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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Souma Jinno Hiroshi Toki Masayuki Abe Dept. of Electrical and Electronic Systems Engineering Osaka Institute of Technology Osaka Japan Research Center for Nuclear Physics (RCNP) Osaka University Osaka Japan Grahuate School of Engineering Science Osaka University Osaka Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper presents a near-field antenna analysis using electromagnetic potentials. We introduce the concept of source-potential energy representation and visualize the spatiotemporal distribution of energy densities around a dipole antenna. Our results demonstrate that the source-potential expression enables clear distinction between the conductive energy within the antenna and the radiated energy from the antenna. This visualization provides a novel perspective on energy dynamics in antenna systems, offering insights into the interplay between internal conduction and external radiation processes.

关键词： Antenna measurements Visualization Electric potential Dipole antennas Statistical analysis Conductors Timing Electromagnetics Antenna accessories Time-varying systems

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Optimal planning strategy for charging and discharging an electric vehicle connected to the grid through wireless recharger

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Frontiers in Energy Research 2024年 12卷

作者： Boukhchana, Asma Flah, Aymen Alkuhayli, Abdulaziz Ullah, Rahmat El-Bayeh, Claude Ziad Processes Energy Environment and Electrical Systems National Engineering School of Gabes University of Gabes Gabes Tunisia MEU Research Unit Middle East University Amman Jordan University of Gabes Gabes Tunisia Applied Science Research Center Applied Science Private University Amman Jordan Electrical Engineering Department College of Engineering King Saud University Riyadh Saudi Arabia Advanced High Voltage Engineering Research Centre Cardiff University Cardiff United Kingdom Department of Electrical Engineering Bayeh Institute Amchit Lebanon

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity. Copyright © 2024 Boukhchana, Flah, Alkuhayli, Ullah and El-Bayeh.

关键词： State of charge

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L₁ Adaptive Control-Based Formation Tracking of Multiple Quadrotors Without Linear Velocity Feedback Under Unknown Disturbances

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IEEE Transactions on Automation science and engineering 2024年 22卷 5804-5815页

作者： Yang Hu Zhiqiang Miao Yaonan Wang Haoming Tang Xiangke Wang Wei He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Intelligence Science and Technology and the Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

This paper addresses the problem of formation control for a quadrotor swarm (QS) system with directed graph topology under external environmental disturbances and unreliable internal state acquisition. The proposed distributed robust control framework, based on a gemetric controller, incorporates ${\mathcal {L}}_systems$ adaptive controllers and differentiator systems. First, the geometric formation controller is designed to implement the formation control of the nominal system. Then, ${\mathcal {L}}_systems$ adaptive controllers are designed separately for each quadrotor’s position loop and attitude loop subsystems to address the effects of uncertainties such as external time-varying disturbances (matched and unmatched disturbances) and different mass variations of quadrotors. Furthermore, the differentiator system is devised to accurately estimate the higher-order derivatives of the non-directly-measurable velocity information and the virtual translation control signal, which enhances system accuracy while reducing computational complexity. The Lyapunov stability theory is employed to analyze the stability of the closed-loop system. Finally, the effectiveness and exceptional performance of this approach in QS formation control were validated through numerical simulation and experimental results. Note to Practitioners—The inspiration for this article comes from the issue of formation control in a cluster of quadrotor drones, which is also applicable to formation control in other types of drones. In this paper, a formation control algorithm based on ${\mathcal {L}}_systems$ adaptive control strategy and arbitrary-order differentiation is designed. This algorithm can address not only the issue of time-varying wind disturbances frequently encountered during quadrotor drone flights but also the effects of unpredictable velocities and inconsistent masses of quadrotor drones. The disturbance rejection capability of this scheme enables quadrotor drones to be applied more safely and r

关键词： Quadrotors Formation control Adaptive control Topology Drones Autonomous aerial vehicles Uncertainty

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seongwook Kim Gwangeun Byeon Seungeui Lee Youngseok Bae Seokin Hong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Advanced R&D Center Hanwha Systems Co. Ltd. Emerging Science and Technology Directorate Agency for Defense Development

