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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Li, Ze Zheng, Xingyu Xia, Jinhui Liu, Hangan Gao, Xiaonan Rodriguez, Jose Wang, Liling Zhang, Zhaoyan Liu, Zhiheng Hebei University College of Electronic and Information Engineering China Southeast University School of Automation China Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Sweden Universidad San Sebastian Faculty of Engineering Chile

ISBN: (纸本)9798350351330

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme. © 2024 IEEE.

关键词： Synchronous motors

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Controller Placement with Respect to Controller Reachability 7

Controller Placement with Respect to Controller Reachability

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7th International Conference on System Reliability and Safety, ICSRS 2023

作者： Xu, Ran Wang, Fenghua Kooij, Robert E. Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Unit ICT Strategy and Policy Den Haag Netherlands

ISBN: (纸本)9798350306057

In this paper we investigate the controller placement problem on networks using controller reachability as the network performance metric. This metric is defined as the probability that each node can reach at least one controller, given that each link is operational with a fixed probability. By exploring placements for more than 100 real-world networks and by varying the number of controllers from two to five, we find that controller reachability varies greatly with different placements. Obviously, increasing the number of controllers increases the controller reachability. However, the extent of this increase depends on the strategy with which the controllers are placed. The findings indicate that efficient controller placement strategies should be developed to ensure good network performance. In this research, we propose four controller placement strategies. One strategy is based on topological network metrics: node degree and path length between controllers and nodes. The other three heuristic strategies are the greedy algorithm, the classic genetic algorithm and the heuristic genetic algorithm. By validating strategies on real-world networks, we find that all four strategies work well to solve the controller placement problem with respect to controller reachability. © 2023 IEEE.

关键词： Controllers

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yang, Ze Jiang, Xianliang Jin, Guang Huang, Junkai Bai, Jie Yu, Dingxin Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

ISBN: (纸本)9798350337020

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection. © 2023 IEEE.

关键词： crop pests deep learning object detection pest detection

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Decentralized Federated Graph Learning With Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku 102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Plugged-in electric vehicle-assisted demand response strategy for residential energy management

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Energy Informatics 2023年第1期6卷 6页

作者： Alfaverh, Khaldoon Alfaverh, Fayiz Szamel, Laszlo Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 Budapest1521 Hungary School of Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip. © 2023, The Author(s).

关键词： Energy utilization

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XCAE: Deep Neural Network for X-ray Coronary Angiograms Quality Enhancement 28

XCAE: Deep Neural Network for X-ray Coronary Angiograms Qual...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Dinescu, Andrei Damian Miron, Radu Itu, Lucian Mihai Cristina Plajer, Ioana Turcea, Alexandru Transilvania University Faculty of Mathematics and Computer Science Braşov Romania Siemens Advanta Development & Technology Braşov Romania Transilvania University Siemens Advanta Development & Technology Faculty of Electrical Engineering and Computer Science Braşov Romania

ISBN: (纸本)9798350339918

X-ray Angiography (XA) is the gold standard medical imaging modality used to assess Coronary Artery Disease (CAD), and also the imaging modality used during Percutaneous Coronary Interventions (PCI), and while performing invasive hemodynamic measurements and intravascular imaging. Aside from visually inspecting the coronary arteries, in recent years computer Aided Diagnosis (CADx) systems have been employed to assist clinicians in the detection and evaluation of CAD. However, both visual inspection and CADx systems rely heavily on the quality of the acquisitions, which, due to factors such as lower cost or older equipment and the desirability of a low radiation dose, among others, can be sub-optimal. In this paper, a Deep Learning (DL) based method is presented to address this issue, by enhancing the quality of coronary angiograms. A quantitative evaluation of the proposed method is performed and additional evaluation methods are proposed for future work. © 2023 IEEE.

关键词： computer aided diagnosis

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Design and Implementation of a Slant Polarized Wideband Vivaldi Antenna Array Feed for Monopulse Radar Reflectors

Design and Implementation of a Slant Polarized Wideband Viva...

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IEEE Radio and Wireless Symposium (RWS formerly RAWCON)

作者： Reza Masoumi Robab Kazemi Aly E. Fathy Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA

ISBN: (数字)9798350377743

ISBN: (纸本)9798350377750

Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar feed structure that offers a wider bandwidth and simplifies manufacturing. The innovative design of the Vivaldi antenna incorporates strategically placed grooves and a circular dielectric lens to enhance gain and reduce sidelobe levels. Operating from 6 GHz to 18 GHz with proper impedance matching, the antenna is 23 mm wide ($0.9 \lambda$ at 12 GHz), making it over 66% smaller than traditional designs. The antennas are angled in $2 \times 2$ array configuration to overlap, with a center-to-center spacing of $11.3 \mathrm{~mm}(0.68 \lambda$ at 18 GHz), ensuring that no grating lobes are present across the entire bandwidth. This innovative antenna effectively feeds an elliptical reflector, providing high gain, broadband capabilities, and significant null depth, ensuring accurate and efficient performance in monopulse radar applications.

关键词： Impedance matching Horn antennas Vivaldi antennas Radar antennas Reflector antennas Radar applications Manufacturing Gratings Antenna arrays Lenses

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Portable Magnetic Detectors Based on TMR Sensors 2

Portable Magnetic Detectors Based on TMR Sensors

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2nd International Conference on Electronic Information and Communication engineering, EICE 2023

作者： Li, Zihao He, Anran Zhu, Haobo Wang, Hai Zhao, Yinhan Kong, Xiangyan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute for Future Wireless Research Ningbo University IFWR China

The tunnel magnetoresistance sensor (TMR) is the fourth generation of the industrial magnetic sensor, which has the characteristics of low power consumption, high sensitivity, and low- temperature dependence. The portable magnetic detector based on the TMR sensor can effectively pick up the magnetic signal by gradient probes based on TMR sensors. After the magnetic signal is amplified, filtered, and normalized, the magnetic source orientation and amplitude are displayed on the LCD screen. The experiment proves that the portable magnetic detector can effectively detect a magnetic field larger than 100 NT at a distance of 30 cm, and the design meets the experimental requirement. It is of great practical significance to detect the magnetic source in an unshielded indoor environment. © Published under licence by IOP Publishing Ltd.

关键词： Liquid crystal displays

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