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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Adham, Midrar Keene, Sean J. Slay, Tylor Kolln, Jaime T. Bass, Robert B. Portland State University Department of Electrical & Computer Engineering PortlandOR97201 United States Pacific Northwest National Laboratory RichlandWA99354 United States

ISBN: (纸本)9798350352528

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers' privacy and ensuring their agency over their Distributed Energy Resources. © 2025 IEEE.

关键词： DER DERMS Energy Service Interface Grid Services GridAPPS-D

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Grid Sweep: Active and Passive Signal Processing

Grid Sweep: Active and Passive Signal Processing

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Yop, Philip Shehada, Halah Lawrence Livermore National Laboratory LivermoreCA94550 United States Virginia Tech Electrical and Computer Engineering Department BlacksburgVA24060 United States

ISBN: (纸本)9798350352528

The GridSweep device is an actively probing wave-form measurement unit developed by McEachern Laboratories in conjunction with Lawrence Livermore National Lab and Lawrence Berkeley National Lab. The active probing of the device combined with high sensitivity, and GPS time synchronization allow coordinated measurements of distribution system frequency response and other grid characteristics from common electrical outlets. This paper presents an initial review of the signal processing techniques applied to the collected data, a summary of interesting results from several different processing modalities including the active signal response, machine learning techniques, and ambient signal correlation. The early results indicate the measurements from the GridSweep device have potential for use to detect local grid properties like system impedance, and characteristics and identification of distributed energy resources connected on the system. © 2025 IEEE.

关键词： Signal analysis

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On Strong Secrecy for Multiple Access Channels With States and Causal CSI

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IEEE Transactions on Information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

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Comprehensive Design, Modeling and Analysis of Grid-Forming Type IV Wind Turbine Generators Using State-Space Methods

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IEEE Access 2025年 13卷 91089-91112页

作者： Harith E. Udawatte Mohammad H. Ravanji Behrooz Bahrani Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia Department of Electrical Engineering Sharif University of Technology Tehran Iran

Grid-forming (GFM) control has emerged as a promising solution to the challenges posed by the increasing reliance on inverter-based resources (IBRs). However, unlike in a battery-based IBR, the implementation of GFM in wind turbine generators (WTGs) introduces challenges due to multiple machine-side converter (MSC) and grid-side converter (GSC) interactions. In this work, a GFM-WTG control structure is adopted in which the MSC primarily regulates the DC-link voltage, while the GSC emulates grid-forming behavior using virtual synchronous generator principles. Accordingly, this paper presents a practical control implementation scheme and a systematic small-signal modeling framework for GFM WTGs using the component connection method, enabling a unified state-space representation that captures key electromechanical, aerodynamic and control interactions inside the GFM-WTG system. The proposed model is validated through electromagnetic transient simulations, and eigenvalue and participation factor analyses reveal strong MSC-GSC inter-dependencies. Sensitivity analysis further confirms model accuracy across varying operating conditions. Additionally, a reduced-order model is derived to balance computational efficiency with dynamic fidelity. The findings provide a robust foundation for stability analysis and control tuning of GFM WTGs, supporting their reliable integration into future power grids.

关键词： Aerodynamics Wind turbines Grid forming Power system stability Computational modeling Analytical models Wind energy Voltage control Generators Eigenvalues and eigenfunctions

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A Hybrid Adversarial-TransUnet Architecture for Improving LV Segmentation Using Cardiac Cine Images 1st

A Hybrid Adversarial-TransUnet Architecture for Improving L...

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1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Abdeltawab, Hisham Jani, Md. Rafsan Hasan, Md Rakibul Yasser, Ibrahim Khalifa, Fahmi Department of Biomedical Engineering and Systems Cairo University Cairo12613 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States Department of Electronics and Communications Engineering Mansoura University Mansoura35516 Egypt

ISBN: (纸本)9783031823763

Cardiac image segmentation is crucial for the assessment of heart anatomy and function. Deep learning (DL) has been widely employed for cardiac image segmentation and the accuracy of the segmentation has improved due to the advances in DL research and the existence of large cardiac datasets. In this study, we introduce a DL framework based on adversarial training for the delineation of the left ventricle (LV) from cardiac magnetic resonance imagining (CMRI). Our framework consists of two networks: the segmenter which is a neural network that predicts segmentation maps for the myocardium, the discriminator which is a convolutional neural network that discriminates between the ground truth and the predicted segmentation maps. We investigate two different architectures for the segmenter (Unet and TransUnet). The adversarial training can identify and correct the differences between the predicted and the ground truth maps and results in better segmentation accuracy than stand-alone Unet or TransUnet. The proposed framework has been evaluated on cine CMRI, collected from 100 patients, and evaluated using different metrics (Dice coefficient, Jaccard Index, and Hausdorff distance). The results documented an improvement of the segmentation accuracy (0.91, 0.82, 4.69 mm) using the proposed framework with TransUnet compared to the segmentation obtained by stand-alone Unet (0.75, 0.68, 7.54 mm). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Improved Single DC Source Hybrid Neutral Point Clamped Converter with 13 Levels

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IEEE Transactions on Power Electronics 2025年

