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Choice of the Optimal Parameters of Measuring the Shaft Rotation Frequency of the Hydraulic Transmission of the Locomotive Using Microcontroller
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2017) Zhukovytskyy, Igor V.; Kliushnyk, Ihor A.
ENG: Purpose. The article provides for finding solution to the problem of developing and improving the means for measuring tachometric data of the previously created information and measuring system for testing hydraulic locomotive transmission by substantiating the optimal sensor design and signal processing algorithms. At the same time first of all it is necessary to start from the possibility of modifying the already existing test bench for hydraulic locomotive transmissions at the Dnipropetrovsk diesel locomotive repair plant «Promteplovoz». Methodology. In the work, the researchers proposed a methodology for modifying the sensor design and the algorithm for processing its signals. It is grounded on previous developments of tachometric sensor of the optical type on the basis of D-2MMU-2 sensor of the microprocessor automated test bench system of hydraulic locomotive transmission in the locomotive repair plant conditions. Selection of the necessary measurement algorithm and the number of sensor teeth is substantiated by calculating instrumental and methodological errors. Also, the studies aimed at identifying the source of interference in the measurement of rotational speed are described and solution for its elimination has been found. Findings. For the designed rotation speed sensor of the optical type based on the existing D-2MMU-2 sensor, the authors analyzed the dependence of the methodological and instrumental errors. Based on the obtained data more rational variant of the rotation speed calculation algorithm is proposed, and the number of teeth of the sensor disk is justified. Further, the main source of measurement interference was established and a method for improving the hardware of the hydraulic locomotive test bench was proposed. Originality. There were conducted the studies according to the methodological and instrumental errors of the designed rotation speed of sensor. The mechanisms of interference filtering arising from the sensor rotation speed fixing were proposed. Additional studies have shown the need for a hardware revision of signal conditioner scheme. Practical value. Conducted studies make it possible to establish a rational number of sensor disk teeth, which allows improving the measurement algorithm. It was also performed a hardware improvement of signal conditioner scheme from the sensor, helping to get rid of interferences. The results of measurements in studies are the initial data to perform further studies in order to determine the technical condition of hydraulic transmission UGP 750-1200 during factory testing after repair.
Conceptual Resource-Saving Design of Real-Time Socio-Technical Systems
(IOP Publishing Ltd, England, 2021) Kosolapov, Anatolii A.; Ivin, Pavlo
EN: The article discusses the methodological resource-saving approach to the System design of socio-technical systems for real-time managing (STS RTM). The definition of STS RTM from the point of view of computer systems architecture is introduced. This approach made it possible to classify the main paradigms of using computer systems, including the modern stage, corresponding to STS RTM. For such complex systems, it is important to ensure their energy efficiency, which is ensured by the use of resource-saving models and methods for optimizing structural solutions. This is ensured by minimizing the total length of communications at the enterprise, saving computing and financial resources during system migration from a centralized structure to a functionally distributed hierarchical multi-microcontroller system. STS is a human a real-time machine system, therefore, the system's response time to the processing of requests related to the issuance of messages to the system's operating personnel is determined. The complex of formulated tasks and their solution on a common information and analytical base is extremely difficult without a unified system design methodology and automation tools for solving problems. Such a Framework is described in this article.
Cемиотико-агентно-онтологическая модель интеллектуальных систем
(ООО "Научный мир", Иваново, 2017) Косолапов, Анатолий Аркадьевич
RU: В работе рассматривается предложенная автором гибридная интегрированная семиотико-агентная-онтологическая модель интеллектуальных систем. САО-модель является развитием семиотической модели Д.А. Поспелова на основе новых парных агентных моделей и онтологических баз знаний для хранения агентов и описания системной семантики и прагматики. Дополненная средствами имитационного моделирования, организации диалога и процедурами принятия решений в условиях неполноты и неопределённости, а также большого количества данных САО-модель будет интеллектуальным инструментарием для создания, познания и развития интеллектуальных систем.
