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Blackout Mitigation via Physics-Guided RL

IEEE Transactions on Power Systems 2025年第3期40卷 2363-2375页

作者： Dwivedi, Anmol Paternain, Santiago Tajer, Ali Rensselaer Polytechnic Institute Electrical Computer and Systems Engineering Department TroyNY12180 United States

This paper considers the sequential design of remedial control actions in response to system anomalies to prevent blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions involving both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black-box counterparts. One important observation is that strategically removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts. © 1969-2012 IEEE.

关键词： Electric load flow

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A 500kHz Wide Output LLC-T Resonant Converter with Narrow Frequency Range, Reduced Circulating Energy, and Low Voltage Stress

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IEEE Transactions on Power Electronics 2025年第8期40卷 11749-11762页

作者： Hou, Zhengming Jiao, Dong Lai, Jih-Sheng Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

The resonant converters are promising topologies for the electric vehicle charging applications due to the soft switching feature. However, the conventional LLC resonant converter regulates the wide output voltage through a wide frequency range which leads to low efficiency with large circulating energy and the difficulty of the resonant tank optimization. This article proposes a novel LLC-T resonant converter which parallels an auxiliary transformer with the LC resonant tank for the wide output applications. The proposed LLC-T resonant converter narrows the frequency range with less circulating energy, lower voltage stress on the resonant capacitor, and smaller flux linkage in the resonant inductor compared to the conventional LLC resonant converter. Meanwhile, the zero-voltage switching turn-on is maintained throughout the wide output voltage range and load range. Furthermore, a precise time-domain based analysis and the design methodology are proposed. A 500-kHz, 1.35kW, 400V input /250V-450V output prototype is built to verify the concepts of the proposed topology and demonstrates a 97.82% peak efficiency. © 2025 IEEE.

关键词： Resonant converters

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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic Systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Liver Necrotic Mapping Using Diffusing Illumination Photoacoustic Imaging

Liver Necrotic Mapping Using Diffusing Illumination Photoaco...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Gao, Shang Hua, Yanbo Bhagavatula, Sharath K. Liu, Guigen Jonas, Oliver Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute MA United States Department of Biomedical Engineering Worcester Polytechnic Institute MA United States Department of Computer Science Worcester Polytechnic Institute MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School MA United States

ISBN: (纸本)9781510683860

Liver cancer treatments often face challenges, particularly when tumors are considered surgically unresectable. Percutaneous radiofrequency ablation (RFA) offers a minimally invasive alternative but can have higher recurrence rates due to incomplete ablations. Standard non-contrast ultrasound imaging cannot distinguish ablation-induced necrosis, limiting ability to monitor treatment and ensure complete tumor coverage during the ablation procedure. Photoacoustic (PA) imaging, which uses light to generate ultrasound signals, has shown promise in distinguishing between ablated and normal tissues through spectroscopic PA (sPA) imaging. However, conventional light delivery methods are unsuitable for minimally invasive procedures due to bulky fiber bundles. Optical fibers, though suitable for less invasive insertion, have limited illumination volume because of the confined light delivery only at the fiber tip. This study introduces a diffusing fiber approach for effective light delivery within the liver, enhancing the PA excitation area compared to conventional tip illumination. The diffusing fiber was tested in both phantom and ex vivo liver tissue studies. Results demonstrated that the diffusing fiber provided a wider PA excitation area and more homogeneous illumination. In an ex vivo porcine liver, the diffusing fiber accurately mapped the ablated region with minimal deviation from gross pathological measurements. Our findings suggest that this diffusing fiber approach could significantly improve the accuracy of PA imaging for liver ablation, potentially reducing recurrence rates by ensuring complete tumor ablation and minimizing complications by enabling real time monitoring. This innovation paves the way for more reliable and effective minimally invasive treatments for liver cancer. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534,I0011页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China Photonics Research Institute The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia

Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

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Hybrid Islanding Detection of an Inverter-Based High-Noise Microgrid

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IEEE Access 2025年 13卷 73161-73172页

作者： Amini, Hossein Mehrizi-Sani, Ali Noroozian, Reza Quanta Technology Transmission & Regulatory RaleighNC27607 United States Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States University of Zanjan University Blvd Department of Electrical Engineering Zanjan45371-38791 Iran

Unintentional islanding occurs when a microgrid continues operating independently after disconnection from the main grid, which can lead to voltage and frequency instability, power quality degradation, and safety risks. Few local and remote methods consider islanding detection in noisy environments. Noise can interfere with measurements and cause failed or delayed islanding detection. This paper proposes a hybrid islanding detection method that utilizes a Van der Pol Duffing oscillator (VDPDO), focusing on decreasing the detection period, zero power mismatch nondetection zone, and power quality degradation. The proposed method uses the fast Fourier transform (FFT) symmetry characteristic of a hybrid VDPDO (HVDPDO) output signal and addresses the set point threshold using the Melnikov function. This method detects the changes in frequency, phase, and voltage amplitude of the point of common coupling (PCC) simultaneously to distinguish islanded operation from other faults. For the asymmetric FFT waveform of the passive HVDPDO, the islanding system sends a signal to the inverter to change the mode from grid-following to grid-forming and injects perturbations into the frequency, phase, and voltage amplitude, simultaneously. The active HVDPDO detects islanded operations with a small non-detection zone. The inverter reverts to grid-following mode for other faults, and the main grid damps the intentionally small perturbations. Simulation and experimental results validate that the proposed method meets the IEEE standard 1547 criteria. © 2013 IEEE.

