Pub Date : 2023-11-13DOI: 10.34185/1562-9945-6-143-2022-06
Rvach Dmytro, Yevgeniya Sulema
The synchronization of multimodal data is one of the essential tasks related to mulse-media data processing. The concept of mulsemedia (MULtiple SEnsorial MEDIA) involves the registration, storage, processing, transmission and reproduction by computer-based tools of multimodal information about a physical object that humans can perceive through their senses. Such information includes audiovisual information (object's appearance, acoustic properties, etc.), tactile information (surface texture, temperature), kinesthetic information (weight, object's centre of gravity), information about its taste, smell, etc. The perception of mulsemedia information by a person is the process that exists over time. Because of this, the registration of mulsemedia data should be carried out with the fixation of the moments of time when the relevant mulsemedia information existed or its perception made sense for a human who supervises the object as mulsemedia data is temporal. This paper presents a method that enables the consolidation and synchronization of mulsemedia data using the principles of multithreading. The universal method was designed to support combining data of different modalities in parallel threads. The application of the proposed method solves problems associated with integrating data of different modalities and formats in the same time interval. The effectiveness of applying this method increases by us-ing multithreaded distributed computing. This method is designed for use in the development of mulsemedia software systems. The modified JSON format (TJSON – Timeline JSON) was proposed in the paper, as well. TJSON-object is a complex data structure for representing the synchronized mulsemedia data and their further processing. The proposed method can be further extended with other approaches and technologies. For example, artificial intelligence methods can be applied to assess the correlation between data from different modalities. This can help improve the method's accuracy and the output files' quality.
{"title":"Mulsemedia data consolidation method","authors":"Rvach Dmytro, Yevgeniya Sulema","doi":"10.34185/1562-9945-6-143-2022-06","DOIUrl":"https://doi.org/10.34185/1562-9945-6-143-2022-06","url":null,"abstract":"The synchronization of multimodal data is one of the essential tasks related to mulse-media data processing. The concept of mulsemedia (MULtiple SEnsorial MEDIA) involves the registration, storage, processing, transmission and reproduction by computer-based tools of multimodal information about a physical object that humans can perceive through their senses. Such information includes audiovisual information (object's appearance, acoustic properties, etc.), tactile information (surface texture, temperature), kinesthetic information (weight, object's centre of gravity), information about its taste, smell, etc. The perception of mulsemedia information by a person is the process that exists over time. Because of this, the registration of mulsemedia data should be carried out with the fixation of the moments of time when the relevant mulsemedia information existed or its perception made sense for a human who supervises the object as mulsemedia data is temporal. This paper presents a method that enables the consolidation and synchronization of mulsemedia data using the principles of multithreading. The universal method was designed to support combining data of different modalities in parallel threads. The application of the proposed method solves problems associated with integrating data of different modalities and formats in the same time interval. The effectiveness of applying this method increases by us-ing multithreaded distributed computing. This method is designed for use in the development of mulsemedia software systems. The modified JSON format (TJSON – Timeline JSON) was proposed in the paper, as well. TJSON-object is a complex data structure for representing the synchronized mulsemedia data and their further processing. The proposed method can be further extended with other approaches and technologies. For example, artificial intelligence methods can be applied to assess the correlation between data from different modalities. This can help improve the method's accuracy and the output files' quality.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"130 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.34185/1562-9945-4-147-2023-05
Labutkina Tetyana, Ananko Ruslan
The results of the study are presented within the framework of the task of ensuring full coverage of a given area of heights above the Earth's surface (the area of space between two spheres with a common center at the center of the Earth) by instantaneous zones of possible application of orbital-based surveillance devices located on spacecraft in orbital groups of different heights in circular orbits. In the general case, the solution of the problem involves the use of several orbital groupings of different heights on circular quasi-polar orbits, which in the simplified statement of the problem are assumed to be polar. The instantaneous zone of possible application of the surveillance device is simplified in the form of a cone. The cases of using observation devices "up" (above the plane of the instantaneous local horizon of the spacecraft, which is the carrier of the observation device) and observations "down" (below this plane) are considered. The concept of solving the problem is proposed, which is based on the selection (based on the development of methods of applying known algorithms) of such a structure of each orbital grouping, which will ensure continuous coverage of a part of the given observation space (area of guaranteed observation), the boundaries of which are moved away from the location of observation devices, and then - filling the space with these areas. The work is devoted to the space theme, but by generalizing the statement of the prob-lem, varying a number of conditions of this statement and changing the "scale" of the input data, it is possible to arrive at a variety of technical problems where the proposed concept and algorithms used in its implementation will be appropriate and acceptable (in part or in full). In particular, when some surveillance systems or systems of complex application of technical operations devices are created.
