Pub Date : 2022-12-26DOI: 10.37791/2687-0649-2022-17-6-68-80
K. Klyshnikov, E. Ovcharenko, V. Danilov, P. Onishchenko, V. Ganyukov
Significant interest in the field of application of machine learning for the analysis of medical images stimulates the search for promising algorithms for solving routine diagnostic problems in cardiology. In relation to cardiovascular diseases, such a procedure is coronary angiography, which assesses the state of the vascular network and the presence of stenotic areas. This paper demonstrates an example of using modern models of neural networks: SSD MobileNet V2, SSD ResNet-50, Faster-RCNN Inception ResNet for localizing a single-vessel coronary artery lesion on a set of clinical data (3200 images). It is shown that the Faster-RCNN Inception ResNet V2 model was the most accurate in terms of the chosen metric mAP[0.5:0.95], reaching 0.9434 and 0.95 for the validation and test sets, respectively. However, the data processing speed was 0.363 seconds per frame, which corresponds to a speed of 2.8 frames/sec, which does not correspond to the speed of coronary angiography (15 frames/sec). Neural networks with a more “simple” architecture demonstrated an unsatisfactory quality of stenosis localization, expressed in a low characteristic mAP[0.5:0.95]. The results of this study demonstrate a key problem in the application of machine learning algorithms on graphic data – high accuracy, which may be acceptable for medical diagnostic procedures, is “decompensated” by long-term image analysis, as a result, the use of unmodified neural network architectures does not provide real-time data processing.
{"title":"Neural network analysis of coronary artery stenoses: assessment of the accuracy and speed of promising architectures","authors":"K. Klyshnikov, E. Ovcharenko, V. Danilov, P. Onishchenko, V. Ganyukov","doi":"10.37791/2687-0649-2022-17-6-68-80","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-68-80","url":null,"abstract":"Significant interest in the field of application of machine learning for the analysis of medical images stimulates the search for promising algorithms for solving routine diagnostic problems in cardiology. In relation to cardiovascular diseases, such a procedure is coronary angiography, which assesses the state of the vascular network and the presence of stenotic areas. This paper demonstrates an example of using modern models of neural networks: SSD MobileNet V2, SSD ResNet-50, Faster-RCNN Inception ResNet for localizing a single-vessel coronary artery lesion on a set of clinical data (3200 images). It is shown that the Faster-RCNN Inception ResNet V2 model was the most accurate in terms of the chosen metric mAP[0.5:0.95], reaching 0.9434 and 0.95 for the validation and test sets, respectively. However, the data processing speed was 0.363 seconds per frame, which corresponds to a speed of 2.8 frames/sec, which does not correspond to the speed of coronary angiography (15 frames/sec). Neural networks with a more “simple” architecture demonstrated an unsatisfactory quality of stenosis localization, expressed in a low characteristic mAP[0.5:0.95]. The results of this study demonstrate a key problem in the application of machine learning algorithms on graphic data – high accuracy, which may be acceptable for medical diagnostic procedures, is “decompensated” by long-term image analysis, as a result, the use of unmodified neural network architectures does not provide real-time data processing.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"25 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76157483","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: 10.37791/2687-0649-2022-17-6-5-17
Nikolay A. Kondratenko, E. Filimonova, P. Mashegov, Oleg P. Kultygin, Andrey M. Nechaev
This work is devoted to the study of pricing issues for obtaining maximum profit when selling consumer goods at a constant purchase price. The said goods come in from either manufacturers or warehouses where the retail companies buy the goods in order to sell them directly to the consumers. The dependence of the selling rate per unit of time on the level of the added price in relation to the purchase price of the item is established by the means of sales price variation. The object of the research is the specific case of a linear approximation of said dependence, which is usually actualized in the event of either more elastic or less elastic demand for goods, when they are sold through Internet platforms. The proposed approach to determining prices of all the goods which are being sold for maximizing the total profit from the sales of all consumer goods or maximizing the total revenue throughout the whole period of sales time, based on the search of extremum points of the profit and revenue functions for each item of goods remains valid in the case of more complex approximations by quadratic and cubic functions of demand function. The type of the function of maximum value added revenue and the type of the function of maximum profit can be both found per unit of time depending on the variable level of the added price included into the sales price of the item. The type of maximum revenue function can be found per unit of time depending on the sales price of the item. The extremum points of the found functions are being determined. The theorems have been proved, that the extremum points which are being determined appear to be the maximum points of the researched functions for each item of goods, when the maximum profit or the maximum revenues are reached by selling goods to consumers. All common variables of said functions are found by summing up these functions among the multitude of goods on the interval of the whole sales time. The received data is used for the practical implementation of an effective sales strategy that ensures maximum profits for companies specializing in direct sales to consumers of the purchased goods. An applied methodicalэф approach to the sales of goods which ensures maximum profit from the sales in the field of elastic demand approximated by a linear function and under the condition of a constant purchase price for goods is proposed and theoretically substantiated.
