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2023 IEEE International Conference on Big Data, BigData 2023

作者： Carey, Alycia N. Bhaila, Karuna Wu, Xintao University of Arkansas Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350324457

Differential privacy, the current gold standard for data anonymization and protection, is commonly known to cause degraded utility, and exacerbate unfairness, for different demographic groups when it is used to train a private machine learning model. However, in contrast with this long-held perception, recent work has shown that local differential privacy, a variant of differential privacy where users perturb their data on their device before it is aggregated, can surprisingly lead to improved fairness measures without significantly affecting the utility of the underlying machine learning model. Motivated by this previous work, in this paper we further show that applying randomized response, a popular local differential privacy method, does not incur disparate impact on the private model's accuracy for different demographic groups. Specifically, through conducting thorough empirical analysis in which we perform randomized response on the labels, the features, or on both the features and labels across multiple data modalities and model architectures, we empirically show that the absolute difference in utility loss for different demographic groups is negligible. © 2023 IEEE.

关键词： fairness local differential privacy machine learning randomized response

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Dynamic Defense Policies Learning Through Soft Actor-Critic for Soccer Robot Goalkeepers

Dynamic Defense Policies Learning Through Soft Actor-Critic ...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Li, Shih-An Huang, Chun-Jui Wong, Ching-Chang Feng, Hsuan-Ming Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan National Quemoy University Department of Computer Science and Information Engineering Kinmen Taiwan

ISBN: (纸本)9798350352788

This paper implements a dynamic goalkeeper defense policy based on Deep Reinforcement Learning (DRL) for a soccer robot. This study proposed a multi-directional training approach to enable soccer goalkeepers to learn defense strategies for acting an appropriate manner. This paper using the Soft Actor-Critic (SAC) as DRL architecture and the Gazebo simulator as an environment simulation platform. Utilizing progressive learning with good reward function design to enable soccer robot goalkeepers to explore and learn in a simulated environment. It efficiently completed the better defense task. Finally, the correctness of the defense policy is evaluated by testing and validating it in a multi-directional area. The experimental results demonstrated the designed SAC containing the superior performance for successful defense rates in this paper. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Load Insensitive Model Predictive Control for a Three-phase Inverter with L-C Filter

A Load Insensitive Model Predictive Control for a Three-phas...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Zeng, Jianwu Kim, Taesic Winstead, Vincent Minnesota State University Department of Electrical and Computer Engineering and Technology MankatoMN56001 United States University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65201 United States

ISBN: (纸本)9798350376067

The traditional model predictive control (MPC) for power inverters is designed upon a certain operating point and is solved with quadratic programming (QP). The performance may deteriorate under variations of converter parameters or the load. This paper proposes a load-insensitive MPC (LI-MPC) for a three-phase inverter. The load current is first estimated from the current and voltage measured from the L-C filter, then the optimization problem is online solved with the gradient descent (GD) method, which makes the solution insensitive to the parameter's variation in the power converter. Experiment results show that the LI-MPC not only has a lower and more stable total harmonic distortion value than the QP-MPC, but has a small voltage tracking error as well. Therefore, the proposed LI-MPC can achieve better power quality than the traditional MPC. © 2024 IEEE.

关键词： Predictive control systems

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Fuzzing for Power Grids: A Comparative Study of Existing Frameworks and a New Method for Detecting Silent Crashes in Control Devices

Fuzzing for Power Grids: A Comparative Study of Existing Fra...

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2023 IEEE Design Methodologies Conference, DMC 2023

作者： Uwibambe, Marie Louise Pan, Yanjun Li, Qinghua University of Arkansas Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350315547

Power grids serve as the backbone of modern society, supplying electricity for a wide range of critical uses, including the residential, commercial, and industrial sectors. As the power grid infrastructure becomes more reliant on networked digital technologies, it also becomes more vulnerable to cyberattacks. To ensure the resilience and security of the power grid, it is necessary to identify security vulnerabilities in power electronic devices. Fuzzing is an effective technique for detecting security flaws in software/firmware by subjecting the system under test to a series of unexpected inputs, which elicits unexpected behavior such as system crashes. While some work has been done on fuzzing power grid systems, there still lacks a comprehensive, comparative study of existing solutions. Furthermore, new methods are still needed to detect unexpected behaviors of power electronic devices during fuzzing. In this paper, we first conduct a survey and a comparative study of existing fuzzing frameworks for power grid systems. We then hypothesize and verify the use of electromagnetic waves as a method to detect silent system crashes in power electronic devices, aiming to advance fuzzing in this field. © 2023 IEEE.