作者： Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research United States

In this paper, a 13-level circuit topology is proposed, which features a single dc source, extended linear modulation range, and a high ratio of output voltage levels to semiconductor devices. The proposed converter, which consists of a series-connected floating H-bridge (FHB) supplementing a five-level active neutral point clamped converter (5L-ANPC), can generate 13 pole voltage levels in the normal modulation region and 15 levels in the extended modulation region. The key performance improvements offered by the proposed topology are enhanced power quality, reduced losses, and increased power density. Whereas existing schemes with a similar circuit structure regulate the FHB to one-eighth of the dc-link voltage and only achieve nine levels, the proposed joint-phase redundancy balancing approach regulates the FHB stage to one-twelfth of the total dc-link voltage on a switching-frequency basis, thereby increasing output power quality while reducing the converter's cost and size without the need for multiple isolated supplies. Further, the proposed modulation scheme also allows for a switching frequency reduction of the higher voltage blocking 5L-ANPC stage by shifting the high-frequency switching to the low voltage blocking FHB stage, thus reducing losses. This work also evaluates operation in the topology's extended linear modulation range which is shown to be increased to 0.612 times the dc-link voltage (modulation index, M = 1.223) without limitations on load power factors, compared to the typical limit of to 0.577 times the dc-link voltage (M = 1.154) for non-boosting topologies such as 3L-ANPC and 5L-ANPC. The proposed converter uses boosting states enabled by the FHB in this extended modulation region, which increases the number of output levels to 15. The effectiveness of the proposed topology and capacitor balancing scheme is validated using experimental and analytical results. Based on experimental and analytical results, the proposed topology is shown to h

关键词： Model predictive control

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Research on Enterprise Risk Decision Support System Optimization based on Ensemble Machine Learning 24

Research on Enterprise Risk Decision Support System Optimiza...

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5th International Conference on computer Science and Management Technology, ICCSMT 2024

作者： Gong, Chenwei Lin, Yuzhen Cao, Jinming Wang, Jun Henry Samueli School of Engineering Department of Computer University of California Los Angeles United States School of Information Systems and Management Carnegie Mellon University Jersey City United States Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland United States School of Economics and Management Wuhan University Hubei Wuhan China

ISBN: (纸本)9798400709999

Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner. By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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FlyWheel: A Robotic Platform for Modeling Fly Visual Behavior 13th

FlyWheel: A Robotic Platform for Modeling Fly Visual Behavi...

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13th International Conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Nourse, William R. P. Quinn, Roger D. Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031725968

An ongoing problem in robotics is the calculation of body motion given motion in the visual field, also known as ego-motion estimation. This is a problem which has been solved in the visual system of most animals, including the fruit fly Drosophila melanogaster. Here we present FlyWheel, an open-source robotic platform for studying models of the visual motion-processing system in insects. We showcase a dataset of rotational motion data in real-world conditions using the robot, and use a simplified model of the motion pathway in Drosophila as a baseline for further comparison and development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Flywheels

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The Role of Generative AI in Deepfake Detection: A Systematic Literature Review

The Role of Generative AI in Deepfake Detection: A Systemati...

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International Conference on Smart Business and technology, ICSBT 2024

作者： Acim, Btissam Kharmoum, Nassim Lagmiri, Souad Najoua Ziti, Soumia Department of Computer Science Intelligent Processing & Security of Systems Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco IT Networking Security and Management Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Intelligence (AI) does expand possibilities and give an opportunity to achieve terrific outcomes and has innumerable potential uses. This systematic literature review focuses on the role of generative AI in the improvement of deepfake detection technologies, providing a comparison between different methods and assessing their effectiveness. By analyzing recent studies, it depicts the techniques employed most frequently in deepfake detection, such as the ones concentrating on the intricacies between the visual and auditory elements, the temporal domains, as well as any minor signs of alteration. Due to the fact that generative AI is rapidly improving, the methods that are used to detect its use are often inadequate, which calls for more studies on the subject matter. Besides, by combining audio and visual elements when performing the analysis, calling it as a method that can be useful in focusing the detection growth potential with diverse and unstructured data. Also, this study evaluates the advantages and limitations of the techniques existing today and demonstrates their relative effectiveness in controlled and real conditions. Clarifying that measures aimed at detecting AI generated content should be preoccupied with a technique that is able to keep pace with changes in generative AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Data-Driven Adaptive Control for Frequency Perturbations in Power systems with PV-SmartParks

Data-Driven Adaptive Control for Frequency Perturbations in ...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Ali Arzani Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Seamless operation of PV plants during both transient and dynamic conditions is imperative for all stakeholders including energy control centers. Fast and dampened transient response, low steady-state error, and stability are sought for power system operation with PV plants. For PV power perturbation at point of interconnection (POI), this can be further supported by deploying auxiliary fast-acting non-synchronous generation such as SmartParks (SPs). SmartParks are parking lots with a large number of electric vehicles performing bidirectional power transfers, namely, vehicle-to-grid and grid-to-vehicle. In this study, a data-driven adaptive frequency controller (DD-AFC) was developed for SPs to compensate for frequency perturbations at PV-POI. The DD-AFC was incorporated into the controller of SPs to provide a fast response for frequency perturbations caused by PV-site power perturbations. The adaptiveness of the frequency droop comes from data-driven empirical characterization of the PV plant operation at the PV-site location using time-series data analytics. The DD-AFC supports fast-frequency control response by SPs for mitigating fast power perturbation at PV-POIs. The effectiveness of the DD-AFC was illustrated via real-time simulations of a bulk power system with a 210MW p PV plant and 100MW p of SmartParks in RTDS.

关键词： Automatic frequency control Time-frequency analysis Vehicle-to-grid Transient response Data analysis Perturbation methods Power system stability Adaptive control Transient analysis Vehicle dynamics

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