{"title":"Global near-earth space coverage by zones of the use of its observation devices: concept and algorithms","authors":"Labutkina Tetyana, Ananko Ruslan","doi":"10.34185/1562-9945-4-147-2023-05","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-05","url":null,"abstract":"The results of the study are presented within the framework of the task of ensuring full coverage of a given area of heights above the Earth's surface (the area of space between two spheres with a common center at the center of the Earth) by instantaneous zones of possible application of orbital-based surveillance devices located on spacecraft in orbital groups of different heights in circular orbits. In the general case, the solution of the problem involves the use of several orbital groupings of different heights on circular quasi-polar orbits, which in the simplified statement of the problem are assumed to be polar. The instantaneous zone of possible application of the surveillance device is simplified in the form of a cone. The cases of using observation devices \"up\" (above the plane of the instantaneous local horizon of the spacecraft, which is the carrier of the observation device) and observations \"down\" (below this plane) are considered. The concept of solving the problem is proposed, which is based on the selection (based on the development of methods of applying known algorithms) of such a structure of each orbital grouping, which will ensure continuous coverage of a part of the given observation space (area of guaranteed observation), the boundaries of which are moved away from the location of observation devices, and then - filling the space with these areas. The work is devoted to the space theme, but by generalizing the statement of the prob-lem, varying a number of conditions of this statement and changing the \"scale\" of the input data, it is possible to arrive at a variety of technical problems where the proposed concept and algorithms used in its implementation will be appropriate and acceptable (in part or in full). In particular, when some surveillance systems or systems of complex application of technical operations devices are created.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"128 29","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351576","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}
The article is devoted to the development of combined models, methods and tools designed to solve the current problems of modeling and analysis of monitoring process data, which are repre-sented by time series and differ in variable or fuzzy observation intervals (CHRPNI). In the article, a new relational separable model (RSM) and a combined quantile algorithm are proposed to in-crease the accuracy and efficiency of modeling and analysis of the processes of CHRPNI. The rela-tional model is defined by a system of fuzzy relational relations of the first and second order ob-tained on the basis of the original sequence of data. In the combined algorithm, the results of calcu-lations obtained by SPM and models of fuzzy relational relationships were generalized with the op-timal selection of weighting factors for individual components. As a result of the conducted research by means of numerical modeling, it was established that the introduction of combined process models in the case of PNEU is rational and effective. Exam-ples of data analysis of monitoring processes of rehabilitation of diabetic patients showed certain possibilities of ensuring the accuracy of the results of the analysis of indicators and their short-term forecasting.