{"title":"Building the mathematical model of the decision support system in the field of pricing for e-commerce","authors":"Nikolay A. Kondratenko, E. Filimonova, P. Mashegov, Oleg P. Kultygin, Andrey M. Nechaev","doi":"10.37791/2687-0649-2022-17-6-5-17","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-5-17","url":null,"abstract":"This work is devoted to the study of pricing issues for obtaining maximum profit when selling consumer goods at a constant purchase price. The said goods come in from either manufacturers or warehouses where the retail companies buy the goods in order to sell them directly to the consumers. The dependence of the selling rate per unit of time on the level of the added price in relation to the purchase price of the item is established by the means of sales price variation. The object of the research is the specific case of a linear approximation of said dependence, which is usually actualized in the event of either more elastic or less elastic demand for goods, when they are sold through Internet platforms. The proposed approach to determining prices of all the goods which are being sold for maximizing the total profit from the sales of all consumer goods or maximizing the total revenue throughout the whole period of sales time, based on the search of extremum points of the profit and revenue functions for each item of goods remains valid in the case of more complex approximations by quadratic and cubic functions of demand function. The type of the function of maximum value added revenue and the type of the function of maximum profit can be both found per unit of time depending on the variable level of the added price included into the sales price of the item. The type of maximum revenue function can be found per unit of time depending on the sales price of the item. The extremum points of the found functions are being determined. The theorems have been proved, that the extremum points which are being determined appear to be the maximum points of the researched functions for each item of goods, when the maximum profit or the maximum revenues are reached by selling goods to consumers. All common variables of said functions are found by summing up these functions among the multitude of goods on the interval of the whole sales time. The received data is used for the practical implementation of an effective sales strategy that ensures maximum profits for companies specializing in direct sales to consumers of the purchased goods. An applied methodicalэф approach to the sales of goods which ensures maximum profit from the sales in the field of elastic demand approximated by a linear function and under the condition of a constant purchase price for goods is proposed and theoretically substantiated.","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":"81336529","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: 10.37791/2687-0649-2022-17-6-102-121
A. Puchkov, M. Dli, Nikolay N. Prokimnov, D. Y. Shutova
The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented.
{"title":"Multilevel algorithms for evaluating and making decisions on the optimal control of an integrated system for processing fine ore raw materials","authors":"A. Puchkov, M. Dli, Nikolay N. Prokimnov, D. Y. Shutova","doi":"10.37791/2687-0649-2022-17-6-102-121","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-102-121","url":null,"abstract":"The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"24 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91290084","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: 10.37791/2687-0649-2022-17-6-81-93
E. Shchetinin
Anomaly detection is an important task in various applications and areas of technology and production, such as structural defects, malicious intrusions into management and control systems, financial supervision and risk management, digital health screening, etc. The ever-increasing flows of diverse data and their structural complexity require the development of advanced approaches to their solution. In recent years, deep learning methods have achieved significant success in detecting anomalies, and unsupervised deep learning methods have become especially popular. Methods of anomaly detection by methods of deep learning without a teacher are investigated in the work on the example of a set of electrocardiograms containing normal ECG signals and ECG signals of people with various cardiovascular diseases (anomalies). To detect abnormal electrocardiograms, an autoencoder model has been developed in the form of a deep neural network with several fully connected layers. Also, to solve this problem, a method is proposed for selecting the threshold for separating abnormal ECG signals from normal ones, consisting in optimizing the ratio of performance indicators of the autoencoder model by methods. The paper presents a comparative analysis of the effectiveness of applying various machine learning models, such as the one class Support Vector Method, Isolation Forest, Random Forest and the presented autoencoder model to solving the problem of detecting abnormal ECG signals. For this purpose, metrics such as accuracy, recall, completeness, and f-score were used. His results showed that the proposed model surpassed the other models in solving the problem with accuracy = 98.8% precision = 95.75%, recall = 99.12%, f1-score = 98.75%.
