Pub Date : 2023-02-24DOI: 10.15588/1607-3274-2023-1-2
Yuliia Parfenenko, V. Shendryk, Yevhenii Kholiavka, P. M. Pavlenko
Context. The current stage of development of the electric power industry is characterized by an intensive process of microgrid development and management. The feasibility of using a microgrid is determined by the fact that it has a number of advantages compared to classical methods of energy generation, transmission, and distribution. It is much easier to ensure the reliability of electricity supply within the microgrid than in large energy systems. Energy consumers in a microgrid can affect the power balancing process by regulating their loads, generating, storing, and releasing electricity. One of the main tasks of the microgrid is to provide consumers with electrical energy in a balance between its generation and consumption. This is achieved thanks to the intelligent management of the microgrid operation, which uses energy consumption forecasting data. This allows to increase the efficiency of energy infrastructure management. Objective. The purpose of this work is to develop short-term electricity consumption forecasting models for various types of microgrid electricity consumers, which will improve the efficiency of energy infrastructure management and reduce electricity consumption. Method. The SARIMA autoregressive model and the LSTM machine learning model are used to obtain forecast values of electricity consumption. AIC and BIC information criteria are used to compare autoregressive models. The accuracy of forecasting models is evaluated using MAE, RMSE, MAPE errors. Results. The experiments that forecast the amount of electricity consumption for the different types of consumers were conducted. Forecasting was carried out for both LSTM and AR models on formed data sets at intervals of 6 hours, 1 day, and 3 days. The forecasting results of the LSTM model met the forecasting requirements, providing better forecasting quality compared to AR models. Conclusions. The conducted study of electricity consumption forecasting made it possible to find universal forecasting models that meet the requirements of forecasting quality. A comparative analysis of developed time series forecasting models was performed, as a result of which the advantages of ML models over AR models were revealed. The predictive quality of the LSTM model showed the accuracy of the MAPE of forecasting electricity consumption for a private house – 0.1%, a dairy plant – 3.74%, and a gas station – 3.67%. The obtained results will allow to increase the efficiency of microgrid management, the distribution of electricity between electricity consumers to reduce the amount of energy consumption and prevent peak loads on the power grid.
{"title":"COMPARISON OF SHORT-TERM FORECASTING METHODS OF ELECTRICITY CONSUMPTION IN MICROGRIDS","authors":"Yuliia Parfenenko, V. Shendryk, Yevhenii Kholiavka, P. M. Pavlenko","doi":"10.15588/1607-3274-2023-1-2","DOIUrl":"https://doi.org/10.15588/1607-3274-2023-1-2","url":null,"abstract":"Context. The current stage of development of the electric power industry is characterized by an intensive process of microgrid development and management. The feasibility of using a microgrid is determined by the fact that it has a number of advantages compared to classical methods of energy generation, transmission, and distribution. It is much easier to ensure the reliability of electricity supply within the microgrid than in large energy systems. Energy consumers in a microgrid can affect the power balancing process by regulating their loads, generating, storing, and releasing electricity. One of the main tasks of the microgrid is to provide consumers with electrical energy in a balance between its generation and consumption. This is achieved thanks to the intelligent management of the microgrid operation, which uses energy consumption forecasting data. This allows to increase the efficiency of energy infrastructure management. \u0000Objective. The purpose of this work is to develop short-term electricity consumption forecasting models for various types of microgrid electricity consumers, which will improve the efficiency of energy infrastructure management and reduce electricity consumption. \u0000Method. The SARIMA autoregressive model and the LSTM machine learning model are used to obtain forecast values of electricity consumption. AIC and BIC information criteria are used to compare autoregressive models. The accuracy of forecasting models is evaluated using MAE, RMSE, MAPE errors. \u0000Results. The experiments that forecast the amount of electricity consumption for the different types of consumers were conducted. Forecasting was carried out for both LSTM and AR models on formed data sets at intervals of 6 hours, 1 day, and 3 days. The forecasting results of the LSTM model met the forecasting requirements, providing better forecasting quality compared to AR models. \u0000Conclusions. The conducted study of electricity consumption forecasting made it possible to find universal forecasting models that meet the requirements of forecasting quality. A comparative analysis of developed time series forecasting models was performed, as a result of which the advantages of ML models over AR models were revealed. The predictive quality of the LSTM model showed the accuracy of the MAPE of forecasting electricity consumption for a private house – 0.1%, a dairy plant – 3.74%, and a gas station – 3.67%. The obtained results will allow to increase the efficiency of microgrid management, the distribution of electricity between electricity consumers to reduce the amount of energy consumption and prevent peak loads on the power grid.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"68 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86073198","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 : 2022-12-15DOI: 10.15588/1607-3274-2022-4-9
Ye. S. Sulema, L. V. Drozdenko, A. Dychka
