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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Anmol Aggarwal Bhuvan Khoshoo John Agapiou Shanelle N. Foster General Motors Pontiac MI USA Department of Electrical & Computer Engineering Michigan State University East Lansing MI USA Retired Manufacturing Systems Research Laboratory General Motors Warren MI USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Using oriented steel for segmented stator construction in AC electric machine can improve the efficiency of machine by decreasing core losses compared to a machine designed with non-oriented steel. Therefore, analysis of the oriented steel stator in AC electric machines in finite analysis is of great interest. Several attempts were made to accurately simulate oriented steel in segmented stators; however, the insight into the flux distribution and consequently the perceived properties of the oriented steel is at-large missing. In this work, a physics-based approach to model oriented steel for radial flux AC machines is developed to accurately capture the magnetic properties by considering the direction of flux. The determined magnetic properties are used to model an equivalent isotropic model in FEA. The accuracy of developed FEA model is validated experimentally.

关键词： Stator cores Accuracy Correlation AC machines Core loss Energy conversion Finite element analysis Steel Magnetic properties

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Seven-level Cascade Multilevel Inverter based Minimum Switches for Photovoltaic applications

Seven-level Cascade Multilevel Inverter based Minimum Switch...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ali Abedaljabar Al-Samawi Hafedh Trabelsi Department of Electronic and Communications Engineering College of Engineering Al-Muthanna University Al Muthanna Iraq Department of Electrical Engineering National Engineering School of Sfax (ENIS) Computer and Embedded Systems Laboratory (CES-Lab) Sfax University Sfax Tunisia

To enhance power quality and obtain high power from photovoltaic(PV), and mitigate the impact of the PV system's partial shading condition(PSC) and electrical mismatch power problems. This paper presents a single-phase, simple seven-level multilevel inverter architecture With a lower number of power components connected with four individual group PV systems integration converter DC/DC with independent Maximum Power Point Tracking(MPPT)in each group. The proposed inverter provides a reduced total harmonic distortion (THD) of voltage and unity active power. This reduction in the number of switches generates a decrease in the losses of switching and the number of drive circuits, which in turn causes a decrease in the control complication and decreases the cost and size of the control circuit. We compared this cascaded multilevel topology architecture to evaluate the performance of the suggested inverter, which needed modern cascade multilevel topologies with the same seven levels of voltage. The achievement of the comparison is dependent on several elements, including a set of parameters(diodes and capacitors), the overall harmonic distortion, and the number of switches included inside the inverter. The program(Simulink/Matlab) was used to verify the critical evaluation of the presented topology through simulation results.

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Towards A Secured SCADA Architecture: A DNP3 Test Case

Towards A Secured SCADA Architecture: A DNP3 Test Case

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IEEE Southeastcon

作者： Ahmed S. Soliman Rocio Devine Diego Landi S M Sajjad Hossain Rafin M. H. Cintuglu Osama A. Mohammed Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA ECE Department Energy System Research Laboratory Florida International University Miami FL USA Dept. of Electrical and Computer Engineering Florida International University Miami Florida USA

The foundation of a country's critical infrastructures (CI), which provide crucial services, is its security, healthcare, and economic systems. By lowering CI's operational costs and associated expenses and enhancing its effectiveness and safety, appropriately functional CI infrastructure plays an essential role in the prosperity of the nations. To do so, businesses that manage CIs frequently combine the Internet of Things (IoT) with Cyber-Physical systems (CPS), such as SCADA systems. SCADA systems were not initially built with web security in mind, exposing CIs open to security risks. National security issues can arise when these technologies are connected to power plants and water treatment facilities. This study aims to demonstrate how security features can be implemented in DNP3-based SCADA infrastructures. By creating a functional configuration and consensus amongst the communicating devices, the DNP3 protocol will support digital signatures. The team proposed a SCADA architecture to accomplish this, making it possible for these systems more secure manner.

关键词： Protocols Industrial control SCADA systems Medical services Safety Security Internet of Things

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Ciphertext-only fault analysis of GIFT lightweight cryptosystem

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Science China(Information Sciences) 2022年第3期65卷 263-265页

作者： Wei LI Shan CAO Dawu GU Jiayao LI Tianpei CAI Menglin WANG Li SUN Zhiqiang LIU Ya LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Computing and Systems Shanghai Jiao Tong University Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The GIFT cryptosystem was proposed by Banik et al. [1]in CHES 2017. It can be widely applied to protect RFID tags and other low-resource devices. It has an SPN structure with a fixed 128-bit key size and two flexible variants of 64-bit and 128-bit block sizes. In simulations, GIFT achieves good performance and surpasses both SIMON and SKINNY [1]. In 2013, Fuhr et al. [2] proposed a ciphertextonly fault analysis(CFA) of AES for three types of faults:zero-byte fault, zero-nibble fault,

关键词： lightweight cryptosystem GIFT ciphertext-only fault analysis cryptanalysis fault analysis

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Multi-microgrid Energy Management systems: Architecture, Communication, and Scheduling Strategies

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Journal of Modern Power systems and Clean Energy 2021年第3期9卷 463-476页

作者： Bin Zhou Jianting Zou Chi Yung Chung Huaizhi Wang Nian Liu Nikolai Voropai Daosen Xu the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China the Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonSK S7N5A9Canada the College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China the Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences Irkutsk 664033Russia

The increasing penetration of various distributed and renewable energy resources at the consumption premises,along with the advanced metering,control and communication technologies,promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids(MMGs).The networked MMG system is an interconnected cluster of distributed generators,energy storage as well as controllable loads in a distribution *** its operation complexity can be decomposed to decrease the burdens of communi-cation and control with a decentralized ***,the multi-microgrid energy management system(MIVIGEIV1S)plays a significant role in improving energy efficiency,power quality and reliability of distribution systems,especially in enhancing system resiliency during contingencies.A comprehensive overview on typical functionalities and architectures of MMGEMS is ***,the emerging communication technologies for information monitoring and interaction among MMG clusters are ***,various energy scheduling and control strategies of MMGs for interactive energy trading,multi-energy management,and resilient operations are thoroughly analyzed and ***,some challenges with great importance in the future research are presented.

