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Joint Access Selection and Computation Offloading in LEO Ubiquitous Edge Computing Networks: An Alternating DRL-Based Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1870-1886页

作者： Yang, Junyi Zhang, Yuanjun Xiao, Zhenyu Han, Zhu Beihang University School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beijing100191 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the increase in users’ service diversity and demand for quality of experience (QoE), the utilization of low earth orbit (LEO) satellite networks for assisted or independent offloading of computation tasks has become a promising trend. However, due to the high mobility and uneven resource distribution of LEO satellites, it is difficult to meet the requirements of low delay and low overhead by using traditional optimization algorithms or common reinforcement learning (RL) algorithms. Therefore, in this paper, we consider a scenario in which terrestrial users offload delay-sensitive (DS) computation tasks to a LEO satellite network in order to study the joint access selection and computation offloading problem. First, we analyze the characteristics of the scenario and the computation tasks, and establish a generic mathematical model. Then, based on the block descent coordinate (BCD) principle, we propose a novel algorithm of alternating Dueling DQN (ADDQN) for the joint decision-making problem, where access selection and computation offloading are performed with corresponding independent agent respectively. Comprehensive simulations show that compared with other benchmark algorithms, the proposed method not only has better convergence, but also can maximize the number of successfully completed sub-tasks and the optimization objective value meanwhile reducing the unnecessary access handovers. © 2015 IEEE.

关键词： Satellites Computational modeling Decision making Low earth orbit satellites Handover Benchmark testing Mathematical models Quality of experience Optimization Convergence

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Implementation of Variable Capacitance to Improve Efficiency for Asymmetric LCC-LCC Compensated Wireless Power Transfer Systems

Implementation of Variable Capacitance to Improve Efficiency...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zhu, Gangwei Dong, Jianning Wang, Jundong Deng, Zichen Li, Yongpeng Bauer, Pavol Delft University of Technology Mathematics and Computer Science Department of Electrical Engineering Delft Netherlands

ISBN: (纸本)9798350364446

This paper proposes a variable-capacitance-based control strategy to improve efficiency for asymmetric LCC-LCC compensated wireless power transfer (WPT) systems. While the existing triple-phase-shift (TPS) method can achieve power regulation and wide-range zero-voltage-switching (ZVS), it results in significantly increased reactive power under asymmetric LCC-LCC compensation topology. To this end, this paper incorporates a switch-controlled-capacitor (SCC) on the primary side. The impact of variable capacitance on the system characteristics is first investigated. Furthermore, the optimal capacitor tuning factor is derived to achieve the inverter ZVS with minimal reactive power. Through the implementation of variable capacitance, the primary inductor current is notably reduced within a wide range of power. Moreover, the turn-off currents of power switches are minimized. These factors contribute to a reduction in inductor and inverter losses, thus improving the overall efficiency. Experimental results confirm that the proposed method improves the efficiency of an asymmetric LCC-LCC compensated WPT prototype, with a maximum efficiency improvement of up to 1.8%. © 2024 IEEE.

关键词： Capacitor bank

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Multi-Level-Resistance Gate Driver for SiC Motor Drives to Control dv/dt and Reduce Switching Loss 11

Multi-Level-Resistance Gate Driver for SiC Motor Drives to C...

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11th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2024

作者： Bose, Abhishek Jiang, Peiwen Higgins, Hayden Huang, Qingyun University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States

ISBN: (纸本)9798350375602

Designing an optimal gate driver resistance for SiC motor drive inverters to meet the maximum dv/dt requirement is extremely critical in prolonging the motor lifetime. However, for SiC MOSFETs, turn on voltage slew rate dv/dt, decreases with increasing switching current, leading to an additional and undesirable increase in switching turn-on losses. This paper investigates how gate driver could be optimized to meet the maximum voltage slew rate through a sinusoidal load current by changing external gate resistance in limited change levels. Therefore, the switching turn-on losses under the maximum dv/dt will be significantly reduced compared to using the fixed gate driver resistance. In the proposed approach, the dynamic turn-on behaviour of SiC MOSFETs with regards to increasing miller platform voltage and decreasing dv/dt, is taken into consideration. In this paper, We theoretically demonstrate the effectiveness of the proposed multi-level resistance gate driver for SiC motor drive based on the analytical dv/dt and power loss model. The results show that by adjusting gate resistances with a few changing levels while meeting the maximum specified dv/dt requirement, turn-on switching loss can be reduced from 10 mJ to 4.5 mJ at device transconductance 12 S. © 2024 IEEE.