{"title":"On anomalies detection in electrocardiograms with unsupervised deep learning methods","authors":"E. Shchetinin","doi":"10.37791/2687-0649-2022-17-6-81-93","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-81-93","url":null,"abstract":"Anomaly detection is an important task in various applications and areas of technology and production, such as structural defects, malicious intrusions into management and control systems, financial supervision and risk management, digital health screening, etc. The ever-increasing flows of diverse data and their structural complexity require the development of advanced approaches to their solution. In recent years, deep learning methods have achieved significant success in detecting anomalies, and unsupervised deep learning methods have become especially popular. Methods of anomaly detection by methods of deep learning without a teacher are investigated in the work on the example of a set of electrocardiograms containing normal ECG signals and ECG signals of people with various cardiovascular diseases (anomalies). To detect abnormal electrocardiograms, an autoencoder model has been developed in the form of a deep neural network with several fully connected layers. Also, to solve this problem, a method is proposed for selecting the threshold for separating abnormal ECG signals from normal ones, consisting in optimizing the ratio of performance indicators of the autoencoder model by methods. The paper presents a comparative analysis of the effectiveness of applying various machine learning models, such as the one class Support Vector Method, Isolation Forest, Random Forest and the presented autoencoder model to solving the problem of detecting abnormal ECG signals. For this purpose, metrics such as accuracy, recall, completeness, and f-score were used. His results showed that the proposed model surpassed the other models in solving the problem with accuracy = 98.8% precision = 95.75%, recall = 99.12%, f1-score = 98.75%.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"5 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784947","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: 10.37791/2687-0649-2022-17-6-18-35
E. Gumerov, T. V. Alekseeva
In IIoT (Industrial Internet of Things) systems designed for enterprise management in real time, it is required to perform operational and intelligent processing of Big Data and issue a control signal to the actuators in a predictable time (on the order of several milliseconds). The high speeds of Big Data continuously generated by sensors of the industrial Internet of Things system make it difficult to obtain a control effect at a predictable time. The purpose of the study is to develop the architecture of a complex of IIoT systems to obtain a control effect at a predictable time in real time. The central issue of the task is the high-speed processing of structured data at the place of their occurrence to solve the contradiction between a large number of continuously generated necessary data and the need to process them at a predictable time. The decomposition of the IIoT system into separate IIoT systems according to the structures of the data used by them, followed by synthesis into a single complex of enterprise IIoT systems, is applied. The developed architecture of the IIoT system complex makes it possible to effectively implement distributed management of production processes in a predictable time, perform operational and intelligent processing of huge amounts of data of various formats continuously generated by industrial facilities. The complex of IIoT systems consists of separate systems of the industrial Internet of Things, each of which has its own structure of transmitted data and is implemented on the basis of a multi-level bus, which provides a high data transfer rate in a structured form, the ability to attach to the bus any IIoT device and any program used, including the Big Data system to identify hidden patterns in the work of the enterprise. The proposed solution of the architecture of the IIoT system complex based on intelligent sensors and touchsensors allows for effective management of enterprise equipment and technological process operations in real time with the immediate use of the new patterns found in the continuously incoming new data. The solution can be used by developers of industrial Internet of Things systems for the effective launch and implementation of projects, for the development and commissioning of IIoT systems.
