediction of the quality of life on the time interval [2022;2026] years within the framework of the implementation of three scenarios are shown. The values of system variables in 2021 normalized relative to 2010 were used as initial conditions for the calculations. The developed software can be used to form scenarios for the socio-economic development of the region. Models and algorithms can be used as part of an information-advising system for making decisions at various levels of management.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"47 3 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83186720","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 : 2023-02-10DOI: 10.37791/2687-0649-2023-18-1-22-36
A. Puchkov, M. Dli, Nikolay N. Prokimnov, A. M. Sokolov
The results of studies on the development of the structure of an intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials are presented. Such devices are involved in all cycles of the technological process, so the assessment of this risk for them is an urgent task. A method for assessing such risks is proposed, which is based on the assessment of the useful life of equipment, performed on the basis of the prediction of characteristics by a deep recurrent neural network, with further generalization of the results of such an assessment in a fuzzy inference block. Recurrent neural networks with long short-term memory were used, which are one of the most powerful tools for solving time series regression problems, including predicting their values for long intervals. The use of deep neural networks to predict the characteristics of electromechanical devices made it possible to obtain a high prediction accuracy, which made it possible to apply a relatively less accurate recurrent least squares method for the iterative process of estimating the useful life of equipment. This approach made it possible to build a computational evaluation process with its constant refinement as new results of measurements of the characteristics of electromechanical devices become available. The results of a model experiment with a software implementation of the proposed method, performed in the MatLab 2021a environment, are presented, which showed the consistency of the program modules and obtaining a risk assessment result that is consistent with the expected dynamics of its change.
{"title":"An intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials","authors":"A. Puchkov, M. Dli, Nikolay N. Prokimnov, A. M. Sokolov","doi":"10.37791/2687-0649-2023-18-1-22-36","DOIUrl":"https://doi.org/10.37791/2687-0649-2023-18-1-22-36","url":null,"abstract":"The results of studies on the development of the structure of an intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials are presented. Such devices are involved in all cycles of the technological process, so the assessment of this risk for them is an urgent task. A method for assessing such risks is proposed, which is based on the assessment of the useful life of equipment, performed on the basis of the prediction of characteristics by a deep recurrent neural network, with further generalization of the results of such an assessment in a fuzzy inference block. Recurrent neural networks with long short-term memory were used, which are one of the most powerful tools for solving time series regression problems, including predicting their values for long intervals. The use of deep neural networks to predict the characteristics of electromechanical devices made it possible to obtain a high prediction accuracy, which made it possible to apply a relatively less accurate recurrent least squares method for the iterative process of estimating the useful life of equipment. This approach made it possible to build a computational evaluation process with its constant refinement as new results of measurements of the characteristics of electromechanical devices become available. The results of a model experiment with a software implementation of the proposed method, performed in the MatLab 2021a environment, are presented, which showed the consistency of the program modules and obtaining a risk assessment result that is consistent with the expected dynamics of its change.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"14 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81792966","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 : 2022-12-26DOI: 10.37791/2687-0649-2022-17-6-122-134
Almaz R. Iskhakov
The article presents the results of a study of the problem of structural synthesis of a vision system and its parametric identification using a new method based on the mathematical apparatus of the theory of modified descriptive image algebras. The theory of modified descriptive image algebras is a mathematical apparatus that allows one to formally describe the processing and analysis of images. In this mathematical apparatus, it is possible to describe the mathematical model of the measurement function of the technical vision system for the selected attribute of the observed object. To develop mathematical models, procedural and parametric transformations of images are used. Any mathematical model in the theory of modified descriptive image algebras has at least one variational parameter. In the course of parametric identification, it is required to calculate their values. This problem is multimodal and always has at least one solution. Numerical methods are usually used to solve the optimization problem. The article describes the algorithm for constructing a mathematical model for measuring the area using procedural and parametric transformations. The parametric identification problem is solved in the form of a nonlinear optimization problem. The visualization of the objective function has been carried out and recommendations for choosing the values of its variational parameters have been formulated. The collection of statistical data was carried out and a histogram was constructed, on the basis of which the distribution law for the measured value is selected. The statistical task of testing the hypothesis with the selected law of distribution of the general population according to the Pearson criterion is solved for a given level of significance. For the unknown parameters of the chosen distribution law, the estimation of confidence intervals was carried out. The materials of the article are applied in nature and have practical value. Using the proposed approach, it is possible to develop a measurement function for any feature of the observed object on a series of images.