Cемиотико-агентно-онтологическая модель интеллектуальных систем (препринт)
(ООО "Научный мир", Иваново, 2017) Косолапов, Анатолий Аркадьевич
RU: В работе рассматривается предложенная автором гибридная интегрированная семиотико-агентная-онтологическая модель интеллектуальных систем. САО-модель является развитием семиотической модели Д.А. Поспелова на основе новых парных агентных моделей и онтологических баз знаний для хранения агентов и описания системной семантики и прагматики. Дополненная средствами имитационного моделирования, организации диалога и процедурами принятия решений в условиях неполноты и неопределённости, а также большого количества данных САО-модель будет интеллектуальным инструментарием для создания, познания и развития интеллектуальных систем.
Design of Databases by Bachelor’s Degree Applicants when Writing a Qualification Paper
(2023) Pakhomova, Victoria M.
ENG: For use by applicants for a bachelor's degree when writing qualification papers, the «BachelorDesignDB» methodology is proposed, which consists of the following stages: review of sources on existing databases; study of the subject area in order to form an initial attitude; database design using well-known methods («Normal Forms» and «Essence-Relation») and analysis of the design results obtained; creation of a designed database with the help of the selected software application and its protection; optimization and performance improvement of the created database; formulating conclusions and providing recommendations for the practical use of the created database.
Detection of Attacks of the U2R Category by Means of the SOM on Database NSL-KDD
(Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Pakhomova, Victoria M.; Mehelbei, Yehor O.
ENG: Creating an effective system for detecting network attacks requires the use of qualitatively new approaches to information processing, which should be based on adaptive algorithms capable of self-learning. The mathematical apparatus of the Kohonen self-organizing map (SOM) was used as a research method. Python language with a wide range of modern standard tools was used as a software implementation of the Kohonen SOM addition, this section compiles the Python software model «SOM_U2R» using a Kohonen SOM. Created «SOM_U2R» software model on database NSL-KDD an error research was performed for different number of epochs with different map sizes. On the «SOM_U2R» model the research of parameters of quality of detection of attacks is carried out. It is determined that on the «SOM_U2R» created software model the error of the second kind of detection of network classes of attacks Buffer_overflow and Rootkit is 6 %, and for the class Loadmodule reached 16 %. In addition, a survey of the Fmeasure was conducted for a different number of epochs of learning the Kohonen SOM. It is determined that for all network attack classes (except Buffer_overflow) the F-measure increases, reaching its maximum value at 50 epochs.
Detection of Attacks on a Computer Network Based on the Use of Neural Networks Complex
(Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2020) Zhukovyts’kyy, Igor V.; Pakhomova, Victoria M.; Ostapets, Denis O.; Tsyhanok, O. I.
ENG: Purpose. The article is aimed at the development of a methodology for detecting attacks on a computer network. To achieve this goal the following tasks were solved: to develop a methodology for detecting attacks on a computer network based on an ensemble of neural networks using normalized data from the open KDD Cup 99 database; when performing machine training to identify the optimal parameters of the neural network which will provide a sufficiently high level of reliability of detection of intrusions into the computer network. Methodology. As an architectural solution of the attack detection module, a two-level network system is proposed, based on an ensemble of five neural networks of the multilayer perceptron type. The first neural network to determine the category of attack class (DoS, R2L, U2R, Probe) or the fact that there was no attack; other neural networks – to detect the type of attack, if any (each of these four neural networks corresponds to one class of attack and is able to identify types that belong only to this class). Findings. The created software model was used to study the parameters of the neural network configuration 41–1–132–5, which determines the category of the attack class on the computer network. It is determined that the optimal training speed is 0.001. The ADAM algorithm proved to be the best for optimization. The ReLU function is the most suitable activation function for the hidden layer, and the hyperbolic tangent function – for the output layer activation function. Accuracy in test and validation samples was 92.86 % and 91.03 %, respectively. Originality. The developed software model, which uses the Python 3.5 programming lan-guage, the integrated development environment PyCharm 2016.3 and the Tensorflow 1.2 framework, makes it pos-sible to detect all types of attacks of DoS, U2R, R2L, Probe classes. Practical value. Graphical dependencies of accuracy of neural networks at various parameters are received: speed of training; activation function; optimization algorithm. The optimal parameters of neural networks have been determined, which will ensure a sufficiently high level of reliability of intrusion detection into a computer network.
