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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Abdelrahman, Mahmoud S. Kassem, A. Saad, Ahmed A. Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States Helwan University Faculty of Engineering Department of Electrical Engineering Cairo Egypt

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

Event detection and classification are crucial to power system stability. The Wide Area Measurement System (WAMS) technology helps in enhancing wide area situational awareness by providing useful synchronized information to the grid control center in order to accurately identify various power system events. This paper demonstrates the viability of using EWAMS (Egyptian Wide Area Measurement System) data as one of the evolving technologies of smart grid to identify extreme events within the Egyptian power grid. The proposed scheme is based on online synchronized measurements of wide-area monitoring devices known as Frequency Disturbance Recorders (FDRs) deployed at selected substations within the grid. The FDR measures the voltage, voltage angle, and frequency at the substation and streams the processed results to the Helwan University Host Server (HUHS). Each FDR is associated with a timestamp reference to the Global Positioning System (GPS) base. An EWAMS-based frequency disturbance detection algorithm based on the rate of frequency deviation is developed to identify varies types of events such as generator trip and load shedding. Based on proper thresholding on the frequency and rate of change of frequency of the Egyptian grid, different types of events have been captured in many locations during the supervision and monitoring the operation of the grid. EWAMS historical data is used to analyze a wide range of data pre-event, during and post-event for future enhancement of situational awareness as well as decision making. © 2022 IEEE.

关键词： Electric power transmission networks

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A Lightweight AI Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 IEEE.

关键词： 5G mobile communication systems

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Integrated Magnetoelectrics: A Path Toward Novel Electronics with Ultra-low SWaP-C

Integrated Magnetoelectrics: A Path Toward Novel Electronics...

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International Workshop on Thin Films for Electronics, Electro-Optics, Energy and Sensors 2022

作者： Sun, Nian X. W.M. Keck Laboratory for Integrated Ferroics Electrical and Computer Engineering Department Northeastern University BostonMA United States

ISBN: (纸本)9781510660618

The coexistence of electric polarization and magnetization in multiferroic materials provides great opportunities for realizing magnetoelectric coupling, including electric field control of magnetism, or vice versa, through a strain mediated magnetoelectric coupling in layered magnetic/ferroelectric multiferroic heterostructures. Strong magnetoelectric coupling has been the enabling factor for different multiferroic devices, which, however, has been elusive, particularly at RF/microwave frequencies. In this presentation, I will cover the most recent progress on new integrated RF magnetoelectric materials and devices [1-7]. Specifically, we will introduce magnetoelectric multiferroic materials and their applications in different devices, including voltage tunable RF magnetic components, ultracompact magnetoelectric mechanical antennas immune from ground plane effect with © 2023 SPIE. All rights reserved.

关键词： Slot antennas

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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A Comprehensive Structural Study and Frequency Control of Inductive Power Transfer System

A Comprehensive Structural Study and Frequency Control of In...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Onai, Kuseso Ojo, Olorunfemi Arogunjo, Ezekiel Tennessee Technological University Laboratory for Electric Machine and Power Electronics Department of Electrical and Computer Engineering CookevilleTN38505 United States

ISBN: (纸本)9798350316445

In this paper, a structural study of the inductive power transfer system charging a Li-ion battery is presented. Instead of representing the battery by a constant resistor, an actual model of the battery is included. The inclusion of the battery reveals that the dynamics of the system is dominated by the battery parameters. It is shown that the system is both completely controllable and observable. For frequency control operation of the inductive power transfer system, it is shown that the states associated with the inductive power transfer are the most controllable and observable states whilst those associated with the battery are the least controllable and observable states. Based on these findings, a PID controller is designed to control the charging of the battery in both constant current and constant voltage modes. Simulation and experimental results of the designed controller are presented. © 2023 IEEE.