{"title":"Relational-separable models of monitoring processes at variable and unclear observation intervals","authors":"Skalozub Vladyslav, Horiachkin Vadim, Murashov Oleg","doi":"10.34185/1562-9945-4-147-2023-01","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-01","url":null,"abstract":"The article is devoted to the development of combined models, methods and tools designed to solve the current problems of modeling and analysis of monitoring process data, which are repre-sented by time series and differ in variable or fuzzy observation intervals (CHRPNI). In the article, a new relational separable model (RSM) and a combined quantile algorithm are proposed to in-crease the accuracy and efficiency of modeling and analysis of the processes of CHRPNI. The rela-tional model is defined by a system of fuzzy relational relations of the first and second order ob-tained on the basis of the original sequence of data. In the combined algorithm, the results of calcu-lations obtained by SPM and models of fuzzy relational relationships were generalized with the op-timal selection of weighting factors for individual components. As a result of the conducted research by means of numerical modeling, it was established that the introduction of combined process models in the case of PNEU is rational and effective. Exam-ples of data analysis of monitoring processes of rehabilitation of diabetic patients showed certain possibilities of ensuring the accuracy of the results of the analysis of indicators and their short-term forecasting.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.34185/1562-9945-4-147-2023-09
Mohylnyi Oleksandr
The article presents a study devoted to the development and research of an automated model of visual information processing. The goal of the research was to create a comprehen-sive model capable of automatically processing and analyzing various forms of visual data, such as images and videos. The model is developed on the basis of a combined approach that combines various algorithms and methods of visual information processing. The literature review conducted within the scope of this study allowed us to study the existing methods and algorithms for visual information processing. Various image processing approaches were analyzed, including segmentation, pattern recognition, object classification and detection, video analysis, and other aspects. As a result of the review, the advantages and limitations of each approach were identified, as well as the areas of their application were determined. The developed model showed high accuracy and efficiency in visual data processing. It can suc-cessfully cope with the tasks of segmentation, recognition and classification of objects, as well as video analysis. The results of the study confirmed the superiority of the proposed model. Potential applications of the automated model are considered, such as medicine, robotics, security, and many others. However, limitations of the model such as computational resource requirements and quality of input data are also noted. Further development of this research can be aimed at optimizing the model, adapting it to specific tasks and expanding its func-tionality. In general, the study confirms the importance of automated models of visual infor-mation processing and its important place in modern technologies. The results of the research can be useful for the development of new systems based on visual data processing and con-tribute to progress in the field of computer vision and artificial intelligence.
{"title":"Automated models of visual information processing","authors":"Mohylnyi Oleksandr","doi":"10.34185/1562-9945-4-147-2023-09","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-09","url":null,"abstract":"The article presents a study devoted to the development and research of an automated model of visual information processing. The goal of the research was to create a comprehen-sive model capable of automatically processing and analyzing various forms of visual data, such as images and videos. The model is developed on the basis of a combined approach that combines various algorithms and methods of visual information processing. The literature review conducted within the scope of this study allowed us to study the existing methods and algorithms for visual information processing. Various image processing approaches were analyzed, including segmentation, pattern recognition, object classification and detection, video analysis, and other aspects. As a result of the review, the advantages and limitations of each approach were identified, as well as the areas of their application were determined. The developed model showed high accuracy and efficiency in visual data processing. It can suc-cessfully cope with the tasks of segmentation, recognition and classification of objects, as well as video analysis. The results of the study confirmed the superiority of the proposed model. Potential applications of the automated model are considered, such as medicine, robotics, security, and many others. However, limitations of the model such as computational resource requirements and quality of input data are also noted. Further development of this research can be aimed at optimizing the model, adapting it to specific tasks and expanding its func-tionality. In general, the study confirms the importance of automated models of visual infor-mation processing and its important place in modern technologies. The results of the research can be useful for the development of new systems based on visual data processing and con-tribute to progress in the field of computer vision and artificial intelligence.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"123 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136352150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.34185/1562-9945-6-143-2022-02
Gromova Viktoria, Borysenko Pavlo
Blockchain is a distributed and decentralized database for recording transactions. It is shared and maintained by network nodes, which ensures its operations using cryptography and consensus rules that allow all nodes to agree on a unique structure of the blockchain. However, modern blockchain solutions face network scalability issues due to different protocol design decisions. In this paper, we discuss sharding as a possible solution to overcome the technical limitations of existing blockchain systems and different forms of its practical realization presented in recent research spurred by blockchain popularity.
