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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2014 IEEE.

关键词： Inductive power transmission

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Development of a String Transfer Assist Device for Electric Bass Picking 13

Development of a String Transfer Assist Device for Electric ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Marutsuka, Ryo Tsurumi, Kazuki Koutaki, Gou Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto860-8555 Japan

ISBN: (纸本)9798350355079

This laboratory is developing a semi-automatic performance robot that involves human interaction. The robot is designed to provide an active performance experience. It is not fully automated, but rather semi-automated to allow for human involvement. While there have been robots in the past that assist with string pressing, playing the bass guitar can be challenging for beginners due to the need to pluck the strings separately with single notes. This presentation discusses the development of a device that facilitates picking motions in electric bass playing. © 2024 IEEE.

关键词： User centered design

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Dual-Buck Bipolar Buck-Boost AC-AC Converter With Reduced Semiconductor Devices

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IEEE Transactions on Industrial Electronics 2024年第9期71卷 10495-10511页

作者： Ahmed, Hafiz Furqan Chien, Howard Khan, Ashraf Ali Islam, Shirazul Akbar, Fazal Alzaabi, Omar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Memorial University of Newfoundland Faculty of Engineering And Applied Science Department of Electrical And Computer Engineering St. John'sNLA1B 3X9 Canada Qatar University Department of Electrical Engineering Doha2713 Qatar University of Edinburgh EdinburghEH9 3JL United Kingdom Khalifa University Department of Electrical Engineering And Computer Science Advanced Power And Energy Center Abu Dhabi127788 United Arab Emirates

Dual-buck (DB) structured ac-ac converters are becoming advanced due to their inherent protection from open- and short-circuit risks, and elimination of commutation issue. However, the existing DB ac-ac converters provide restricted bipolar voltage operations with a large semiconductor device count and underutilized inductors. This article proposed a new reduced switch DB ac-ac converter without commutation issue. The propose converter provides multiple buck-boost voltage operations with noninverting and inverting outputs, making it a flexible topology for various ac voltage applications. The propose converter exhibits the continuous input and output currents irrespective of operation mode, and performs well with nonunity power factor loads. The positive and negative voltage gains of the proposed converter can address the grid voltage sag and swell issues, respectively, making it right candidate for application as dynamic voltage restorer. A detailed comparative evaluation is carried out with existing DB converters, which validated the effectiveness of the proposed converter, including;reduced semiconductor device count and their power ratings, reduced inductor sizes with improved magnetic utilization, enhanced voltage compensation ranges, and better efficiency. A laboratory scale hardware prototype of the proposed converter is built and thoroughly tested to validate its operation. © 1982-2012 IEEE.

关键词： Semiconductor devices

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Passive islanding detection in microgrids using artificial neural networks

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Applied Nanoscience (Switzerland) 2023年第4期13卷 2885-2900页

作者： Alyasiri, Ali Majeed Mohammed Kurnaz, Sefer Department of Electrical and Computer Engineering Institute of Science Altinbas University Istanbul Turkey

This research focuses on modeling and simulating voltage control of passive islanding detections with distributed generation. This research presents how reactive power generation and/or absorption can be utilized to partake voltage control in medium voltage distribution through multi-microgrids for passive islanding detection with rule-based mathematical model and artificial neural network (ANN). With the increased emphasis on renewable energy, modern power grids have become more reliant to smaller distributed generation units. Unlike traditional power grids that rely on larger centralized units, the detection of islanding events in these grids is more complex. However, it is still important to maintain the connection to the power grid to maintain high stability of the system and is also important to deenergize the grid when an islanding event happens, to protect the workers that may be working on the grid to clear the cause of the islanding. Islanding detection methods are categorized into passive, active and communication based. Active methods detect islanding by measuring the influence of noise they add to the grid, to predict the size of the grid, which reduces the quality of the power provided on that grid. Communication-based methods are expensive and highly reliable on the communications infrastructure, which limits their application. Unlike existing methods, the proposed method of ANN relies on the instantaneous current and voltage values to detect any islanding with an accuracy of 97.89% precisely. These values are directly fed to the ANN, i.e., without applying any feature extraction, so that, faster and more accurate decisions are made. The proposed method is expected to combine the high accuracy of ANNs with the faster change in instantaneous values, so that, faster and more accurate detection can be achieved. © 2022, King Abdulaziz City for science and Technology.