{"title":"Development of the architecture of a complex of industrial Internet of Things systems based on intelligent sensors and touchsensors","authors":"E. Gumerov, T. V. Alekseeva","doi":"10.37791/2687-0649-2022-17-6-18-35","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-18-35","url":null,"abstract":"In IIoT (Industrial Internet of Things) systems designed for enterprise management in real time, it is required to perform operational and intelligent processing of Big Data and issue a control signal to the actuators in a predictable time (on the order of several milliseconds). The high speeds of Big Data continuously generated by sensors of the industrial Internet of Things system make it difficult to obtain a control effect at a predictable time. The purpose of the study is to develop the architecture of a complex of IIoT systems to obtain a control effect at a predictable time in real time. The central issue of the task is the high-speed processing of structured data at the place of their occurrence to solve the contradiction between a large number of continuously generated necessary data and the need to process them at a predictable time. The decomposition of the IIoT system into separate IIoT systems according to the structures of the data used by them, followed by synthesis into a single complex of enterprise IIoT systems, is applied. The developed architecture of the IIoT system complex makes it possible to effectively implement distributed management of production processes in a predictable time, perform operational and intelligent processing of huge amounts of data of various formats continuously generated by industrial facilities. The complex of IIoT systems consists of separate systems of the industrial Internet of Things, each of which has its own structure of transmitted data and is implemented on the basis of a multi-level bus, which provides a high data transfer rate in a structured form, the ability to attach to the bus any IIoT device and any program used, including the Big Data system to identify hidden patterns in the work of the enterprise. The proposed solution of the architecture of the IIoT system complex based on intelligent sensors and touchsensors allows for effective management of enterprise equipment and technological process operations in real time with the immediate use of the new patterns found in the continuously incoming new data. The solution can be used by developers of industrial Internet of Things systems for the effective launch and implementation of projects, for the development and commissioning of IIoT systems.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"14 3","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72413797","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: 10.37791/2687-0649-2022-17-6-94-101
V. Bobkov, O. Bulygina, Elizaveta K. Vereikina
The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes based on the results of modeling heat and mass transfer processes occurring in various zones of the roasting conveyor machine. The impossibility of carrying out expensive full-scale experiments leads to the need to use computer simulation methods. Nonlinearity, large dimension of the search space, high computational complexity make it difficult to use traditional deterministic search methods. Under these conditions, the stochastic methods that deliberately introduce an element of randomness into the search algorithm show good results. Today, population algorithms based on modeling the collective behavior of living organisms and characterized by the ability to simultaneously process several options have become widespread. To solve the optimization problem, it is proposed to use a modified Cuckoo search algorithm (by introducing fuzzy elements), which provides a comprehensive account of a huge number of parameters set for each vacuum chamber of the roasting conveyor machine. The control of the chemical-energy-technological system for the processing of apatite-nepheline ores waste, taking into account the obtained data and based on the existing neural network model of the high-temperature process, will make it possible to minimize the amount of return and provide energy-saving conditions for the operation of roasting units.
{"title":"Using population algorithms to optimize the temperature regime of roasting phosphorite pellets","authors":"V. Bobkov, O. Bulygina, Elizaveta K. Vereikina","doi":"10.37791/2687-0649-2022-17-6-94-101","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-6-94-101","url":null,"abstract":"The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes based on the results of modeling heat and mass transfer processes occurring in various zones of the roasting conveyor machine. The impossibility of carrying out expensive full-scale experiments leads to the need to use computer simulation methods. Nonlinearity, large dimension of the search space, high computational complexity make it difficult to use traditional deterministic search methods. Under these conditions, the stochastic methods that deliberately introduce an element of randomness into the search algorithm show good results. Today, population algorithms based on modeling the collective behavior of living organisms and characterized by the ability to simultaneously process several options have become widespread. To solve the optimization problem, it is proposed to use a modified Cuckoo search algorithm (by introducing fuzzy elements), which provides a comprehensive account of a huge number of parameters set for each vacuum chamber of the roasting conveyor machine. The control of the chemical-energy-technological system for the processing of apatite-nepheline ores waste, taking into account the obtained data and based on the existing neural network model of the high-temperature process, will make it possible to minimize the amount of return and provide energy-saving conditions for the operation of roasting units.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"62 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84492524","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: 10.37791/2687-0649-2022-17-5-15-33
Irik Z. Mukhametzyanov
The paper presents an overview and comparative analysis of four weighting methods for multi-criteria decision-making problem based on pairwise comparisons: AHP, Dematel, BWM and SWARA. It is demonstrate, by examples that the reliability of evaluations largely depends on the correct use of the pairwise comparison tool: evaluations are given on a verbal scale, then converted into quantitative values and then the criteria priorities are calculated. All stages of pairwise comparisons are multivariate. In particular, the validity of this decision-making tool depends on the choice of numerical scale and the method of prioritization. Given the importance, a set of concepts relating to linguistic variables, linguistic pairwise comparison matrices, and numerical scale (scale function) are presented in detail. It is demonstrate that the information of the pairwise comparison matrix in AHP is higher and is sufficient for the unambiguous implementation of the Dematel, BWM and SWARA methods. Although the reliability of the solution for a larger number of input information is considered higher, nevertheless, it cannot be argued that the decision of the AHP are more significant. The emphasis in this study is on the transformation of the numerical scale. The transformation of the numerical scale a directly related to the mental representation of the verbal scale, since the decision maker forms the scale according to his mental representation. It is demonstrate that the compression of the numerical scale leads to the alignment of priorities. The trend is the same for all types of numerical scales and prioritization methods, but the process occurs at different speeds. For scales with a smaller number of gradations, a decrease in the degree of priority on the numerical scale is characteristic, which leads to a decrease in the difference in weights. In particular, this difference can be adjusted by scaling.