关键词： Power quality

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Efficient Path Selection Design for Large Scale LEO Satellite Constellations Using Graph Embedding-Based Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2007-2020页

作者： Kang, Yuhan Zhu, Yifei Wang, Dan Han, Zhu University of Houston Electrical and Computer Engineering Department HoustonTX77004 United States Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai200240 China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The rapid expansion of large-scale Low Earth Orbit (LEO) satellite constellations marks a new phase in global connectivity and communication. However, efficient path selection among numerous satellites connected by inter-satellite links (ISL) in such dynamic networks poses substantial challenges. This paper introduces a novel Path Selection Mechanism (PSM-LEO) utilizing Graph Embedding-Based Reinforcement Learning (GERL). Our GERL-based PSM-LEO method employs Graph Embedding (GE) to model the satellite network in a simplified low-dimensional space, simplifying the analysis of complex satellite relationships. Combining Reinforcement Learning (RL) with GE allows for dynamic adaptation of path choices in response to dynamic network conditions and communication needs. To the best of our knowledge, we are the first to propose GERL for PSM-LEO. We evaluate our method's performances through simulations in the Ansys Systems Tool Kit (STK), focusing on diverse LEO scenarios. Our results reveal that our approach surpasses several benchmarks in convergence speed, end-to-end latency, and energy consumption, demonstrating enhanced data transfer efficiency, reduced latency, and improved reliability. Additionally, we assess the scalability of the proposed method by analyzing its performance with increasing satellite constellation sizes. Our results confirm the framework's high scalability, demonstrating its suitability for addressing path selection challenges in future larger satellite networks. © 2013 IEEE.

关键词： Graph embeddings

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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method

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Photoacoustics 2025年 44卷

作者： Lin, Fangzhou Gao, Shang Tang, Yichuan Ma, Xihan Murakami, Ryo Zhang, Ziming Obayemi, John D. Soboyejo, Winston O. Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States 60 Prescott Street WorcesterMA01605 United States Department of Mechanical & Materials Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate and quantify chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, PA imaging is highly susceptible to noise, leading to a low signal-to-noise ratio (SNR) and compromised image quality. Furthermore, low SNR in spectral data adversely affects spectral unmixing outcomes, hindering accurate quantitative PA imaging. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. Moreover, spectral information preservation is unclear, limiting their adaptability for clinical usage. Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, in vivo, and ex vivo studies. These studies demonstrated that SPADE improved image SNR by over 15 dB in high noise cases and preserved spectral information (R > 0.8), outperforming conventional methods, especially in low SNR conditions. SPADE presents a promising solution for preserving the accuracy of quantitative PA imaging in clinical applications where noise reduction and spectral preservation are critical. © 2025 The Authors

关键词： Image denoising

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G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Xingyu Tang, Bo Mississippi State University Department of Electrical and Computer Engineering Mississippi StateMS39762 United States Worcester Polytechnic Instiute Department of Electrical and Computer Engineering WorcesterMA United States

Deep neural networks (DNNs) have demonstrated promising results in various complex tasks. However, such DNN models face challenges related to over-parameterization, particularly in scenarios where training data is scarce. In response to these challenges and to improve the generalization capabilities of DNNs, the Mixup technique has emerged, which effectively addresses the limitations posed by over-parameterization. Nevertheless, it still produces suboptimal outcomes. Inspired by the successful Sharpness-Aware Minimization (SAM) method, which establishes a connection between the sharpness of the training loss landscape and model generalization, we propose a new learning framework called Generalized-Mixup, which combines the strengths of Mixup and SAM for training DNN models. The theoretical analysis provided demonstrates how the developed G-Mix framework enhances generalization. Additionally, to further optimize DNN performance with the G-Mix framework, we introduce two novel algorithms: Binary G-Mix (BG-Mix) and Decomposed G-Mix (DG-Mix). These algorithms partition the training data into two subsets based on the sharpness-sensitivity of each example to address the issue of "manifold intrusion"in Mixup. Both theoretical explanations and experimental results reveal that the proposed BG-Mix and DG-Mix algorithms further enhance model generalization across multiple datasets and models, achieving state-of-the-art performance. © 2020 IEEE.

关键词： Deep neural networks

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