{"title":"Method of structural synthesis of a technical vision system for the problem of area measurement","authors":"Almaz R. Iskhakov","doi":"10.37791/2687-0649-2022-17-6-122-134","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-122-134","url":null,"abstract":"The article presents the results of a study of the problem of structural synthesis of a vision system and its parametric identification using a new method based on the mathematical apparatus of the theory of modified descriptive image algebras. The theory of modified descriptive image algebras is a mathematical apparatus that allows one to formally describe the processing and analysis of images. In this mathematical apparatus, it is possible to describe the mathematical model of the measurement function of the technical vision system for the selected attribute of the observed object. To develop mathematical models, procedural and parametric transformations of images are used. Any mathematical model in the theory of modified descriptive image algebras has at least one variational parameter. In the course of parametric identification, it is required to calculate their values. This problem is multimodal and always has at least one solution. Numerical methods are usually used to solve the optimization problem. The article describes the algorithm for constructing a mathematical model for measuring the area using procedural and parametric transformations. The parametric identification problem is solved in the form of a nonlinear optimization problem. The visualization of the objective function has been carried out and recommendations for choosing the values of its variational parameters have been formulated. The collection of statistical data was carried out and a histogram was constructed, on the basis of which the distribution law for the measured value is selected. The statistical task of testing the hypothesis with the selected law of distribution of the general population according to the Pearson criterion is solved for a given level of significance. For the unknown parameters of the chosen distribution law, the estimation of confidence intervals was carried out. The materials of the article are applied in nature and have practical value. Using the proposed approach, it is possible to develop a measurement function for any feature of the observed object on a series of images.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"4 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87904130","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 : 2022-12-26DOI: 10.37791/2687-0649-2022-17-6-36-55
V. Rozhkov, V. V. Fedotov
By means of simulation computer modeling, an effective variant of constructing an identifier for the speed of an asynchronous motor of an electromechanical system of a sintering machine is analyzed. The mathematical and algorithmic basis of the adaptive speed identifier (ASI) of an induction motor with a squirrel-cage rotor (ACIM) is given. Using the developed mathematical description of ASI with a reference model and using the apparatus of Lyapunov functions, an adequate computer simulation model was created. Compared with the existing methods for constructing identifiers in sensorless asynchronous electric drives, the proposed version of the ASI allows taking into account the discrete nature of the supply voltage of the ACIM at the output of the frequency converter with pulse-width modulation (PWM) of the output voltage and changing a larger number of equivalent circuit parameters. The stability of the speed identification process is provided in a wide range, sufficient to stabilize the speed of the trolleys according to the requirements of the technological process of sintering machines. As a result, the accuracy of speed identification in static and dynamic modes of operation of the electric drive increases. Simulation confirmed the operability of the proposed version of the identifier, proposed options for setting the AIS components. Universal, important for practical application results have been obtained, which allow both to build a high-precision system for identifying the ACIM speed in general and to refine the setting of the coefficients of the proposed version of the identifier in particular. An important property of the developed version of the ASI is its operability without loss of accuracy at near-zero and zero speeds of rotation and close to the nominal load torque on the ACIM shaft. In this regard, the practical application of the developed version, in addition to the drive of the sintering machine, is also possible in high- precision positioning systems for electric drives for various purposes.
