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arXiv 2022年

作者： Madurasinghe, Dulip Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition. © 2022, CC BY.

关键词： Network topology

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Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

arXiv

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

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Leveraging Attribute Knowledge for Open-set Action Recognition

Leveraging Attribute Knowledge for Open-set Action Recogniti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Kaixiang Yang Junyu Gao Yangbo Feng Changsheng Xu School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Open-set action recognition(OSAR) aims to recognize known classes and reject unknown classes. Most OSAR methods focus on learning a favorable threshold to distinguish known and unknown samples in a pure data-driven manner. However, these methods do not utilize the prior knowledge of action classes. In this paper, we propose to Leverage Attribute Knowledge (LAK) for OSAR. Specifically, the class-attribute knowledge learning is designed to integrate attribute knowledge into the model based on spatial-temporal features. Here, attributes are used as a bridge, linking known and unknown classes implicitly to make up the knowledge gap. Furthermore, a learnable relation matrix is adaptively adjusted during training to obtain the class-attribute relations that are expected to be generalized in open-set settings. Extensive experiments on three popular datasets show that the proposed method achieves state-of-the-art performance.

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction

Improved Particle Swarm Optimization Algorithm for EIT Image...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Oumayma Kahouli Mariem Hafsa Hiba Hellara Imed Eddine Bennour Najoua Essoukri Ben Amara Olfa Kanoun Department of Computer Science National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse Laboratory of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax University of Sfax Tunisia

electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles.

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Spatial Re-parameterization for N:M Sparsity

arXiv

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arXiv 2023年

作者： Zhang, Yuxin Xu, Mingliang Tian, Yonghong Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Zhengzhou China School of Electronics Engineering and Computer Science Peking University Beijing China

This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity. SpRe stems from an observation regarding the restricted variety in spatial sparsity presented in N:M sparsity compared with unstructured sparsity. Particularly, N:M sparsity exhibits a fixed sparsity rate within the spatial domains due to its distinctive pattern that mandates N non-zero components among M successive weights in the input channel dimension of convolution filters. On the contrary, we observe that conventional unstructured sparsity displays a substantial divergence in sparsity across the spatial domains, which we experimentally verify to be very crucial for its robust performance retention compared with N:M sparsity. Therefore, SpRe employs the spatial-sparsity distribution of unstructured sparsity by assigning an extra branch in conjunction with the original N:M branch at training time, which allows the N:M sparse network to sustain a similar distribution of spatial sparsity with unstructured sparsity. During inference, the extra branch can be further re-parameterized into the main N:M branch, without exerting any distortion on the sparse pattern or additional computation costs. SpRe has achieved a commendable feat by matching the performance of N:M sparsity methods with state-of-the-art unstructured sparsity methods across various benchmarks. Our project is available at https://***/zyxxmu/SpRE. © 2023, CC BY.

关键词： Convolution

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Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

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Numerical simulation for the motions of nonautonomous solitary waves of a variable-coefficient forced Burgers equation via the lattice Boltzmann method

arXiv

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arXiv 2022年

作者： Guan, Qing-Feng Chen, Wei-Qin Li, Ying School of Mathematics Shandong University Jinan China 250100 Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY USA 12180 School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China 410114

The lattice Boltzmann method (LBM) for the variable-coefficient forced Burgers equation (vc-FBE) is studied by choosing the equilibrium distribution and compensatory functions properly. In our model, the vc-FBE is correctly recovered via the Chapman-Enskog analysis. We numerically investigate the dynamic characteristics of solitons caused by the dispersive and external-force terms. Four numerical examples are given, which align well with the theoretical solutions. Our research proves that LBM is a satisfactory and efficient method for nonlinear evolution equations with variable coefficients. Copyright © 2022, The Authors. All rights reserved.

关键词： Solitons

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A Novel Switching Asynchronous Sampled-Data Scheme: Implementations in Interconnected Feedback systems

A Novel Switching Asynchronous Sampled-Data Scheme: Implemen...

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American Control Conference (ACC)

作者： Xiaoyu Wang Feng Xiao Qian Feng State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Sampled-data control is continually under research exploration and development to optimally coordinate the limited communication resources in networked control systems. In this paper, a novel switching asynchronous sampleddata framework with double-checking consisting of two distinct sampling schemes is proposed. An established method involves designing the switching sampling scheme with event-triggered and time-triggered mechanisms. We also introduce the concept of switching event-triggered control (SETC), by which a positive minimum sampling interval can be guaranteed effectively. By an integral method and Barbalat's Lemma, sufficient conditions that ensure the stability of interconnected linear systems are derived under the SETC. Numerical examples are presented to demonstrate the effectiveness of the proposed methodology.

关键词： Linear systems Sufficient conditions Networked control systems Delay effects Switches Trajectory Numerical stability

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A Grid-Connected PV System Based on Z-source Inverter with Maximum Power Extraction 6

A Grid-Connected PV System Based on Z-source Inverter with M...

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6th IEEE International Conference on Computing, Communication and Automation, ICCCA 2021

作者： Al Essawy, Ihab Jamal Elmorshedy, Mahmoud F. Dabour, Sherif M. Rashad, Essam M. Islam, Md Rabiul Tawam United Electromechanical Enterprises Company Abu Dhabi United Arab Emirates Tanta University Faculty of Engineering Department of Electrical Power Systems and Machines Egypt School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia

ISBN: (纸本)9781665414739

The research introduces a Z-source inverter (ZSI) as an interface for a grid-connected Photovoltaic (PV) system. The ZSI performs both boosting and inversion processes in a single stage to diminish the overall system bulk volume, and cost. The proposed control technique of the entire system aims to stabilize the inverter's input voltage and controls the power injected to the grid. The system also implements maximum power point tracking (MPPT) in addition to transferring it to the grid with a unity power factor. Two independent control variables are used to decouple the control of the DC and AC sides of the ZSI. These variables are the shoot-through (ST) duty cycle and inverter modulation index. Output results of simulation are carried out to check the effectiveness of the suggested control techniques and their capability to elicit the ultimate power while regulating the DC-link voltage and achieving unity power factor process. © 2021 IEEE.

关键词： Electric inverters

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