Context. The problem of constructing a set of barcode patterns for multicolor barcodes that are resistant to distortions of one or two elements within each pattern is considered. Objective. The goal of the work is ensuring the reliability of the reading of multi-color barcode images. Method. A multicolor barcode pattern has the property of interference immunity if its digital equivalent (vector) is a codeword of a multi-valued (non-binary) correcting code capable to correct errors (distortions of the pattern elements). It is shown that the construction of barcode patterns should be performed on the basis of a multi-valued correcting BCH code capable to correct two errors. A method is proposed for constructing a set of interference-resistant barcode patterns of a given capacity, which ensure reliable reproduction of data when they are read from a carrier. A procedure for encoding data with a multi-valued BCH code based on the generator matrix of the code using operations by the modulo of a prime number has been developed. A new method of constructing the check matrix of the multivalued BCH code based on the vector representation of the elements of the finite field is proposed. A generalized algorithm for generating symbologies of a multi-color barcode with the possibility of correcting double errors in barcode patterns has been developed. The method also makes it possible to build symbology of a given capacity based on shortened BCH codes. A method of reducing the generator and check matrices of a multi-valued full BCH code to obtain a shortened code of a given length is proposed. It is shown that, in addition to correction double errors, multi-valued BCH codes also make it possible to detect errors of higher multiplicity – this property is enhanced when using shortened BCH codes. The method provides for the construction of a family of multicolor noise-immune barcodes. Results. On the basis of the developed software tools, statistical data were obtained that characterize the ability of multi-valued BCH codes to detect and correct errors, and on their basis to design multi-color interference-resistant bar codes. Conclusions. The conducted experiments have confirmed the operability of the proposed algorithmic tools and allow to recommend it for use in practice for developing interference-resistant multi-color barcodes in automatic identification systems.
{"title":"SYNTHESIS OF THE SYMBOLOGIES OF MULTICOLOR INTERFERENCE-RESISTANT BAR CODES ON THE BASE OF MULTI-VALUED BCH CODES","authors":"Ye. S. Sulema, L. V. Drozdenko, A. Dychka","doi":"10.15588/1607-3274-2022-4-9","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-9","url":null,"abstract":"Context. The problem of constructing a set of barcode patterns for multicolor barcodes that are resistant to distortions of one or two elements within each pattern is considered. \u0000Objective. The goal of the work is ensuring the reliability of the reading of multi-color barcode images. \u0000Method. A multicolor barcode pattern has the property of interference immunity if its digital equivalent (vector) is a codeword of a multi-valued (non-binary) correcting code capable to correct errors (distortions of the pattern elements). It is shown that the construction of barcode patterns should be performed on the basis of a multi-valued correcting BCH code capable to correct two errors. A method is proposed for constructing a set of interference-resistant barcode patterns of a given capacity, which ensure reliable reproduction of data when they are read from a carrier. A procedure for encoding data with a multi-valued BCH code based on the generator matrix of the code using operations by the modulo of a prime number has been developed. A new method of constructing the check matrix of the multivalued BCH code based on the vector representation of the elements of the finite field is proposed. A generalized algorithm for generating symbologies of a multi-color barcode with the possibility of correcting double errors in barcode patterns has been developed. The method also makes it possible to build symbology of a given capacity based on shortened BCH codes. A method of reducing the generator and check matrices of a multi-valued full BCH code to obtain a shortened code of a given length is proposed. It is shown that, in addition to correction double errors, multi-valued BCH codes also make it possible to detect errors of higher multiplicity – this property is enhanced when using shortened BCH codes. The method provides for the construction of a family of multicolor noise-immune barcodes. \u0000Results. On the basis of the developed software tools, statistical data were obtained that characterize the ability of multi-valued BCH codes to detect and correct errors, and on their basis to design multi-color interference-resistant bar codes. \u0000Conclusions. The conducted experiments have confirmed the operability of the proposed algorithmic tools and allow to recommend it for use in practice for developing interference-resistant multi-color barcodes in automatic identification systems.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"3 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80719738","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 : 2022-12-15DOI: 10.15588/1607-3274-2022-4-7
S. Danshyna, A. Nechausov, S. Andrieiev
Context. In the light of current road network monitoring practices, this study aims to explore the capability of remote sensing technologies to solve the problems of increasing the objectivity of preliminary evaluations of the condition of the infrastructure as a whole. The object of the study was to process the monitoring of transport infrastructure (TI) to find ways to improve it in the implementation of development projects. Objective. The goal of the work is to increase objectivity of decision-making on the evaluation, reconstruction, development of the transport network structure due to the visual presentation and disclosure of open data for monitoring the transport value. Method. Existing approaches to TI monitoring and evaluating its condition are analyzed. The identified shortcomings, as well as the development of remote sensing technologies, open up prospects for the use of remote sensing data in the TI monitoring process. A set-theoretic model of the monitoring process information flows is proposed, the consistent refinement of the elements of which made it possible to develop information technology (IT). Formation of a set of input and output parameters of IT, the set of its operations, their representation with IDEFX-models set explains how a set of heterogeneous (graphic, text, digital, cartographic, etc.) data