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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Rajaie Nassar Promit Datta Guo-Quan Lu Christina DiMarino Khai Ngo The Bradley Department of Electrical and Computer Engineering Center for Power Electronics Systems (CPES) Virginia Polytechnic Institute and State University Blacksburg VA USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

There is a constant push towards more compact and efficient power electronic converters. However, the fringing losses of gapped magnetic components inhibit size reduction by requiring extra space to keep the winding away from the gaps. Concurrent high efficiency and high power density cannot thus be achieved with conventional gapping approaches. A core structure that reduces the fringing and overall losses without requiring additional space is presented here, simultaneously enabling higher efficiency and power density. The geometry also reduces the sensitivity of the winding to its position in the winding window compared to the conventional discrete gapping approach. The idea is verified in a planar inductor design, decreasing the power loss of a soft-switched unloaded buck converter by more than 32%.

关键词： Resistance Geometry Power system measurements Sensitivity Density measurement Magnetic cores Windings Power electronics Magnetic losses Inductors

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Stability Analysis of Distribution Networks With High Penetration of Distributed Energy Resources

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IEEE Access 2025年 13卷 83867-83880页

作者： Venkataramanan, Ashwin Wang, Wenzong Ramasubramanian, Deepak Huque, Aminul Mehrizi-Sani, Ali Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg 24061 VA United States Electric Power Research Institute (EPRI) Knoxville 37932 TN United States

Increasing penetration of distributed energy resources (DER) has already resulted in several operational challenges in the power system. While literature focuses on stability challenges to the bulk power system due to high penetration of inverter-based resources (IBR), the challenges from a distribution system becoming unstable due to high DER penetration are not sufficiently addressed. Increasing DER penetration also creates opportunities for a local resource to improve stability in the distribution network without fully relying on support from the transmission system. This work utilizes real-world distribution feeders to first analyze stability challenges due to high-DER penetration and evaluates solutions from GFM-based local resources in a distribution network. This paper then proposes a new screening or system strength index that is based on the equal area method and utilizes only steady-state information in evaluating relative stability of DER interconnections in a large distribution area. The proposed index is further utilized in identifying distribution system strength improvement with advanced inverter controls. © 2013 IEEE.

关键词： Distributed energy resources distribution stability grid-following grid-forming system strength transient stability

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Ga2O3 TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network

arXiv

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arXiv 2025年

作者： Nguyen, Le Minh Long Ong, Edric Eng, Matthew Zhang, Yuhao Wong, Hiu Yung Department of Electrical Engineering San Jose State University San JoseCA95192 United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

In this paper, we demonstrate the possibility of performing automatic Technology computer-Aided-Design (TCAD) parameter calibration using machine learning, verified with experimental data. The machine only needs to be trained by TCAD data. Schottky Barrier Diode (SBD) fabricated with emerging ultra-wide-bandgap material, Gallium Oxide (Ga2O3), is measured and its current-voltage (IV) is used for Ga2O3 Philips Unified Mobility (PhuMob) model parameters, effective anode workfunction, and ambient temperature extraction (7 parameters). A machine comprised of an autoencoder (AE) and a neural network (NN) (AE-NN) is used. Ga2O3 PhuMob parameters are extracted from the noisy experimental curves. TCAD simulation with the extracted parameters shows that the quality of the parameters is as good as an expert’s calibration at the pre-turned-on regime but not in the on-state regime. By using a simple physics-informed neural network (PINN) (AE-PINN), the machine performs as well as the human expert in all regimes. © 2025, CC BY.

关键词： Schottky barrier diodes

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INTERFERENCE-AWARE EDGE RUNTIME PREDICTION WITH CONFORMAL MATRIX COMPLETION

arXiv

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arXiv 2025年

作者： Huang, Tianshu Ramesh, Arjun Ruppel, Emily Pereira, Nuno Rowe, Anthony Joe-Wong, Carlee Department of Electrical and Computer Engineering Carnegie Mellon University United States Robert Bosch GmbH Germany INESC TEC Polytechnic Institute of Porto Portugal

Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation;these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% – 2x better than a naive application of existing methods. © 2025, CC BY.

