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2022 IEEE International Power Modulator and High Voltage Conference, IPMHVC 2022

作者： Wei, Zhuo Alkhalid, Khalid Alsaif, Faisal Wang, Jin Grosjean, Dennis Schweickart, Daniel The Ohio State University Department of Electrical and Computer Engineering ColumbusOH United States Innovative Scientific Solutions Inc. DaytonOH United States Air Force Research Laboratory Aerospace Systems Directorate Ohio United States

ISBN: (纸本)9781665471657

Partial discharge (PD) is a common and detrimental phenomenon that can cause damage and potential breakdown in insulation systems. According to the air breakdown voltage behaviors shown by Paschen's curve, from sea level to high altitude, the partial discharge inception voltage (PDIV) of the air is expected to decrease with decreasing pressure. So far, there has not been a widely accepted PD testing method for aviation wires in low pressure environments with pulse width modulated (PWM) voltage excitations. There have been studies of the PD behaviors of aviation wires with conventional triangular impulses, ac voltages, and PWM pulses with relatively low dv/dt. But existing test methods and study results cannot be directly utilized to test and predict PDIV of aviation wires when high dv/dt PWM excitation is applied with wide-bandgap (WBG) power devices, such as Silicon Carbide (SiC) Metal Oxide Semiconductor Field Effect Transistors (MOSFETs). Thus, this paper aims to provide more insights on PD behaviors of aviation wires under high dv/dt PWM excitations and possible ways to improve the existing PD test methods. A test setup including an ultra-high dv/dt PWM generator, a test sample fixture, and associated PD sensors including a photomultiplier tube (PMT), a high frequency current transducer (HFCT), and an ultra-high-frequency (UHF) antenna will be introduced. Experimental results and associated analysis for aviation wire test samples at various pressures under high dv/dt PWM excitations will be presented. The effect of the rise time of the PWM will be discussed in detail. Suggestions on how to improve test methods and test standards will be provided. © 2022 IEEE.

关键词： Silicon carbide

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Integrated Modeling and Optimal Operation of Multi-Energy Sy...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Chen, Yang Chen, Jun Liu, Chenang Liu, Guodong Ferrari, Maximiliano Sundararajan, Aditya Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University Greensboro United States Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States School of Industrial Engineeering and Management Oklahoma State University Stillwater United States Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9781665493765

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands. © 2023 IEEE.

关键词： Desalination

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Evaluating Large Language Models in Interaction with Open Government Data 24

Evaluating Large Language Models in Interaction with Open Go...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Ioannis Maslaris Areti Karamanou Evangelos Kalampokis Konstantinos Tarabanis Database & Information Retrieval Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece Information Technologies Institute Center for Research & Technology HELLAS (CERTH) Thessaloniki Greece and Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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Interacting diffusion processes for event sequence forecasting 24

Interacting diffusion processes for event sequence forecasti...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mai Zeng Florence Regol Mark Coates Department of Electrical and Computer Engineering McGill University Montreal QC Canada and International Laboratory on Learning Systems (ILLS) Montreal QC Canada and Mila Québec AI Institute Montreal QC Canada

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.

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Outlier-Robust Training of Machine Learning Models

arXiv

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arXiv 2024年

作者： Talak, Rajat Georgiou, Charis Shi, Jingnan Carlone, Luca Laboratory of Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to mitigate the impact of outliers. We bring to light two literatures that have diverged in their ways of designing robust losses: one using M-estimation, which is popular in robotics and computer vision, and another using a risk-minimization framework, which is popular in deep learning. We first show that a simple modification of the Black-Rangarajan duality provides a unifying view. The modified duality brings out a definition of a robust loss kernel σ that is satisfied by robust losses in both the literatures. Secondly, using the modified duality, we propose an Adaptive Alternation Algorithm (AAA) for training machine learning models with outliers. The algorithm iteratively trains the model by using a weighted version of the non-robust loss, while updating the weights at each iteration. The algorithm is augmented with a novel parameter update rule by interpreting the weights as inlier probabilities, and obviates the need for complex parameter tuning. Thirdly, we investigate convergence of the adaptive alternation algorithm to outlier-free optima. Considering arbitrary outliers (i.e., with no distributional assumption on the outliers), we show that the use of robust loss kernels σ increases the region of convergence. We experimentally show the efficacy of our algorithm on regression, classification, and neural scene reconstruction problems. © 2024, CC BY.

关键词： Contrastive Learning

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Statistical Delay and Error-Rate Bounded QoS Control for URLLC in the Non-Asymptotic Regime

Statistical Delay and Error-Rate Bounded QoS Control for URL...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&amp M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing mobile wireless network architectures for ultra-reliable and low-latency communications (URLLC). One of the major design issues raised by URLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying wireless fading channels. To efficiently accommodate statistical QoS provisioning for URLLC traffic, it is crucial to model and investigate wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, outage capacity, etc., in the non-asymptotic regime. However, how to rigorously and efficiently characterize the stochastic dynamics of mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither well understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling frameworks and controlling mechanisms for statistical delay and error-rate bounded QoS provisioning in the non-asymptotic regime. First, we establish FBC-based system models by characterizing various information-theoretic specifications. Second, we characterize the outage-probability and outage capacity functions in the non-asymptotic regime. Third, we develop a set of new statistical delay and error-rate bounded QoS metrics and control mechanisms including delay-bound-violation probability, QoS-exponent functions, and the -effective capacity in the non-asymptotic regime. Finally, the obtained simulation results validate and evaluate our proposed controlling mechanisms for statistical QoS in support

关键词： Measurement Fading channels Analytical models Wireless networks Simulation Stochastic processes Quality of service

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Statistical QoS-Driven Beamforming and Trajectory Optimizations in UAV/IRS-Based 6G Wireless Networks in the Non-Asymptotic Regime

Statistical QoS-Driven Beamforming and Trajectory Optimizati...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&#x0026 M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.

关键词： 6G mobile communication Three-dimensional displays Array signal processing Wireless networks systems architecture Quality of service Ultra reliable low latency communication

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Naceur, Ferdaws Ben Toumi, Sana Salah, Chokri Ben Mahjoub, Mohamed Ali National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse4011 Tunisia Department of Electrical Engineering Monastir Tunisia Brest29238 France Issat Sousse Sousse Tunisia

ISBN: (纸本)9798350325539

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm 'fuzzy logic'. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)). © 2023 IEEE.

关键词： Decision making

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