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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Kwon, Man Jae Kang, Seung Hyun Hwang, Yun Seong Kim, Hyeon Soo Yoo, Dong-Wook Lee, Byoung Kuk Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Hanyang University Department of Electrical and Biomedical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

For conventional domestic induction cooktop using series resonant inverter, the reference power of system can be controlled by frequency variation. In this case, the controller only monitors the input power condition to satisfy target power level. However, when the double-bottom pot is located on a working coil, system can be damaged by large magnitude of output current under the same target input power since the equivalent impedance of load highly decreases compared to ferromagnetic pot. Therefore, in this digest, active power control strategy is established with mathematical analysis of load impedance without obtaining input power curve data. Through experimental results using 3.2 kW test bed, the validity of proposed method is evaluated. © 2024 IEEE.

关键词： Electric load management

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Bidirectional Performance-considered 11 kW Integrated Wired/Wireless EV Charging System with Effective Operation Strategy

Bidirectional Performance-considered 11 kW Integrated Wired/...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lee, Ju-A Sim, Dong Hyeon Jo, Hyeonu Kim, Chae-Lyn Yoo, Dong-Wook Lee, Byoung Kuk Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Hanyang University Department of Electrical and Biomedical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

In this paper, a circuit configuration of a bidirectional integrated wired/wireless charging system is proposed for electric vehicle (EV). In the conventionally proposed integrated charging system, a structural limitation is identified. Through consideration of the structural limitation, an enhanced integrated charging system is proposed to address the fundamental problems of the conventional system. As a result, the control strategy for the effective operation of the proposed system is explained in wired/wireless operation modes. Additionally, superior performance is demonstrated through experimental validation considering battery charging and discharging situations. © 2024 IEEE.

关键词： Charging stations

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Optimized Design Strategy of CLLC converter Considering High Efficiency Operation in 22kW Bidirectional On-Board Charger

Optimized Design Strategy of CLLC converter Considering High...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sim, Dong Hyeon Lee, Ju-A Jo, Hyeonu Kim, Chae-Lyn Yoo, Dong-Wook Lee, Byoung Kuk Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Hanyang University Department of Electrical and Biomedical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes a high-efficiency design methodology for a CLLC converter within a 22kW bidirectional On-Board Charger (OBC). Further analysis determines the converter's operational range in relation to the transformer turn ratio for different operational modes. Following the design of a resonance network that aligns with these turn ratios, the study identifies an optimal design strategy for high-efficiency operation through a comparative loss analysis. Experimental verification on a CLLC converter testbed confirms the effectiveness and validity of the proposed design method. © 2024 IEEE.

关键词： Resonant converters

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Eighteen-Element Antenna for Metal-Rimmed Smartphone Sub-6 GHz LTE42 Band Applications

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computers, Materials & Continua 2022年第11期73卷 3181-3198页

作者： Saqer S.Alja’Afreh Mallak H.Alshamaileh Eqab Almajali Jawad Yousaf Amir Altaf Electrical Engineering Department Mutah UniversityAl-karak61710Jordan Electrical Engineering Department University of SharjahSharjahUAE Department of Electrical Computer&Biomedical EngineeringAbu Dhabi UniversityAbu DhabiUAE Department of Electrical and Computer Engineering Sungkyunkwan UniversityKorea

Due to the limited space and large mutual coupling levels,the design of sub-6 GHz massive Multi-Input Multi-Output(m-MIMO)smartphone antenna system attracts antennas’designers and engineers ***,this paper presents 18-element m-MIMO antenna system that covers the long-term evolution 42(LTE42)frequency band(3.4–3.6 GHz)for the fifth generation(5G)applications in metallic frame *** proposed array system is etched on the long sides of a metal rim of the mobile chassis symmetrically,which is electrically connected to the system ground plane with zero ground clearance.A low-profile frame of height 7 mm(λ/12.3)is symmetrically placed below&above the ground system *** two frame parts(above/below the ground level)are utilized separately for the implantation of the antenna elements to achieve good space exploitation and perfect pattern diversity between *** feeds are used to further increase the level of the isolation where each antenna element above the ground level is fed by a 50-ohm microstrip line,and each antenna element below the ground level is fed by a 50-ohm coplanar waveguide(CPW).The proposed antenna structure is a capacitive coupled-fed open-slot antenna with a small footprint area of 12×3.5 mm2(λ/7.2×λ/24.5,whereλrepresents the free space wavelength at 3.5 GHz).A small coupling L-shaped strip provides a capacitive coupling for the proposed 5G antenna *** establish the contribution of the proposed 18-element m-MIMO,the prototype of the proposed system was manufactured and successfully *** measured and simulated results are shown to be in good *** proves that the proposed antenna provides coverage for the LTE42 band(3.4–3.6 GHz)with acceptable isolation and ***,the performance of the proposed m-MIMO were further verified via channel capacity calculation and the envelope correlation coefficient(ECC)*** on that,the proposed m-MIMO is shown to provide a desira

关键词： Metal-rim smartphone antennas massive MIMO 5G band

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method 31

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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31st National and 9th International Iranian Conference on biomedical engineering, ICBME 2024

作者： Karampour, Ramin Aghili Bakhtiar, Amir Arshia Hajizamani, Kiarash Fallahi, Alireza Zadeh, Hamid Soltanian Hamedan University of Technology Biomedical Engineering Department Hamedan Iran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of 9 1.2%, 84.3%, 72.5%, and 88.9% for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes. © 2024 IEEE.

