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13th International electrical engineering Congress, iEECON 2025

作者： Ritnathikul, Somkid Duangmalai, Danupat Kaewdang, Khanittha Kwuanphet, Settasuk Chalermpong, Thanawat Pitaksuttayaprot, Koson Phetchabun Rajabhat University Faculty of Agricultural and IndustrialTechnology Department of Computer Engineering Phetchabun6700 Thailand Ubon Ratchathani University Faculty of Engineering Department of Electrical and Electronic Engineering Ubon Ratchathani34190 Thailand Nakhon Phanom University Faculty of Industrial Technology Department of Electrical Engineering Nakhon Phanom48000 Thailand Phetchabun Rajabhat University Faculty of Agricultural and Industrial Technology Department of Electronics and Automation Systems Engineering Phetchabun6700 Thailand

ISBN: (纸本)9798331543952

This paper presents an electronically controlled biquadratic filter designed for precise frequency selection in applications such as audio processing, telecommunications, and cochlear implants. The design utilizes AD830, AD844, and LM13700 integrated circuits, facilitating high-pass, band-pass, and low-pass responses with improved stability and real-time tunability. PSPICE simulations validate the filter's adaptability and performance, demonstrating its effectiveness in dynamic environments and its suitability for high-frequency applications requiring flexible frequency control. The results indicate a calculated pole frequency of 2.23 MHz, while simulations yield a slightly lower value of 2.0 MHz, affirming the design's practical viability and alignment with theoretical expectations. © 2025 IEEE.

关键词： LSI circuits

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Electric Bike Security and Protection System 2

Electric Bike Security and Protection System

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Maniraj, P. Murugan, A. Durai Gokul, R. Shalini, A.S. Kesavan, K. Kavin, P. M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350349221

With the developing prevalence of electric bicycles (e-bicycles) as a feasible and proficient method of transportation, guaranteeing their security has turned into a foremost concern. Electric bicycles are important resources helpless against robbery and harm, to conquer this issue the venture proposes the improvement of an Electric Vehicle Burglary Location and Warning Framework utilizing IOT innovation. The framework integrates sensors to screen electric vehicles for unapproved access and utilizes Radio Recurrence ID innovation to improve security. After recognizing an unfair RFID section or any dubious movement, the framework sets off a caution to tell the proprietor and naturally locks the back tire, forestalling robbery. This framework could be an instrument for deflecting the robbery of electric vehicles and keeping proprietors from monetary misfortunes. In a period of developing IOT-based security arrangements, this framework offers an important expansion to safeguard electric vehicle and their venture. © 2024 IEEE.

关键词： Electric vehicles

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Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation

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Soft Computing 2024年第20期28卷 11873-11894页

作者： Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Department of Computer Science Ryerson University TorontoON Canada Department of Electrical and Computer Engineering University of Windsor WindsorON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Combination of Interleaved Single-input Multiple-output DC-DC Converters

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CSEE Journal of Power and Energy systems 2022年第1期8卷 132-142页

作者： Eladio Dur´an Aranda Salvador P´erez Litr´an Mar´ıa Bella Ferrera Prieto Electronic Engineering Computer Systems and Automation DepartmentUniversity of HuelvaHuelvaSpain Electrical and Thermal Engineering Design and Projects DepartmentUniversity of HuelvaHuelvaSpain

This paper analyses,simulates and verifies an experimental prototype of a four-phase interleaved DC-DC *** is based on a SEPIC-Cuk *** developed prototype has been used in single-input multiple-output(SIMO)*** combined converter allows obtaining dual output voltages of the same value,from a single input DC voltage and with only a power *** interleaved DC-DC converters achieve a better dynamic response and low ripple,maintaining their *** converter is connected in parallel,thereby managing their losses by distributing them between more components,which facilitates the thermal management of the multiphase converter and allows handling high power values in small sizes with respect to solutions for a single *** control strategies were applied:synchronous operation mode(SOM)and interleaved operation mode(IOM).The simulation results allow the comparison of both operational modes,verifying that the IOM presents advantages with respect to the ripple at the input and output *** experimental prototype was designed for a distributed power architecture and bipolar DC microgrid(MG).