Determination of the Optimal Parameters of Wireless Local Network on the Created Program Using the Ant Algorithm
(ProConference in conjunction with KindleDP Seattle, Washington, USA, 2022) Pakhomova, Victoria M.; Salohub, Maksym V.
ENG: The «WLAN_EliteAS» program, created in the JavaScript language of the ant algorithm, determines the optimal number of base stations of wireless local networks and their location on the territory of USUST. Initial data of the «WLAN_EliteAS» program: parameters of the territory of USUST (coordinates of vacant places; number of clients that need to be connected to base stations); wireless local network parameters (base station coverage radius, maximum number of clients to one base station); parameters of the ant algorithm (number of ordinary and elite ants, irrigation and evaporation, greed and laziness). The quality of the obtained solutions depends significantly on the choice of parameters of the ant algorithm.
Development of a Self-Diagnostics Subsystem of the Information-Measuring System Using Anfis Controllers
(НВП ПП «Технологічний центр», 2018) Zhukovyts’kyy, Igor V.; Kliushnyk, Ihor A.
EN: A hybrid self-diagnostic system was designed to evaluate correctness of functioning of sensors of the information-measuring system of testing hydraulic transmissions of diesel locomotives of UHP 750 type. The system features the possibility of checking certain four parameters in steady-state operation conditions using known mathematical dependencies. For the other 14 parameters (for which mathematical dependencies were not studied and which have a high complexity of calculations), 14 neural-fuzzy ANFIS networks were developed. Self-diagnostic algorithms using ANFIS controllers were elaborated. The algorithms provide prediction of individual system parameters with the help of ANFIS controllers and a further comparison of the predicted parameters with the measured parameters. The ANFIS controller structure with the proposed Sugeno rule set was constructed and its efficiency was shown. Network training and test of the diagnostic subsystem were performed using the data sets obtained in a series of tests of hydraulic transmissions conducted at Promteplovoz diesel locomotive repair plant. The test results have shown that application of the proposed procedure ensures obtaining of correct result of the self-diagnostic subsystem operation.
Distribution of Information Flows in the Advanced Network of MPLS of Railway Transport by Means of a Neural Model
(Dnipro National University of Railway Transport named after Academician V. Lazaryan, 2019) Zhukovyts’kyy, Ihor; Pakhomova, Victoria N.; Domanskay, Halyna; Nechaiev, Andrew
ENG: Abstract. Ensuring interoperability of railway transport is possible only due to the developed information structure. Today, Ukraine uses the information-telecommunication system (ITS) of railway transport, which is based on a data communication network. The effectiveness of its work is largely determined by the routing system. The current algorithm for choosing the shortest route, which is used in the existing routing protocol (OSPF), does not always lead to an effective result. However, there is MPLS technology, which could improve the quality of the ITS network by creating virtual channels between its nodes. The authors proposed a scheme for selecting tunnels for the flows in the MPLS network, which is based on the neural model of a multilayer perceptron of configuration 18–3–3–10 with the activation function Softmax in hidden layers and a linear activation function in the input layer. To simulate the network operation, flow data is needed: class of service (CoS), sender and recipient identifiers, average flow rate vector and tunnel data (their initial load). The final load of the tunnels is taken as the resulting output of the neural network, on the basis of which the tunnel is selected for the flow of the k-th class of service.
Forecasting Network Traffic in the Information and Telecommunication System of Railway Transport by Means of a Neural Network
(MATEC Web of Conferences, 2023) Zhukovytskyy, Igor V.; Pakhomova, Victoria M.
