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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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Photonics Research Interview with Professor Marlan Scully

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Photonics Research 2022年第1期10卷 256-260页

作者： LAN YANG Department of Electrical and Systems Engineering Washington University in St.LouisSt.LouisMissouri 63130USA

Professor Marlan Scully discusses his career in quantum optics with the Editor-in-Chief of Photonics Research,*** Yang.

关键词： optics Photonics quantum

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Introduction

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in Applied Sciences and Technology 2022年 1-5页

作者： de Simón-Martín, Miguel Bracco, Stefano Piazza, Giorgio Pagnini, Luisa Carlotta González-Martínez, Alberto Delfino, Federico Department of Electrical Systems and Automation Engineering University of León León Spain Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Department of Civil Chemical and Environmental Engineering University of Genoa Genoa Italy

The deployment of Energy Communities can help to increase the provision of sustainable energy from renewable energy sources at urban, industrial, and commercial levels, granting benefits to all the community members. Their effective integration in the form of microgrids (and nanogrids) will undoubtedly help to decarbonize the energy sector and, at the same time, increase energy resiliency. The main purpose of this book is to provide a systematic framework for the calculation of the Levelized Cost of Energy (LCOEn) for Energy Communities proposing a general formulation and its application to three representative real case studies. To the authors’ knowledge, the existing literature does not address this issue yet. This book is intended for researchers, academics and postgraduate students in energy engineering and economics, promoters of energy projects, policymakers and, in general, for anyone interested in energy sustainability and renewable energy integration. In this chapter, we present the context and the aim of the book, its organization and a brief biographical sketch of the authors.` © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Accelerating CNNs for Pneumonia Disease Diagnosis via Heterogeneous FPGA systems 9

Accelerating CNNs for Pneumonia Disease Diagnosis via Hetero...

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9th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Sanida, Theodora Sideris, Argyrios Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350342482

Early and accurate diagnosis of pneumonia is critical for effective treatment and improved patient outcomes. This paper introduces a cutting-edge framework for accelerating the diagnosis of pneumonia using Convolutional Neural Networks (CNNs) on heterogeneous Field-programmable Gate Array (FPGA) systems. The framework is designed to leverage FPGAs' parallel processing capabilities and reconfigurability, enabling significant enhancements in speed and efficiency for processing medical images. Our approach involves the development of an optimized CNN model tailored to the features and characteristics typical of pneumonia in radiographic images. This model is then mapped onto an FPGA, utilizing high-level synthesis and custom hardware descriptions to maximize data throughput and minimize latency. We conducted testing using a diverse chest X-ray dataset, comparing our FPGA-based system's performance against traditional computing platforms such as GPUs and CPUs. Results indicate that our system accelerates the diagnostic process over 5 times and achieves a 97.58% accuracy rate comparable to state-of-the-art methods. The proposed framework enhances medical imaging devices' capabilities, leading to quicker diagnoses, thus reducing hospitalization time and improving patient care. © 2024 IEEE.

关键词： Digital storage

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Physical Layer Security in AmBC-NOMA Networks with Random Eavesdroppers

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IEEE Internet of Things Journal 2025年第12期12卷 18628-18642页

作者： Pei, Xinyue Wang, Xingwei Huang, Min Chen, Yingyang Li, Xiaofan Tsiftsis, Theodoros A. Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Jinan University College of Information Science and Technology Guangzhou510632 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China University of Thessaly Department of Informatics Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided. © 2014 IEEE.

关键词： Signal to noise ratio

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A Novel Approach for Frequency Control in Smart Grids Utilizing Thermal Equipment for Emergency Situations 13

A Novel Approach for Frequency Control in Smart Grids Utiliz...

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13th Smart Grid Conference, SGC 2023

作者： Golshaeian, Sajad Kiani, Hossein Nazari, Mohammad Hassan Hosseinian, Seyed Hossein Gharehpetian, Gevork B. Amirkabir University of Technolog Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350396065

To control power grid frequency, the establishment of a balance between generation and consumption is always a deemed necessary. The main loops of Load-Frequency Control (LFC) system are only responsive to low-amplitude and slow changing load/frequency, and cannot manage emergency situations and power imbalances in some cases. Typically, in critical and emergency situations, loads are removed using emergency control, which is one of the control levels in the frequency control system. Load Shedding (LS), in fact, is one of the applications of the emergency frequency control employed to guarantee system security. In this paper, a novel technique for two-area emergency frequency control is proposed. In this control scheme, instead of power system load reduction during emergency frequency control, the control of Cooling-Heating systems (CHSs) is implemented. Furthermore, the integration of Advanced Metering Infrastructure (AMI) of smart grids with the support of advanced communication technologies and strengthened control algorithms, is necessary to achieve a stable system, which is considered in simulations in MATLAB/Simulink environment. © 2023 IEEE.