about TI elements coming from different sources are processed and presented to support decision-making on the survey of existing infrastructure and its improvement. The developed IT makes it possible to obtain complex indicators for analyzing the TI of a particular area, to solve the problems of inventorying objects, TI and its elements modeling, taking into account the physical and geographical location, which makes it possible to consider it as an auxiliary tool that complements existing methods of TI monitoring. Results. The developed IT was studied in solving the problem of monitoring the TI section of the Kharkiv region using satellite imagery of medium (Sentinel–2) and high (SuperView-1) resolution and the results of laser survey of the road bridge across the river Mzha (as an element of infrastructure). Conclusions. The conducted experiments confirmed the operability of the proposed information technology and showed expediency of its practical use in solving the problems of obtaining generalizing characteristics of the infrastructure, inventory of TI objects and their modeling. This opens up opportunities for substantiating project decisions for the reconstruction of the transport network and planning procedures for examining its condition. Prospects for further research may include: creating reference models of TI objects, expanding the table of decryption signs of road transport infrastructure objects, integrating remote data, survey results of TI sections and engineering surveys of objects to obtain evaluations of the condition of TI in general.
{"title":"INFORMATION TECHNOLOGY OF TRANSPORT INFRASTRUCTURE MONITORING BASED ON REMOTE SENSING DATA","authors":"S. Danshyna, A. Nechausov, S. Andrieiev","doi":"10.15588/1607-3274-2022-4-7","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-7","url":null,"abstract":"Context. In the light of current road network monitoring practices, this study aims to explore the capability of remote sensing technologies to solve the problems of increasing the objectivity of preliminary evaluations of the condition of the infrastructure as a whole. The object of the study was to process the monitoring of transport infrastructure (TI) to find ways to improve it in the implementation of development projects. \u0000Objective. The goal of the work is to increase objectivity of decision-making on the evaluation, reconstruction, development of the transport network structure due to the visual presentation and disclosure of open data for monitoring the transport value. \u0000Method. Existing approaches to TI monitoring and evaluating its condition are analyzed. The identified shortcomings, as well as the development of remote sensing technologies, open up prospects for the use of remote sensing data in the TI monitoring process. A set-theoretic model of the monitoring process information flows is proposed, the consistent refinement of the elements of which made it possible to develop information technology (IT). Formation of a set of input and output parameters of IT, the set of its operations, their representation with IDEFX-models set explains how a set of heterogeneous (graphic, text, digital, cartographic, etc.) data about TI elements coming from different sources are processed and presented to support decision-making on the survey of existing infrastructure and its improvement. The developed IT makes it possible to obtain complex indicators for analyzing the TI of a particular area, to solve the problems of inventorying objects, TI and its elements modeling, taking into account the physical and geographical location, which makes it possible to consider it as an auxiliary tool that complements existing methods of TI monitoring. \u0000Results. The developed IT was studied in solving the problem of monitoring the TI section of the Kharkiv region using satellite imagery of medium (Sentinel–2) and high (SuperView-1) resolution and the results of laser survey of the road bridge across the river Mzha (as an element of infrastructure). \u0000Conclusions. The conducted experiments confirmed the operability of the proposed information technology and showed expediency of its practical use in solving the problems of obtaining generalizing characteristics of the infrastructure, inventory of TI objects and their modeling. This opens up opportunities for substantiating project decisions for the reconstruction of the transport network and planning procedures for examining its condition. Prospects for further research may include: creating reference models of TI objects, expanding the table of decryption signs of road transport infrastructure objects, integrating remote data, survey results of TI sections and engineering surveys of objects to obtain evaluations of the condition of TI in general.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"76 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86307667","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 : 2022-12-13DOI: 10.15588/1607-3274-2022-4-11
N. Kholodna, V. Vysotska
Context. Paraphrased textual content or rewriting is one of the difficult problems of detecting academic plagiarism. Most plagiarism detection systems are designed to detect common words, sequences of linguistic units, and minor changes, but are unable to detect significant semantic and structural changes. Therefore, most cases of plagiarism using paraphrasing remain unnoticed. Objective of the study is to develop a technology for detecting paraphrasing in text based on a classification model and machine learning methods through the use of Siamese neural network based on recurrent and Transformer type – RoBERTa to analyze the level of similarity of sentences of text content. Method. For this study, the following semantic similarity metrics or indicators were chosen as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover’s Distance, distances according to WordNet dictionaries, prediction of two ML models: Siamese neural network based on recurrent and Transformer type - RoBERTa. Results. An intelligent system for detecting paraphrasing in text based on a classification model and machine learning methods has been developed. The developed system uses the principle of model stacking and feature engineering. Additional features indicate the semantic affiliation of the sentences or the normalized number of common N-grams. An additional fine-tuned RoBERTa neural network (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This specificity of the model may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both the overall classification accuracy and the model’s sensitivity to pairs of sentences that are not paraphrases of each other. Conclusions. The created model shows excellent classification results on PAWS test data: precision – 93%, recall – 92%, F1score – 92%, accuracy – 92%. The results of the study showed that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without the need for additional feature generation.