关键词： Detection Distributed energy Distribution grid Islanding Microgrid Reactive power Voltage control

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Implementation of Variable Capacitance to Improve Efficiency for Asymmetric LCC-LCC Compensated Wireless Power Transfer Systems

Implementation of Variable Capacitance to Improve Efficiency...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zhu, Gangwei Dong, Jianning Wang, Jundong Deng, Zichen Li, Yongpeng Bauer, Pavol Delft University of Technology Mathematics and Computer Science Department of Electrical Engineering Delft Netherlands

ISBN: (纸本)9798350364446

This paper proposes a variable-capacitance-based control strategy to improve efficiency for asymmetric LCC-LCC compensated wireless power transfer (WPT) systems. While the existing triple-phase-shift (TPS) method can achieve power regulation and wide-range zero-voltage-switching (ZVS), it results in significantly increased reactive power under asymmetric LCC-LCC compensation topology. To this end, this paper incorporates a switch-controlled-capacitor (SCC) on the primary side. The impact of variable capacitance on the system characteristics is first investigated. Furthermore, the optimal capacitor tuning factor is derived to achieve the inverter ZVS with minimal reactive power. Through the implementation of variable capacitance, the primary inductor current is notably reduced within a wide range of power. Moreover, the turn-off currents of power switches are minimized. These factors contribute to a reduction in inductor and inverter losses, thus improving the overall efficiency. Experimental results confirm that the proposed method improves the efficiency of an asymmetric LCC-LCC compensated WPT prototype, with a maximum efficiency improvement of up to 1.8%. © 2024 IEEE.

关键词： Capacitor bank

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Multi-Level-Resistance Gate Driver for SiC Motor Drives to Control dv/dt and Reduce Switching Loss 11

Multi-Level-Resistance Gate Driver for SiC Motor Drives to C...

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11th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2024

作者： Bose, Abhishek Jiang, Peiwen Higgins, Hayden Huang, Qingyun University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States

ISBN: (纸本)9798350375602

Designing an optimal gate driver resistance for SiC motor drive inverters to meet the maximum dv/dt requirement is extremely critical in prolonging the motor lifetime. However, for SiC MOSFETs, turn on voltage slew rate dv/dt, decreases with increasing switching current, leading to an additional and undesirable increase in switching turn-on losses. This paper investigates how gate driver could be optimized to meet the maximum voltage slew rate through a sinusoidal load current by changing external gate resistance in limited change levels. Therefore, the switching turn-on losses under the maximum dv/dt will be significantly reduced compared to using the fixed gate driver resistance. In the proposed approach, the dynamic turn-on behaviour of SiC MOSFETs with regards to increasing miller platform voltage and decreasing dv/dt, is taken into consideration. In this paper, We theoretically demonstrate the effectiveness of the proposed multi-level resistance gate driver for SiC motor drive based on the analytical dv/dt and power loss model. The results show that by adjusting gate resistances with a few changing levels while meeting the maximum specified dv/dt requirement, turn-on switching loss can be reduced from 10 mJ to 4.5 mJ at device transconductance 12 S. © 2024 IEEE.

