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10th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2023

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

ISBN: (纸本)9798350337136

The ultra-wide bandgap energy (***) of gallium oxide (Ga2O3) enables high breakdown electrical strength, low intrinsic carrier concentration and high operation temperatures, making it a promising material for high-Temperature, high-density power electronics. The low thermal conductivity of Ga2O3 is a limiting factor, which impedes efficient heat removal from the device junction, and increases the junction to case thermal resistance. However, a quantitative study of how much higher thermal resistances and device junction temperature will rise compared to other semiconductor devices like silicon carbide (SiC) are not fully investigated. In this paper, a finite element analysis model of Ga2O3 power module is built, and the junction temperature rise of Ga2O3 power module is simulated under both bottom sided cooling and double sided cooling packaging. The thermal performance of Ga2O3 is compared with SiC power modules under the same heat load and cooling conditions, and the breakdown of total thermal resistance of Ga2O3 power module is also calculated and presented to better understand the influence of Ga2O3's low thermal conductivity on the power module's overall thermal performances. © 2023 IEEE.

关键词： Heat resistance

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A Private Blockchain Based P2P Energy Trading Platform for Energy Users 9

A Private Blockchain Based P2P Energy Trading Platform for E...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Mololoth, Vidya Krishnan Ahlund, Christer Saguna, Saguna Electrical and Space Engineering Luleå University of Technology Department of Computer Science Skellefteå Sweden

ISBN: (纸本)9798350397758

With the rapid rise of renewable energy sources (RES) in energy grids, the need for secure peer-to-peer (P2P) energy trading platforms are increasing. Such platforms should allow energy users to buy/sell energy, participate in bids, and negotiate prizes. The decentralized blockchain architecture makes it suitable for building an efficient energy trading platform. In this paper, we propose a private blockchain-based energy trading platform using a proof of authority (PoA) consensus algorithm for energy users within a neighbourhood or a microgrid. The private Ethereum blockchain is configured using three Raspberry Pi 4 models, which act as three nodes on the network. The trading process is implemented using a smart contract deployed on the nodes by configuring each node as a buyer and seller. The nodes can add offers, choose offers, and trade energy within the nodes in the network. All the transactions are recorded on the blockchain. For ease of demonstration, Python Flask and HTTP-based interaction environment is developed. © 2023 IEEE.

关键词： Smart contract

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Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment 50

Deep Learning Based Corona Discharge Severity Classification...

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

作者： Messaoudi, Maher Kameli, Sayed Mohammad Refaat, Shady S. Abu-Rub, Haitham Trabelsi, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (纸本)9781665464543

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. © 2024 IEEE.

关键词： Long short-term memory

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Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach 4

Forecasting EV Charging Demand: A Graph Convolutional Neural...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 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 Texas A&M University at Qatar Electrical & Computer Engineering Department Doha23874 Qatar Florida State University Electrical & Computer Engineering Department TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

ISBN: (纸本)9798350306262

Electric vehicles (EVs) are expected to revolutionize the global transportation sector by promoting sustainability and eco-friendliness. The continuous proliferation of EVs requires an expansion of the existing charging infrastructure to meet the corresponding increase in electricity demand. Such an expansion requires an accurate forecasting of charging demand in both space and time domains for a well-planned allocation of charging stations (CSs). This paper proposes a Graph Convolutional Neural Networks (GCNN) based approach combined with a Long Short-Term Memory (LSTM) to predict the future charging demands of EVs. The proposed architecture fuses the benefits of both GCNN and LSTM to extract the underlying spatio-temporal features from the collected dataset. The training dataset reflects the coupling between the power and transportation systems, and thereby it helps the proposed deep learning architecture to capture the spatio-temporal patterns of the inter-connected environment. A comparative analysis is conducted with other state-of-the-art EV charging load prediction models to assess the prediction performance of the proposed load forecasting strategy. © 2024 IEEE.

关键词： Forecasting

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Advancing Brain Tumor Classification: Exploring Two Deep Learning Architectures for Improved Accuracy 16

Advancing Brain Tumor Classification: Exploring Two Deep Lea...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kumar, Vendra Durga Ratna Ethel Muchina, Fadzai Singh, Priyanka Khan Nizami, Tousif Srm University Ap Computer Science and Engineering AndhraPradesh India Srm University Ap Department of Electrical and Electronics Engineering AndhraPradesh India

ISBN: (纸本)9798350383119

A mass of abnormal cells that form inside or outside the brain is called a brain tumor. Adults are at high risk of developing brain tumors, which can cause serious organ dysfunction and even death. Detecting tumors manually is a tedious and difficult method that might yield erroneous findings. As a result, these tumors must be meticulously classified to offer a complete medical diagnosis and design an appropriate treatment plan. Estimating the patient's chances of survival is difficult since tumors are uncommon and can vary greatly in size, location, and history. In order to address these issues, the use of two different deep learning frameworks for multi-class brain tumor classification utilizing Magnetic Resonance Imaging (MRI) data was examined in this study. Significant evaluation metrics, including F1 score, recall, accuracy, and precision, were applied to these models. Both models demonstrated significant improvements over prior brain tumor classification studies, illustrating that deep learning algorithms may be used in the future to accurately diagnose brain tumors and enable medical personnel to make well-informed judgments regarding patients' treatment courses. This study proposes two classification algorithms: ResNet50, that obtained a success rate of 99.39%, and EfficientNetB0, obtained accuracy rate of 99.75%. © 2024 IEEE.

