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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Amini, Hossein Mehrizi-Sani, Ali The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

ISBN: (纸本)9781665464543

Power system operation needs to match generation to load demand. Accurate load forecasting helps operators maintain the power balance, reducing generation costs and preventing outages. This paper proposes an improved fuzzy clustering load forecasting method based on similar day and normalization approaches, especially for short-term power system load forecasting. Simulation results utilizing data from PJM, demonstrate a reasonable forecast error compared to PJM day-ahead hourly load forecasts. The superiority of this method is the simple calculations and accuracy based on the historical data. © 2024 IEEE.

关键词： Electric load forecasting

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Artificial Neural Network-based Simplified Model Predictive Control for Enhancing Voltage Source Inverters: CMV Reduction and Weighting Factor Optimization 50

Artificial Neural Network-based Simplified Model Predictive ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shokrani, Mohammad Imanlou, Arash Behkam, Reza Nazari-Heris, Morteza Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Polytechnic Tehran Iran Department of Engineering East Carolina University GreenvilleNC United States

ISBN: (纸本)9781665464543

Controlling voltage source inverters (VSIs) is crucial for ensuring the efficient operation of inverter-based systems. Model predictive control (MPC), notably finite control set MPC (FCS-MPC), is increasingly recognized as an effective control strategy within power electronics, particularly for addressing multi-objective control problems. This article proposes a novel approach for controlling voltage source inverters using a simplified FCS-MPC scheme with common-mode voltage (CMV) reduction, limited switching frequency, and optimized weighting factors (WFs). The proposed method simplifies the MPC controller by replacing the computational burden of current predictions with a single voltage reference estimation, thereby reducing computational complexity. Moreover, offline optimization based on the non-dominated sorting genetic algorithm II (NSGA-II) is employed to determine optimized weighting factors for the cost function, as well as enhance the controller's performance. For further alleviation of computational burden, the controller is imitated using an artificial neural network (ANN) trained offline with data labeled by the original FCS-MPC algorithm. A simulation study is presented to control both conventional two-level VSI (2L-VSI) and three-level neutral point clamped (3L-NPC) inverters to demonstrate the effectiveness and validity of the proposed method. The results underline that the ANN imitator of simplified MPC reduces computational complexity while maintaining performance comparable to optimal control. © 2024 IEEE.

关键词： Cost functions

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Mitigating False Data Injection Attacks on Inverter Set Points in a 100% Inverter-Based Microgrid

Mitigating False Data Injection Attacks on Inverter Set Poin...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Beikbabaei, Milad Montano, Mario Mehrizi-Sani, Ali Liu, Chen-Ching Virginia Polytechnic Institute and State University The Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350313604

The increasing number of 100% inverter-based microgrids is introducing new challenges in their control and cybersecurity. Previous work has studied the cyber vulnerabilities of microgrids;however, very few work has studied methods to mitigate and detect cyberattacks in a 100% inverter-based microgrid. Attackers can utilize communication-based devices in a microgrid to launch false data injection (FDI) attacks and cause voltage and frequency instability. This paper studies the effects of FDI attacks on the real and reactive power set points of inverter-based resources (IBR) in a 100% inverter-based microgrid. This work co-simulates a power system using PSCAD and a communication system using Python to study FDI attacks. The communication system is modeled as a first in first out (FIFO) queue model. A long short-Term memory (LSTM)based method is used to mitigate and detect ramp and bias FDI attacks. The proposed strategy is tested on a microgrid with four IBRs subject to different FDI attacks. © 2024 IEEE.

关键词： Long short-term memory

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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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Analysis of Blockchain Sustainability through the Comparison of Different Smart Contracts Programming Languages 6

Analysis of Blockchain Sustainability through the Comparison...

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6th Distributed Ledger Technology Workshop, DLT 2024

作者： Bistarelli, Stefano Fiore, Marco Lazzizera, Antonio Ignazio Mercanti, Ivan Mongiello, Marina Department of Mathematics and Computer Science University of Perugia Perugia Italy Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy

This paper aims to analyze the correlation between programming languages used to implement smart contracts and the related Blockchain platforms, seeking an economically and environmentally viable solution. The research selects four programming languages commonly employed for smart contract creation, defining three standard functions that will be tested on the Ethereum, Tezos, Solana, and Polkadot Blockchains. The study investigates how these programming languages directly impact the amount of energy resources required and subsequently influence the cost of executing a smart contract. © 2024 Copyright for this paper by its authors.

