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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Cochlear Decomposition: A Novel Bio-Inspired Multiscale Analysis Framework 48

Cochlear Decomposition: A Novel Bio-Inspired Multiscale Anal...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Alfalahi, Hessa Khandoker, Ahsan Alhussein, Ghada Hadjileontiadis, Leontios Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9781728163277

Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal information in time-domain signals. So far, various SMD approaches, such as the Multiresolution Wavelet Transform (MWT), the Empirical Mode Decomposition (EMD), and the Variational Mode Decomosition (VMD) have been proposed. However, issues, such as mode mixing for signals with closelyspaced modes, have been identified. To confront such problems, we propose here a novel spatial auditory decomposition framework for non-stationary signals, namely the Cochlear Decomposition (CD). CD is inspired by the biological rules of the spiral human cochlea and it is built upon the concept of 'place-pitch' or the tonotopic organization of the spiral cochlea. The new insight here is to formulate a set of basis functions, namely cochlear wavelets, whose dilation factors are determined by their angular position on the cochlear spiral (i.e., coupled rotation and dilation). Under proper parameterization, iterative application of spatial filters eventually results in signal's mono-components, each arising from specific angular positions along the spiral cochlea, with high time and frequency localization. The performance of the proposed CD is validated via synthetic acoustic signals and real non-stationary speech signals analysis. The analysis results show that CD outperforms the performance of MWT, EMD, and VMD, exhibiting, at the same time, high noise robustness. We also show that the CD disentangles lowfrequency temporal modulations of sounds, supporting perceptual phenomena in both speech and music. Clearly, CD paves the way for higher-level speech perception deep learning models and also for efficient cochlear implants design. Future work to incorporate the nonlinearity of the cochlea into a binaural hearing framework is underway. © 2023 IEEE.

关键词： Audition

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High Efficiency InGaN/GaN Nanowire LED Utilizing Exciton Recombination Dynamics

High Efficiency InGaN/GaN Nanowire LED Utilizing Exciton Rec...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Malhotra, Yakshita Pandey, Ayush Min, Jungwook Reddeppa, Maddaka Xiao, Yixin Li, Zidong Kan, Yuchen Guo, Yifu Wu, Yuanpeng Sun, Kai Deotare, Parag Mi, Zetian Department of Electrical Engineering and Computer Science University of Michigan 1301 Beal Avenue Ann ArborMI48109 United States Department of Material Science and Engineering University of Michigan 1301 Beal Avenue Ann ArborMI48109 United States

Facet formation in InGaN disc-in-wire LEDs leads to increased exciton binding energy due to strain relaxation and reduced polarization fields resulting in an ultra-high efficiency of 25.2% for green emission utilizing... 详细信息

ISBN: (纸本)9781957171395

关键词： Excitons

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Improved Convolutional Neural Network and Transfer Learning with VGG16 Approach for Image Classification

Improved Convolutional Neural Network and Transfer Learning ...

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2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Habibullah Ullah, Md. Oli Kamal, Minhaz Sohag, Md. Sabbir Hasan Rahman, Md. Saifur Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307771

Transfer learning may boost modeling speed. This research unifies the improved VGG16 model. Skipping VGG16's entirely connected layer and tying it to the layer following it improves CNN's architecture and reduces its processing burden. CNNs are classifying cat and dog photos. With a small dataset, CNN training may take time and resources. Transfer learning uses ImageNet models to handle similar challenges in new contexts. Transfer learning enhances CNN cat-dog classification. The proposed technique selects a suitable pre-trained model, freezes its layers to retain acquired properties, adds layers to learn task-specific features, and tunes it using the cat and dog dataset. Data augmentation improves model performance and prevents overfitting. Hyperparameter optimization and validation enhance model accuracy and speed. This method enhances cat and dog photo classification even without adequate data to train CNNs with an accuracy of 97%. © 2023 IEEE.

关键词： Convolutional neural networks

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PREDICTIVE MACHINE LEARNING MODELS FOR LIDAR SENSOR RELIABILITY IN AUTONOMOUS VEHICLES

PREDICTIVE MACHINE LEARNING MODELS FOR LIDAR SENSOR RELIABIL...

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ASME 2024 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, InterPACK 2024

作者： Farahani, Saba A. Lee, Jae Yun Kim, Hunjae Won, Yoonjin Department of Electrical Engineering and Computer Science University of California Irvine United States Department of Mechanical and Aerospace Engineering University of California Irvine United States Hyundai Motor Company Seoul Korea Republic of

ISBN: (纸本)9780791888469

The emergence of autonomous vehicles marks a transformative moment in the transportation sector, significantly propelled by the integration of Light Detection and Ranging (LiDAR) technology. LiDAR revolutionizes how vehicles perceive their environment, emitting laser beams to measure distances to objects and creating highly accurate three-dimensional maps. This innovation is pivotal for enhancing the operational efficiency and safety of autonomous vehicles by providing instantaneous and detailed environmental mappings. However, the efficacy of LiDAR sensors is compromised by environmental factors such as dust, dirt, snow, and rain, which can severely affect their accuracy and, consequently, the safety of the vehicles they guide. Despite the importance of this issue, the research field has shown a notable lack of investigation into predicting LiDAR sensor contamination and its reliability. This gap underscores a critical need for dedicated research efforts to ensure the reliability and safety of autonomous driving technologies, making it a pressing challenge for researchers and developers alike. Therefore, it is imperative to develop a novel way to detect LiDAR sensors'reliability like contamination level. In response to the urgent need to address LiDAR sensor contamination, our team have initiated a comprehensive data collection journey, spanning from California's diverse climates to Michigan's variable weather conditions. This dataset contains multi-level features such as contamination levels on multiple sensors, environmental factors, and sensor images across varying weather conditions and geographical locales. The dataset is a groundbreaking contribution to the field, playing a vital role in developing our machine learning models and offering novel insights that could dramatically advance research and practical applications. In this work, our framework employs machine learning methods across two distinct but complementary strategies: sensor data analysis and imag