关键词： MOSFET devices

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SADA: Unsupervised Domain Adaptation for Reliable Scene Awareness 59

SADA: Unsupervised Domain Adaptation for Reliable Scene Awar...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Maghsoumi, Hossein Fallah, Yaser P. Atia, George University of Central Department of Electrical and Computer Engineering OrlandoFL United States University of Central Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798331513269

Although deep neural networks are extensively employed in autonomous driving, they must be adjusted to handle unforeseen environmental conditions that were not included in their initial training. Unsupervised domain adaptation (UDA) techniques have proven effective in transferring learned knowledge from one domain to another when the labels in the target domain are unavailable. However, their potential application in scene awareness remains underexplored. This paper introduces SADA (Scene Awareness with Unsupervised Domain Adaptation), a novel approach designed to enhance the reliability of drivable area and lane detection tasks in autonomous driving systems. We leverage UDA techniques in unified multi-task networks to address domain shift challenges, ensuring robust feature extraction under diverse and complex conditions. The proposed technique is evaluated on different real-world datasets and the results demonstrate that the proposed methods outperform the baseline scheme in terms of detection accuracy and consistency. © 2025 IEEE.

关键词： Deep neural networks

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ClinicalGraph: An Applied Approach in Clinical EHR Knowledge Graph Generation for Optimized Clinical Decision Support System

ClinicalGraph: An Applied Approach in Clinical EHR Knowledge...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ruiz Mejia, Jose M. Rawat, Danda B. Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States

ISBN: (纸本)9798350350548

Electronic Health Records (EHR) are well-known for their extensive capabilities in storing pertinent patient information across various specializations. However, EHRs are part of a broader ecosystem of applications that support the medical treatment process. This ecosystem, although somewhat centralized, does not have the interoperability that many assume. The main issue is that current triage models are not personalized to each patient. Context matters in a medical emergency situation especially when resources are low. In this research, we propose a possible solution by applying state of the art techniques in order to develop a clinical knowledge graph that contains relevant data used for triage optimization. We utilize a fine-tuned named entity recognition model (NER) to extract 41 label entity categories from previous medical records. Additionally, we employed prompt engineering utilizing a large language text generation model with medical knowledge to generate relationships. This resulted in a sum of 1,429 relationship type categories and approximately 999 entity nodes were created with 2,387 relationships. © 2024 IEEE.

关键词： Knowledge graph

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A SiC MOSFET Based Multi-Port Solid State Circuit Breaker for DC Protection

A SiC MOSFET Based Multi-Port Solid State Circuit Breaker fo...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Song, Xiaoqing Nawafleh, Yannal Rahman, Mohammad Dehan University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350376067

Direct current (DC) systems require fast fault protection speed due to ultrafast transients with high currents in short circuit faults, where solid state circuit breakers (SSCBs) can perfectly fit. Compared to the traditional mechanical circuit breakers (MCBs), SSCBs demonstrate 10 to 100 orders of magnitude faster fault breaking speed and enhanced safety with arc-less fault current interruption. One remaining challenge of the SSCB is large conduction losses, resulting in heavy and bulky cooling systems. In this paper, a novel high efficiency multi-port SSCB is proposed, which reduces >50% conduction losses compared to conventional SSCB topology. The power loss reduction benefits and operation principles of the proposed multiport SSCB are analyzed. A SiC MOSFET based SSCB hardware prototype is built, and experimental results validate the feasibility and effectiveness of the proposed SSCB concept. © 2024 IEEE.

关键词： MOSFET devices

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A Comparative Study on Prosumers Load Demand and Production Forecasting Using Machine Learning and Time Series Techniques 16

A Comparative Study on Prosumers Load Demand and Production ...