关键词： Matrix factorization

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Modelling and Comparative Analysis of Robust Fuzzy Controllers for LFC of an Islanded AC Microgrid

Modelling and Comparative Analysis of Robust Fuzzy Controlle...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Sunil Kumar Bhatta Prakash Chandra Sahu Sarat Chandra Swain Buddhadeva Sahoo Amiya Kumar Sahoo Department of Electrical KIIT Polytechnic Bhubaneswar India School of Electrical and Comp. Science Indian Institute of Technology Bhubaneswar India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India Department of Electrical & Electronics Engineering SR University Warangal Telengana India Department of Computer Science Aryan Institute of Engineering & Technology Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The innovative research work presented in this manuscript addresses different Fuzzy ruled based controllers as closed frequency stabilization loop of an energy efficiented stand alone Microgrid. The Microgrid model recommended for this investigation is single area based where several distributed generation (DG) featured power producing setups are assembled together. After assembling all DG s to a common platform, the established grid is known as the microgrid. The term microgrid comes from the micro-MW generating capability of the this grid by combining all capacity of all connected individual DGs. The DGs like wind electricity plant, Solar photovoltaic (PV) electricity generating system, micro-tubine, Fuel-cell, Geo-thermal, diesel-generator and thermo-electric generation system. The microgrid is also incorporated with energetic power accumulating banks like battery bank, Super-capacitor (ultra-capacitors) and flywheel system. The microgrid faces frequency instability problem when load dynamics and wind, solar power uncertainties come to the picture. The LFC makes an accurate control strategy to build quick stability over frequency dynamic. The LFC employs several fuzzy ruled controller such as conventional Fuzzy-PID (F-PID), Fractional ordered Fuzzy-PID (FO-FPID), Multi-stage Fuzzy PID (MS-FPID) and an ideal Type-2 Fuzzy PID (T2-FPID) controllers as secondary control loop for this study. The performance of all above discussed controllers are synthesized in an unique Vortex Search algorithm (VSA).

关键词： Uncertainty Protocols Electricity Microgrids Power system stability Stability analysis Distributed power generation Smart grids Frequency control Numerical stability

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Dimensionality and dynamics for next-generation artificial neural networks

Patterns

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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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Nonlinear hybrid vector finite element method for modeling wave propagation in nonlinear media

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RADIO SCIENCE 1996年第4期31卷 913-922页

作者： Polstyanko, SV Lee, JF Department of Electrical and Computer Engineering Worcester Polytechnic Institute

In this paper a unified computer-aided approach based on the vector finite element method is proposed to study the propagation of optical waves in dielectric nonlinear waveguides. Most of the existing methods are based on the scalar Helmholtz wave equation and cannot adequately describe the vectorial properties of fields in nonlinear optical structures. To take into account vectorial field properties and properties of the medium without limitations, a new nonlinear hybrid vector finite element method based on the vectorial wave equation is developed. Several numerical examples are given to demonstrate the applicability of this method.

关键词： Optical waveguides Finite element analysis Nonlinear optics Optical beams Electric fields Mathematical model

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Mitigating Power System Oscillations in PMSGWind Integrated Grids Using Power Electronics and Inertia Control: A Comprehensive Review

Mitigating Power System Oscillations in PMSGWind Integrated ...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Aliyu Sabo Kabir Garba Jibia Hossein Shahinzadeh Mohammad Mohsen Hayati Mehdi Abapour Gevork B. Gharehpetian Department of Electrical and Electronic Engineering Advanced Lightning Power and Energy Research (ALPER) Universiti Putra Malaysia Malaysia Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tabriz Iran Energy Research Institute (ESRI) Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids.

关键词： Damping Renewable energy sources Uncertainty Reviews Power system stability Power electronics Synchronous generators Permanent magnets Oscillators Wind energy conversion

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A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues

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IEEE Communications Surveys and Tutorials 2025年

作者： Wang, Zibo Ji, Haichao Zhu, Yifei Wang, Dan Han, Zhu Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering Houston United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has restricted the traditional data analytics workflow, where the edge data are gathered by a centralized server to be further utilized by data analysts. To continue leveraging vast edge data to support various data-incentive applications, computing paradigms have promoted a transformative shift from centralized data processing to privacy-preserved distributed data processing. The need to perform data analytics on private edge data motivates federated analytics (FA), an emerging technique to support collaborative data analytics among diverse data owners without centralizing the raw data. Despite the wide applications of FA in industry and academia, a comprehensive examination of existing research efforts in FA has been notably absent. This survey aims to bridge this gap by first providing an overview of FA, elucidating key concepts, and discussing its relationship with similar concepts. We then thoroughly examine FA, including its key challenges, taxonomy, and enabling techniques. Diverse FA applications, including statistical metrics, frequency-related applications, database query operations, FL-assisting FA tasks, and other wireless network applications are then carefully reviewed. We complete the survey with several open research issues, future directions, and a comprehensive lessons learned part. This survey intends to provide a holistic understanding of the emerging FA techniques and foster the continued evolution of privacy-preserving distributed data processing in the emerging networked society. © 1998-2012 IEEE.

关键词： Data assimilation

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