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Ontological Analysis of the Digital Crime Conceptual Model 18

Ontological Analysis of the Digital Crime Conceptual Model

引用

18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Vlasenko, Lidiia Lutska, Nataliia Zaiets, Nataliia Lysenko, Vitaliy State University of Trade and Economics dept. of Software Engineering and Cyber Security Kyiv Ukraine National University of Food Technologies dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine dept. of Automation and Robotic Systems Kyiv Ukraine

ISBN: (纸本)9798350360462

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance. © 2023 IEEE.

关键词： Ontology

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks 4

Improving the Efficiency of a Multistage Evaporator Station ...

引用

4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zaiets, Nataliia Lutska, Nataliia Vlasenko, Lidiia National University of Life and Environmental Sciences of Ukraine Det. of Automation and Robotic Systems Kyiv Ukraine National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9798350346831

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department. © 2022 IEEE.

关键词： Evaporation

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

引用

5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Intellectual control system for unmanned energy crop combine 1

Intellectual control system for unmanned energy crop combine

引用

1st International Conference on Intellectual systems and Information Technologies, ISIT 2019

作者： Gunchenko, Yurii Shvorov, Sergey Lukin, Volodymyr Mezhuyev, Vitalii Dept. of Mathematical Support of Computer Systems Odessa I.I.Mechnikov National University Odessa Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

The purpose of the work is to develop methodical bases for the construction of an intelligent control system (ISC) for unmanned combines (UMs) of energy crops (EC) for biogas plants (BP). In order to achieve this goal, the functional structure of the ISC was substantiated in the work, the method of recognition and determination of the volume of biomass EC in fields with unmanned aerial vehicles (UAVs) was developed, the method of synthesis of compromise-optimal routes of the BP with the minimum length of their routes in the process of biomass collection was proposed. and taking into account passive (unmoving) and active (moving) obstacles. With the help of the proposed ISC, the following tasks are solved: monitoring of the EC cultivation process and the determination of EC volumes based on the use of UAVs;determination of the density of changes in the yield of EC, the coordinates of active and passive obstacles in the way of the UMs movement;distribution of UMs by fields and planning of optimal routes and speed parameters of their movement for EC collection. A prerequisite for efficient harvesting combine driving is the constant control of the technological process of harvesting energy crops. Deviations from the work plan in most cases occur due to malfunction of technical equipment or adverse weather conditions. However, there are situations in which the deviation of the "plan-fact" is influenced by other factors, which may lead to a failure of the plan, and vice versa. Failure to complete the plan may be due to a reduction in the intensity of the work or the assumption by the user of errors when entering the initial data into the system, which reduces the adequacy of the model to the real process. The increase in planned indicators may be due to an increase in the speed of technological operations, which may lead to additional losses of biomass or a decrease in its quality. To solve the tasks listed, the ISC is divided into a subsystem of monitoring, planning and

关键词： Biomass

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Development of Method for Generating Trajectories of Movement of End Effectors and Control Programs for Industrial Robots in an Uncertain Working Environment

Development of Method for Generating Trajectories of Movemen...

引用

2021 International Russian automation Conference, RusAutoCon 2021

作者： Yukhimets, Dmitry Gubankov, Anton Mursalimov, Eduard Institute of Automation and Control Processes Robotic Laboratory Vladivostok Russia Far Eastern Federal University Dept. of Automation and Robotics Vladivostok Russia Institute of Marine Technology Problems Laboratory of Intelligent Information Systems for Marine Robots Vladivostok Russia

ISBN: (纸本)9780738131566

the deals with the implementation of the method of the trajectories generating for end effectors and control programs for industrial robots in an uncertain working environment. To generate correct trajectories, it is proposed to sequentially process data from vision systems, correct them in the human-machine interface, generate a control program in a generalized form using universal commands and then translate it into the form that a specific industrial robot requires. Experimental studies of the proposed method were carried out, which confirmed the operability and effectiveness of the proposed method. © 2021 IEEE.

关键词： Trajectories

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Synchronization Implementations for 5G Mobile Networks 16

Synchronization Implementations for 5G Mobile Networks

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022

作者： Vakas, Vyacheslav Koval, Valerii Fedorova, Nataliia Manko, Oleksandr Domin, Dmytro Private Joint Stock Company 'Kyivstar' Kyiv Ukraine National University of Life and Environmental Sciences of Ukrain Dept. of Automation and Robotic Systems Named by I.I.Martynenko Kyiv Ukraine Automation of Design of Energy Processes and Systems National Technical University O Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Kyiv Professional College of Communications Kyiv Ukraine 'Qualitek' Llc Kyiv Ukraine

In paper were presented aspects of building a synchronization network for 5G mobile networks. Reviewed stability parameters for PTP and regulatory requirements. Analyzed methods for inter-operator synchronization. ... 详细信息

ISBN: (纸本)9781665468619

关键词： Synchronization

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Modeling the Productivity of a Sugar Factory using Machine Learning Methods

Modeling the Productivity of a Sugar Factory using Machine L...

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International Computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

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Ontological Analysis of the Digital Crime Conceptual Model

Ontological Analysis of the Digital Crime Conceptual Model

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International Computer Sciences and Information Technologies (CSIT)

作者： Lidiia Vlasenko Nataliia Lutska Nataliia Zaiets Vitaliy Lysenko dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

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