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6th International Symposium on Advanced electrical and Communication Technologies, ISAECT 2024

作者： Alayedi, Mohanad Jaradat, Ahmad M. Elgammal, Ziad Malkawi, Malek Haliç University Department of Software Engineering Istanbul Turkey University of Detroit Mercy Department of Electrical and Computer Engineering and Computer Science MI United States Istanbul Medipol University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331529987

This study introduces a novel 2-dimensional (2D) spectral/temporal code, termed 2D-cyclic shift (2D-CS) code, optimized for non-coherent Spectral Amplitude Coding Optical Code division Multiple Access (SAC-OCDMA) systems. Computational results demonstrate that the 2D-CS code has fostered the SAC-OCDMA system's performance whence spectral efficiency (SE), phase induced intensity noise (PIIN), and cardinality. Specifically, the 2D-CS code can increase cardinality by up to 117%, 89%, and 61% compared to one-dimensional CS (1D-CS), 2D-multi service (2D-MS), and 2D-diluted perfect difference (2D-DPD) codes, respectively. The system's performance was evaluated using Optisystem software, showing significant improvements in quality factor (QF) and bit error rate (BER) with LED and laser light sources for four active users at 115 dBm transmitted power and 20,000 m distance. © 2024 IEEE.

关键词： Code division multiple access

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Dynamic Model Selection for Asynchronous Federated Learning in IoBT Scenarios

Dynamic Model Selection for Asynchronous Federated Learning ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tankard, Earl Haileselassie Hagos, Desta Rawat, Danda B. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

This paper explores the potential of Dynamic Model Selection (DMS) combined with asynchronous Federated Learning (FL) to enhance military devices within the Internet of Battlefield Things (IoBT). While previous studies have primarily focused on synchronous FL, we argue this approach has limitations. Hence, this study addresses this research gap by investigating asynchronous FL and its advantages over synchronous methods. Our main contribution lies in developing a dynamic client selection algorithm that optimizes the utility function, incorporating communication delay or latency, available computational resources, and model accuracy. This algorithm ensures clients are selected based on their potential contribution to the global model's performance, thereby improving the efficiency and effectiveness of the training process. Through experimentation with standard datasets (MNIST, CIFAR-10) and our custom dataset (Common Objects in Battlefield (COBA)) tailored for battlefield scenarios, we demonstrate the effectiveness of asynchronous FL and dynamic client selection. By addressing this critical research gap and providing empirical evidence, our study advances the use of FL for battlefield decision-making, significantly contributes to developing IoBT, and offers practical insights for military applications. © 2024 IEEE.

关键词： Federated learning

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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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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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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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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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TILE: Input Structure Optimization for Neural Networks to Accelerate Secure Inference 40

TILE: Input Structure Optimization for Neural Networks to Ac...

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40th Annual computer Security Applications Conference, ACSAC 2024

作者： Feng, Yizhou Zhang, Qiao Cai, Yifei Wu, Hongyi Xin, Chunsheng Department of Electrical and Computer Engineering Old Dominion University NorfolkVA United States College of Computer Science Chongqing University Chongqing China Department of Electrical and Computer Engineering University of Arizona TucsonAZ United States

ISBN: (纸本)9798331520885

Machine Learning as a Service (MLaaS) is an innovative framework that enables a broad range of users to capitalize on the powerful Artificial Intelligence (AI) technologies. Nevertheless, MLaaS raises a privacy concern for both the client data and server model. To address this issue, several Secure Inference (SI) frameworks for MLaaS have been proposed in the literature that take advantage of Homomorphic Encryption (HE) operations. However, the computation cost of these frameworks is still high, especially for real-time applications. In this paper, we propose a novel system called input structure optimization for neural networks (TILE) to accelerate SI. The goal of TILE is to reduce both linear and non-linear computation costs, as well as non-linear communication costs in MLaaS, while maintaining the model accuracy. TILE defines two novel HE-friendly input structures: Internal Tile and External Tile Structures, aimed at reducing the HE operations for SI. We also develop a search mechanism to identify optimal application locations for these input structures. We apply TILE to widely used models such as VGG and ResNet, and datasets including Cifar10 and Tiny-ImageNet. The experimental results demonstrate that TILE effectively reduces the computation time, with up to 51.57% reduction for a state-of-the-art SI framework. Furthermore, TILE can also be applied to models that have already been pruned to significantly reduce the computation time, to further reduce the overall computation time by 25.90%. © 2024 IEEE.

关键词： Structural optimization

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High Temperature Characterization and Degradation Test of a Cascode Gallium Nitride Field Effect Transistor 11

High Temperature Characterization and Degradation Test of a ...

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

作者： Rahman, Mohammad Dehan Hasan, Abu Shahir Md Khalid Mantooth, H. Alan Song, Xiaoqing University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350375602

Gallium Nitride (GaN) is a wide bandgap semiconductor material known for its exceptional performance in high-Temperature and high-voltage environments. This makes GaN a preferred material for field-effect transistors (FETs) used in demanding applications. While GaN FETs are theoretically capable of operating at temperatures exceeding 200 {C}, practical applications often limit their maximum junction temperature to around 150 {C}, primarily due to packaging limitations, such as those associated with the standard TO-247 package. This paper presents an experimental analysis of the high-Temperature performance of GaN FETs, focusing on how elevated temperatures influence various operational parameters. Key areas of investigation include the impact on output characteristics, on-resistance ({R}-{{dson}}), threshold voltage ({V}-{{th}}), transfer characteristics, and gate leakage currents. Furthermore, the study examines potential degradation effects that may result from prolonged operation at elevated temperatures. The insights gained from this study are crucial for advancing the deployment of GaN FETs in applications that demand robust performance under extreme conditions, including automotive, aerospace, and industrial power systems. Ultimately, the findings will contribute to the development of more reliable and efficient power electronics, pushing the boundaries of GaN technology. © 2024 IEEE.

关键词： III-V semiconductors

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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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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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