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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Gracheva, Elena Ivanovna Petrova, Renata Maratovna Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Caruso, Massimo Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia University Nova of Lisbon Faculty Fct and Uninova -CTS Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

The article describes the reliability of transformers, the failure rate and uptime to the first failure, the durability and maintainability of transformers and their service life, the allowable temperature rises and the dependence of steady-state overheating on the load factor of the transformer. An assessment of the effect of ambient temperature on the rated power of the transformer is shown. Dependences of heating on the changing load of transformers are given. The heating time constant is calculated depending on the power and cooling system of the transformer. The winding heating temperature is considered for real daily load schedules of a 33/11 kV transformer, the maximum actual overheats are compared when loaded with full power with allowable overheats. These studies can be used in transformers 33/11 kV and others with subsequent interpolation of values. © 2023 IEEE.

关键词： Failure rate

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Simulation of the Functional Characteristics of Low-Voltage Switching Devices, Based on the Example of Automatic Circuit Breakers

Simulation of the Functional Characteristics of Low-Voltage ...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Petrov, Almaz Radikovich Ivanovna Gracheva, Elena Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Rahman, Aqeel Ur Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Cts University Nova Faculty Fct and Uninova Lisbon Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

The article discusses the main features of mechanical, chemical, thermal, and electrical processes occurring in the contact zones of the surfaces of contact connections of electrical devices. The parameters determining the value of power losses and power consumption in low voltage switching equipment are analyzed. The main features of the characteristics of the topology and technical condition in the operating conditions of the equipment of shop low-voltage industrial power supply networks are shown. A comparative analysis of the technical characteristics of automatic circuit breakers. Using catalog data, the dependences of active power losses in circuit breakers on rated currents are established. Algorithms have been developed and the obtained dependencies have been modeled using approximating functions. The standard deviation of the compiled approximating functions is calculated. Analytical expressions of the dynamics of power losses per pole in the studied machines of some companies of the function of the rated current are presented. The graphical dependencies of the investigated parameters of low-voltage equipment are presented. The developed models are recommended to be used to increase the reliability of the assessment and refinement of the amount of active power and electricity losses in low-voltage electrical networks of industrial power supply systems. © 2023 IEEE.

关键词： Electric circuit breakers

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Application of the Harmonic Analysis to Estimate the Gain of the Mathematical Model of the Aircraft Structure Strength Test Bench 23

Application of the Harmonic Analysis to Estimate the Gain of...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Trubin, Maksim V. Yurkevich, Valery D. Novosibirsk State Technical University Faculty of Automation and Computer Engineering Novosibirsk Russia Institute of Automation and Electrometry Siberian Branch Russian Academy of Sciences Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9781665498043

The problem of estimating the amplification factor of the channel of force loading of the bench for strength tests of aircraft structures is considered. The solution of this problem may allow in the future automating the process of setting up regulators for electric hydraulic valves of the stand and thereby accelerate the process of carrying out fatigue and strength tests. In this paper, it is proposed to use the harmonic analysis of the signal from the load force sensor and the control signal at the input of the electrohydraulic distributor to estimate the gain of the force loading channel. In this paper, it has been shown that on the basis of this analysis an estimate of the gain of the power loading channel of the bench can be obtained. The results of application of the proposed approach on an experimental stand are presented. Finally, the proposed approach was compared with another approach based on the least squares method with sliding window. © 2022 IEEE.

关键词： Harmonic analysis

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers 21

Network Traffic Prediction with Decomposition and Multi-Scal...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Meijia Yuan, Haitao Kuang, Zhenwei Ma, Hanbo Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models. © 2024 IEEE.

关键词： Autocorrelation

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A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Fast Algorithm for Dynamic Range Expansion of Images in Optoelectronic Measurement Systems

Fast Algorithm for Dynamic Range Expansion of Images in Opto...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Vladimir I. Guzhov Sergey P. Ilinykh Vladimir K. Shperling Dmitry S. Khaidukov Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation

The paper proposes a new fast method for increasing the dynamic range of brightness of images projected onto an object and registered by an optoelectronic system. The influence of photon noise on the accuracy of brightness values determination is analyzed. An algorithm realizing the increase of the dynamic range of brightness of images registered by the optoelectronic system is proposed. For this purpose, a three-color RGB image is projected onto an object. Each channel contains encoded information about the brightness field in one of the selected modules of the residual class system. An experimental validation of the proposed algorithm is carried out. The dynamic range extension up to 20 binary digits of digital image digitization is achieved. The speed of data processing was increased several times due to combining several test images into one and using a cascade scheme of their processing.

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Reconfigurable and Multiband Graphene THz Antenna Design Optimized Using Machine Learning

Reconfigurable and Multiband Graphene THz Antenna Design Opt...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Patel, Shobhit K. Agravat, Dhruvik Nguyen, Tien Dung Nguyen, Thi Thanh Kieu Nguyen, Truong Khang Marwadi University Department of Computer Engineering Gujrat Rajkot360003 India Marwadi University Department of Physics Gujrat Rajkot360003 India Electric Power University Faculty of Control and Automation Ha Noi Viet Nam Binh Thuan College Faculty of Engineering Binh Thuan Viet Nam School of Technology Van Lang University Faculty of Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350353983

This study investigates the concept of multiband graphene THz antennas. We investigate the advantages of graphene in THz antenna applications with standard materials. Using a graphene strip, we built a multiband GSSPlp-MIMO antenna in this study that achieves triple operating frequencies of 7.64 THz, 9.57 THz, and 10.90 THz. The reflection response (S11) of -29.2135 dB, which shows excellent signal confinement within the antenna, was greatly improved upon the inclusion of graphene. Superior impedance matching was the trade-off for this, even if the gain decreased slightly (10.41 dB) in comparison to the antenna without graphene (12.30 dB). The parameters are optimized using machine learning, yielding a maximum accuracy (R2) of 0.996622 and a mean square error of 5.3934 ×10-5. This indicates that high-performance terahertz antennas that balance gain and effective signal transmission can be designed using graphene. The antenna's capacity to handle multiple bands makes it ideal for future ultra-broadband wireless communication systems. This results in higher data transfer rates and supports emerging technologies such as 6G and beyond. © 2024 IEEE.

关键词： Data transfer rates

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Enhancing the Recommender System Algorithm's Quality Through the Utilization of Affinity Analysis

Enhancing the Recommender System Algorithm's Quality Through...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Igor M. Stubarev Olga K. Alsova Alexander A. Yakimenko Faculty of Automation and Computer Engineering FB Consult Novosibirsk State Technical University Novosibirsk Russia Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Faculty of Automation and Computer Engineering Institute of Computational Mathematics and Mathematical Geophysics SB RAS Laboratory of Geophysical Informatics Novosibirsk State Technical University Novosibirsk Russia

The demand for auxiliary systems that utilize methods and technologies of intellectual data analysis and machine learning is increasing in the CRM system development field. These systems are capable of generating valuable insights from vast amounts of data collected in CRM. The article shows the results of the development and study of the CRM system using the methods of affinity analysis. Earlier, the authors developed and implemented the base version of the recommender algorithm based on the use of cluster data analysis and collaborative filtration [1],[2]. The new version of the algorithm additionally uses affinity analysis methods to form recommendations for the selection of products, which made it possible to increase the accuracy of the recommendation system (service) by metrics F2 on average from 67.98% to 81.24% with an increase in the time for issuing recommendations (On average by 2.47 ms). The study and comparison of the basic and modified versions of the algorithm were executed using data sourced from insurance companies, furnished by "FB Consult".

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