{"title":"REWRITING IDENTIFICATION TECHNOLOGY FOR TEXT CONTENT BASED ON MACHINE LEARNING METHODS","authors":"N. Kholodna, V. Vysotska","doi":"10.15588/1607-3274-2022-4-11","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-11","url":null,"abstract":"Context. Paraphrased textual content or rewriting is one of the difficult problems of detecting academic plagiarism. Most plagiarism detection systems are designed to detect common words, sequences of linguistic units, and minor changes, but are unable to detect significant semantic and structural changes. Therefore, most cases of plagiarism using paraphrasing remain unnoticed. \u0000Objective of the study is to develop a technology for detecting paraphrasing in text based on a classification model and machine learning methods through the use of Siamese neural network based on recurrent and Transformer type – RoBERTa to analyze the level of similarity of sentences of text content. \u0000Method. For this study, the following semantic similarity metrics or indicators were chosen as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover’s Distance, distances according to WordNet dictionaries, prediction of two ML models: Siamese neural network based on recurrent and Transformer type - RoBERTa. \u0000Results. An intelligent system for detecting paraphrasing in text based on a classification model and machine learning methods has been developed. The developed system uses the principle of model stacking and feature engineering. Additional features indicate the semantic affiliation of the sentences or the normalized number of common N-grams. An additional fine-tuned RoBERTa neural network (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This specificity of the model may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both the overall classification accuracy and the model’s sensitivity to pairs of sentences that are not paraphrases of each other. \u0000Conclusions. The created model shows excellent classification results on PAWS test data: precision – 93%, recall – 92%, F1score – 92%, accuracy – 92%. The results of the study showed that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without the need for additional feature generation.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"76 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90996717","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 : 2022-12-13DOI: 10.15588/1607-3274-2022-4-12
A. D. Kozhukhivskyi, O. Kozhukhivska
Context. Because assessing security risks is a complex and complete uncertainty process, and uncertainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to noncomputed data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of security professionals. Therefore, it will be advisable to use models that can ade-quately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Models/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized. Objective. Development of a fuzzy model of quality of security risk assessment and protection of ERP systems through the use of fuzzy neural models. Method. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized. Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters was substantiated and a fuzzy product model of risk assessment and a database of rules of fuzzy logical conclusion using the MATLAB application package and the Fuzzy Logic Toolbox extension package was implemented, as well as improved by introducing the adaptability of the model to experimental data by introducing neuro-fuzzy components into the model. The use of fuzzy models to solve the problems of security risk assessment, as well as the concept and construction of ERP systems and the analyzed problems of their security and vulnerabilities are considered. Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of
{"title":"RISK ASSESSMENT MODELING OF ERP-SYSTEMS","authors":"A. D. Kozhukhivskyi, O. Kozhukhivska","doi":"10.15588/1607-3274-2022-4-12","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-12","url":null,"abstract":"Context. Because assessing security risks is a complex and complete uncertainty process, and uncertainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to noncomputed data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of security professionals. Therefore, it will be advisable to use models that can ade-quately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Models/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized. \u0000Objective. Development of a fuzzy model of quality of security risk assessment and protection of ERP systems through the use of fuzzy neural models. \u0000Method. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized. \u0000Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters was substantiated and a fuzzy product model of risk assessment and a database of rules of fuzzy logical conclusion using the MATLAB application package and the Fuzzy Logic Toolbox extension package was implemented, as well as improved by introducing the adaptability of the model to experimental data by introducing neuro-fuzzy components into the model. The use of fuzzy models to solve the problems of security risk assessment, as well as the concept and construction of ERP systems and the analyzed problems of their security and vulnerabilities are considered. \u0000Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of ","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"98 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76195514","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 : 2022-12-11DOI: 10.15588/1607-3274-2022-4-10
Y. Turbal, S. Babych, N. Kunanets