关键词： Bipolar DC DC-DC converter interleaved

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Multi-Layer Network Formation Through HAPS Base Station and Transmissive RIS-Equipped UAV

Multi-Layer Network Formation Through HAPS Base Station and ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled El-Oued University Lgeere Lab Department of Electrical Engineering El-Oued Algeria Carleton University Ntn Lab Department of Systems and Computer Engineering Ottawa Canada Ankara Yildirim Beyazit University Electrical and Electronics Engineering Ankara Turkey University of Tripoli Department of Electrical and Electronic Engineering Libya Kth Royal Institute of Technology Division of Communications Systems Sweden Zonguldak Bulent Ecevit University Department of Computer Engineering Zonguldak Turkey Department of Computer Engineering Center for Communication Systems and Sensing Dhahran Saudi Arabia

ISBN: (纸本)9798350368369

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels. © 2025 IEEE.

关键词： Enterprise resource planning

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Feasibility Study of Using Modified Single-Phase Wireless Power Transfer System for Three-Phases Motor Driving 32

Feasibility Study of Using Modified Single-Phase Wireless Po...

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32nd IEEE International Symposium on industrial Electronics, ISIE 2023

作者： Jafari-Natanzi, Alireza Babaki, Amir Ebel, Thomas Vaez-Zadeh, Sadegh University of Tehran School of Electrical and Computer Engineering College of Engineering Iran University of Southern Denmark Centre for Industrial Electronics Department of Mechanical and Electrical Engineering Denmark

ISBN: (纸本)9798350399714

Conventional integration of Wireless Power Transmission (WPT) system with electric motor drive system seems not to be a desirable solution in some aspects like reliability, economic, sizing and numbers of power conversion. This is because of using two independent sets of switching converters at the primary and secondary side of WPT system as well as isolating DC link between these two systems. The primary sets act as high-frequency amplifier and the secondary one converts the induced power to low frequency one to feed the motor. These drawbacks will be more dominant even in three-phases motor drive applications. In this paper, a modified nine-switches topology is proposed in the secondary side of WPT system which is using high-frequency AC link voltage and converting it directly to three-phases low-frequency one to supply three-phases motor. This is carried out without using any rectification circuit and DC link as interface. Despite the lower cost of this system, it is also capable of direct drive of the motor from the primary side of WPT system. Unlike the conventional topology, the topology proposed in this paper has less switches as well as lower voltage oscillation with the same performance. Some quantified comparisons have been done in both theory and simulation which shows the superiority of the proposed system. © 2023 IEEE.

关键词： Inductive power transmission

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Compact deep-groove-assisted bends on SiN platform for monolithic integrated laser

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Optics Express 2025年第3期33卷 4383-4395页

作者： Zhang, Ziyao Skandalos, Ilias Ma, Rui Chen, Minjia Sun, Bohao Zhong, Chuan Gardes, Frederic Cheng, Qixiang Penty, Richard Centre for Photonic Systems Electrical Engineering Division Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

To build a photonic integration platform that incorporates monolithically integrated light sources which require minimized loss and reflection, a micro-meter scale passive layer is needed in addition to the III-V layer. Low temperature plasma-enhanced chemical vapor deposition (PECVD) silicon nitride (SiN) is a promising candidate, owing to its back-end-of-line (BEOL) integration capabilities, along with its amorphous structure that allows the growth of defect-free thick layers. In view of the III-V layer stack, the optimized SiN waveguides are thick and in a rib formation. However, bends on this thick SiN platform suffer from high radiation losses, resulting in the need for a bending radius as large as 800 µm that poses challenges for compact photonic integrated circuits (PICs). This paper describes and demonstrates a novel SiN bending structure with a deep etched groove along the outer side. This structure significantly reduces the bending radii to 37 µm with a bending loss of 0.1 dB/90°. A compact micro-ring resonator (MRR) with deep etched grooves is also investigated, exhibiting a substantial enhancement in the free spectral range (FSR) compared with the rib waveguide MRRs, while offering a passband of 39.1 GHz. These notable performance improvements pave the way for compact integrated emitters on a single substrate. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Silicon nitride

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Next Generation Memristor Reservoir Computing 3

Next Generation Memristor Reservoir Computing

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Nikiruy, K. Ivanov, T. Ziegler, M. Rossetti, D. Corinto, F. Ascoli, A. Tetzlaff, R. Demirkol, A.S. Schmitt, N. Micro-and Nanoelectronic Systems Tu Ilmenau Department of Electrical Engineering and Information Technology Ilmenau Germany Turin Italy Institute of Circuits and Systems Tu Dresden Faculty of Electrical and Computer Engineering Dresden Germany

ISBN: (纸本)9798350378009

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators. © 2024 IEEE.

关键词： Memristors

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