{"title":"USING SHARDING TO IMPROVE BLOCKCHAIN NETWORK SCALABILITY","authors":"Gromova Viktoria, Borysenko Pavlo","doi":"10.34185/1562-9945-6-143-2022-02","DOIUrl":"https://doi.org/10.34185/1562-9945-6-143-2022-02","url":null,"abstract":"Blockchain is a distributed and decentralized database for recording transactions. It is shared and maintained by network nodes, which ensures its operations using cryptography and consensus rules that allow all nodes to agree on a unique structure of the blockchain. However, modern blockchain solutions face network scalability issues due to different protocol design decisions. In this paper, we discuss sharding as a possible solution to overcome the technical limitations of existing blockchain systems and different forms of its practical realization presented in recent research spurred by blockchain popularity.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"123 44","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351333","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}
The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.
{"title":"Research of methods based on neural networks for the analysis of the tonality of the corps of the texts","authors":"Ostrovska Kateryna, Stovpchenko Ivan, Pechenyi Denys","doi":"10.34185/1562-9945-4-147-2023-14","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-14","url":null,"abstract":"The object of the study is methods based on neural networks for analyzing the tonality of a corpus of texts. To achieve the goal set in the work, it is necessary to solve the following tasks: study the theoretical material for learning deep neural networks and their features in relation to natural language processing; study the documentation of the Tensorflow library; develop models of convolutional and recurrent neural networks; to develop the implementation of linear and non-linear classification methods on bag of words and Word2Vec models; to compare the accuracy and other quality indicators of implemented neural network models with classical methods. Tensorboard is used for learning visualization. The work shows the superiority of classifiers based on deep neural networks over classical classification methods, even if the Word2Vec model is used for vector representations of words. The model of recurrent neural network with LSTM blocks has the highest accuracy for this corpus of texts.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"128 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.34185/1562-9945-4-147-2023-11
Hnatushenko Viktoriia, Sytnyk Roman
Recent research and publications. "Industry 4.0" is a concept of the industrial revolu-tion, which is based on the use of modern technologies and digital innovations in production and distribution processes. The introduction of the concept of "Industry 4.0" was designed to improve the competitiveness of European industry and increase productivity and product quality. A blockchain is a distributed data structure that is replicated and distributed among network members. The purpose of the study is to improve automation processes, increase efficiency, re-duce delays and errors in information systems of industry and supply chains by using block-chain technologies in the construction of information systems. Main material of the study. The paper makes an analysis of approaches and algorithms to data management in "Industry 4.0" information systems. Blockchain algorithms are com-pared to classical approach with other databases in the client-server architecture. Conclusions. By implementing algorithms based on blockchain technology, namely by using the Merkle Tree, digital signature technology, and by using consensus algorithms in the framework of decentralized data storage in Distributed Ledger Technology, the processes of automation and efficiency in data flow management are improved, providing a secure and transparent way to store and share data that reduces delays and errors in industry informa-tion systems and supply chains.
{"title":"Management of data flows in modern industry using blockchain","authors":"Hnatushenko Viktoriia, Sytnyk Roman","doi":"10.34185/1562-9945-4-147-2023-11","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-11","url":null,"abstract":"Recent research and publications. \"Industry 4.0\" is a concept of the industrial revolu-tion, which is based on the use of modern technologies and digital innovations in production and distribution processes. The introduction of the concept of \"Industry 4.0\" was designed to improve the competitiveness of European industry and increase productivity and product quality. A blockchain is a distributed data structure that is replicated and distributed among network members. The purpose of the study is to improve automation processes, increase efficiency, re-duce delays and errors in information systems of industry and supply chains by using block-chain technologies in the construction of information systems. Main material of the study. The paper makes an analysis of approaches and algorithms to data management in \"Industry 4.0\" information systems. Blockchain algorithms are com-pared to classical approach with other databases in the client-server architecture. Conclusions. By implementing algorithms based on blockchain technology, namely by using the Merkle Tree, digital signature technology, and by using consensus algorithms in the framework of decentralized data storage in Distributed Ledger Technology, the processes of automation and efficiency in data flow management are improved, providing a secure and transparent way to store and share data that reduces delays and errors in industry informa-tion systems and supply chains.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"123 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136352146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.34185/1562-9945-6-143-2022-08
Kirichenko Lyudmila, Zinchenko Petro