Context. The problem of generating vectors consisting of different representatives of a given set of sets is considered. Such problems arise, in particular, in scheduling theory, when scheduling appointments. A special case of this problem is the problem of generating permutations. Objective. Problem is considered from the point of view of a permanent approach and a well-known one, based on the concept of lexicographic order. Method. In many tasks, it becomes necessary to generate various combinatorial objects: permutations, combinations with and without repetitions, various subsets. In this paper we consider a new approach to the combinatorial objects generation, which is based on the procedure of the permanent decomposition. Permanent is built for the special matrix of incidence. The main idea of this approach is including to the process of the algebraic permanent decomposition by row additional function for the column identifiers writing into corresponding data structures. In this case, the algebraic permanent in not calculated, but we get a specific recursive algorithm for generating a combinatorial object. The computational complexity of this algorithm is analyzed. Results. It is investigated a new approach to the generation of complex combinatorial objects, based on the procedure of decomposition of the modified permanent of the incidence matrix by line with memorization of index elements. Conclusions. The permanent algorithms of the combinatorial objects generation is investigated. The complexity of our approach in the case of permutation is compared with the lexicographic algorithm and the Johnson-Trotter algorithm. The obtained results showed that our algorithm belongs to the same complexity class as the lexicographic algorithm and the Johnson-Trotter method. Numerical results confirmed the effectiveness of our approach.
{"title":"PERMANENT DECOMPOSITION ALGORITHM FOR THE COMBINATORIAL OBJECTS GENERATION","authors":"Y. Turbal, S. Babych, N. Kunanets","doi":"10.15588/1607-3274-2022-4-10","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-10","url":null,"abstract":"Context. The problem of generating vectors consisting of different representatives of a given set of sets is considered. Such problems arise, in particular, in scheduling theory, when scheduling appointments. A special case of this problem is the problem of generating permutations. \u0000Objective. Problem is considered from the point of view of a permanent approach and a well-known one, based on the concept of lexicographic order. \u0000Method. In many tasks, it becomes necessary to generate various combinatorial objects: permutations, combinations with and without repetitions, various subsets. In this paper we consider a new approach to the combinatorial objects generation, which is based on the procedure of the permanent decomposition. Permanent is built for the special matrix of incidence. The main idea of this approach is including to the process of the algebraic permanent decomposition by row additional function for the column identifiers writing into corresponding data structures. In this case, the algebraic permanent in not calculated, but we get a specific recursive algorithm for generating a combinatorial object. The computational complexity of this algorithm is analyzed. \u0000Results. It is investigated a new approach to the generation of complex combinatorial objects, based on the procedure of decomposition of the modified permanent of the incidence matrix by line with memorization of index elements. \u0000Conclusions. The permanent algorithms of the combinatorial objects generation is investigated. The complexity of our approach in the case of permutation is compared with the lexicographic algorithm and the Johnson-Trotter algorithm. \u0000The obtained results showed that our algorithm belongs to the same complexity class as the lexicographic algorithm and the Johnson-Trotter method. Numerical results confirmed the effectiveness of our approach.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"38 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76005217","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 : 2022-12-10DOI: 10.15588/1607-3274-2022-4-8
O. Kungurtsev, A. I. Vytnova
Context. The implementation of different use-cases may be performed by different development teams at different times. This results in a poorly structured code. The problem is exacerbated when developing medium and large projects in a short time. Objective. Since inheritance is one of the effective ways to structure and improve the quality of code, the aim of the study is to determine possible inheritance relationships for a variety of class models. Method. It is proposed to select from the entire set of classes representing the class model at a certain design stage, subsets for which a common parent class (in a particular case, an abstract class) is possible. To solve the problem, signs of the generality of classes have been formulated. The mathematical model of the conceptual class has been improved by including information about the responsibilities of the class, its methods and attributes. The connection of each class with the script items for which it is used has been established. A system of data types for class model elements is proposed. Description of class method signatures has been extended. A method for restructuring the class model, which involves 3 stages, has been developed. At the first stage, the proximity coefficients of classes are determined. At the second, subsets of possible child classes are created. At the third stage, an automated transformation of the class structure is performed, considering the identified inheritance relationships. Results. A software product for conducting experiments to identify possible inheritance relationships depending on the number of classes and the degree of their similarity has been developed. The results of the conducted tests showed the effectiveness of the decisions made. Conclusions. The method uses an algorithm for forming subsets of classes that can have one parent and an algorithm for automatically creating and converting classes to build a two-level class hierarchy. An experiment showed a threefold reduction in errors in detecting inheritance and a multiple reduction in time in comparison with the existing technology.
