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14th International Conference on Advanced Computer Information Technologies, ACIT 2024

作者： Hoholyuk, Oksana Paranchuk, Yaroslav Stakhiv, Petro Shabatura, Yuriy Kuznyetsov, Oleksiy Rudyy, Taras Lviv Polytechnic National University Institute of Power Engineering and Control Systems Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Department of Electromechanics and Electronics Lviv Ukraine Lviv State University of Internal Affairs Lviv Department of Informatics Ukraine

ISBN: (纸本)9798350350036

A double-loop structure of the high-speed fuzzy control of the electric arc furnace (AF) electric mode (EM) coordinates for the equalization of phase loads is proposed. System-technical solutions have been developed to increase the control speed of the EM coordinates and the implementation of phase-by-phase independent adjustment of the arc lengths. The components of the system control vector for the implementation of multi-criteria optimization of melting modes are substantiated. The proposed solutions are aimed at comprehensive improvement of the energy efficiency as well as the electromagnetic compatibility of AF and power grid modes. A computer Simulink-model of the developed double-loop AF EM control system was created and a study of the efficiency of the proposed solutions was carried out. A significant decrease of the phase loads asymmetry coefficient, a decrease in the of the EM coordinate dispersion, as well as an improvement in the indicators of electrotechnological efficiency and electromagnetic compatibility of the AF and the power grid are confirmed by the analysis of the results of their use. © 2024 IEEE.

关键词： Electric arc furnaces

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Machine learning for predicting energy efficiency of buildings: a small data approach 14

Machine learning for predicting energy efficiency of buildin...

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14th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Izonin, Ivan Tkachenko, Roman Mitoulis, Stergios Aristoteles Faramarzi, Asaad Tsmots, Ivan Mashtalir, Danylo Department of Artificial Intelligence Lviv Polytechnic National University Kniazia Romana str. 5 Lviv79905 Ukraine Department of Publishing Information Technologies Lviv Polytechnic National University S. Bandera str. 12 Lviv79013 Ukraine Department of Civil Engineering School of Engineering University of Birmingham BirminghamB15 2TT United Kingdom Department of Automated Control Systems Lviv Polytechnic National University Kniazia Romana str. 5 Lviv79905 Ukraine

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. © 2024 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

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Journal of Computer and Communications 2022年第11期10卷 44-55页

作者： Javier Vives Juan Palací Janverly Heart Department of Systems Engineering and Automation University Polytechnic of Valencia Valencia Spain Red Engineering Technology Limited London UK

With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine’s electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

关键词： Vibrations Wind Turbines Fault Diagnosis Machine Learning Condition Monitoring Deep Learning

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Validation of Moment Monitoring Results in Rolling Mill Stand Spindles

Validation of Moment Monitoring Results in Rolling Mill Stan...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Gasiyarova, Olga A. Loginov, Boris M. Radionov, Andrey A. Gasiyarov, Vadim R. Khramshin, Vadim R. Llc Npp Uchtekh-Profi Department of Material Science Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Moscow Polytechnic University Department of Automation and Control Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

ISBN: (纸本)9798350395044

The authors highlight the need for the development and industrial deployment of online monitoring systems for the elastic moment on the rolling mill stand spindles. An example of a 5000 plate mill was used to demonstrate that these systems are made due to the need for a reliable moment assessment in dynamic modes. This problem is relevant for the development of control systems limiting the maximum (amplitude) values of moments when the workpieces enter the stand. The shock loading causes a multifold increase of the dynamic moment over the steady rolling moment. This results in the fatigue breakdown of spindle connections., as well as mill failures and emergency stops. The authors provide information on a telemetric elastic moment monitoring system installed on a 5000 mill. The data were validated in the industrial context. To achieve this, the authors propose a procedure where experimental research is carried out in two stages. During the first stage, the electromagnetic moment of the motor is compared to the spindle moment in the quasi-steady rolling mode when the elastic properties of the spindle are not manifested. During the second stage, the elastic moments of the upper and lower roll motors are assessed during 9 passes of reverse rolling. Some conclusions are made about the proportion of roll motor loads, spindle moment amplitudes, and the relevance of aligning rolling moments through the improvement of electric drive speed control algorithms. © 2024 IEEE.

关键词： Ductile fracture

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Improved material descriptors for bulk modulus in intermetallic compounds via machine learning

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Rare Metals 2023年第7期42卷 2396-2405页

作者： De-Xin Zhu Kun-Ming Pan Yuan Wu Xiao-Ye Zhou Xiang-Yue Li Yong-Peng Ren Sai-Ru Shi Hua Yu Shi-Zhong Wei Hong-Hui Wu Xu-Sheng Yang National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials Henan Key Laboratory of High-Temperature Structural and Functional MaterialsHenan University of Science and TechnologyLuoyang471003China Longmen Laboratory Luoyang471003China State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing100083China Guangdong Province Key Laboratory of Durability for Marine Civil Engineering School of Civil EngineeringShenzhen UniversityShenzhen518060China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong999077China

Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic *** this study,bulk modulus datasets of intermetallic compounds were collected,and the features affecting the bulk modulus of intermetallics were screened via feature *** features B_(cal),dB_(avg),and TIE(corresponding to calculated bulk modulus,mean bulk modulus,and third ionization energy,respectively)were found to be the dominant factors influencing bulk modulus and can be extended to other multi-component ***,we predicted the bulk modulus with an accuracy of 95%using surrogate machine learning models with the selected features,and these features were also demonstrated to be effective for high-entropy ***,symbolic regression provided an expression for the relationship between bulk modulus and the screened *** machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds.

