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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaobing Zhou, Xiangwei Zhang, Songyang Sun, Mingxuan Division of Electrical and Computer Engineering Louisiana State University United States Department of Electrical and Computer Engineering University of Louisiana Lafayette United States Division of Computer Science and Engineering Louisiana State University United States

ISBN: (纸本)157735897X

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients’ preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions’ winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL’s effectiveness in improving both server utility and client utility. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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Nonlinear Distortion Correlation Aware Power Allocation for Massive MIMO systems

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IEEE Transactions on Communications 2024年第6期73卷 4033-4048页

作者： Salman, Murat Babek Bjornson, Emil Guvensen, Gokhan Muzaffer Ciloglu, Tolga KTH Royal Institute of Technology Division Of Communication Systems Stockholm16440 Sweden Department of Electrical and Electronics Engineering Ankara Turkey

This article provides a comprehensive analysis aimed at addressing the impact of power amplifier nonlinearities on massive multiple-input multiple-output (MIMO) communication systems. Specifically, we derive a closed-form expression for the received distortion power, a critical aspect that has not been explored in literature. Our derived expression takes into account the combined influence of spatial diversity and frequency-selectivity on received distortion, with a particular emphasis on the slowly varying parameters, primarily the locations of users and delay spread. In the context of massive MIMO systems, characterized by an exceptionally large number of antennas, our work highlights the crucial role of distortion correlation among antennas. Neglecting distortion correlation can result in significant inaccuracies in system evaluation and algorithmic design. Utilizing the analytical framework we have developed, we formulate a power allocation problem that explicitly exploits the distortion correlation. The proposed solution outperforms methods that neglect the spatial distortion correlation, particularly in terms of spectral efficiency (SE). This superior performance is crucial in optimizing the overall system performance and ensuring desirable power allocation decisions. © 1972-2012 IEEE.

关键词： MIMO systems

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Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

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IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a Network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in electrical Networks

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computer Modeling in engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Dilated space-and-wavelength selective crosspoint optical switch

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Photonics Research 2025年第4期13卷 924-934页

作者： ZIYAO ZHANG MINJIA CHEN RUI MA BOHAO SUN ADRIAN WONFOR RICHARD PENTY QIXIANG CHENG Department of Engineering Centre for Photonic Systems Electrical Engineering Division University of Cambridge GlitterinTech Limited

Photonic integrated switches that are both space and wavelength selective are a highly promising technology for data-intensive applications as they benefit from multi-dimensional manipulation of optical signals. However, scaling these switches normally poses stringent challenges such as increased fabrication complexity and control difficulties, due to the growing number of switching elements. In this work, we propose a new type of dilated crosspoint topology, which efficiently handles both space and wavelength selective switching, while reducing the required switching element count by an order of magnitude compared to reported designs.

关键词： Arrayed waveguide gratings Optical interconnects Optical manipulation Optical signals Variable optical attenuators Wavelength selective switches

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Current and Voltage Scaling for Maximum Transferable Active Power Improvement of Weak-Grid-Tied Grid-Following Inverters 10

Current and Voltage Scaling for Maximum Transferable Active ...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

The grid-following inverter's dq admittance model manifests a negative resistance in the low-frequency range due to the phase-locked loop, potentially leading to low-frequency instabilities and limiting the maximum transferable active power (MTAP), especially in weak grid scenarios. This paper introduces a simple and efficient current and voltage scaling scheme to counteract the phase-locked loop's adverse effects, enabling the grid-following inverter to inject per-unit active power even under extremely weak grid conditions. Complemented by a detailed design procedure, this proposed method stands out for its simplicity and effectiveness. Time-domain simulations validate the proposed scheme's efficacy in MTAP enhancement. © 2024 IEEE.

关键词： Voltage scaling

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TRANSIENT STABILITY ENHANCEMENT OF GRID-FORMING TYPE-3 WIND TURBINE GENERATORS 23

TRANSIENT STABILITY ENHANCEMENT OF GRID-FORMING TYPE-3 WIND ...

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23rd Wind and Solar Integration Workshop, WIW 2024

作者： Hajtaleb, Salah Bahrani, Behrooz Electrical and Computer Systems Engineering Department Monash University Melbourne Australia

ISBN: (纸本)9781837242689

Grid-forming control is considered to be essential in the transition to renewables-dominated power systems. However, grid-forming Type-3 wind turbine generators present unique challenges due to their electromechanical coupling with the grid and the limited kinetic energy in their drive-trains. This prevents active power control methods that emulate synchronous-machine behaviour from delivering robust and stable performance in Type-3 turbines. These limitations become more evident during recovery from severe grid disturbances. To overcome these limitations, this paper proposes a lead-compensator (LC)-based approach for synchronisation and active-power control as an alternative to virtual-synchronous-generators (VSG). The proposed approach is intended to demonstrate that the system damping and the overall transient stability can be improved solely by altering the control structure. Therefore, several time-domain studies have been conducted under different grid-strength levels and current-limitation mechanisms, and the comparison of post-fault recovery performance of the two controllers are presented. The results show that excessive active power oscillations and rotor speed deviations observed with VSG control are substantially mitigated using the proposed LC approach. More importantly, this is achieved without compromising inertia and droop settings, or utilising frequency measurements. Reduced turbine mechanical stress and enhanced robustness are among the benefits of this approach. © Energynautics GmbH.

关键词： Kinetic energy

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Python-Based Cross-Platform Impedance Benchmarking Tool (CroZBenMa) for IBR's EMT Models in Matlab/Simulink and PSCAD/EMTDC 10

Python-Based Cross-Platform Impedance Benchmarking Tool (Cro...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Mohammed, Nabil Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

This paper presents a Python-driven graphical user interface (GUI) explicitly crafted for benchmarking the electromagnetic transient (EMT) models of inverter-based resources (IBRs) across Matlab/Simulink and PSCAD/EMTDC platforms through impedance measurement techniques. The GUI ensures a user-friendly experience, enabling seamless interactions between these platforms while offering advanced visualization of impedance measurement results and associated benchmarking errors. This paper delves into the Python-based implementation that powers these features. Through a series of case studies, the tool's efficacy in benchmarking IBR's EMT models is validated. The benchmarking methodology and corresponding GUI design are extensible to other leading EMT platforms such as DIgSILENT/PowerFactory, PSSE, EMTP-RV, EMTP-ATP, and DSAToolsTM/SSAT, fostering expansive and holistic research in the stability analysis of the IBR-based modern power systems. © 2024 IEEE.

关键词： Benchmarking

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Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games 63

Conjectural Online Learning with First-order Beliefs in Asym...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Li, Tao Hammar, Kim Stadler, Rolf Zhu, Quanyan New York University Department of Electrical and Computer Engineering United States Kth Royal Institute of Technology Division of Network and Systems Engineering Sweden

ISBN: (纸本)9798350316339

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyberphysical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies. Considering these limitations, we propose conjectural online learning (COL), an online learning method under generic information structures in AISGs. COL uses a forecaster-actorcritic (FAC) architecture, where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments based on information feedback, COL uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by COL are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by COL converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate COL's faster convergence over state-of-the-art reinforcement learning methods against nonstationary attacks. © 2024 IEEE.

关键词： Cost functions

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