{"title":"DETERMINATION OF INHERITANCE RELATIONS AND RESTRUCTURING OF SOFTWARE CLASS MODELS IN THE PROCESS OF DEVELOPING INFORMATION SYSTEMS","authors":"O. Kungurtsev, A. I. Vytnova","doi":"10.15588/1607-3274-2022-4-8","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-8","url":null,"abstract":"Context. The implementation of different use-cases may be performed by different development teams at different times. This results in a poorly structured code. The problem is exacerbated when developing medium and large projects in a short time. \u0000Objective. Since inheritance is one of the effective ways to structure and improve the quality of code, the aim of the study is to determine possible inheritance relationships for a variety of class models. \u0000Method. It is proposed to select from the entire set of classes representing the class model at a certain design stage, subsets for which a common parent class (in a particular case, an abstract class) is possible. To solve the problem, signs of the generality of classes have been formulated. The mathematical model of the conceptual class has been improved by including information about the responsibilities of the class, its methods and attributes. The connection of each class with the script items for which it is used has been established. A system of data types for class model elements is proposed. Description of class method signatures has been extended. A method for restructuring the class model, which involves 3 stages, has been developed. At the first stage, the proximity coefficients of classes are determined. At the second, subsets of possible child classes are created. At the third stage, an automated transformation of the class structure is performed, considering the identified inheritance relationships. \u0000Results. A software product for conducting experiments to identify possible inheritance relationships depending on the number of classes and the degree of their similarity has been developed. The results of the conducted tests showed the effectiveness of the decisions made. \u0000Conclusions. The method uses an algorithm for forming subsets of classes that can have one parent and an algorithm for automatically creating and converting classes to build a two-level class hierarchy. An experiment showed a threefold reduction in errors in detecting inheritance and a multiple reduction in time in comparison with the existing technology.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"9 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78464232","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 : 2022-12-09DOI: 10.15588/1607-3274-2022-4-6
S. Subbotin
Context. Cluster analysis is widely used to analyze data of various nature and dimensions. However, the known methods of cluster analysis are characterized by low speed and are demanding on computer memory resources due to the need to calculate pairwise distances between instances in a multidimensional feature space. In addition, the results of known methods of cluster analysis are difficult for human perception and analysis with a large number of features. Objective. The purpose of the work is to increase the speed of cluster analysis, the interpretability of the resulting partition into clusters, as well as to reduce the requirements of cluster analysis to computer memory. Method. A method for cluster analysis of multidimensional data is proposed, which for each instance calculates its hash based on the distance to the conditional center of coordinates, uses a one-dimensional coordinate along the hash axis to determine the distances between instances, considers the resulting hash as a pseudo-output feature, breaking it into intervals, which matches the labels pseudo-classes – clusters, having received a rough crisp partition of the feature space and sample instances, automatically generates a partition of input features into fuzzy terms, determines the rules for referring instances to clusters and, as a result, forms a fuzzy inference system of the Mamdani-Zadeh classifier type, which is further trained in the form of a neuro-fuzzy network to ensure acceptable values of the clustering quality functional. This makes it possible to reduce the number of terms and features used, to evaluate their contribution to making decisions about assigning instances to clusters, to increase the speed of data cluster analysis, and to increase the interpretability of the resulting data splitting into clusters. Results. The mathematical support for solving the problem of cluster data analysis in conditions of large data dimensions has been developed. The experiments confirmed the operability of the developed mathematical support have been carried out. Conclusions. . The developed method and its software implementation can be recommended for use in practice in the problems of analyzing data of various nature and dimensions.