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Solea, Claudiu Hera, Corina Florea, Adrian Morariu, Daniel Stanescu, Dorel Vintan, Maria Lucian Blaga University of Sibiu Computer Science and Electrical Engineering Department Sibiu Romania

ISBN: (纸本)9798350388107

A data-driven and realistic policy could overcome the current turmoil in the intricate energy landscape. With solar and wind expanding faster than liquified natural gas or nuclear ever have, local, bottom-up solutions, such as organising collaboratively residential prosumers into electricity trading communities and the efficient use of aggregators, are one way to resilience and control over these resources. This paper analyses the features and characteristics of some prosumers and facilitates the identification of relevant algorithms for the specific tasks of both load demand and production forecasting using machine learning and time series techniques, thus enabling them to operate on a level playing field in the market. Load demand and production forecast simulations on two data sets, one collected in 2019, corresponding to 50 residential households and a public building from Portugal, and the other from 2023, that aggregates values from 29 household prosumers from Romania, show that both simple and double exponential smoothing techniques are more accurate than Markov predictors. Provided that the external contingencies influencing either the consumption or the production are considered, the latter gives a better prediction accuracy for energy production, whereas the former provides a better forecast for the consumption. © 2024 IEEE.

关键词： Demand response

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Passive islanding detection in microgrids using artificial neural networks

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Applied Nanoscience (Switzerland) 2023年第4期13卷 2885-2900页

作者： Alyasiri, Ali Majeed Mohammed Kurnaz, Sefer Department of Electrical and Computer Engineering Institute of Science Altinbas University Istanbul Turkey

This research focuses on modeling and simulating voltage control of passive islanding detections with distributed generation. This research presents how reactive power generation and/or absorption can be utilized to partake voltage control in medium voltage distribution through multi-microgrids for passive islanding detection with rule-based mathematical model and artificial neural network (ANN). With the increased emphasis on renewable energy, modern power grids have become more reliant to smaller distributed generation units. Unlike traditional power grids that rely on larger centralized units, the detection of islanding events in these grids is more complex. However, it is still important to maintain the connection to the power grid to maintain high stability of the system and is also important to deenergize the grid when an islanding event happens, to protect the workers that may be working on the grid to clear the cause of the islanding. Islanding detection methods are categorized into passive, active and communication based. Active methods detect islanding by measuring the influence of noise they add to the grid, to predict the size of the grid, which reduces the quality of the power provided on that grid. Communication-based methods are expensive and highly reliable on the communications infrastructure, which limits their application. Unlike existing methods, the proposed method of ANN relies on the instantaneous current and voltage values to detect any islanding with an accuracy of 97.89% precisely. These values are directly fed to the ANN, i.e., without applying any feature extraction, so that, faster and more accurate decisions are made. The proposed method is expected to combine the high accuracy of ANNs with the faster change in instantaneous values, so that, faster and more accurate detection can be achieved. © 2022, King Abdulaziz City for science and Technology.

关键词： Detection Distributed energy Distribution grid Islanding Microgrid Reactive power Voltage control

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Prompt Distillation for Emotion Analysis 16

Prompt Distillation for Emotion Analysis

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16th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2024

作者： Mackey, Andrew L. Gauch, Susan Cuevas, Israel Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR United States

ISBN: (纸本)9789897587160

Emotion Analysis (EA) is a field of study closely aligned with sentiment analysis whereby a discrete set of emotions are extracted from a given document. Existing methods of EA have traditionally explored both lexicon and machine learning techniques for this task. Recent advancements in large language models have achieved success in a wide range of tasks, including language, images, speech, and videos. In this work, we construct a model that applies knowledge distillation techniques to extract information from a large language model which instructs a lightweight student model to improve its performance with the EA task. Specifically, the teacher model, which is much larger in terms of parameters and training inputs, performs an analysis of the document and shares this information with the student model to predict the target emotions for a given document. Experimental results demonstrate the efficacy of our proposed prompt-based knowledge distillation approach for EA. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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A Hybrid Rectifier Mode Control for Communication-Free Wireless Power Transfer

A Hybrid Rectifier Mode Control for Communication-Free Wirel...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Zhu, Gangwei Dong, Jianning Bauer, Pavol Delft University of Technology Department of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9798350376067

This paper presents a hybrid rectifier mode control for broad-range output power regulation in wireless power transfer (WPT) systems. The proposed control method employs a secondary-side active rectifier for output tuning, thereby eliminating the necessity for communication links. Furthermore, leveraging the pulse-skipping technique, two hybrid modes are introduced in the proposed approach. These hybrid modes reduce circulating reactive power within the resonant circuits, thereby optimizing the transmission efficiency of the WPT systems. To validate the effectiveness of the proposed method, experiments were conducted using a WPT prototype. Experimental results demonstrate that the proposed approach achieves higher efficiency than the conventional phase-shift control strategy, with a maximum efficiency improvement of up to 3.7%. © 2024 IEEE.

关键词： Inductive power transmission

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