关键词： Weather forecasting

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Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

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Performance Analysis of Real-Time Safety Vest and Helmet Detection Using Yolov8 18

Performance Analysis of Real-Time Safety Vest and Helmet Det...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Saputra, Mochammad Agung Munastha, Ketut Abimanyu Mulyono, Tanto Trisno Fitriadi, Budi Setiawan, Asep Trisna Lestari, Nina Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Universitas Bandar Lampung Faculty of Computer Science Lampung Indonesia

ISBN: (纸本)9798331527686

Workplace safety is a critical priority in the industry, particularly in enforcing occupational health and safety (OHS) standards. This study introduces an innovative OHS compliance inspection system integrating access control with object recognition technology. The system captures images of workers using an ESP32Cam as the pre-processing step for safety helmet and vest detection. The ESP32 Cam captures the best image for detection at a maximum distance of 100 cm. The OpenCV library accesses these images, while the YOLOv8 library performs object recognition. The dataset includes 761 images for helmets and 314 images for vests, with the best results achieved using 100 training epochs of the pre-trained *** model. The system demonstrates a confidence level of 0.89 and an accuracy rate 0.75 in identifying safety helmets and vests. Recognition results are monitored in real-time via a web-based dashboard, with data stored in a database for further analysis. This study highlights the potential of integrating object recognition technology to enhance OHS compliance and workplace safety. © 2024 IEEE.

关键词： Inspection equipment

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Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Multiuser Resource Allocation for Semantic-Relay-Aided Text ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hu, Zeyang Liu, Tianyu You, Changsheng Yang, Zhaohui Chen, Mingzhe Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China College of Information Science and Electronic Engineering ZheJiang University Hangzhou China Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States

ISBN: (纸本)9798350370218

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region. © 2023 IEEE.

关键词： joint power and bandwidth allocation Semantic communication semantic relay

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Cyberattack Aware Load Frequency Control in Power Systems 58

Cyberattack Aware Load Frequency Control in Power Systems

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, Muhammad Davis, Katherine R. Serpedin, Erchin Texas A&m University Electrical & Computer Engineering Department College StationTX77843 United States Lebanese American University Department of Computer Science & Mathematics Beirut Lebanon Florida State University Department of Electrical & Computer Engineering TallahasseeFL32310 United States Research and Outreach Center Tennessee Tech University Department of Cybersecurity Education CookevilleTN38505 United States

ISBN: (纸本)9798350354058

The load frequency control (LFC) is crucial for stabilizing the frequency of the power grid in the intermittency of renewable energy sources. Modern LFC systems utilize open communication and automation networks that expose power systems to potential cyberattacks, which could lead to frequency instability throughout the network. Current research often treats frequency control and cybersecurity independently, which could lead to ineffective solutions and increased system instability. This paper introduces a cyber-resilient LFC strategy that improves both control performance and security. We employ reinforcement learning for adaptive frequency control and integrate it with a graph convolutional neural network to enhance control responses during a cyberattack event. Our strategy also includes a graph auto encoder-based attack detector that is trained in various scenarios and has shown more than 92% in detection rate while tested on the Iberian power system topology, which includes 486 buses. This highlights the robustness and reliability of our approach in ensuring resilience against cyberattacks within the realm of frequency control in power systems. © 2024 IEEE.

关键词： Graph neural networks

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A Robust Finite Time Longitudinal Speed Control for a Catenary-based Electric Vehicle 63

A Robust Finite Time Longitudinal Speed Control for a Catena...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rinaldi, Gianmario Ferrara, Antonella Menon, Prathyush P University of Exeter Faculty of Environment Science and Economy Department of Engineering EX4 4QF United Kingdom University of Pavia Computer and Biomedical Engineering Department of Electrical Pavia Italy

ISBN: (纸本)9798350316339

The electrification of the transportation sector plays a crucial role in reducing the global carbon footprint. Among the various solutions, catenary-based electric vehicles have attracted a great deal of interest from researchers and practitioners. This article considers a vehicle powered by a battery energy storage system (BESS) and a catenary system and proposes a robust finite-time longitudinal speed control strategy. Drawing inspiration from the Second-Order Sliding Mode (2-SOSM) methodology, the approach ensures that the control input remains within the limits set by the maximum torque of the motor. The strategy demonstrates robustness against uncertainties and disturbances, eliminating the need to know the model parameters. The simulation case study considers the effects of real road data, including slope variations, on power management. A comparative analysis with the conventional Proportional Integral (PI) method highlights the superior performance of the proposed strategy. © 2024 IEEE.

关键词： Carbon footprint

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