{"title":"DATA CLUSTERING BASED ON INDUCTIVE LEARNING OF NEURO-FUZZY NETWORK WITH DISTANCE HASHING","authors":"S. Subbotin","doi":"10.15588/1607-3274-2022-4-6","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-6","url":null,"abstract":"Context. Cluster analysis is widely used to analyze data of various nature and dimensions. However, the known methods of cluster analysis are characterized by low speed and are demanding on computer memory resources due to the need to calculate pairwise distances between instances in a multidimensional feature space. In addition, the results of known methods of cluster analysis are difficult for human perception and analysis with a large number of features. \u0000Objective. The purpose of the work is to increase the speed of cluster analysis, the interpretability of the resulting partition into clusters, as well as to reduce the requirements of cluster analysis to computer memory. \u0000Method. A method for cluster analysis of multidimensional data is proposed, which for each instance calculates its hash based on the distance to the conditional center of coordinates, uses a one-dimensional coordinate along the hash axis to determine the distances between instances, considers the resulting hash as a pseudo-output feature, breaking it into intervals, which matches the labels pseudo-classes – clusters, having received a rough crisp partition of the feature space and sample instances, automatically generates a partition of input features into fuzzy terms, determines the rules for referring instances to clusters and, as a result, forms a fuzzy inference system of the Mamdani-Zadeh classifier type, which is further trained in the form of a neuro-fuzzy network to ensure acceptable values of the clustering quality functional. This makes it possible to reduce the number of terms and features used, to evaluate their contribution to making decisions about assigning instances to clusters, to increase the speed of data cluster analysis, and to increase the interpretability of the resulting data splitting into clusters. \u0000Results. The mathematical support for solving the problem of cluster data analysis in conditions of large data dimensions has been developed. The experiments confirmed the operability of the developed mathematical support have been carried out. \u0000Conclusions. . The developed method and its software implementation can be recommended for use in practice in the problems of analyzing data of various nature and dimensions.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"167 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83248160","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 : 2022-12-05DOI: 10.15588/1607-3274-2022-4-5
Y. Bodyanskiy, I. Pliss, A. Shafronenko
Context. The task of clustering arrays of observations of an arbitrary nature is an integral part of Data Mining, and in the more general case of Data Science, a huge number of approaches have been proposed for its solution, which differ from each other both in a priori assumptions regarding the physical nature of the data and the problem, and in the mathematical apparatus. From a computational point of view, the clustering problem turns into a problem of finding local extrema of a multiextremal function of the vector density argument using gradient procedures that are repeatedly launched from different points of the initial data array. It is possible to speed up the process of searching for these extremes by using the ideas of evolutionary optimization, which includes algorithms inspired by nature, swarm algorithms, population algorithms, etc. Objective. The purpose of the work is to introduce a data clustering procedure based on the peaks of the data distribution density and the evolutionary method of cat swarms, that combines the main advantages of methods for working with data in conditions of overlapping classes, is characterized by high-quality clustering, high speed and accuracy of the obtained results. Method. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarms was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap. Results. The results of the experiments confirm the effectiveness of the proposed approach in clustering problems under the condition of classes that overlap and allow us to recommend the proposed method for use in practice to solve problems of automatic clustering big data. Conclusions. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarm was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap. The method is quite simple from the numerical implementation and is not critical for choosing an optimization procedure. The experimental results confirm the effectiveness of the proposed approach in clustering problems under conditions of overlapping clusters.