The current state of science and technology is characterized by a variety of methods and approaches to solving various tasks, including in the fields of time series analysis and computer vision. This abstract explores a novel approach to the classification of time series based on the analysis of brain activity using recurrent diagrams and deep neural networks. The work begins with an overview of recent achievements in the field of time series analysis and the application of machine learning methods. The importance of time series classification in various domains, including medicine, finance, technology, and others, is em-phasized. Next, the methodology is described, in which time series are transformed into gray-scale images using recurrent diagrams. The key idea is to use recurrent diagrams to visualize the structure of time series and identify their nonlinear properties. This transformed informa-tion serves as input data for deep neural networks. An important aspect of the work is the selection of deep neural networks as classifiers for the obtained images. Specifically, residual neural networks are applied, known for their ability to effectively learn and classify large volumes of data. The structure of such networks and their advantages over other architectures are discussed. The experimental part of the work describes the use of a dataset of brain activity, which includes realizations from different states of a person, including epileptic seizures. The ob-tained visualization and classification methods are applied for binary classification of EEG realizations, where the class of epileptic seizure is compared with the rest. The main evalua-tion metrics for classification are accuracy, precision, recall, and F1-score. The experimental results demonstrate high classification accuracy even for short EEG realizations. The quality metrics of classification indicate the potential effectiveness of this method for automated di-agnosis of epileptic seizures based on the analysis of brain signals. The conclusions highlight the importance of the proposed approach and its potential usefulness in various domains where time series classification based on the analysis of brain activity and recurrent diagrams is required.
{"title":"Application of recurrent analysis to classify realizations of encephalograms","authors":"Kirichenko Lyudmila, Zinchenko Petro","doi":"10.34185/1562-9945-6-143-2022-08","DOIUrl":"https://doi.org/10.34185/1562-9945-6-143-2022-08","url":null,"abstract":"The current state of science and technology is characterized by a variety of methods and approaches to solving various tasks, including in the fields of time series analysis and computer vision. This abstract explores a novel approach to the classification of time series based on the analysis of brain activity using recurrent diagrams and deep neural networks. The work begins with an overview of recent achievements in the field of time series analysis and the application of machine learning methods. The importance of time series classification in various domains, including medicine, finance, technology, and others, is em-phasized. Next, the methodology is described, in which time series are transformed into gray-scale images using recurrent diagrams. The key idea is to use recurrent diagrams to visualize the structure of time series and identify their nonlinear properties. This transformed informa-tion serves as input data for deep neural networks. An important aspect of the work is the selection of deep neural networks as classifiers for the obtained images. Specifically, residual neural networks are applied, known for their ability to effectively learn and classify large volumes of data. The structure of such networks and their advantages over other architectures are discussed. The experimental part of the work describes the use of a dataset of brain activity, which includes realizations from different states of a person, including epileptic seizures. The ob-tained visualization and classification methods are applied for binary classification of EEG realizations, where the class of epileptic seizure is compared with the rest. The main evalua-tion metrics for classification are accuracy, precision, recall, and F1-score. The experimental results demonstrate high classification accuracy even for short EEG realizations. The quality metrics of classification indicate the potential effectiveness of this method for automated di-agnosis of epileptic seizures based on the analysis of brain signals. The conclusions highlight the importance of the proposed approach and its potential usefulness in various domains where time series classification based on the analysis of brain activity and recurrent diagrams is required.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"123 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136352254","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}
The use of effective decision-making criteria is very important, especially when it comes to ensuring information security. Controlled attributes, such as keyboard handwriting charac-teristics, intensity of network attacks, and many others, are described by random variables whose distribution laws are usually unknown. Classical nonparametric statistics suggests comparing samples of random variables by rank-based homogeneity criteria that are inde-pendent of the type of distribution. Using the Van der Warden shift criterion and the Klotz scale criterion, Bush and Wind proposed the combined Bush-Wind criterion. It is an asymp-totically optimal nonparametric statistic for equal testing of two normal means and sample variances in a population. The article considers the problem of testing the hypothesis of sta-tistical homogeneity of two experimental measurement samples if the Van der Warden and Klotz criteria, which are formed by approximations of the inverse Gaussian functions, are re-placed by their analogues - the inverse functions of logistic random variables. Computational experiments are carried out and the informativeness of the classical Bush-Wind criterion and its analog, which is formed on the logistic inverse distribution function, is investigated. The analog of the Bush-Wind criterion proposed in this paper differs from the classical criterion by reducing computational complexity while maintaining efficiency. The empirical probabili-ties of recognizing the homogeneity of samples, obtained by conducting computational ex-periments for samples of logistic, Rayleigh and exponential random variables, indicate non-parametricity, high sensitivity and the possibility of applying the criterion in conditions of limited experimental data. The modified Bush-Wind criterion is characterized by high infor-mation content and can be recommended for statistical processing of experimental measure-ments.