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement,

关键词： Deep learning Filters Artificial neural networks Performance gain Complexity theory Hardware acceleration

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Dual-Band Slot Antenna for Miniaturized Biomedical Implants 2

Dual-Band Slot Antenna for Miniaturized Biomedical Implants

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Benaissa, Mohamed Chaabane, Abdelhalim Aissaoui, Djelloul Attia, Hussein Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Science and Technology Djelfa17000 Algeria Université8 Mai 1945 Guelma Laboratoire des Télécommunications-LT BP 401 Guelma24000 Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems Sensing and Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350314243

This paper presents a compact-size antenna designed for biomedical implants. The antenna operates at the dual-band of 910 MHz and 2.45 GHz, which fall within the industrial, scientific, and medical (ISM) band ranges. The proposed antenna achieves a significant size reduction with a minimal form factor measuring only 17.64 mm3(7 mm × 7 mm × 0.36 mm). This size reduction is accomplished by employing a printed antenna with a dual spiral-shape slot and incorporating an inverted U structure, contributing to a more compact design. Additionally, a superstrate layer is introduced to enhance the impedance matching and compactness of the antenna further. The proposed antenna showcases several notable attributes, such as its miniaturization, dual-band operation, and commendable gain values compared to alternative implantable antennas. To evaluate its performance, the antenna is simulated within a multi-layered enclosure comprised of skin, fat, and muscle. The proposed antenna's SARs (specific absorption rates) are calculated within the human tissue model at both operating frequencies. This analysis provides insights into the electromagnetic energy absorption by the surrounding tissues, ensuring the antenna's safety for use in biomedical applications. © 2023 IEEE.

关键词： Microstrip antennas

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Breast Cancer Detection Using Circularly Polarized Printed UWB Antenna 2

Breast Cancer Detection Using Circularly Polarized Printed U...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Boulerbah, Rachida Chaabane, Abdelhalim Attia, Hussein Aissaoui, Djelloul Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Science and Technology Djelfa17000 Algeria Université 8 Mai 1945 Guelma Laboratoire des Télécommunications-LT BP 401 Guelma24000 Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350314243

This paper introduces a breast model system that utilizes a circularly polarized printed ultra-wideband (UWB) antenna for the detection of breast tumors. The antenna, with dimensions of 30 mm × 25 mm × 1.5 mm, offers a wide UWB capability of 115.79% within the frequency range of 4 GHz to 15 GHz, complying with the requirements of The Federal Communications Commission (FCC) band. Additionally, the proposed antenna exhibits a broad axial ratio bandwidth spanning from 4.56 GHz to 7.11 GHz (43.7%). It showcases favorable characteristics, including a maximum gain of 4.5 dBi, approximate radiation efficiency of 85 % within the operating bandwidth, and consistent radiation patterns. The antenna is integrated into a specifically designed system model that incorporates the electromagnetic properties of a human breast, enabling the detection of abnormal objects. Detailed investigations are conducted to analyze the impact of tumor size on the antenna's reflection responses, aiming to enhance tumor detection capabilities. © 2023 IEEE.

关键词： Radiation efficiency

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Zero-Shot Sim2Real Transfer of Deep Reinforcement Learning Controller for Tower Crane System

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IFAC-PapersOnLine 2023年第2期56卷 10016-10020页

作者： Mohammed B. Mohiuddin Abdel Gafoor Haddad Igor Boiko Yahya Zweiri Center for Autonomous Robotic Systems Khalifa University Abu Dhabi UAE Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Aerospace Engineering and Director of Advanced Research and Innovation Center Khalifa University Abu Dhabi UAE

Control of nonlinear systems is a challenging task that often requires linearization, which limits the operating envelope. Moreover, designing a controller for such nonlinear systems requires complex tuning rules and expert system knowledge. Deep Reinforcement Learning (DRL) has been used as a controller for nonlinear systems. However, its applicability is hindered by the Sim2Real gap, an issue where the DRL agent trained in simulation does not transfer to reality. In this work, we propose using DRL for the control of nonlinear systems across the Sim2Real gap by utilizing a detailed dynamic model of the nonlinear system. To demonstrate the proposed methodology, we use a tower crane as the nonlinear system of interest. The control policy of the DRL agent is learned rather than explicitly programmed. To highlight the effectiveness of the proposed DRL-based controller, the results are compared with the PI controller. The experimental results presented demonstrate the successful Sim2Real transfer and effectiveness of the proposed approach.