关键词： MOSFET devices

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AD-Aligning: Emulating Human-Like Generalization for Cognitive Domain Adaptation in Deep Learning 5

AD-Aligning: Emulating Human-Like Generalization for Cogniti...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Li, Zhuoying Wan, Bohua Mu, Cong Zhao, Ruzhang Qiu, Shushan Yan, Chao Johns Hopkins University Department of Computer Science Baltimore United States University of Houston Department of Electrical and Computer Engineering Houston United States Northeastern University Department of Electrical and Computer Engineering Sunnyvale United States

ISBN: (纸本)9798350386943

Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment to enhance generalization capabilities. By pretraining with Coral loss and standard loss, AD-Aligning aligns target domain statistics with those of the pretrained encoder, preserving robustness while accommodating domain shifts. Through extensive experiments on diverse datasets and domain shift scenarios, including noise-induced shifts and cognitive domain adaptation tasks, we demonstrate AD-Aligning's superior performance compared to existing methods such as Deep Coral and ADDA. Our findings highlight AD-Aligning's ability to emulate the nuanced cognitive processes inherent in human perception, making it a promising solution for real-world applications requiring adaptable and robust domain adaptation strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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Absorptive Microwave Filters: From Narrowband to Wideband Operation

Absorptive Microwave Filters: From Narrowband to Wideband Op...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Cui, Jiaqian Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering Macau China

ISBN: (纸本)9798350355581

This paper presents a method to control the out-ofband performance of absorptive filters in both narrowband and wideband cases. To verify the method, a narrowband absorptive filter is designed with wideband matching, and a wideband one is proposed to achieve an infinite low-reflection range. Finally, the narrowband filter is implemented in microstrip-line platform, not only attaining symmetrical geometry but also improving impedance-matching performance. © 2024 IEEE.

关键词： Wiener filtering

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Dynamic Model Selection for Asynchronous Federated Learning in IoBT Scenarios

Dynamic Model Selection for Asynchronous Federated Learning ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tankard, Earl Haileselassie Hagos, Desta Rawat, Danda B. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

This paper explores the potential of Dynamic Model Selection (DMS) combined with asynchronous Federated Learning (FL) to enhance military devices within the Internet of Battlefield Things (IoBT). While previous studies have primarily focused on synchronous FL, we argue this approach has limitations. Hence, this study addresses this research gap by investigating asynchronous FL and its advantages over synchronous methods. Our main contribution lies in developing a dynamic client selection algorithm that optimizes the utility function, incorporating communication delay or latency, available computational resources, and model accuracy. This algorithm ensures clients are selected based on their potential contribution to the global model's performance, thereby improving the efficiency and effectiveness of the training process. Through experimentation with standard datasets (MNIST, CIFAR-10) and our custom dataset (Common Objects in Battlefield (COBA)) tailored for battlefield scenarios, we demonstrate the effectiveness of asynchronous FL and dynamic client selection. By addressing this critical research gap and providing empirical evidence, our study advances the use of FL for battlefield decision-making, significantly contributes to developing IoBT, and offers practical insights for military applications. © 2024 IEEE.

关键词： Federated learning

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ClinicalGraph: An Applied Approach in Clinical EHR Knowledge Graph Generation for Optimized Clinical Decision Support System

ClinicalGraph: An Applied Approach in Clinical EHR Knowledge...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ruiz Mejia, Jose M. Rawat, Danda B. Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States

ISBN: (纸本)9798350350548

Electronic Health Records (EHR) are well-known for their extensive capabilities in storing pertinent patient information across various specializations. However, EHRs are part of a broader ecosystem of applications that support the medical treatment process. This ecosystem, although somewhat centralized, does not have the interoperability that many assume. The main issue is that current triage models are not personalized to each patient. Context matters in a medical emergency situation especially when resources are low. In this research, we propose a possible solution by applying state of the art techniques in order to develop a clinical knowledge graph that contains relevant data used for triage optimization. We utilize a fine-tuned named entity recognition model (NER) to extract 41 label entity categories from previous medical records. Additionally, we employed prompt engineering utilizing a large language text generation model with medical knowledge to generate relationships. This resulted in a sum of 1,429 relationship type categories and approximately 999 entity nodes were created with 2,387 relationships. © 2024 IEEE.

关键词： Knowledge graph

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