使用有效的决策标准非常重要，特别是在确保信息安全方面。受控制的属性，如键盘手写特征、网络攻击强度等，都是由随机变量描述的，其分布规律通常是未知的。经典的非参数统计建议通过独立于分布类型的基于秩的同质性标准来比较随机变量的样本。利用Van der Warden位移准则和Klotz尺度准则，Bush和Wind提出了Bush-Wind联合准则。它是总体中两个正态均值和样本方差相等检验的渐近最优非参数统计量。本文考虑了如果由反高斯函数的近似形成的Van der Warden准则和Klotz准则被它们的类似物——logistic随机变量的逆函数所取代，那么检验两个实验测量样本的统计同质性假设的问题。通过计算实验，研究了基于logistic逆分布函数的经典Bush-Wind判据及其类似判据的信息量。本文提出的Bush-Wind准则的模拟与经典准则的不同之处在于在保持效率的同时降低了计算复杂度。通过对logistic、Rayleigh和指数随机变量样本进行计算实验得到的样本同质性识别的经验概率表明，该准则具有非参数性、高灵敏度和在实验数据有限的情况下应用该准则的可能性。改进的Bush-Wind判据具有信息量大的特点，可推荐用于实验测量的统计处理。
{"title":"Informativeness of statistical processing of experimental measurements by the modified Bush-Wind criterion","authors":"Malaichuk Valentin, Klymenko Svitlana, Lysenko Nataliia","doi":"10.34185/1562-9945-6-143-2022-03","DOIUrl":"https://doi.org/10.34185/1562-9945-6-143-2022-03","url":null,"abstract":"The use of effective decision-making criteria is very important, especially when it comes to ensuring information security. Controlled attributes, such as keyboard handwriting charac-teristics, intensity of network attacks, and many others, are described by random variables whose distribution laws are usually unknown. Classical nonparametric statistics suggests comparing samples of random variables by rank-based homogeneity criteria that are inde-pendent of the type of distribution. Using the Van der Warden shift criterion and the Klotz scale criterion, Bush and Wind proposed the combined Bush-Wind criterion. It is an asymp-totically optimal nonparametric statistic for equal testing of two normal means and sample variances in a population. The article considers the problem of testing the hypothesis of sta-tistical homogeneity of two experimental measurement samples if the Van der Warden and Klotz criteria, which are formed by approximations of the inverse Gaussian functions, are re-placed by their analogues - the inverse functions of logistic random variables. Computational experiments are carried out and the informativeness of the classical Bush-Wind criterion and its analog, which is formed on the logistic inverse distribution function, is investigated. The analog of the Bush-Wind criterion proposed in this paper differs from the classical criterion by reducing computational complexity while maintaining efficiency. The empirical probabili-ties of recognizing the homogeneity of samples, obtained by conducting computational ex-periments for samples of logistic, Rayleigh and exponential random variables, indicate non-parametricity, high sensitivity and the possibility of applying the criterion in conditions of limited experimental data. The modified Bush-Wind criterion is characterized by high infor-mation content and can be recommended for statistical processing of experimental measure-ments.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"124 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351323","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}
Increasing the complexity and size of systems of various nature requires constant improvement of modeling and verification of the obtained results by experiment. It is possible to clearly conduct each experiment, objectively evaluate the summaries of the researched process, and spread the material obtained in one study to a series of other studies only if they are correctly set up and processed. On the basis of experimental data, algebraic expressions are selected, which are called empirical formulas, which are used if the analytical expression of some function is complex or does not exist at this stage of the description of the object, system or phenomenon. When selecting empirical formulas, polynomials of the form: у = А0 + А1х+ А2х2+ А3х3+…+ Аnхn are widely used, which can be used to approximate any measurement results if they are expressed as continuous functions. It is especially valuable that even if the exact expression of the solution (polynomial) is unknown, it is possible to determine the value of the coefficients An using the methods of mean and