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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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An Injection Circuit Coupling Low-DC Voltage Impedance Matching System with Flexible Resonance Points for Square-Wave Voltage Power Amplifier in EM Exploration

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IEEE Transactions on Power Electronics 2025年第8期40卷 11032-11050页

作者： Pang, Ying Zhu, Yongji Ji, Yanju Li, Gang Xiang, Zeng Gong, Cheng Han, Rong Tian, Wei Guo, Qun Wong, Man-Chung Lin, Jun State Key Laboratory of Deep Earth Exploration and Imaging College of Instrumentation and Electrical Engineering Jilin University Chаngсhun China College of Instrumentation and Electrical Engineering Jilin University Changchun China Tongda Electromagnetic Energy Company Ltd. Changsha China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macao China Institute of Microelectronics University of Macau Macao China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macao China School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian China School of Electrical Engineering Guangxi University Nanning China College of Marine Electrical Engineering Dalian Maritime University Dalian China State Key Laboratory of Internet of Things for Smart City University of Macau Macao China

This paper focuses on reactive power compensation and impedance matching of square-wave voltages. Square-wave voltage is the operating voltage in electromagnetic method (EM) exploration. The characteristics of a square-wave voltage are very different from those of a sinusoidal voltage in the power grid. Although some papers proposed impedance matching converters for square-wave voltages, they solved the high DC voltage requirement problem. The problem is caused by square-wave voltage having multiple and variable spectral components. An impedance-matching converter with an injection circuit (IMFR) for square-wave voltage is proposed. IMFR is composed of a thyristor-controlled inductor and capacitor (TCLC) part and injection circuit coupling active Hbridge part. TCLC can be controlled to resonate at the highest energy fundamental frequency for damping the largest part of the DC voltage requirement. The use of TCLC offsets the varying fundamental frequency demand on the DC voltage. First, this paper gives a joint design and optimization approach for the lowest DC voltage requirement. Second, a synergistic control method of proposed IMFR is provided implementing IMFR. Third, two groups of comparative experiments show the proposed IMFR can enlarge the current by 11.8% – 38.5%. The effect on the current increase is 4.4% – 7.9% higher than using only the H-bridge of the previous method when they are operated with the same DC voltage. ©1986-2012 IEEE.

关键词： Natural frequencies

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Acharya, Samrat Khan, Hafiz Anwar Ullah Karri, Ramesh Dvorkin, Yury Pacific Northwest National Laboratory RichlandWA99354 United States New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States Johns Hopkins University Ralph o'Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and Systems Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350313604

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs. © 2024 IEEE.

关键词： Relay protection

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Joint Optimizations for Double-IRS' Cooperative Positioning and Beamforming Over Massive-MIMOAP Based 6G Secure Mobile Wireless Networks

Joint Optimizations for Double-IRS' Cooperative Positioning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Jiaojie Wang, Fei Zhang, Xi Yang, Yuanyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A&m University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (纸本)9798350351255

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS' positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs' aggregate secrecy rates when Eve's exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve's exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs' positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve's deploying areas. © 2024 IEEE.

关键词： Deep reinforcement learning

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Hybrid Sub-Harmonic Synchronous Machines Using Series and Parallel Consequent Permanent Magnet

Hybrid Sub-Harmonic Synchronous Machines Using Series and Pa...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Sajjad Hossain Rafin, S.M. Ali, Qasim Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Dept. of Electrical and Computer Engineering MiamiFL United States Sukkur Iba University Department of Electrical Engineering Sukkur Pakistan

ISBN: (纸本)9798350316445

This paper presents novel sub-harmonic synchronous machines having series or parallel permanent magnet structures on rotors for better performance in terms of greater torque density. The stator of the machines has two sets of three-phase winding, where the additional winding is used to generate sub-harmonic magnetomotive force components for brushless operation. Similar to the stator, the rotor of them uses an additional winding to induce that sub-harmonic component. Rotor also uses a permanent magnet to improve machine performance in series or parallel hybrid structures having conventional or consequent pole permanent magnet structures. In this paper, 4 different rotors, 3 having hybrid excitation, are used and their performance is compared. To validate the performance, a 2D finite element analysis of 8 poles and 48 slots of sub-harmonic synchronous machines is carried out with different rotors. Finally, the wide-speed operation of the proposed machine is discussed. © 2023 IEEE.

关键词： brushless motors consequent pole parallel PM excitation series PM excitation sub-harmonic synchronous machine

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Guest Editorial Enabling Technologies and systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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Reflection Type Phase Shifter with Commercial BST Capacitors

Reflection Type Phase Shifter with Commercial BST Capacitors

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Asia-Pacific Conference on Microwave

作者： John Mortensen Dubari Borah Thottam S. Kalkur Department of Electrical and Computer Engineering Microelectronics Research Laboratory University of Colorado CO USA

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

In this paper, a novel wideband reflection type phase shifter (RTPS) is developed for the first time using commercial BST capacitor on PCB technology. BST has a particular advantage of decoupling RF signal and DC biasing voltage which enables tuning of its ferroelectric permittivity without any DC blocking capacitor. The proposed phase shifter provides a phase shift of 103° at 2.5GHz and a phase shift of 150° at 3GHz, with an insertion loss of <1dB in the frequency range of 1GHz-3GHz. The phase shift is nearly constant from 2.5GHz to 3GHz for bias voltages between 4V and 20V. With increase in DC bias voltages from 4V to 20V, the phase shift increases from 35° to 110° in the frequency range of 2.5GHz-3GHz due to the non-linear variation of capacitance with voltage for the BST capacitor.

关键词： Power dividers Phase shifters Capacitors RF signals Voltage Insertion loss Reflection Permittivity Wideband Tuning

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Training Graph Neural Networks on Growing Stochastic Graphs 48

Training Graph Neural Networks on Growing Stochastic Graphs

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cerviño, Juan Ruiz, Luana Ribeiro, Alejandro University of Pennsylvania Department of Electrical and Systems Engineering Philadelphia United States Mit Computer Science & Artificial Intelligence Laboratory Cambridge United States

ISBN: (纸本)9781728163277

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational costs leading to scalability limitations in practice. To overcome these limitations, proposed methods rely on training GNNs in smaller number of nodes, and then transferring the GNN to larger graphs. Even though these methods are able to bound the difference between the output of the GNN with different number of nodes, they do not provide guarantees against the optimal GNN on the very large graph. In this paper, we propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon. We propose to grow the size of the graph as we train, and we show that our proposed methodology - learning by transference - converges to a neighborhood of a first order stationary point on the graphon data. A numerical experiment validates our proposed approach. © 2023 IEEE.

关键词： Graph Neural Networks Graphons

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