关键词： Bulk modulus Intermetallic compounds Machine learning Symbolic regression

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Relationship Between Mechanical Deformation and Contact Force Applied by Catheter Tip on Cardiac Muscle: Experimentation and Computer Modeling 5

Relationship Between Mechanical Deformation and Contact Forc...

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5th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Ijima, Yukako Masnok, Kriengsak Perez, Juan J. Gonzalez-Suarez, Ana Berjano, Enrique Watanabe, Nobuo Master's Program Global Course of Engineering and Science Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan Functional Control Systems Graduate School of Engineering and Science Shibaura Institute of Technology Biofluid Science and Engineering Laboratory Saitama Japan Universitat Politecnica de Valencia BioMIT Department of Electronic Engineering Valencia Spain School of Engineering University of Galway Translational Medical Device Lab Galway Ireland Functional Control Systems Graduate School of Engineering and Science College of Systems Engineering and Science Shibaura Institute of Technology Biofluid Science and Engineering Laboratory Department of Bio-Science and Engineering Saitama Japan

ISBN: (纸本)9798350320978

The cardiac muscle is elastic and deformable. Pushing a catheter in contact with the cardiac muscle surface to conduct focal energy-based ablative therapies, such as RF ablation, requires an adequate electrode-tissue contact surface to transfer the energy to the target site. In this regard, the relationship between the contact force (CF) and the resulting mechanical response is still unclear, in particular, the insertion depth (ID) and the diameter of the surface deformation. The objective of this study was to quantify these relationships using an ex vivo model and a computational model. A rigid bar with a 2.3 mm diameter blunt tip (mimicking a 7Fr standard ablation catheter) was placed at a perpendicular orientation on a fragment of the porcine heart. CF values ranged from 10 to 80g. We used ANSYS to build a Mooney-Rivlig model of 3 parameters based on hyperelastic material and to simulate the same conditions as in the experiments. The experimental results showed a strong linear correlation between CF and insertion depth ID ($\mathrm{R}^{2}=0.97, \mathrm{P} © 2023 IEEE.

关键词： Catheters

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Development of a UAV-based System for Technical Diagnostics of Overhead Power Lines 24

Development of a UAV-based System for Technical Diagnostics ...

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24th International Conference on Computational Problems of Electrical engineering, CPEE 2023

作者： Kozlovskyi, Oleksandr Trushakov, Dmitro Rendzinyak, Serhiy Korud, Vasyl Central Ukrainian National Technical University Department of Electrical Systems and Energy Management Kropyvnytskyi Ukraine Central Ukrainian National Technical University Department of Automation and Control in Technical Systems Kropyvnytskyi Ukraine Ivan Franko National University of Lviv Faculty of Electronics and Computer Technologies Lviv Ukraine Lviv Polytechnic National University Institute of Power Engineering and Control System Lviv Ukraine

ISBN: (纸本)9798350330342

The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics of overhead lines based on UAVs was developed, for which the necessary diagnostic parameters were selected according to informative criteria. An analysis of types of UAVs was carried out in order to determine their suitability and efficiency of use for diagnosing the condition of overhead lines. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Starting System of Induction Motor with Dynamic Torque control Powered by an Autonomous Sources of Energy Supply 4

Starting System of Induction Motor with Dynamic Torque Contr...

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

作者： Zachepa, Iurii Zachepa, Nataliia Antonov, Mykola Pyrozhok, Andrii Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine National University Zaporizhzhya Polytechnic Electrical Engineering Department Zaporizhzhya Ukraine

ISBN: (纸本)9798350346831

The paper considers the application of starting systems for induction motors powered by autonomous sources of limited power. The conditions for the formation of the starting torque of an induction motor with the formation of the required transient characteristics and the control of electromagnetic transient moments under conditions of power supply from a stationary network and an autonomous sources of energy supply of limited power are analyzed. Studies have been carried out on the implementation of the 'favorable' switching on mode of a three-phase induction motor powered by an autonomous generator according to the principle of creating non-zero initial conditions. An implementation algorithm has been developed and a technical solution has been proposed for the implementation of a starting device with the implementation of a 'quasi-favorable' method for switching on an induction motor under conditions of power supply from autonomous sources of energy supply. © 2022 IEEE.

关键词： Induction motors

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

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