关键词： Energy management system(EMS) microgrid communication renewable energy multi-energy system

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Quality Assessment of Screen Content Videos

Quality Assessment of Screen Content Videos

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International Conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Hossein Motamednia Pooryaa Cheraaqee Azadeh Mansouri Ahmad Mahmoudi-Aznaveh Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran

Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human visual system. With the diversity in the contents of multimedia signals, the conventional methods for traditional media seems no longer satisfying. One of these emerging media, is the screen content images/videos (SCINs), Containing texts and computer generated graphics, SCVs cannot be sufficiently expressed with features designed for natural sceneries. Therefore, new researches tried to devise objective quality assessment metrics, specificly for screen contents. Recently, a dataset was proposed for quality assessment of screen content videos. Since screen contents are full of structures that spread in cardinal directions, we were motivated to employ the horizontal and vertical subbands of the wavelet transform to characterize these types of visual contents. The features were incorporated in a full-reference method that showed promising results on the publicly available dataset for SCV quality assessment. The method can bo accessed via: https://***/motamedNia/QASCV.

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Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

arXiv

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arXiv 2025年

作者： Zhao, Changyuan Zhang, Ruichen Wang, Jiacheng Niyato, Dusit Sun, Geng Du, Hongyang Li, Zan Jamalipour, Abbas Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

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IEEE Transactions on Mobile computing 2025年

作者： Zhang, Rongyu Chi, Xiaowei Zhang, Wenyi Liu, Guiliang Wang, Dan Wang, Fangxin Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong The Hong Kong University of Science and Technology Hong Kong Department of Electrical Engineering and Computer Science University of California Irvine United States Shenzhen China Shenzhen China Guangdong Provincial Key Laboratory of Future Networks of Intelligence China

Multimodal learning has significantly advanced the extraction of features from varied data sources, enhancing model performance. Federated learning (FL) complements this by enabling collaborative training while maintaining data privacy. The fusion of these two fields, multimodal federated learning, offers considerable promise. Yet, standard methods often incorrectly assume that each node in the FL network has a full complement of multimodal data, which is rare in real-world applications. In our study, we present a novel architecture designed to surmount these challenges, termed the Unimodal Training - Multimodal Prediction (UTMP) framework, positioned within the multimodal federated learning paradigm. Our proposed model, the HA-Fedformer, is a transformer-based model crafted to facilitate unimodal training on the client-side using exclusively unimodal datasets and to execute multimodal inference by synthesizing insights from multiple clients. Our HA-Fedformer model effectively handles non-IID data through a novel uncertainty-aware aggregation technique and layer-wise Markov Chain Monte Carlo sampling in local encoders. It also resolves misaligned language sequences via cross-modal decoder aggregation, capturing correlations between decoders trained on different modalities. Our comprehensive evaluations conducted on widely recognized sentiment analysis benchmarks demonstrate the superiority of the HA-Fedformer. The results show that our model achieves a substantial uplift in performance. © 2002-2012 IEEE.

关键词： Federated learning

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Interconnectedness of Circular Economy, Artificial Intelligence and Vertical Farming as a Technosignature Model for Sustainable Space Food Production 15

Interconnectedness of Circular Economy, Artificial Intellige...

引用

15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Ii, Ronnie Concepcion Cabaccan, Carlo Ducut, Jullian Dominic Relano, R-Jay Alejandrino, Jonnel Agulto, Ronnel Bandala, Argel Mohamad, Roslina Dadios, Elmer Culaba, Alvin Department of Manufacturing Engineering and Management De la Salle University Manila Philippines De la Salle University Center for Engineering and Sustainability Development Research Intelligent Systems Research Unit Manila Philippines De la Salle University Bio-energy Environment and Renewables Laboratory Center for Engineering and Sustainability Development Research Manila Philippines Plant and Soil Health Research Unit Center for Natural Sciences and Environmental Research De la Salle University Manila Philippines School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of Electrical Engineering College of Engineering Universiti Teknologi Mara Shah Alam Malaysia De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9798350381177

The degradation of Earth's natural resources and environmental health due to improper anthropological activities, mainly through unoptimized food production, is manifested through climate change that negatively alters the productivity in the agricultural sector. Because of this challenge in open terrestrial agriculture, the concept of space farming is believed to be significant in delivering fresh and quality food for the people on top of providing for astronauts and making them healthy in space. However, another problem emerges in the view of circular economy (CE) which is a production and consumption idealism that avoids degradation of natural resources and environment in producing one food unit and despite the collaborative advances in space technology, particularly in astrobotany, the materialization of CE and the absolutism of artificial intelligence (AI) are still partly imaginary. This study employed a bibliometric, scientometric and systematic analysis to highlight the advantages, limitations, and connections of CE, AI, and vertical farming (VF) which led to establishing a technosignature model for sustainable space food production. Here, CE, AI and VF were singularized as a model for sustainable space food production and identified their potential interplay. Based on scientific correlations, it is undoubtedly true that CE, AI, and VF have existing mutualism in Earth-based systems, but the degree of its importance as a whole is not yet completely appreciated for space farming. The developed CE-AI-VF technosignature model is scalable and adaptable if the government and scientific community put it into action accompanied by science policies. © 2023 IEEE.

关键词： Climate change

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