ENG: Network traffic is one of the most important actual indicators of the information and telecommunication system (ITS) of railway transport. Recent studies show that network traffic in the ITS of railway transport is self-similar (fractal), for the study of which the Hirst indicator can be used. One of the possible solutions is a method of network traffic forecasting using neural network technology, which will allow you to manage traffic in real time, avoid server overload and improve the quality of services, which confirms the relevance of this topic. The method of forecasting the parameters of network traffic in the ITS of railway transport using neural network technology is proposed: for long-term forecasting (day-ahead) of network traffic volume based on network traffic volumes for the previous three days using the created multilayer neuro-fuzzy network; for short-term prediction (one step forward, which takes five minutes) of network traffic intensity based on network traffic intensities for the previous fifteen minutes using the created multilayer neural network. The corresponding samples are formed on the basis of real values of network traffic parameters in the ITS of railway transport. Studies of optimal parameters of the created multilayer neural network, which can be integrated into specialized analytical servers of the ITS of railway transport, are carried out, which will provide a sufficiently high level of short-term forecasting of network traffic parameters (in particular intensity) in the ITS of railway transport at the stage of deepening the integration of the national transport network into the Trans-European Transport Network.
Formation of Competencies Among Applicants of Foreign Origin in Blended Learning «Local Networks» Discipline
(Sergeieva&Co, Germany, Karlsruhe, 2022) Pakhomova, Victoria M.
ENG: The proposed «BlenLearnEnglLAN» methodology for the formation of competences among applicants of foreign origin bachelor's degree in the «Computer Engineering» specialty during blended learning in the «Local Networks» discipline: 1) study of basic concepts and fundamental principles of various network technologies during lectures held using the «Zoom» system; 2) compilation of the structure of the local network and assessment of its correctness, according to the compiled structure, creation of a simulation model of the local network in NetCracker Pro and conducting research on it during laboratory work carried out face-to-face; 3) research of network traffic parameters using neural network technology and obtained data on a simulation model during independent work using recommended sources; 4) development of theoretical material using the lecturer's presentations and testing in the «Leader» system, arguing the choice of network technology based on the obtained results of research on simulation models.
Formation of Competencies in Applicants of the Bachelor’s Degree of Foreign Origin in Distance Learning in the «Database» Discipline
(Sergeieva&Co, Germany, Karlsruhe, 2022) Pakhomova, Victoria M.
ENG: The «ForeignDistLearnDB» methodology on the formation of competencies of applicants for foreign origin «Bachelor» in «Computer Engineering» in distance learning in the «Databases» discipline, consisting of the following stages: 1) familiarization with the basic models of data representation (during lectures); 2) study of DDL, DML and DQL constructs that form the basis of SQL (during laboratory work); 3) designing a relational database using the «Normal Forms» and «Essence-Relation» methods (during the individual task); 4) analysis of the process and results of database design by different methods (mathematical and graphical); 5) elaboration of theoretical material with the use of lecturer presentations and modular testing in the «Lider» system.
Framework Conceptual Design of Complex Real-Time Management System (CoDeCS)
(Luminary Publication, India, 2018) Kosolapov, Anatolii A.; Loboda, Dmytro H.
EN: The modern information management systems are the complicated systems with territorial and by the functionally distributed complexes integrated on the basis of networks technologies and workings in the real time. When designing and developing such systems, the role of their conceptual design increases. A large number of used parameters, incompleteness of data require the use of special techniques and frameworks of the system designing. The work deals with the concept of the modern information management systems architecture and the main stages of system design based on the national standard 34. The methodology of designing complex management (information) systems working in real time is proposed. The methodology is based on ontological models of knowledge about the problem domain.
Identifying Threats in Computer Network Based on Multilayer Neural Network
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2018) Zhukovyts’kyy, Igor V.; Pakhomova, Victoria M.
