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27th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Hall, Janne Tanner, April Eldek, Abdelnasser Division of Information Technology Jackson State University JacksonMS39217 United States Department of Elect. & Comp. Engineering and Computer Science Jackson State University JacksonMS39217 United States Department of Electrical Engineering Lamar University BeaumontTX77710 United States

ISBN: (纸本)9781713880325

The focus of this research was to analyze current methods for online education for electrical and computer engineering undergraduate programs. From this research, an online model for virtual labs in undergraduate engineering is introduced. Experiential learning was exhibited by students through the hands-on experience with lab equipment combined with user feedback. Software used in this research to simulate traditional lab equipment affords institutions the opportunity to provide instructive methods to remote students who are unable to complete on-campus curricula. The devices used to remotely complete the virtual labs facilitated high-value lab practices and skills comparable to those presented in an on-campus lab. As examined during this research, the opportunity to operate virtual devices with the simulation software delivered an extensive laboratory experience with the capability for creating a teaching method for engineering concepts. Data from the students' feedback on the simulated, interactive learning is presented in this paper. © 2023 Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Students

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Evaluating a multicast backhaul protocol for reliable low latency communication over mmWave links

Evaluating a multicast backhaul protocol for reliable low la...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Mastrogiannis, Dimitrios Fund, Fraida Panwar, Shivendra S. Division of Engineering NYU Abu Dhabi United Arab Emirates Tandon School of Engineering NYU Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665494274

Millimeter wave (mmWave) wireless links are an important part of next generation cellular networks because of the abundant available bandwidth in this frequency band. However, these frequencies are highly susceptible to blockages due to obstructions in the signal path, making it difficult to reliably offer high-data-rate, low-latency service over mmWave links. In this work, we demonstrate a prototype of a previously proposed multicast backhaul architecture, which takes advantage of the excess capacity on mmWave backhaul links and multi-connectivity support at user devices to mitigate the latency associated with frequent blockages on the mmWave access network. The testbed implementation of this protocol overcomes some limitations of a previous mininet- and P4-based prototype, allowing us to run real-time experiments with live application traffic and evaluate the delay benefits of the proposed architecture. © 2023 IEEE.

关键词： Millimeter waves

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2D Structure Pushing Nanocrystalline Common Mode Inductor First Resonant Frequency Above 10 MHz

2D Structure Pushing Nanocrystalline Common Mode Inductor Fi...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Zhang, Rongrong Zhao, Hui Jiang, Chaoqiang Wang, Shuo Long, Teng Liu, Kefu Qiu, Jian Fudan University The School of Information Science and Technology Shanghai China City University of Hong Kong The Department of Electrical Engineering Hong Kong University of Florida The Electrical and Computer Engineering GainesvilleFL United States University of Cambridge The Electrical Engineering Division Department of Engineering Cambridge United Kingdom

ISBN: (纸本)9798350376067

Nanocrystalline materials, known for their excellent magnetic properties, are widely used in the design of inductors and transformers, yet their application has been primarily limited to low-frequency scenarios due to their low resonance frequency. This paper introduces an innovative 2D nanocrystalline inductor design that not only capitalizes on the excellent magnetic characteristics of nanocrystalline materials but also effectively increases the resonance frequency to above 10 MHz, enabling the use of nanocrystalline materials in high-frequency power electronics. This design is not only suitable for mass production but also breakthrough in frequency limitations of nanocrystalline Materials. Experimental and simulation results confirm the superiority of the proposed design in enhancing high-frequency impedance, increasing high-frequency insertion loss, exhibiting improvements in EMI conduction frequency range (150 kHz - 30 MHz). The performance of the filter with the proposed 2D inductor is superior to the performance of EMI filters using other inductors within high frequency range, marking a significant breakthrough in magnetic device design. © 2024 IEEE.

关键词： Natural frequencies

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AC magnetic susceptibility of magnetic nanoparticles measured under DC bias magnetic field

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Journal of the Magnetics Society of Japan 2022年第2期46卷 42-48页

作者： Noguchi, Shoya Trisnanto, Suko Bagus Yamada, Tsutomu Ota, Satoshi Takemura, Yasushi Division of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan Department of Electrical and Electronic Engineering Shizuoka University Hamamatsu432-8561 Japan

The magnetization dynamics of magnetic nanoparticles are essential for the medical applications of hyperthermia and magnetic particle imaging. In this paper, we clarified the AC magnetic susceptibility of superparamagnetic nanoparticles and their frequency dependence. We measured the AC magnetic susceptibility under an applied DC bias magnetic field. The frequency dependence of AC magnetic susceptibility was clearly observed. The experimentally obtained characteristics of the samples with different effective core diameters and dependence on the intensity of the DC bias magnetic field agreed with the theory assuming a simple particle model. The sample comprising effective core diameters with a wide distribution exhibited different characteristics than the samples comprising magnetically fractionated nanoparticles with a narrower core diameter distribution. © 2022, Magnetics Society of Japan. All rights reserved.