least squares. But in the method of least squares, there is a shift in estimates when the noise in the data is increased, as it is affected by the noise of the previous stages of information processing. Therefore, for real-time information processing procedures, a pseudo-reverse operation is proposed, which is performed using recurrent formulas. This procedure is a procedure of successive updating (with a shift) along the columns of the matrix of given sizes and pseudo-reversal at each step of information change. This approach is straightforward and takes advantage of the bounding method. With pseudo-inversion, it is possible to control the correctness of calculations at each step, using Penrose conditions. The need for pseudo-inversion may arise during optimization, forecasting of certain parameters and characteristics of systems of various purposes, in various problems of linear algebra, statistics, presentation of the structure of the obtained solutions, to understand the content of the incorrectness of the resulting solution, in the sense of Adomar-Tikhonov, and to see the ways of regularization of such solutions.
{"title":"Alternative to mean and least squares methods used in processing the results of scientific and technical experiments","authors":"Ignatkin Valery, Dudnikov Volodymyr, Luchyshyn Taras, Alekseenko Serhii, Yushkevich Oleh, Karpova Tetyana, Khokhlova Tetyana, Khomosh Yuriy, Tikhonov Vasyl","doi":"10.34185/1562-9945-4-147-2023-04","DOIUrl":"https://doi.org/10.34185/1562-9945-4-147-2023-04","url":null,"abstract":"Increasing the complexity and size of systems of various nature requires constant improvement of modeling and verification of the obtained results by experiment. It is possible to clearly conduct each experiment, objectively evaluate the summaries of the researched process, and spread the material obtained in one study to a series of other studies only if they are correctly set up and processed. On the basis of experimental data, algebraic expressions are selected, which are called empirical formulas, which are used if the analytical expression of some function is complex or does not exist at this stage of the description of the object, system or phenomenon. When selecting empirical formulas, polynomials of the form: у = А0 + А1х+ А2х2+ А3х3+…+ Аnхn are widely used, which can be used to approximate any measurement results if they are expressed as continuous functions. It is especially valuable that even if the exact expression of the solution (polynomial) is unknown, it is possible to determine the value of the coefficients An using the methods of mean and least squares. But in the method of least squares, there is a shift in estimates when the noise in the data is increased, as it is affected by the noise of the previous stages of information processing. Therefore, for real-time information processing procedures, a pseudo-reverse operation is proposed, which is performed using recurrent formulas. This procedure is a procedure of successive updating (with a shift) along the columns of the matrix of given sizes and pseudo-reversal at each step of information change. This approach is straightforward and takes advantage of the bounding method. With pseudo-inversion, it is possible to control the correctness of calculations at each step, using Penrose conditions. The need for pseudo-inversion may arise during optimization, forecasting of certain parameters and characteristics of systems of various purposes, in various problems of linear algebra, statistics, presentation of the structure of the obtained solutions, to understand the content of the incorrectness of the resulting solution, in the sense of Adomar-Tikhonov, and to see the ways of regularization of such solutions.","PeriodicalId":493145,"journal":{"name":"Sistemnì tehnologìï","volume":"128 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351421","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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