ENG: Purpose. Currently, there appear more often the reports of penetration into computer networks and attacks on the Web-server. Attacks are divided into the following categories: DoS, U2R, R2L, Probe. The purpose of the article is to identify threats in a computer network based on network traffic parameters using neural network technology, which will protect the server. Methodology. The detection of such threats as Back, Buffer_overflow, Quess_password, Ipsweep, Neptune in the computer network is implemented on the basis of analysis and processing of data on the parameters of network connections that use the TCP/IP protocol stack using the 19-1-25-5 neural network configuration in the Fann Explorer program. When simulating the operation of the neural network, a training (430 examples), a testing (200 examples) and a control sample (25 examples) were used, based on an open KDDCUP-99 database of 500000 connection records. Findings. The neural network created on the control sample determined an error of 0.322. It is determined that the configuration network 19-1-25-5 copes well with such attacks as Back, Buffer_overflow and Ipsweep. To detect the attacks of Quess_password and Neptune, the task of 19 network traffic parameters is not enough. Originality. We obtained dependencies of the neural network training time (number of epochs) on the number of neurons in the hidden layer (from 10 to 55) and the number of hidden layers (from 1 to 4). When the number of neurons in the hidden layer increases, the neural network by Batch algorithm is trained almost three times faster than the neural network by Resilient algorithm. When the number of hidden layers increases, the neural network by Resilient algorithm is trained almost twice as fast as that by Incremental algorithm. Practical value. Based on the network traffic parameters, the use of 19-1-25-5 configuration neural network will allow to detect in real time the computer network threats Back, Buffer_overflow, Quess_password, Ipsweep, Neptune and to perform appropriate monitoring.
Information Analytics of Solve a Problem of Software Reliability Evaluation for Sociotechnical Systems at the Conceptual Design Stag
(ДВНЗ «ПДТУ», Маріуполь, 2021) Kosolapov, Anatolii A.; Ivin, Pavlo
EN: The scientific works proposes a method for evaluation the software reliability of a computer-based process control in real time system at the at the conceptual design stage. The proposed approach makes it possible to evaluate the possibilities of reserving transactions in real time.
Information-Measuring Test System of Diesel Locomotive Hydraulic Transmissions
(Дніпропетровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпропетровськ, 2015) Zhukovytskyy, Igor V.; Kliushnyk, Ihor A.; Ochkasov, Oleksandr B.; Korenyuk, Roman O.
ENG: The article describes the process of developing the information-measuring test system of diesel locomotives hydraulic transmission, which gives the possibility to obtain baseline data to conduct further studies for the determination of the technical condition of diesel locomotives hydraulic transmission. The improvement of factorytechnology of post-repair tests of hydraulic transmissions by automating the existing hydraulic transmission test stands according to the specifications of the diesel locomotive repair enterprises was analyzed. It is achieved based on a detailed review of existing foreign information-measuring test systems for hydraulic transmission of diesel locomotives, BelAZ earthmover, aircraft tug, slag car, truck, BelAZ wheel dozer, some brands of tractors, etc. The problem for creation the information-measuring test systems for diesel locomotive hydraulic transmission is being solved, starting in the first place from the possibility of automation of the existing test stand of diesel locomotives hydraulic transmission at Dnipropetrovsk Diesel Locomotive Repair Plant «Promteplovoz». Methodology. In the work the researchers proposed the method to create a microprocessor automated system of diesel locomotives hydraulic transmission stand testing in the locomotive plant conditions. It acts by justifying the selection of the necessary sensors, as well as the application of the necessary hardware and software for information-measuring systems. Findings. Based on the conducted analysis there was grounded the necessity of improvement the plant hydraulic transmission stand testing by creating a microprocessor testing system, supported by the experience of developing such systems abroad. Further research should be aimed to improve the accuracy and frequency of data collection by adopting the more modern and reliable sensors in tandem with the use of filtering software for electromagnetic and other interference. Originality. The authors developed the information-measuring system that improves the hydraulic transmission test process by automating and increasing the accuracy of measurements of control parameters. The measurement results are initial data for carrying out further studies to determine the technical condition of the hydraulic transmission UGP750-1200 during the plant post-repair tests. Practical value. The paper proposed the alternate design of microprocessor hydraulic transmission test system for diesel locomotives, which has no analogues in Ukraine. Automated data collection during the tests will allow capturing the fast processes to determine the technical condition of hydraulic transmission.