{"title":"Simulation modeling of the adaptive speed identifier of an induction motor of a sintering machine","authors":"V. Rozhkov, V. V. Fedotov","doi":"10.37791/2687-0649-2022-17-6-36-55","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-36-55","url":null,"abstract":"By means of simulation computer modeling, an effective variant of constructing an identifier for the speed of an asynchronous motor of an electromechanical system of a sintering machine is analyzed. The mathematical and algorithmic basis of the adaptive speed identifier (ASI) of an induction motor with a squirrel-cage rotor (ACIM) is given. Using the developed mathematical description of ASI with a reference model and using the apparatus of Lyapunov functions, an adequate computer simulation model was created. Compared with the existing methods for constructing identifiers in sensorless asynchronous electric drives, the proposed version of the ASI allows taking into account the discrete nature of the supply voltage of the ACIM at the output of the frequency converter with pulse-width modulation (PWM) of the output voltage and changing a larger number of equivalent circuit parameters. The stability of the speed identification process is provided in a wide range, sufficient to stabilize the speed of the trolleys according to the requirements of the technological process of sintering machines. As a result, the accuracy of speed identification in static and dynamic modes of operation of the electric drive increases. Simulation confirmed the operability of the proposed version of the identifier, proposed options for setting the AIS components. Universal, important for practical application results have been obtained, which allow both to build a high-precision system for identifying the ACIM speed in general and to refine the setting of the coefficients of the proposed version of the identifier in particular. An important property of the developed version of the ASI is its operability without loss of accuracy at near-zero and zero speeds of rotation and close to the nominal load torque on the ACIM shaft. In this regard, the practical application of the developed version, in addition to the drive of the sintering machine, is also possible in high- precision positioning systems for electric drives for various purposes.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"28 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79270652","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 : 2022-12-26DOI: 10.37791/2687-0649-2022-17-6-56-67
A. S. Mezentsev, L. Yasnitsky
Machine learning methods are currently widely used to solve various production problems, the problems of defects diagnosing and predicting for items in mass production, in particular. One of the most important problems is defects diagnosing and predicting, basing on its solution the regulations for the technological processes parameters and raw materials used can be determined, that insures the minimum probability of defects and the highest possible quality of manufactured products. The solution of this urgent problem with the help of a neural network model is shown on the example of the technological process for manufacturing products from fine ore material. The proposed model is based on the neural network trained on the set of historical data including examples of manufacturing products with different sets of technological parameters and raw ore material. The predicted parameter is warping of the product in one of its sections. Designing and training of the proposed neural network structure allowed achieving the coefficient of determination R2 between the predicted and actual warpage values of 92%. The dependences for the warpage value on the most significant parameters of the technological process, including thermophysical and chemical power technological processes of raw materials processing were constructed by conducting computer experiments using the method of partial freezing for input parameters. Due to these dependencies, the regulations for the most significant parameters of the production process are determined, which ensures the product to be without violating the tolerance for the warpage value specified by the design documentation. Thus, a specific example shows the possibility of using neural network modeling to solve the problem of setting regulations for the production process parameters, which compliance ensures the minimum amount of rejects and, accordingly, a higher quality of a production batch.
{"title":"Neural network model for determining the regulations parameters in the technological process of ore raw materials processing","authors":"A. S. Mezentsev, L. Yasnitsky","doi":"10.37791/2687-0649-2022-17-6-56-67","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-56-67","url":null,"abstract":"Machine learning methods are currently widely used to solve various production problems, the problems of defects diagnosing and predicting for items in mass production, in particular. One of the most important problems is defects diagnosing and predicting, basing on its solution the regulations for the technological processes parameters and raw materials used can be determined, that insures the minimum probability of defects and the highest possible quality of manufactured products. The solution of this urgent problem with the help of a neural network model is shown on the example of the technological process for manufacturing products from fine ore material. The proposed model is based on the neural network trained on the set of historical data including examples of manufacturing products with different sets of technological parameters and raw ore material. The predicted parameter is warping of the product in one of its sections. Designing and training of the proposed neural network structure allowed achieving the coefficient of determination R2 between the predicted and actual warpage values of 92%. The dependences for the warpage value on the most significant parameters of the technological process, including thermophysical and chemical power technological processes of raw materials processing were constructed by conducting computer experiments using the method of partial freezing for input parameters. Due to these dependencies, the regulations for the most significant parameters of the production process are determined, which ensures the product to be without violating the tolerance for the warpage value specified by the design documentation. Thus, a specific example shows the possibility of using neural network modeling to solve the problem of setting regulations for the production process parameters, which compliance ensures the minimum amount of rejects and, accordingly, a higher quality of a production batch.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"52 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90842556","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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