Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode

Zimovets, V. I.; Shamatrin, S. V.; Olada, D. E.; Kalashnykova, Nataliya I.

doi:10.21272/jes.2020.7(1).e4

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Lab Environment. Based on the .NET digital art education information system, there is only one server, which can make the system maintenance more convenient [8,33]. The system does not use a dedicated server server, only a dual-purpose database [10].…”

Section: Application Experiments Of Cluster Analysismentioning

confidence: 99%

Application of Cluster Analysis Algorithm in the Online Intelligent Teaching Art Resource Platform

2022

Wireless Communications and Mobile Computing

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With the explosion of knowledge and the high-speed dissemination of information, people’s desire for knowledge and information is getting stronger and stronger. At the same time, the updating of knowledge and information is going on at an unprecedented speed. The traditional teaching mode is affected by time and space. Its limitations have become more and more prominent; the traditional classroom teaching has been unable to meet the existing teaching needs. At present, there are many methods for the analysis of network user behavior, such as statistical methods, association analysis methods, and clustering algorithms. Among them, clustering algorithms are more widely used in network user behavior analysis, which is closely related to the unsupervised and high efficiency of clustering algorithms. This paper combines the advantages of clustering algorithm in network user behavior analysis and, on the basis of the existing clustering algorithm research, proposes an improved algorithm for the analysis of online intelligent teaching art resources, so as to obtain the law of online behavior of student users in campus network. Provide some help for students’ Internet management and network optimization. Finally, summarize and put forward the concept of intelligent teaching and design and implement an online intelligent teaching art resource platform based on cluster analysis algorithm. Studies have shown that the average number of transactions processed by the platform per second is 65.21, which can well simulate real information query use cases. The transaction processing time of the platform will eventually stabilize between 30 s and meet the performance requirements.

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Section: Application Experiments Of Cluster Analysismentioning

confidence: 99%

Application of Cluster Analysis Algorithm in the Online Intelligent Teaching Art Resource Platform

2022

Wireless Communications and Mobile Computing

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“…Since the use of functional diagnostics is expedient at high power of the alphabet of recognition classes, which characterize the possible technical conditions of the device, it is necessary to retrain the SFD in automatic mode. To this end, [23][24][25] considers the functioning of the SFD in the mode of information-extreme hierarchical machine learning, which allows you to automatically retrain the system when expanding the alphabet of recognition classes. But these works do not explore the problem of building a new hierarchical data structure, which inevitably arises when retraining SFD.…”

Section: Review Of the Literaturementioning

confidence: 99%

Information-Extreme Machine Training System of Functional Diagnosis System With Hierarchical Data Structure

Shelehov

Barchenko

Prylepa

et al. 2022

RIC

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Context. The problem of information-extreme machine learning of the functional diagnosis system is considered by the example of recognizing the technical state of a laser printer by typical defects of the printed material. The object of the research is the process of hierarchical machine learning of the functional diagnosis system of an electromechanical device. Objective. The main objective is to improve the functional efficiency of machine learning during functional diagnostics system retraining using automatically forming a new hierarchical data structure for an expanded alphabet of recognition classes. Method. A method of information-extreme hierarchical machine learning of the system of functional diagnosis of a laser printer based on typical defects of the printed material is proposed. The method was developed with functional approach of modeling the cognitive processes of natural intelligence, which makes it possible to give the diagnostic system the properties of adaptability under arbitrary initial conditions for the formation of images of printing defects and flexibility during retraining of the system due to an increase in the power of the alphabet of recognition classes. The method is based on the principle of maximizing the amount of information in the process of machine learning. The process of information-extreme machine learning is considered as an iterative procedure for optimizing the parameters of the functioning of the functional diagnostics system according to the information criterion. As a criterion for optimizing machine learning parameters, a modified Kullback’s information measure is considered, which is a functional of the exact characteristics of classification solutions. According to the proposed categorical functional model, an information-extreme machine learning algorithm has been developed based on a hierarchical data structure in the form of a binary decomposition tree. The use of such a data structure makes it possible to split a large number of recognition classes into pairs of nearest neighbors, for which the optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. Results. Information, algorithmic software for the system of functional diagnostics of a laser printer based on images of typical defects in printed material has been developed. The influence of machine learning parameters on the functional efficiency of the system of functional diagnostics of a laser printer based on images of defects in printed material has been investigated. Conclusions. The results of physical modeling have confirmed the efficiency of the proposed method of information-extreme machine learning of the system of functional diagnosis of a laser printer based on typical defects in printed material and can be recommended for practical use. The prospect of increasing the functional efficiency of information-extremal learning of the functional diagnostics system is to increase the depth of machine learning by optimizing additional parameters of the system’s functions, including the parameters of the formation of the input training matrix.

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“…According to the results of comparative analysis of the advantages and disadvantages of the above methods of detecting attacks, a promising way to increase the functional efficiency of ADS is the use of data mining based on machine learning ideas and methods of and pattern recognition [10,11]. As a result, the prospect of creating combined ADS, in which the rules based on the results of machine learning play the role of signatures and are able to automatically fill in the mode of factor cluster analysis [12].…”

Section: Introductionmentioning

confidence: 99%

Information-extreme machine learning of a cyber attack detection system

Dovbysh

Lyubchak

Shelehov

et al. 2022

reks

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The study aims to increase the functional efficiency of a machine learning cyber attack detection system. An information-extreme machine learning method of the cyberattack detection system with optimization of control tolerances for recognition features that reflect the traffic properties of the info-communication system has been developed. The method is developed within the framework of the functional approach to modeling of cognitive processes of natural intelligence at the formation and acceptance of classification decisions. This approach, in contrast to known methods of data mining, including neuron-like structures, allows giving the recognition system adaptability to arbitrary initial conditions of the learning matrix and flexibility in retraining the system by expanding the recognition classes alphabet. The method idea is to maximize the information capacity of the attack detection system in the machine learning process. A modified Kullback information measure is used as a criterion for optimizing machine learning parameters. According to the proposed categorical functional model, algorithmic software for attack detection system in the mode of machine learning with the depth of the second level has been developed and implemented. However, the depth level is determined by the number of machine learning parameters, which were optimized. The geometric parameters of the recognition hyperspherical containers classes and the control tolerances on the recognition features were considered as optimization parameters, which played the role of input data quantization levels in the transformation of the input Euclidean learning matrix of the type "object-property" into a working binary learning matrix given in the Hamming space. Admissible transformations of the working training matrix of the offered method allow adapting the input mathematical description of the attacks detection system to the maximum full probability of the correct classification decisions acceptance. Based on the results of information-extreme machine learning within the geometric approach, decisive rules are constructed as practically invariant to the multidimensionality of the recognition features space. The computer simulation results of information-extreme machine learning of the attack detection system to recognize four host traffic of different profiles confirm the developed method's efficiency.

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Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode

Cited by 4 publications

References 9 publications

Application of Cluster Analysis Algorithm in the Online Intelligent Teaching Art Resource Platform

Application of Cluster Analysis Algorithm in the Online Intelligent Teaching Art Resource Platform

Information-Extreme Machine Training System of Functional Diagnosis System With Hierarchical Data Structure

Information-extreme machine learning of a cyber attack detection system

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