{"title":"CLUSTERIZATION OF DATA ARRAYS BASED ON COMBINED OPTIMIZATION OF DISTRIBUTION DENSITY FUNCTIONS AND THE EVOLUTIONARY METHOD OF CAT SWARM","authors":"Y. Bodyanskiy, I. Pliss, A. Shafronenko","doi":"10.15588/1607-3274-2022-4-5","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-5","url":null,"abstract":"Context. The task of clustering arrays of observations of an arbitrary nature is an integral part of Data Mining, and in the more general case of Data Science, a huge number of approaches have been proposed for its solution, which differ from each other both in a priori assumptions regarding the physical nature of the data and the problem, and in the mathematical apparatus. From a computational point of view, the clustering problem turns into a problem of finding local extrema of a multiextremal function of the vector density argument using gradient procedures that are repeatedly launched from different points of the initial data array. It is possible to speed up the process of searching for these extremes by using the ideas of evolutionary optimization, which includes algorithms inspired by nature, swarm algorithms, population algorithms, etc. \u0000Objective. The purpose of the work is to introduce a data clustering procedure based on the peaks of the data distribution density and the evolutionary method of cat swarms, that combines the main advantages of methods for working with data in conditions of overlapping classes, is characterized by high-quality clustering, high speed and accuracy of the obtained results. \u0000Method. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarms was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap. \u0000Results. The results of the experiments confirm the effectiveness of the proposed approach in clustering problems under the condition of classes that overlap and allow us to recommend the proposed method for use in practice to solve problems of automatic clustering big data. \u0000Conclusions. The method for clustering data arrays based on the combined optimization of distribution density functions and the evolutionary method of cat swarm was proposed. The advantage of the proposed approach is to reduce the time for solving optimization problems in conditions where clusters are overlap. The method is quite simple from the numerical implementation and is not critical for choosing an optimization procedure. The experimental results confirm the effectiveness of the proposed approach in clustering problems under conditions of overlapping clusters.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"12 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85160949","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 : 2022-12-04DOI: 10.15588/1607-3274-2022-4-3
I. Pershyna
Context. The problem of development and research of methods for approximation of discontinuous functions by discontinuous interlination splines and its further application to problems of computed tomography. The object of the study was the modeling of objects with a discontinuous internal structure. Objective. The aim of this study is to develop a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiated splines. Method. Modern methods of restoring functions are characterized by new approaches to obtaining, processing and analyzing information. There is a need to build mathematical models in which information can be represented not only by function values at points, but also in the form of a set of function traces on planes or straight lines. At the same time, practice shows that among the multidimensional objects that need to be investigated, more problems are described by a discontinuous functions. The paper develops a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiable splines. It is considered that the domain of the definition of the required twodimensional function is divided into rectangular elements. Theorems on interlination and approximation properties of such discontinuous constructions are formulated and proved. The method is developed for approximating discontinuous functions of two variables based on the constructed discontinuous splines. The input data are the traces of an unknown function along a given system of mutually perpendicular straight lines. The proposed method has not only theoretical significance but also practical application in the IT domain, especially in computing tomography, allowing more accurately restore the internal structure of the body. Results. The discontinuous interlination operator from known traces of the function of two variables on a system of mutually perpendicular straight lines is researched. Conclusions. The functions of two variables that are discontinuous at some points or on some lines are better approximated by discontinuous spline interlinants. At the same time, equally high approximation estimates can be obtained. The results obtained have significant advantages over existing methods of interpolation and approximation of discontinuous functions. In further research, the authors plan to develop a theory of discontinuous splines on areas of complex shape bounded by arcs of known curves.
{"title":"RESTORATION OF DISCONTINUOUS FUNCTIONS BY DISCONTINUOUS INTERLINATION SPLINES","authors":"I. Pershyna","doi":"10.15588/1607-3274-2022-4-3","DOIUrl":"https://doi.org/10.15588/1607-3274-2022-4-3","url":null,"abstract":"Context. The problem of development and research of methods for approximation of discontinuous functions by discontinuous interlination splines and its further application to problems of computed tomography. The object of the study was the modeling of objects with a discontinuous internal structure. \u0000Objective. The aim of this study is to develop a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiated splines. \u0000Method. Modern methods of restoring functions are characterized by new approaches to obtaining, processing and analyzing information. There is a need to build mathematical models in which information can be represented not only by function values at points, but also in the form of a set of function traces on planes or straight lines. \u0000At the same time, practice shows that among the multidimensional objects that need to be investigated, more problems are described by a discontinuous functions. \u0000The paper develops a general method for constructing discontinuous interlining polynomial splines, which, as a special case, include discontinuous and continuously differentiable splines. It is considered that the domain of the definition of the required twodimensional function is divided into rectangular elements. Theorems on interlination and approximation properties of such discontinuous constructions are formulated and proved. The method is developed for approximating discontinuous functions of two variables based on the constructed discontinuous splines. The input data are the traces of an unknown function along a given system of mutually perpendicular straight lines. The proposed method has not only theoretical significance but also practical application in the IT domain, especially in computing tomography, allowing more accurately restore the internal structure of the body. \u0000Results. The discontinuous interlination operator from known traces of the function of two variables on a system of mutually perpendicular straight lines is researched. \u0000Conclusions. The functions of two variables that are discontinuous at some points or on some lines are better approximated by discontinuous spline interlinants. At the same time, equally high approximation estimates can be obtained. The results obtained have significant advantages over existing methods of interpolation and approximation of discontinuous functions. In further research, the authors plan to develop a theory of discontinuous splines on areas of complex shape bounded by arcs of known curves.","PeriodicalId":43783,"journal":{"name":"Radio Electronics Computer Science Control","volume":"61 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83693485","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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