关键词： Conformal mapping

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Highly sensitive optical biosensor based on electromagnetically induced transparency in a graphene-dielectric metasurface

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Optical and Quantum Electronics 2025年第4期57卷 1-17页

作者： Bagheritabar, Mohsen Molaei Zarasvand, Mahdi Yazdani, Afsoon Molaei Zarasvand, Melika Salemizadeh Parizi, Mohammad Bahadori-Haghighi, Shahram Department of Electrical Engineering University of Cincinnati CincinnatiOH45221-0030 United States Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Communication and Electronics Engineering Shiraz University Shiraz Iran

In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a sheet of graphene and silicon dioxide substrate. Interactions between electric dipoles (bright modes) in silicon bars and magnetic dipoles (dark modes) excited in SRRs lead to a high quality factor resonance based on the phenomenon of electromagnetically induced transparency (EIT). High electric field confinement is achieved within the two gaps of SRRs at the resonance frequency, which result in strong light-analyte interaction. The structural parameters of the proposed metasurface are optimized to achieve the best performance. The biosensor is analyzed using the finite-element method (FEM) and the results are presented. Owing to the high Q-factor EIT resonance and the enhanced light-matter interaction inside the gaps, a high sensitivity of 496 nm/RIU and figure-of-merit (FOM) of as high as 741 RIU−1 are achieved that are higher than those reported in recently published works. The resolution and the linearity R2 value of the proposed biosensor are calculated to be 2.02e-4 and 0.999, respectively. The reported characteristics could be promising for sensing of various biomolecules such as hemoglobin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Q factor measurement

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A Review of Machine Learning Methods for Screening Cardiac Abnormalities using Auscultation 10

A Review of Machine Learning Methods for Screening Cardiac A...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Alhasan, Sarah Poorshamsi, Zahra Nasser, Loubna Alzaabi, Alyaziah Ghazal, Mohammed Yousaf, Jawad Hassan, Taimur Abu Dhabi University Department of Electrical Computer and Biomedical Engineering United Arab Emirates

ISBN: (纸本)9798331532895

This paper presents a thorough review of various methodologies employed in heart sound classification, combining both conventional machine learning (ML) algorithms and complex deep learning (DL) approaches. We systematically evaluate different data collection approaches, preprocessing techniques, methodologies, and classification mechanisms. Further-more, the review analyzes several feature extraction methods required for capturing relevant signal characteristics, spanning Mel-frequency cepstral coefficients (MFCC), continuous wavelet transform (CWT), and convolutional neural networks (CNNs). Additionally, we examine a variety of classification methods, from traditional methods like support vector machines (SVMs), Knearest neighbors (KNNs), and Decision Trees (DTs), to more sophisticated parametric approaches such as recurrent neural networks (RNNs), (CNNs), long-short term memory (LSTM), and even ensemble techniques, signifying the potential of combining classification approaches. Key findings reveal a trend toward employing hybrid models that merge the capabilities of multiple techniques to enhance cardiac diagnostics precision. This comprehensive overview aims to explore existing methodologies and move toward bridging the gap in today's technologies, establishing a robust diagnostic tool for medical applications. © 2024 IEEE.

关键词： Long short-term memory

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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Multi-bit Receivers for High-Speed Communication over CAN Bus 30

Multi-bit Receivers for High-Speed Communication over CAN Bu...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Gallone, Andrea Malcovati, Piero University of Pavia Computer and Biomedical Engineering Department of Electrical Italy

ISBN: (纸本)9798350326499

This paper presents a new CAN (Controller Area Network) receiver, which is suited for multi-bit communication. Two alternative designs are described. The former provides better performance in terms of threshold voltage accuracy and pulse width symmetry, the latter is optimized in terms of area, but provides worse results regarding all the other key parameters. In both receivers, flash analog-to-digital conversion is used, which is performed during the data transmission phase and gives 4 pulses at the output that can be converted to a binary representation by an encoder. During arbitration the receivers behave conventionally. A chip of the second design described was produced using a 0.18- μm high voltage BCD technology. The circuit draws an average current of 5 mA from a 5-V supply. © 2023 IEEE.

关键词： Threshold voltage

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