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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Metwly, Mohamed Y. He, JiangBiao Abdel-Khalik, Ayman S. Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Department of Electrical Engineering Alexandria University Alexandria Egypt

ISBN: (纸本)9781665464543

Aircraft electrification has received increasing attention due to the significant benefits, such as low carbon emissions, high energy efficiency, low operation cost, and reduced acoustic noise. Limitations on both the low energy density with the present battery technologies and slow battery charging speed highly affect the widespread adoption of electric aircraft, especially for megawatt-scale large aircraft. Thus, this paper presents an isolated integrated on-board battery charger (IOBC) using an existing six-phase surface-mounted permanent magnet (SPM) synchronous propulsion machine for MW-scale electric aircraft. Besides exploiting the existing powertrain apparatuses, i.e., the six-phase motor and inverter, in the propulsion and charging modes of operation, the proposed charger offers galvanic isolation to meet safety standards and code compliance. Moreover, zero average torque production during charging and unity power factor operation at the grid side can be simultaneously achieved. The proposed charger concept entails simple hardware reconfiguration to switch between the propulsion and charging modes of operation. Furthermore, this paper adopts a 36-slot/34-pole fractional slot concentrated winding (FSCW)-based SPM machine, which is designed and optimized based on the magnetic equivalent circuit (MEC) model and the multi-objective genetic algorithm (MOGA) design optimization approach, respectively. Eventually, theoretical findings are verified based on 2D and 3D finite element (FE) simulations. © 2024 IEEE.

关键词： Permanent magnets

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Flexible fibres take fabrics into the information age

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NATURE 2024年第7997期626卷 38-39页

作者： Jia, Xiaoting Parrott, Alex Xiaoting Jia is in the Bradley Department of Electrical and Computer Engineering the Department of Materials Science and Engineering and the School of Neuroscience Virginia Tech Blacksburg Virginia 24061 USA. Alex Parrott is in the Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg Virginia 24061 USA.

A technique for embedding fibres with semiconductor devices produces defect-free strands that are hundreds of metres long. Garments woven with these threads offer a tantalizing glimpse of the wearable electronics of t... 详细信息

关键词： engineering Materials science Technology

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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Educational Data Analysis using Generative AI

Educational Data Analysis using Generative AI

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Berr, Abdul Leelaluk, Sukrit Tang, Cheng Chen, Li Okubo, Fumiya Shimada, Atsushi Department of Electrical Engineering and Computer Science Kyushu University Japan Graduate School of Information Science and Electrical Engineering Kyushu University Japan

With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades. © 2024 CEUR-WS. All rights reserved.

关键词： Students

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Stochastic Optimization on Parking Lots for Smart Parking Using Reinforcement Learning Methods 14

Stochastic Optimization on Parking Lots for Smart Parking Us...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Khatermohammadi, Davoud Ghalari, Seyed Amir Ebadollahi, Saeed Gil, Bob Iran University of Science and Technology Department of Electrical Engineering Iran British Columbia Institute of Technology Department of Electrical and Computer Engineering Technology Canada

ISBN: (纸本)9798350360134

Various sciences have always considered the maximum use of limited resources and planning to optimize their use. Parking in urban spaces is regarded as a finite resource. This paper investigates the idea of introducing learning algorithms for parking guidance and information systems that employ a central server. Each driver engages in a systematic search during a cycle to identify the parking space with the highest perceived reward among all available options, the optimal parking is determined based on predefined tunings signifying the pursuit of identifying the best parking places to select. To provide estimated optimal parking searching strategies to travelers there are two options linear programming and dynamic programming. Several scenarios will be argued from basic linear programming to Reinforcement Learning methods. The main contribution of this paper lies in the identification of optimal parking spaces for drivers by considering various factors associated with the parking spaces, employing the MAB approach. The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters, as one of the reinforcement learning implementations used in this investigation. Each driver who played this MAB gained its reward with their probabilities. The reinforcement learning methodology is utilized for which both Greedy and ϵ-Greedy simulations are run considering stationary and nonstationary scenarios, meanwhile, each parking selection can change the environment, and regarding this change, scenario assumptions change which is a new scenario in the area of parking problems finally results of these conditions are analyzed while aspects of each scenario are discussed and compared to their efficiency. © 2024 IEEE.