关键词： Reinforcement learning and deep learning in control Mechatronic systems Control of constrained systems Simulation to reality transfer

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Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Gebrehans, Ghebrebrhan Ilyas, Naveed Eledlebi, Khouloud Lunardi, Willian T. Andreoni, Martin Yeun, Chan Yeob Damiani, Ernesto Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates University of California Riverside United States Khalifa University of Science and Technology Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Center for Secure Cyber-physical Systems Abu Dhabi United Arab Emirates University of Milan Dept. of Computer Science Italy

This paper highlights the critical role of Machine Learning (ML) in combating the dynamic nature of cybersecurity threats. Unlike previous studies focusing mainly on static analysis, this work surveys the literature on dynamic analysis-based malware generation and detection. The study addresses the complexities of applying GANs to tabular data with heavy-tailed and multimodal distributions. It also examines the challenges of generating sequential malware behavior data and categorizes GAN-based models and their primary use cases. Furthermore, the paper evaluates adversarial losses and their limitations in generating dynamic malware behavior. Finally, it identifies existing metrics to assess GAN generalization in malware research and suggests future research directions based on identified limitations. © 2020 IEEE.

关键词： Generative adversarial networks

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Empirical Discovery of Multi-Scale Transfer of Information in Dynamical systems

arXiv

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arXiv 2025年

作者： Curtis, Christopher W. Bollt, Erik M. Department of Mathematics and Statistics San Diego State University United States Department of Electrical and Computer Engineering Clarkson University United States Clarkson Center for Complex Systems Science Clarkson University United States

In this work, we quantify the timescales and information flow associated by multiscale energy transfer in a weakly turbulent system through a novel new interpretation of transfer entropy. Our goal is to provide a detailed understanding of the nature of complex energy transfer in nonlinear dispersive systems driven by wave mixing. Further, we present a modal decomposition method based on the empirical wavelet transform that produces a relatively small number of nearly decorrelated, scale separated modes. Using our method, we are able to track multiscale energy transfer using only scalar time series measurements of a weakly turbulent system. This points to our approach being of broader applicability in real-world data coming from chaotic or turbulent dynamical systems. © 2025, CC BY.

关键词： Wavelet decomposition

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Investigation of Control Algorithm for Electromechanical Vehicle Active Anti-Roll Bar

Investigation of Control Algorithm for Electromechanical Veh...

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International Conference on electrical engineering and Informatics, ICEEI

作者： Zien Zhang Abdul Hadi Abd Rahman Noraishikin Zulkarnain Faculty of Information Science & Technology Center for Artificial Intelligence Technology Bangi Selangor Malaysia Department of Electrical Electronic & Systems Engineering Faculty of Engineering & Built Environment Bangi Selangor Malaysia

The present paper aims to address the issue of improving the roll stability of vehicles by employing an active anti-roll bar (AARB) controller. The AARB controller is established using MATLAB/SIMULINK based on the principle of Proportional-Integral-Derivative (PID) Controller, and the CARSIM software is used to build a testing environment. The evaluation is conducted through specific indicators of vehicle roll stability. A comparative simulation was conducted on the control system by changing the vehicle speed. The joint simulation of SIMULINK and CARSIM software verified the feasibility and correctness of the established model and control system. The simulation results analyzed using MATLAB software show that the AARB control system can improve the vehicle's stability. The significance of the AARB control algorithm for other active safety controllers in vehicles was also briefly explained. We highlight the future research direction of the control algorithm for the AARB controller on the importance and impact of intelligent algorithms on future research.

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