关键词： Brain Tumor Deep Learning EfficientNetB0 MRI Multi-class Classification ResNet50

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR 25

Comparative Analysis of Clusterization Methods Applied to a ...

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25th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2025

作者： Poleselo, Henrique N. Conceição, Andre G. S. Federal University of Bahia Postgraduate Program in Electrical Engineering Salvador Brazil Federal University of Bahia Department of Electrical and Computer Engineering Salvador Brazil

ISBN: (纸本)9798331538606

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons. © 2025 IEEE.

关键词： Mobile robots

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Leveraging Neural Radiance Field and Semantic Communication for Robust 3D Reconstruction 100

Leveraging Neural Radiance Field and Semantic Communication ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Xu, Rui Li, Gaolei Lin, Xi Liu, Yuchen Chen, Mingzhe Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China North Carolina State University Department of Computer Science Raleigh United States University of Miami Electrical and Computer Engineering Department United States

ISBN: (纸本)9798331517786

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, the input images of NeRF transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of 3D reconstruction, thereby limiting its scalability. Fortunately, semantic communication has been proved a effective method to solve the above problem. In this paper, we propose a novel NeRF based 3D semantic communication (NeRF-3DSC) system, which offers robust 3D reconstruction in HIWE. Within NeRF-3DSC, semantic encoder and decoder are employed to extract and recover core semantic features of task-specific specific object (TSO) from the collected images, channel encoder and decoder ensure robust transmission of compressed semantic information in HIWE, lightweight renderer based on NeRF is employed for fast and efficient 3D reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed NeRF-3DSC enables robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality. © 2024 IEEE.

关键词： Decoding

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High-Speed Visible Light Communication Using Phenothiazine/Dimesitylborane Derivatives as Color Conversion Materials in Semipolar Micro-LED-Based White-Light Systems

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ACS Photonics 2024年第2期11卷 489-497页

作者： Sadhu, Annada Sankar Chen, Li-Yin Pai, Yi-Hua Hsieh, Chung-An Lin, Hao-Wu Chow, Chi-Wai Kuo, Hao-Chung Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan

Visible light communication (VLC) has emerged as a cutting-edge high-speed communication technology, poised to meet the surging capacity demands of 6G networks. Micro-light-emitting diodes (μLEDs) are considered as the light sources for achieving high-speed VLC, distinguished by their remarkable modulation bandwidths. However, achieving broadband white light emission hinges on the utilization of color-conversion materials with wide emission spectra. The transmission speed of the white-light system is inherently constrained by the characteristics of these color-conversion materials. In this work, we demonstrate CC-MP7 and CC-MP8, two derivatives of phenothiazine/dimesitylborane, as color conversion materials in a semipolar (20-21) micro-LED-based white-light system for high-speed VLC. The color conversion layers possess wide emission spectra, enabling them to achieve excellent color rendering performance when combined with blue micro-LEDs. CCMP7 and CC-MP8 demonstrate rapid photoluminescence decay characteristics, thereby enhancing the modulation bandwidth of the color-conversion layer in the white-light system. The resulting bandwidths achieved by CC-MP7 and CC-MP8 are 210 and 240 MHz, respectively, which represents an approximately 45-fold increase compared to ordinary phosphors. By combining semipolar (20-21) micro-LEDs with CC-MP7 and CC-MP8, the resulting white-light systems exhibit correlated color temperatures of 6860 and 7500 K, CIE coordinates of (0.3009, 0.3577) and (0.2958, 0.3129), and color-rendering indexes of 80 and 85, respectively. Furthermore, both systems offer high bandwidths of 1063 and 1084 MHz with the data rates of 1.72 Gbps and 1.74 Gbps using non-return-to-zero on-off keying (NRZ-OOK) format, respectively, indicating the significant potential of CC-MP7 and CC-MP8 for practical applications in VLC. © 2024 The Authors.

关键词： Bandwidth

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A Survey on Wireless Sensor Network Routing Performance Optimizing and Security Techniques 4th

A Survey on Wireless Sensor Network Routing Performance Opti...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Nag, Anindya Mandal, Dishari Roy, Nilanjana Firoz Ahmed Fahim, S.M. Chand, Nisarga Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science and Engineering Adamas University Kolkata700126 India Dhaka1229 Bangladesh Department of Electrical and Communication Engineering Adamas University Kolkata700126 India

ISBN: (纸本)9789819726134

Wireless Sensor Network (WSN) is a contemporary topic of research that has proven to be an advantageous technology for a wide variety of applications, including those dealing with the environment, the military, and health, as well as uses at home and in the workplace. There are two different kinds of WSNs: mobile WSN and static WSN. Distribution, location, and tracking are three of the many new conceptual and optimization challenges presented by WSNs. These challenges are particularly important given the fact that a great number of applications are dependent on WSNs for information. There are a number of challenges associated with this type of WSN, including high bandwidth requirements, high energy consumption, providing Quality of Service (QoS), data processing, compression, and cross-layer design. Authors of this review have conducted a survey of the relevant literature and draw the conclusion that routing protocols for WSNs make use of a variety of approaches, such as Hierarchical Based Routing Protocols (HBRP) and Location-Based Routing Protocols (LBRP), in order to accomplish their goals. Each of these approaches is capable of meeting the necessary transmission specifications. The fundamental goal of the analysis is to ensure that both power consumption and security are taken into account while conducting the analysis of performance matrices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

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Light(science & Applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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