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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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Investigation of Factors, Affecting the Behaviour of Id-Vg Shift in MOSFET 6

Investigation of Factors, Affecting the Behaviour of Id-Vg S...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Chukhraev, Igor V. Drach, Vladimir E. Bauman Moscow State Technical University Computer Science and Control Department Moscow Russia Sochi State University Department of Innovative Engineering and Digital Technologies Sochi Russia

ISBN: (纸本)9798350382891

This article investigates the I-V hysteresis (IVH) phenomenon in MOSFETs and the factors affecting it. The study focuses on the effects of oxide traps, interface traps, and 'border traps' on metal-oxide-semiconductor devices. The current paper also discusses a comprehensive method for separating the effects of interface traps and trapped oxide charge in metal-oxide-semiconductor transistors. According to the study border traps and bias-temperature instabilities in MOS devices are significant factors affecting IVH. The findings of this study can be useful in the MOSFET design and optimization of for various applications. © 2024 IEEE.

关键词： MOS devices

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Deep Learning for EEG-Based Motor Imagery Classification: Towards Enhanced Human-Machine Interaction and Assistive Robotics 9

Deep Learning for EEG-Based Motor Imagery Classification: To...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Boutarfaia, Nejia Russo, Samuele Tibermacine, Ahmed Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Psychology Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This study presents a comprehensive exploration of EEG-based motor imagery classification using advanced deep learning architectures. Focusing on six distinct motor imagery classes, we investigate the performance of convolutional neural networks (CNN), CNN with Long Short-Term Memory (CNN-LSTM), and CNN with Bidirectional LSTM (CNN-BILSTM) models. The CNN architecture excels with a remarkable accuracy of 99.86%, while the CNN-LSTM and CNN-BILSTM models achieve 98.39% and 99.27%, respectively, showcasing their effectiveness in decoding EEG signals associated with imagined *** results underscore the potential applications of this research in fields such as assistive robotics and automation, showcasing the ability to translate cognitive intent into robotic actions. This study offers valuable insights into the realm of deep learning for EEG analysis, setting the stage for advancements in brain-computer interfaces and human-machine interaction. © 2023 Copyright for this paper by its authors.

关键词： Brain computer interface

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Dependability Evaluation of Industrial Networks by Using Monte Carlo With Importance Sampling 29

Dependability Evaluation of Industrial Networks by Using Mon...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Guarino, Paolo Nevi, Filippo Pra, Paolo Dai Quaglia, Davide Villa, Tiziano University of Verona Department of Engineering for Innovation Medicine Italy Pure Power Control™S.r.l. Italy University of Verona Department of Computer Science Italy

ISBN: (纸本)9798350361230

Mechanisms for detecting communication errors are crucial in industrial networks where reliability is a primary requirement. Even if the Cyclic Redundancy Check (CRC) polynomial generator is given for each transmission protocol, an application designer could choose a specific protocol according to its robustness, or additional error detection mechanisms can be freely added at the application level (e.g., nested CRC). Therefore, a methodology is desired to evaluate the residual error probability of a given protocol, i.e., considering its packet structure and error detection mechanism. Symbolic approaches proposed in the literature are not scalable for usual packet sizes. Monte Carlo simulation can be a valid alternative to inject errors and see what happens. However, the brute force generation of error patterns takes too much simulation time if the channel error probability is very low, as in realistic scenarios. This paper presents a Monte Carlo approach enriched with Importance Sampling, implemented in a tool11https://***/guarinopaolo/residual-error-probability-simulator. The framework takes the packet structure and the error detection mechanism as input, thus being independent of them. Experimental results validate the approach with respect to state-of-the-art approaches and show its effectiveness in exploring protocol alternatives. © 2024 IEEE.

关键词： Internet protocols

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Consensus Tracking of Multi-Agent Systems in The Presence of Byzantine Agents Using Model Predictive control 8

Consensus Tracking of Multi-Agent Systems in The Presence of...

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8th International Conference on control, Instrumentation and Automation, ICCIA 2022

作者： Rezaei, Mohammad Amin Bagheri, Peyman Hashemzadeh, Farzad University of Tabriz Faculty of Electrical and Computer Engineering Department of Control Engineering Tabriz Iran

ISBN: (纸本)9781665495691

This paper deals with the consensus tracking of multi-agent systems in the presence of some Byzantine agents. Steering the agents toward a predefined reference via model predictive control and, besides, encountering the misbehaving agents, are the main aims of the paper. Using graph properties alongside the Weighted-Mean-Subsequence-Reduced (W-MSR) algorithm led to a control method for special kinds of multi-agent systems. Combining model predictive control with W-MSR results in a new algorithm called Reference-Based-W-MSR (RBW-MSR), which has made the consensus point fixed according to a predetermined reference. The effectiveness of the proposed algorithm would be illustrated via simulation results in which different scenarios have been examined to exhibit the success of the method. © 2022 IEEE.

关键词： Multi agent systems

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Can social media shape the security of next-generation connected vehicles?

Can social media shape the security of next-generation conne...

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Nicola Scarano Luca Mannella Alessandro Savino Stefano Di Carlo Politecnico Di Torino Department of Control Computer Engineering Turin Italy

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

abstract-The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there remains a critical need for comprehensive assessment and analysis of cyber risks. In this context, this paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed for the emerging field of automotive cybersecurity. The framework leverages advanced intelligence techniques and machine learning models to extract valuable insights from social media. Four use cases illustrate The framework’s potential by demonstrating how it can significantly enhance threat assessment procedures within the automotive industry.

关键词： Industries Social networking (online) Shape Machine learning Threat assessment Safety System analysis and design

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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