关键词： Smart contract

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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A Nested Ensemble Method to Bilevel Machine Learning 48

A Nested Ensemble Method to Bilevel Machine Learning

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Lisha Abbas, Momin Chen, Tianyi Rensselaer Polytechnic Institute Electrical Computer and Systems Engineering Department TroyNY United States

ISBN: (纸本)9781728163277

Modern machine learning problems, such as hyperparameter optimization, meta learning, and adversarial training, adopt a bilevel learning formulation. Such problems involve a nested relation between inner- and outer-level problems, which often have suboptimal solutions with poor generalization ability. To address this issue, this paper proposes an ensemble method tailored to bilevel learning. Our method finds a nested ensemble of inner and outer parameters that improve generalization. We instantiate our general results with meta learning. We show theoretically and empirically that the diversity and the smoother loss landscape of the proposed ensemble methods lead to improved generalization over the state-of-the-art method. © 2023 IEEE.

关键词： Machine learning

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Outlier Detection and Removal Signal Processing for Wearable Transcutaneous Oxygen Sensor

Outlier Detection and Removal Signal Processing for Wearable...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Leonardi, Abigail Murphy, Ciara Hobson, Sydney Rohera, Vanshika Guler, Ulkuhan Worcester Polytechnic Institute Electrical and Computer Engineering Department WorcesterMA01609 United States

ISBN: (纸本)9798350308600

Respiratory diseases impact millions of people worldwide. In the smart and connected health era, there is a pressing need for reliable medical devices for remote monitoring of patients with respiratory disorders and patients with chronic diseases in general. For respiratory monitoring, there exist widely-used miniaturized photoplethysmography devices to measure peripheral blood oxygen saturation. More recently, transcutaneous oxygen monitors have been designed in a small form factor to be used as wearable devices to enable remote and continuous measurement of transcutaneous oxygen. In this study, we focus on processing the raw data collected from a noninvasive transcutaneous blood gas monitor that is in the development phase. Post-processing should be applied to remove the outliers in the measurements and smooth out the measurement noise. Various filters to remove outliers were tested in MATLAB, including the Hampel, rmoutliers, filloutliers, and moving average filters. The Hampel filter yielded the best results while maintaining the integrity of the data. The fillmissing function in MATLAB using the previous or linear interpolation methods is used to replace outlier data. The comparison of the raw data and the result of the data processing is presented. © 2023 IEEE.

关键词： Blood

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Additively Manufactured Receiver Design for Wirelessly-Powered Biomedical Applications

Additively Manufactured Receiver Design for Wirelessly-Power...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Marangalou, Amin Hazrati Arturo Gonzalez, Miguel Guler, Ulkuhan Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA01609 United States

ISBN: (纸本)9798350300260

This paper presents a miniaturized receiver prototype fabricated using an additive manufacturing electronics process. We aim to demonstrate the feasibility and advantages of using additive manufacturing for embedding the essential components of an inductive link in a compact form factor for miniaturized biomedical devices. We demonstrated various geometries of planar and helical coils, with average diameters of 20 mm. The wireless power transfer system comprises a class-E power amplifier operating at 6.78 MHz, a 10 μH transmitter coil, and a full-wave active rectifier fabricated in TSMC 180 nm CMOS process technology. The power delivered to a 500 Ω load at the output of the active rectifier varies from 2.7 mW to 42 mW for planar coils and 3 mW to 45 mW for helical coils, over a range of 1 cm to 4 cm. The transmitter's power consumption is kept constant at 1.25 W throughout the experiments. Furthermore, in the case of helical coils, a lateral center-to-center misalignment of 1 cm at a distance of 2 cm between the receiver and transmitter results in a 10 mW drop in the output power. © 2023 IEEE.

关键词： Additive manufacturing electronics Coils Implantables Inductive link Power transfer efficiency Wearables Wireless power transfer system

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An In-depth Investigation on the Structure, Characteristics, and Potential Applications of GaN/SiC Mosfet-Based Systems 6

An In-depth Investigation on the Structure, Characteristics,...

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6th International Conference on Information Technologies and electrical engineering, ICITEE 2023

作者： Ren, Yan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy12180 United States

ISBN: (纸本)9798400708299

In an era of rapid technological innovation, this article provides a comprehensive examination of Gallium Nitride (GaN) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs). The background section highlights the emergence and application of GaN MOSFETs and their significant advantages over silicon-based and Silicon Carbide (SiC) MOSFETs, such as smaller size, faster switching speed, less heat generation, and optimum operation at high temperatures, indicating that GaN MOSFETs are leading us into a more advanced, digitized future. The article delves into the basic theory of GaN MOSFETs, detailing their unique structure and properties that demonstrate massive potential and practicality in real-world applications such as electric vehicles and 5G technology. The conclusion emphasizes that GaN MOSFETs have already shown their importance in a wider range of industrial sectors and are set to revolutionize various industries, thereby driving overall progress. With diversified applications and broad prospects, the impact and value of GaN MOSFETs have gone beyond single-component applications, and their societal significance is undergoing profound changes. © 2023 ACM.

关键词： Gallium nitride

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