关键词： Electromagnetic waves

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Data-Driven Detection of Data Falsification Cyber-Attacks in Distributed Generation

Data-Driven Detection of Data Falsification Cyber-Attacks in...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Anin, Johnson Abdelsalam, Omar Nabil, Mahmoud Hu, Fei Alsharif, Ahmad University of Alabama Department of Electrical and Computer Engineering AL35487 United States Tennessee Tech University Department of Computer Science TN35405 United States University of North Carolina A&T Electrical and Computer Engineering NC27411 United States University of Alabama Department of Computer Science AL35487 United States Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt

ISBN: (纸本)9798350376715

The advancement of smart grid technology has enabled consumers to become active participants in electricity generation, particularly using renewable-based distributed energy resources. While this evolution offers economic benefits by allowing consumers to sell excess energy back to the grid, it has also opened the door to fraudulent activities. Generation theft, a malicious act where consumers report falsified higher energy generation readings for financial gain, has emerged as a significant challenge, leading to estimated annual losses of approximately $6 billion in the United States alone. In response to this issue, our paper introduces a novel approach to the detection of fraudulent electricity generation within the grid. Our primary contribution is the creation of a benchmark dataset, based on actual power usage and generation data from the Ausgrid dataset, which now includes simulated fraudulent activities alongside normal usage patterns. Our investigation further explores the statistical properties of power consumption patterns among fraudulent users, revealing a distinct lack of periodicity and predictability. By employing the day-to-day correlation matrix as an input to a 2D Convolutional Neural Network (CNN), our detection system significantly improves its ability to distinguish between legitimate and fraudulent electricity use. Additionally, we propose a hybrid deep learning model that merges the strengths of 2D CNNs with the sequential pattern recognition capabilities of Long Short-Term Memory (LSTM) networks. This approach effectively captures both spatial and temporal dynamics within the dataset, leading to a remarkable performance with a detection rate of 97.04%, a false alarm rate of 0.67%, and an overall accuracy of 99.70%. © 2024 IEEE.

关键词： Cyber attacks

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Probabilistic Model Checking of Temporal Interaction Dynamics in the Supreme Court 11th

Probabilistic Model Checking of Temporal Interaction Dynami...

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11th International Symposium on From Data to Models and Back, DataMod 2023

作者： Das, Susmoy Sharma, Arpit Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (纸本)9783031872167

The Supreme Court plays an extremely critical role in ensuring adherence to the rule of law and in strengthening the democracy. Due to this reason, modeling and analysis of small group interactions in the courtroom setting is an important task as it can help in understanding court decision-making. We apply probabilistic model checking for the modeling and analysis of temporal interaction dynamics in the context of the Supreme Court of the United States. We have used the transcripts of the oral arguments of cases from the Supreme Court for constructing a discrete-time Markov reward model (DTMRM). Next, we formulate interesting queries over interaction by using probabilistic computation tree logic (PCTL) and PCTL with rewards and verify them using a probabilistic symbolic model checker (PRISM). Our experimental results show that probabilistic model checking is very effective in identifying trends, hidden patterns, and how justices behave during the trials. These results not only provide valuable feedback to the justices but may also be used by the advocates and law students for finding better ways to present their arguments in the court. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

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National science Review 2023年第9期10卷 248-257页

作者： Yi Zhou Tianpeng Ding Yin Cheng Yi Huang Wu Wang Jianmin Yang Lin Xie Ghim Wei Ho Jiaqing He Shenzhen Key Laboratory of Thermoelectric Materials and Department of Physics Southern University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Department of Materials Science and Engineering National University of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research (A*STAR) GuangdongHong Kong-Macao Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices Southern University of Science and Technology

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to diele ctric structure irrationality and *** we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional *** delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mW m-2at 0.2 ***,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased *** enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy *** findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermo electrics and ferroelectrics.

关键词： non-planar dielectrics heat manipulation pyroelectrics weather-adaptive energy harvesting

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 IEEE.

关键词： Efficiency

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Auto-Correlation Properties of Binary Sequences Obtained by Switching Two Bernoulli Chaotic Binary Sequences 14

Auto-Correlation Properties of Binary Sequences Obtained by ...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Tsuneda, Akio Fujikawa, Masahiro Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350313277

In Monte-Carlo simulations, various types of random numbers are necessary for simulating various kinds of stochastic phenomena. Using one-dimensional chaotic maps, we can design statistical properties of the chaotic sequences, which implies that chaotic sequences may be useful for Monte-Carlo methods. In this paper, we examine auto-correlation properties of binary sequences obtained by switching two chaotic binary sequences generated by Bernoulli map. It is shown that binary sequences with various new types of auto-correlation properties can be generated. © 2023 IEEE.

关键词： Auto-correlation function Bernoulli map chaotic binary sequence

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Fast and Accurate Modelling of Laminated Cores in electrical Machines using Homogenization and Neural Networks

Fast and Accurate Modelling of Laminated Cores in Electrical...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Purnode, Florent Henrotte, François Louppe, Gilles Geuzaine, Christophe University of Liège Department of Electrical Engineering and Computer Science Belgium

ISBN: (纸本)9798350338362

Eddy currents induced in the individual laminations of ferromagnetic laminated cores are usually disregarded in conventional electrical machine simulations, and magnetic losses are then only evaluated a posteriori, by means of Steinmetz-like formulas. This approach yields however seriously inaccurate computed fields and losses whenever the operating frequency increases, or in the presence of higher harmonics, which is an issue of growing importance in industrial R&D. In this contribution we introduce a significantly improved and effective approach based on homogenization and neural networks. The method can be easily implemented in standard finite element packages. © 2023 IEEE.

关键词： Laminating

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