关键词： Dynamic programming

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Classification of Parkinson's Disease Using MRMR and ICA Prediction Models - A Comparative Study

Classification of Parkinson's Disease Using MRMR and ICA Pre...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olawoye, Prisca O. Adebiyi, Marion O. Harry, Sotonye Vydah Arowolo, Micheal O. Landmark University Department of Computer Science Omu-Aran Nigeria University of Missouri Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350358155

Parkinson's disease (PD) is a neurodegenerative disorder which impacts the central nervous system and characterized by neuronal loss, particularly in the substantia nigra area of the brain. PD rendered dopamine-producing neurons dead which ultimately leads to a reduction in a variety of motor activities. Patients can have their quality of life improved by receiving an accurate diagnosis and preventative therapy. The primary criteria for diagnosing Parkinson's disease (PD) are medical indicators, particularly motor symptoms. However, traditional diagnostic procedures for PD can be subjective and prone to misdiagnosis due to the difficulty in categorizing small movements accurately. By improving medical professionals' ability to classify Parkinson's disease (PD), this study hopes to expedite the treatment process for those who suffer from it. To select the superior machine learning method, we evaluate the strengths and weaknesses of Independent Component Analysis (ICA) and Minimum Redundancy Maximum Relevance (MRMR). To categorize data, this study employs three well-known methods: the Support Vector Machine (SVM), the K-Nearest Neighbors (KNN), and the Decision Tree. Research used datasets that contained both patients with and without Parkinson's disease for both the training and testing phases of the study. This research suggests significant implications for subtyping Parkinson's disease. A similar level of accuracy (83.05%) is reached by the ICA-SVM model as well as the ICA-KNN model. The ICA-Decision Tree model's classification accuracy of 81.36 percent is also quite good. These results provide new avenues for exploring how machine learning might be used to better categorize people with Parkinson's. Bringing attention to these cutting-edge models is important to us because of their potential to boost diagnostic accuracy and efficiency in Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Diffusion Posterior Sampling is Computationally Intractable 41

Diffusion Posterior Sampling is Computationally Intractable

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41st International Conference on Machine Learning, ICML 2024

作者： Gupta, Shivam Jalal, Ajil Parulekar, Aditya Price, Eric Xun, Zhiyang Department of Computer Science University of Texas Austin United States Department of Electrical Engineering and Computer Science University of California Berkeley United States

Diffusion models are a remarkably effective way of learning and sampling from a distribution p(x). In posterior sampling, one is also given a measurement model p(y | x) and a measurement y, and would like to sample from p(x | y). Posterior sampling is useful for tasks such as inpainting, super-resolution, and MRI reconstruction, so a number of recent works have given algorithms to heuristically approximate it;but none are known to converge to the correct distribution in polynomial time. In this paper we show that posterior sampling is computationally intractable: under the most basic assumption in cryptography-that one-way functions exist-there are instances for which every algorithm takes superpolynomial time, even though unconditional sampling is provably fast. We also show that the exponential-time rejection sampling algorithm is essentially optimal under the stronger plausible assumption that there are one-way functions that take exponential time to invert. Copyright 2024 by the author(s)

关键词： Heuristic algorithms

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Numerical simulation of highly efficient double perovskite solar cell using SCAPS-1D

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Materials Today: Proceedings 2023年 73卷 584-589页

作者： Rana, Ashish D. Pharne, Indrajit D. Bhargava, Kshitij Department of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research And Management Gujarat Ahmedabad 380026 India Department of Electrical Engineering Sanjay Ghodawat University Maharashtra Kolhapur 41610 India

This study focuses on designing of lead-free double perovskite solar cells (DPSCs). Lead-free organic–inorganic DPSCs have achieved very good efficiency within a short period of active research. Formamidinium based double perovskite solar cells ((FA)2BiCuI6) have emerged as a promising replacement of lead-based organic–inorganic PSCs. The electron and hole transport layers (ETLs and HTLs) play important role towards the performance of DPSCs. Herein, we investigate the effect of various combinations of ETLs and HTLs for achieving an optimized highly efficient (FA)2BiCuI6 based DPSCs using SCAPS-1D. The selected ETLs were SnO2, TiO2, PCBM and HTLs were SpiroOMeTAD, NiO, PEDOT: PSS. Total 09 cells were analyzed to achieve the maximum power conversion efficiency (PCE). The best efficiency of 15.49% was yielded by a cell having SnO2 as ETL and NiO as HTL. © 2022

关键词： Perovskite solar cells

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Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 232-236页

作者： Zhou, Changshi Liu, Weiqi Han, Tao Ansari, Nirwan New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States

From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage. © 2019 IEEE.

关键词： Reinforcement learning

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Entanglement-controlled Quantum Federated Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18318-18330页

作者： Park, Soohyun Lee, Hyunsoo Jung, Soyi Park, Jihong Bennis, Mehdi Kim, Joongheon Sookmyung Women’s University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of Pillar Tampines Singapore University of Oulu Centre for Wireless Communications Oulu90014 Finland

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this article develops a novel depth-controllable architecture of entangled slimmable QNNs (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high-entanglement entropy and interdepth interference. Therefore, the proposed method in this article mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing interdepth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions. © 2014 IEEE.

关键词： Federated learning

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