{"title":"Transformation of numerical scales for pairwise comparisons: AHP, Dematel, BWM, SWARA","authors":"Irik Z. Mukhametzyanov","doi":"10.37791/2687-0649-2022-17-5-15-33","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-15-33","url":null,"abstract":"The paper presents an overview and comparative analysis of four weighting methods for multi-criteria decision-making problem based on pairwise comparisons: AHP, Dematel, BWM and SWARA. It is demonstrate, by examples that the reliability of evaluations largely depends on the correct use of the pairwise comparison tool: evaluations are given on a verbal scale, then converted into quantitative values and then the criteria priorities are calculated. All stages of pairwise comparisons are multivariate. In particular, the validity of this decision-making tool depends on the choice of numerical scale and the method of prioritization. Given the importance, a set of concepts relating to linguistic variables, linguistic pairwise comparison matrices, and numerical scale (scale function) are presented in detail. It is demonstrate that the information of the pairwise comparison matrix in AHP is higher and is sufficient for the unambiguous implementation of the Dematel, BWM and SWARA methods. Although the reliability of the solution for a larger number of input information is considered higher, nevertheless, it cannot be argued that the decision of the AHP are more significant. The emphasis in this study is on the transformation of the numerical scale. The transformation of the numerical scale a directly related to the mental representation of the verbal scale, since the decision maker forms the scale according to his mental representation. It is demonstrate that the compression of the numerical scale leads to the alignment of priorities. The trend is the same for all types of numerical scales and prioritization methods, but the process occurs at different speeds. For scales with a smaller number of gradations, a decrease in the degree of priority on the numerical scale is characteristic, which leads to a decrease in the difference in weights. In particular, this difference can be adjusted by scaling.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"70 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86114516","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: 10.37791/2687-0649-2022-17-5-116-124
R. Y. Golikov
The rising trend of computer technology using makes digital signal processing (DSP) techniques converted into numerical data sets particularly relevant. For the most part, they are quite complex and their use is not always justified for a wide range of applications. This determines the ongoing interest in heuristic algorithms that are based on simplified approaches and allow quickly obtaining approximation of estimates with the least work amount. This paper discusses a method of pulsed (single) aperiodic signal with a high level of noise component mathematical processing by approximating its shape by a piecewise linear function, that parameters are determined using the method of least squares. A brief justification for this method is given, based on an analysis of the stochastic nature of the noise component. A numerical analysis of the signals spectral composition before and after processing is performed, as well as a comparison with other common methods: filtering and coherent averaging. It is shown that the waveform piecewise linear approximation can effectively separate the useful signal from the noise component, does not require complex algorithmic designs, and its program code implementation is possible in any high-level languages. The developed method is applicable for all types of signals and is most effective for processing single aperiodic pulses without its repetition possibility. The proposed approach can also be used in the educational process when studying the programming basics and for solving economic problems based on the determination of trend lines by parametric methods.
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Pub Date : 2022-10-21DOI: 10.37791/2687-0649-2022-17-5-77-87
Maxim D. Pysin, Aleksandr A. Egorov, D. V. Zubov
Industry 4.0 is an initiative that involves building smart factories, supply chains and the production process. One of the key related concepts is digital twins, which enable forecasting and planning using real-time data in complex models. The concept involves working with large amounts of data, both when developing systems from scratch, and for building them on the basis of existing modeling software. The tasks of processing, storing and using such data streams are solved daily by large Internet companies operating on the data of millions of users to build business processes. Such companies have been developing systems using microservice architecture for ten or more years, which allows them to build scalable and deterministic systems for processing data flow. However, within the framework of the task, it became necessary to use modeling programs to build a digital twin, which set us the task of integration, since programs for building models are not adapted to work within microservice systems. The way out of this situation is to create data exchange drivers. An example of such a simulation program is Unisim Design. The paper formulates the problem of extracting data from a program that was not originally adapted to work within a software package that implies constant interaction between its parts. A solution has been found and implemented that allows obtaining data from this program without using commercial software and closed libraries.