关键词： Medical applications

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Graph Neural Network-Based Node Clustering for Dual-Focused Power Network Partitioning 50

Graph Neural Network-Based Node Clustering for Dual-Focused ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College Station United States Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago United States Information and Computing Technology Division Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

Partitioning the power system into smaller, manageable units facilitates better grid monitoring and control, thereby improving the grid’s stability and reliability. However, large-scale power networks consist of thousands of nodes and edges, which complicates the process of learning appropriate node embeddings and aggregating information from neighboring nodes. By representing power grids as undirected weighted graphs, this study proposes a novel power network partitioning approach using Graph Neural Networks (GNN). The proposed model simplifies the clustering objective by focusing on a single balancing term, which reduces computational complexity while maintaining competitive clustering performance. The power network is represented as a graph where the proposed GNN uses the normalized graph Laplacian, which effectively captures the complex connectivity of the nodes, instead of the traditional adjacency matrix. Active power levels serve as nodal attributes, ensuring that clusters represent both the physical and operational characteristics of the network. This dual-focused approach promotes a partitioning that is topologically coherent and functionally homogeneous, vital for enhanced grid management. When applied to the IEEE 14, 39, and 118 bus systems, the proposed method has successfully delineated coherent clusters of buses, underlining its potential for improving power grid management. The simulation results confirm the method’s efficacy and applicability. © 2024 IEEE.

关键词： Undirected graphs

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Enhanced Locational FDIA Detection in Smart Grids: A Scalable Distributed Framework 4

Enhanced Locational FDIA Detection in Smart Grids: A Scalabl...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Shadmand, Mohammad Abdallah, Mohamed Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar

ISBN: (纸本)9798350306262

Locational detection of the false data injection attack (FDIA) is essential for smart grid cyber-security. However, the FDIA detection techniques often falter in scalability as power network complexity increases. To address the research gap, this paper introduces an innovative distributed framework for locational FDIA detection that optimizes both performance and scalability. The proposed framework initially partitions the power grid using the improved Louvain community detection algorithm. The proposed solution utilizes the electrical Functional Strength (EFS) matrix and power supply modularity. Subsequently, a dedicated multi-label one-dimensional convolutional neural network model (1D CNN) locational detector is designed for each derived cluster. The proposed methodology is designed to increase detection accuracy and enhance the scalability of the model. This is achieved by reducing training and detection times, as well as lowering memory requirements, compared to traditional centralized approaches. The effectiveness of the proposed framework is validated through simulations on the IEEE 39-bus system. These simulations demonstrate the framework's capability to enhance detection accuracy by simplifying the locational FDIA detection challenge, achieved through strategic grid partitioning. © 2024 IEEE.

关键词： Cybersecurity

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations 63

A Model-Based Approach for Improving Reinforcement Learning ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ozcan, Erhan Can Giammarino, Vittorio Queeney, James Paschalidis, Ioannis Ch. Boston University Division of Systems Engineering BostonMA02215 United States CambridgeMA02139 United States Boston University Division of Systems Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations. © 2024 IEEE.

关键词： Deep reinforcement learning

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Constructive Function Approximation with Local Models 32

Constructive Function Approximation with Local Models

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Mavridis, Christos N. Johansson, Karl Henrik School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798350395440

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach. © 2024 IEEE.

关键词： Heuristic methods

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Vertically Stackable Memcapacitor Crossbar Array based on NAND Flash Array Structure

Vertically Stackable Memcapacitor Crossbar Array based on NA...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Yu, Junsu Hwang, Hwiho Kim, Hyungjin Choi, Woo Young Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Division of Materials Science and Engineering Department of Semiconductor Engineering Hanyang University Seoul04763 Korea Republic of

ISBN: (纸本)9798350365429

In this work, a vertically stackable 4F2 memcapacitor crossbar array based on charge trap flash (CTF) is experimentally demonstrated with a TiN-Al2O3- Si3N4-SiO2-Si (TANOS) gate stack. 4-bit multi-level operation of the fabricated 24 × 48 array is verified with more than 10 years of retention and no read/write disturbance. Also, vector-matrix multiplication (VMM) operations with an error of 0.227 % are validated, along with read operations using a sensing circuit. Based on the measurement data of the planar array, the capability of performing read/write operations with a vertically stacked 3-D structure is verified through TCAD simulations. A weight transfer procedure is also provided to enhance VMM accuracy in a scaled-down vertical structure, resulting in significantly suppressed VMM errors. © 2024 IEEE.

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