Intelligent Routing in the Network of Information and Telecommunication System of Railway Transport
(Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2019) Pakhomova, Victoria M.; Skaballanovich, Tetiana I.; Bondareva, Valentyna S.
ENG: Purpose. At the present stage, the strategy of informatization of railway transport of Ukraine envisages the tran-sition to a three-level management structure with the creation of a single information space, therefore one of the key tasks remains the organization of routing in the network of information and telecommunication system (ITS) of railway transport. In this regard, the purpose of the article is to develop a method for determining the routes in the network of information and telecommunication system of railway transport at the trunk level using neural network technology. Methodology. In order to determine the routes in the network of the information and telecommunica-tion system of railway transport, which at present is working based on the technologies of the Ethernet family, one should create a neural model 21-1-45-21, to the input of which an array of delays on routers is supplied; as a result vector – build tags of communication channels to the routes. Findings. The optimal variant is the neural network of configuration 21-1-45-21 with a sigmoid activation function in a hidden layer and a linear activation function in the resulting layer, which is trained according to the Levenberg-Marquardt algorithm. The most quickly the neural net-work is being trained in the samples of different lengths, it is less susceptible to retraining, reaches the value of the mean square error of 0.2, and in the control sample determines the optimal path with a probability of 0.9, while the length of the training sample of 100 examples is sufficient. Originality. There were constructed the dependencies of mean square error and training time (number of epochs) of the neural network on the number of hidden neurons ac-cording to different learning algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradi-ent on samples of different lengths. Practical value. The use of a multilayered neural model, to the entry of which the delay values of routers are supplied, will make it possible to determine the corresponding routes of transmission of control messages (minimum value graph) in the network of information and telecommunication system of railway transport at the trunk level in the real time.
Investigation of Multilayer Neural Network Parameters for Determination of R2l Category Network Attacks
(Sergeieva&Co, Karlsruhe, Germany, 2021) Pakhomova, Victoria M.; Bikovska, Daria H.
ENG: To determine R2L network attacks, Python created the MLP software model using the open KDDCup database, which was used to study the values of accuracy and error from the number of neural network learning epochs based on various data: activation functions, hidden neurons, optimization methods. The optimal parameters and configuration of the neural network for detecting classes of network attacks are determined: Ftp_write, Guess_passwd, Imap, Multihop, Phf, Spy, Warezclient, Warezmaster.
Investigation of the pPssibility of using Neurofuzzy Network to Determine the Extent of DoS Attack
(Sworld & D.A. Tsenov Academy of Economics – Svishtov, Bulgaria, 2023) Pakhomova, Victoria M.; Kovalov, Rodion
ENG: As a research method, ANFIS configurations 4-5-8-16-16-1 were used, where 4 is the number of input neurons; 5 – total number of layers; 8 – the number of neurons of the first hidden layer; 16 – the number of neurons of the second hidden layer; 16 – the number of neurons of the third hidden layer; 1 – the number of resultant neurons created using the Fuzzy Logic Toolbox of the MatLAB system, the resulting characteristic is the degree of confidence that the DoS attack occurred at the following terms: low; medium; high. Using the open database of NSL-KDD network traffic parameters on the created ANFIS, a study of its error at different affiliation functions on samples of different lengths was carried out using different methods of training optimization. It is determined that the smallest value of the ANFIS error was based on the use of the multiparameter Bell function by the Hybrid learning optimization method, and it is enough to have a training sample of 70 examples.

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