关键词： battery charging control controllability frequency inductive observability stability

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Approximating parameters of photovoltaic models using an amended reptile search algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9073-9088页

作者： Chauhan, Sumika Vashishtha, Govind Kumar, Anil Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal148 106 India Precision Metrology Laboratory Department of Mechanical Engineering Sant Longowal Institute of Engineering and Technology Longowal148 106 India College of Mechanical and Electrical Engineering Wenzhou University Wenzhou325 035 China Department of Computer Science and Engineering National Institute of Technology Delhi New Delhi110036 India

The appropriate selection of parameters of photovoltaic models is necessary for an accurate evaluation, control, and optimization of photovoltaic systems. Even though various strategies have been developed to address this issue, but, a precise and reliable scheme for identifying the model parameters remains a challenge. To improve parameter identification of different photovoltaic models, an opposition-based learning reptile search algorithm with Cauchy mutation strategy (OBL-RSACM) is introduced in this research. In OBL-RSACM, the individuals in search space get doubled by generating their opposite guess of the solution which overcomes the issue of strucking of solution in local minima and also enhances the convergence speed. Cauchy mutation strategy is also incorporated in the basic reptile search algorithm (RSA) which enhances the search mechanism, modifies the control parameter, mutation-driven scheme, and greedy approach of selection during the search process of the RSA. Thus, improves the exploration process and maintains the proper balance between exploration and exploitation. The proposed OBL-RSACM is applied to estimate the parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. A comprehensive comparison of experimental results and analysis demonstrated that OBL-RSACM outperformed other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficacy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Learning algorithms

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Review of Hybrid Transformer Topology

Review of Hybrid Transformer Topology

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2023 IEEE PELS Students and Young Professionals Symposium, SYPS 2023

作者： Wan, Xin Wong, Man-Chung University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering Faculty of Science and Technology China

ISBN: (纸本)9798350307832

Due to the rapid development of power electronics and new energy generation, conventional transformers cannot meet the higher requirements of intelligent grids due to a lack of controllability. While solid-state transformers (SSTs) are currently costly, making it impossible for them to be used widely in power networks. For this reason, a compromise solution of a hybrid transformer (HT) combining the traditional line frequency transformer (LFT) with power electronic devices has attracted more and more attention. Based on existing HT research, topologies of HT are classified according to installation position and connection type of power electronic devices. Rough theoretical analyses were carried out from the point of view of voltage compensation, current compensation, and power flow control. A review of HT topology in recent years is also included. © 2023 IEEE.

关键词： Topology

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Deep Unfolded Tensor Robust PCA With Self-Supervised Learning 48

Deep Unfolded Tensor Robust PCA With Self-Supervised Learnin...

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

作者： Dong, Harry Shah, Megna Donegan, Sean Chi, Yuejie Carnegie Mellon University Department of Electrical and Computer Engineering United States Air Force Research Laboratory Materials and Manufacturing United States

ISBN: (纸本)9781728163277

Tensor robust principal component analysis (RPCA), which seeks to separate a low-rank tensor from its sparse corruptions, has been crucial in data science and machine learning where tensor structures are becoming more prevalent. While powerful, existing tensor RPCA algorithms can be difficult to use in practice, as their performance can be sensitive to the choice of additional hyperparameters, which are not straightforward to tune. In this paper, we describe a fast and simple self-supervised model for tensor RPCA using deep unfolding by only learning four hyperparameters. Despite its simplicity, our model expunges the need for ground truth labels while maintaining competitive or even greater performance compared to supervised deep unfolding. Furthermore, our model is capable of operating in extreme data-starved scenarios. We demonstrate these claims on a mix of synthetic data and real-world tasks, comparing performance against previously studied supervised deep unfolding methods and Bayesian optimization baselines. © 2023 IEEE.

关键词： deep unfolding fine tuning robust principal component analysis self-supervised learning tensors

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Fire Security systems Analysis and Internet of Things Implications

Fire Security Systems Analysis and Internet of Things Implic...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fuerte, Susana Rodriguez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents the current use of the Internet of Things (IoT) in fire evacuation and extinction. It examines the different approaches to the problem and technologies like Building Information Modeling (BIM) and mathematical algorithms that can be used to determine the optimal evacuation route. It also evaluates existing fire security solutions such as smoke, flame, motion, and gas sensors, LED lights, buzzers, and SMS modules. Entities that specialize in Residential and Commercial Security systems and Home Automation are also discussed, along with the services they offer. The main objective of this study is to understand current systems and resources regarding fire evacuation and extinction systems and to analyze different developments in smart buildings to create an efficient system for fire detection and evacuation. © 2023 IEEE.

关键词： Internet of things

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