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Generalized model for the charge transport properties of dressed quantum Hall systems

Physical Review B 2022年第3期105卷 035430-035430页

作者： Kosala Herath Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton 3800 VIC Australia

We present a generalized mathematical model for predicting the transport properties of a quantum system exposed to a stationary magnetic field and a high-intensity electromagnetic field. The new formulation, which applies to two-dimensional (2D) dressed quantum Hall systems, is based on Landau quantization theory and the Floquet-Drude conductivity approach. We model our system as a two-dimensional electron gas (2DEG) that interacts with two external fields. To analyze the strong light coupling with the 2DEG, we employ the Floquet theory as a nonperturbative procedure. Moreover, the Floquet-Fermi golden rule is adopted to explore the impurity scattering effects on charge transport in disordered quantum Hall systems. We derive fully analytical expressions to describe longitudinal components in the conductivity tensor in dressed quantum Hall systems. Subsequently, we demonstrate that the conductivity characteristics of quantum Hall systems can be manipulated using strong external light. Our results align with well-established and experimentally verified theoretical descriptions for undressed systems while providing a more generalized analysis of the conductivity characteristics in quantum Hall systems. Thus our model can be applied to accurately interpret the usage of strong external radiation as a tool in nanoscale quantum devices.

关键词： Hall effect Quantum transport 2-dimensional systems Floquet systems Drude model

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Acoustic Amplifying Diode Using Nonreciprocal Willis Coupling

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Physical Review Letters 2023年第17期130卷 176101-176101页

作者： Xinhua Wen Heung Kit Yip Choonlae Cho Jensen Li Namkyoo Park Department of Physics The Hong Kong University of Science and Technology Kowloon Hong Kong China Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea

We propose a concept called acoustic amplifying diode combining signal isolation and amplification in a single device. The signal is exponentially amplified in one incident direction with no reflection and is perfectly absorbed in another. The reflection is eliminated from the device in both directions with impedance matching, preventing backscattering to the signal source. Here, we demonstrate the amplifying diode using an active metamaterial with nonreciprocal Willis coupling. We also discuss the situation with the presence of both reciprocal and nonreciprocal Willis couplings for more flexibility in implementation. The coexistence of both amplifier and perfect absorber in opposite incident directions extends the regime of sound isolation and further enables applications in sensing and communication, in which nonreciprocity can play an important role.

关键词： Acoustic metamaterials Non-Hermitean systems

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Floquet engineering of dressed surface plasmon polariton modes in plasmonic waveguides

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Physical Review B 2022年第23期106卷 235422-235422页

作者： Kosala Herath Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton 3800 VIC Australia

We present a comprehensive study surface plasmon polariton (SPP) propagation on planar metallic waveguides under a dressing field. We perform a set of analytical calculations of the periodic time-dependent Schrödinger equation to study the interaction of an intense electromagnetic field with a metallic system. We model the strong light coupling to the metallic system using a nonperturbative procedure. In this paper, we show that, by introducing Floquet theory into the dressed metal, the behavior of the Floquet states are similar to the phase-modulated signals in the communication system. This will reveal a new perspective on the underlying science in an intricate, dressed quantum system. Furthermore, we examine the impurity scattering effects on charge transport in disordered plasmonic metals using the generalized Floquet-Fermi golden rule and provide a novel approach to diminish the losses in plasmonic materials using the dressing field. To gauge the effectiveness of our results, we introduce a figure of merit to compare the performance of plasmonic metals, subjective to a dressing field. The calculated dressed SPP characteristics suggest that high-efficiency SPP propagation can be achieved in practice. Our findings open up new directions for state-of-the-art nanoplasmonic devices.

关键词： Surface plasmons Waveguides Nanotechnology

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Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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IEEE Conference on Decision and Control

作者： Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory HUNREN Institute for Computer Science and Control Hungary

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.

关键词： Technological innovation Sensitivity Frequency-domain analysis Stability criteria Prototypes Throughput Frequency response Motion control Optimization MIMO communication

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On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

arXiv

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

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Disturbance Rejection Control for PMSGs Based on Novel Sliding Mode Control Law and Unknown System Disturbance Estimator

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IEEE Transactions on Energy Conversion 2025年

作者： Wei, Chun Yin, Tianqi Qiao, Wei Zhu, Ying Zhejiang University of Technology College of Information Engineering Zhejiang Hangzhou310023 China University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588 United States Hohai University School of Electrical and Power Engineering Nanjing211100 China

This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities. © 1986-2012 IEEE.

关键词： Sliding mode control

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Communication-efficient and Decentralized Federated Minimax Optimization

Communication-efficient and Decentralized Federated Minimax ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Sajjad Ghiasvand Amirhossein Reisizadeh Mahnoosh Alizadeh Ramtin Pedarsani Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

As distributed learning applications like Federated Learning, the Internet of Things (IoT), and Edge Computing expand, addressing their limitations becomes crucial. We approach decentralized learning across a network of communicating clients or nodes, focusing on two primary challenges: data heterogeneity and adversarial robustness. To address these, we introduce a decentralized minimax optimization method incorporating two key components: local updates and gradient tracking. Minimax optimization serves as a fundamental tool for adversarial training, ensuring robustness. Local updates are vital in Federated Learning (FL) to alleviate the communication bottleneck, while gradient tracking is necessary to demonstrate convergence amid data heterogeneity. Our analysis of the proposed algorithm, Dec-Fed Track, in nonconvex-strongly-concave minimax optimization demonstrates its convergence to a stationary point. Additionally, numerical experiments support our theoretical results.

关键词： Training computer aided instruction Federated learning Distance learning Optimization methods Focusing Robustness Internet of Things Convergence Edge computing

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Short-Term Prediction of Solar Photovoltaic Power Generation Using a Digital Twin

Short-Term Prediction of Solar Photovoltaic Power Generation...

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Annual North-American Power Symposium

作者： John Yonce Michael Walters Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variable and uncertain generation sources due to changing weather conditions. This makes operations and controls challenging and complex. To better understand and manage the dynamic nature of solar PV power plants, digital twins (DTs) will be needed. DTs based on artificial intelligence (AI) methods can be applied to replicate the dynamics of PV plants. This study utilizes a popular paradigm of AI - neural networks to create a variety of data-driven DT (DD-DT) prediction models for a 1 MW solar PV plant located at Clemson University in South Carolina, USA. State-of-the-art internet of things (IoT) based real-time measurements are used to develop the DD-DTs. Typical results for short-term PV power prediction for DTs implemented using multilayer perceptron neural networks (MLPNNs) and Elman recurrent neural networks (ERNNs) are presented in this paper.

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Training Graph Neural Networks on Growing Stochastic Graphs

Training Graph Neural Networks on Growing Stochastic Graphs

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

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.

关键词： Training Convolution Scalability Signal processing algorithms Graph neural networks Acoustics Computational efficiency

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