{"title":"The task of extracting data from a simulation program to build a digital twin of production using the example of Unisim Design","authors":"Maxim D. Pysin, Aleksandr A. Egorov, D. V. Zubov","doi":"10.37791/2687-0649-2022-17-5-77-87","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-77-87","url":null,"abstract":"Industry 4.0 is an initiative that involves building smart factories, supply chains and the production process. One of the key related concepts is digital twins, which enable forecasting and planning using real-time data in complex models. The concept involves working with large amounts of data, both when developing systems from scratch, and for building them on the basis of existing modeling software. The tasks of processing, storing and using such data streams are solved daily by large Internet companies operating on the data of millions of users to build business processes. Such companies have been developing systems using microservice architecture for ten or more years, which allows them to build scalable and deterministic systems for processing data flow. However, within the framework of the task, it became necessary to use modeling programs to build a digital twin, which set us the task of integration, since programs for building models are not adapted to work within microservice systems. The way out of this situation is to create data exchange drivers. An example of such a simulation program is Unisim Design. The paper formulates the problem of extracting data from a program that was not originally adapted to work within a software package that implies constant interaction between its parts. A solution has been found and implemented that allows obtaining data from this program without using commercial software and closed libraries.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89832946","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: 10.37791/2687-0649-2022-17-5-62-76
A. Puchkov, A. M. Sokolov, V. V. Fedotov
Currently, there is an acute problem of waste disposal of mining and processing plants, which accumulate in significant volumes in the territories adjacent to them and pose a serious threat to the environment. In this regard, the creation of technological systems for processing ore waste and the improvement of their information support represent an urgent area of research. An example of such a system is a complex chemical and energy technology system for the production of yellow phosphorus from waste apatite-nepheline ores. The purpose of the study was to develop a model for collecting data on the parameters of the processes of heat treatment of pelletized phosphate ore raw materials in such a system, as well as a method for identifying dependencies between these parameters. The identification of dependencies in the information support of the yellow phosphorus production system will improve the quality of its functioning in terms of management criteria, energy and resource efficiency. To achieve this goal, the tasks of choosing a mathematical concept for the basis of the method being developed, constructing an algorithm and creating software implementing this method, conducting model experiments were solved. The method is based on the use of deep recurrent neural networks of long-term short-term memory, which have a high generalizing ability and are used in solving problems of regression and classification of multidimensional time sequences, in the form of which, as a rule, the parameters of a chemical and energy technology system are presented. The method is implemented as an application created in the MatLab 2021 environment. The application interface allows you to interactively conduct experiments with various sets of input and output parameters to identify the relationship between them, as well as change the hyperparameters of neural networks. As a result of the application, a repository of trained neural networks is created that simulate the relationships found between the specified parameters of the technological system and can be applied in decision support systems, management and engineering.
{"title":"Neural network analysis method of heat treatment processes of pelletized phosphate ore raw materials","authors":"A. Puchkov, A. M. Sokolov, V. V. Fedotov","doi":"10.37791/2687-0649-2022-17-5-62-76","DOIUrl":"https://doi.org/10.37791/2687-0649-2022-17-5-62-76","url":null,"abstract":"Currently, there is an acute problem of waste disposal of mining and processing plants, which accumulate in significant volumes in the territories adjacent to them and pose a serious threat to the environment. In this regard, the creation of technological systems for processing ore waste and the improvement of their information support represent an urgent area of research. An example of such a system is a complex chemical and energy technology system for the production of yellow phosphorus from waste apatite-nepheline ores. The purpose of the study was to develop a model for collecting data on the parameters of the processes of heat treatment of pelletized phosphate ore raw materials in such a system, as well as a method for identifying dependencies between these parameters. The identification of dependencies in the information support of the yellow phosphorus production system will improve the quality of its functioning in terms of management criteria, energy and resource efficiency. To achieve this goal, the tasks of choosing a mathematical concept for the basis of the method being developed, constructing an algorithm and creating software implementing this method, conducting model experiments were solved. The method is based on the use of deep recurrent neural networks of long-term short-term memory, which have a high generalizing ability and are used in solving problems of regression and classification of multidimensional time sequences, in the form of which, as a rule, the parameters of a chemical and energy technology system are presented. The method is implemented as an application created in the MatLab 2021 environment. The application interface allows you to interactively conduct experiments with various sets of input and output parameters to identify the relationship between them, as well as change the hyperparameters of neural networks. As a result of the application, a repository of trained neural networks is created that simulate the relationships found between the specified parameters of the technological system and can be applied in decision support systems, management and engineering.","PeriodicalId":44195,"journal":{"name":"Journal of Applied Mathematics & Informatics","volume":"163 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86882844","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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