关键词： Smart power grids

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Average Current Control Design of Active Power Decoupling Converter with Low Filter Inductance Based on Double Sampling Techniques for PV Microinverter

Average Current Control Design of Active Power Decoupling Co...

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2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future, ICUE 2024

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland College Park Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD United States

ISBN: (纸本)9798331517076

Active power decoupling (APD) converter is a power dense and reliable solution in dc-ac power conversion systems to balance the fluctuating ac-side power and constant dc-side power. In boost type APD converter, filter inductor with low inductance can be beneficial in achieving efficient and compact designs or allows the implementation of zero voltage switching (ZVS) based modulation. On the other hand, the reduced inductance increases the difficulty of controller design due to reduction of the average current control loop phase margin and higher requirement in low-pass-filter (LPF). This paper proposes an average current control based on double sampling techniques to reduce the phase delay of LPF and improve the current tracking performance under low inductance. The implementation of double sampling in the digital signal processor (DSP) and the design of control loops are discussed. The proposed approach is experimentally verified using a 40 V, 400 W APD converter prototype. © 2024 IEEE.

关键词： Low pass filters

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Analyzing Phase Imbalance in Smart Charging of Multiple EVs: An Experimental Approach 8

Analyzing Phase Imbalance in Smart Charging of Multiple EVs:...

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8th E-Mobility Power System Integration Symposium, EMOB 2024

作者： Bayram, I. Safak Hunter, Lewis Senol, Murat Sevdari, Kristian Department of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark

ISBN: (纸本)9781837242672

The increasing penetration of electric vehicles (EVs) is causing uneven loading in low voltage distribution networks, primarily due to variations in smart charging rates and simultaneous charging of single-phase and three-phase EVs. This study experimentally analyzes three mainstream EVs (Renault Zoe R90, Peugeot e-2008, Nissan Leaf e+) charging concurrently, with the Zoe and e-2008 using three-phase power and the Leaf using single-phase power. Smart charging capabilities are used to gradually increase charging current from minimum to maximum power for each vehicle. Harmonic, current, and voltage measurements were taken at the point of common coupling using a power quality harmonic analyzer. The results reveal significant harmonic imbalances in three-phase networks, particularly when different types of EVs are charged simultaneously. This highlights the necessity for active load management to ensure EV charging adheres to industry standards and recommendations, such as IEC 61000-3-12 for harmonic emissions and CIGRE C4.07. Such active management strategies can mitigate the negative impacts of uneven loading and ensure the stability and reliability of power distribution networks as EV adoption continues to grow. © Energynautics GmbH.

关键词： State of charge

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Analysis of LiDAR-Camera Fusion for Marine Situational Awareness with Emphasis on Cluster Selection in Camera Frustum 15

Analysis of LiDAR-Camera Fusion for Marine Situational Aware...

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15th IFAC Conference on Control Applications in Marine systems, Robotics and Vehicles, CAMS 2024

作者： Obradović, Juraj Fabijanić, Matej Batoš, Matko Mišković, Nikola Faculty of Electrical Engineering and Computing University of Zagreb Department LABUST Laboratory for Underwater Systems and Technologies Unska 3 Zagreb Croatia

Providing accurate information about all objects in the environment surrounding an autonomous vehicle is essential for the development of a maritime navigation system used on board autonomous and unmanned vessels. In this paper, we introduce a method that integrates LiDAR and camera data for precise 3D object detection and tracking. The approach utilizes LiDAR for acquiring object position information and ensures accurate object classification through data from the camera. The proposed architecture is presented with a detailed explanation of all algorithms used. Several scenarios have been created in a simulated environment to compare three approaches for the selection of target object in the camera frustum (the Nearest Center, Gaussian Likelihood, and Intersection-Over-Union). Finally, the full algorithm setup is tested in a simulator environment with multiple vessels and a background coast. © 2024 Elsevier B.V.. All rights reserved.

关键词： Marine navigation

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