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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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Short-Term Load Forecasting Employing Recurrent Neural Networks

Short-Term Load Forecasting Employing Recurrent Neural Netwo...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Tanzim Mostafa Mostafa M. Fouda Mohammad G. Abdo Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

This research paper explores the domain of Short-Term Load Forecasting (STLF) through the application of Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Focused on the west region of the Electric Reliability Council of Texas (ERCOT), the study employs a dataset spanning 2012–2015, enriched with new features such as day of the month, month of the year, and a detailed holiday classification. The dataset also incorporates hourly temperature, humidity, time of the day, day of the week, and hourly load values, offering a comprehensive perspective for load forecasting. Through meticulous hyperparameter tuning, LSTM and GRU models are optimized to find their best architecture. Four evaluation metrics have been employed to assess the performance of our models, namely, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination or R Squared (R2). With the identified optimal hyperparameters and look-back window, the obtained values for MAE, RMSE, MAPE and R2 for the LSTM model are 8.94, 12.24, 0.80 and 0.9973, respectively. The GRU model also achieves good performance with MAE, RMSE, MAPE, and R2 of 10.72, 14.48, 0.96, and 0.9962, respectively. Our research shows that the accuracy of STLF on the dataset has been improved by adding new features and fine-tuning the LSTM and GRU models, with the LSTM model achieving the best performance.

关键词： Measurement Recurrent neural networks Load forecasting Predictive models Logic gates Reliability Root mean square

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Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

Opioid Use Disorder Prediction Using Machine Learning of fMR...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Temtam, A. Ma, Liangsuo Moeller, F. Gerard Sadique, M.S. Iftekharuddin, K.M. Dept. of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States Institute of Drug and Alcohol Studies Virginia Commonwealth University RichmondVA23284-2520 United States Department of Psychiatry Virginia Commonwealth University RichmondVA23284-2520 United States

ISBN: (纸本)9781510660359

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects' prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development. © 2023 SPIE.

关键词： Machine learning

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Deep Learning-based Physical Layer Authentication in LiFi Networks Under Multi - User Mobility

Deep Learning-based Physical Layer Authentication in LiFi Ne...

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IEEE Southeastcon

作者： Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Department of Computer Science University of New Haven West Haven CT USA Department of Computer Science Tennessee Technological University Cookeville TN USA Cybersecurity Education Research and Outreach Center Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The open nature of wireless networks poses significant security challenges. Upper-layer authentication (ULA) methods face limitations, including vulnerability to cryptanalysis and replay attacks. Physical-layer authentication (PLA) has emerged as a promising technique to complement the ULA, offering robust two-factor authentication. PLA leverages physical-layer characteristics to authenticate transmitters with reduced complexity and latency. In this paper, we focus on passive PLA schemes for indoor Light Fidelity (LiFi) networks, where approximately 80% of data traffic originates indoors. Existing studies overlook channel impulse response (CIR) similarity under multi-user mobility and varying user densities, resulting in a lack of robust PLA solutions. To address this gap, we investigate CIR similarity in indoor LiFi environments. Our simulation results demonstrated that the CIR similarity is significant under multiuser mobility, where the attacker can share more than 30% of the CIR of a legitimate user. We propose a deep learning-based Long Short- Term Memory (LSTM) model that predicts the next CIR value based on historical data to enhance authentication reliability. Authentication is performed if the predicted CIR matches the actual CIR at the access point. Our numerical results show that the proposed LSTM-based PLA method effectively distinguishes legitimate users, achieving a detection probability exceeding 94% even under high multi-user mobility. Furthermore, the proposed LSTM-based PLA method can maintain the missed detection and the false alarm under 6% and 3% in the worst-case scenario, respectively.

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Decision trees for predicting dropout in engineering Course students in Brazil 9th

Decision trees for predicting dropout in Engineering Course ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Mariano, Ari Melo De Magalhães Lelis Ferreira, Arthur Bandeira Santos, Maíra Rocha Castilho, Mara Lucia Bastos, Anna Carla Freire Luna Campêlo Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil Center for Advanced Multidisciplinary Studies University of Brasilia Brazil Federal Institute of Brasilia Brazil Universidade Federal Do sul da Bahia Brazil

The dropout of Brazilian students from higher education is a subject that has been well explored, where high rates of students who drop out are verified. However, despite the vast literature, the problems arising from student's dropout still have no solution since dropout itself is an unsolved problem. This research aims to present a classification via decision trees to predict the evasion of engineering course students in Brazil. To reach this objective, exploratory field research was conducted, where data was collected employing surveys directed to the students, enabling the elaboration of a classificatory decision tree with the C4.5 algorithm. The survey sample consisted of 91 valid answers. The results were analyzed with the RapidMiner tool and presented a decision tree with 86.81% accuracy. Among the main factors preventing dropout is interaction with professors, the course curriculum, and issues related to mental well-being. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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Bioinspired UV-Visible Multispectral Imaging Sensor Enabled by Perovskite Nanocrystal Coating

Bioinspired UV-Visible Multispectral Imaging Sensor Enabled ...

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2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Chen, Cheng Wang, Ziwen Wu, Jiajing Deng, Zhengtao Zhang, Tao Zhu, Zhongming Jin, Yifei Lew, Benjamin Srivastava, Indrajit Liang, Zuodong Nie, Shuming Gruev, Viktor Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States College of Engineering and Applied Sciences Nanjing University Jiangsu Nanjing China Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Inspired by the visual system of Papilio xuthus butterfly, w e f abricated a ultraviolet-visible multispectral imager which combines perovskite nanocrystals and vertically stacked silicon photodetectors. High-resoluti... 详细信息

ISBN: (纸本)9781957171289

关键词： Nanocrystals

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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Edge-Computing Enabled Si Photonics Multimodal Sensor with Integrated Photonic Convolutional Processor

Edge-Computing Enabled Si Photonics Multimodal Sensor with I...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Zian Xiao Zhihao Ren Yangyang Zhuge Zixuan Zhang Jingkai Zhou Siyu Xu Cheng Xu Bowei Dong Chengkuo Lee Department of Electrical & Computer Engineering National University of Singapore Singapore NUS Suzhou Research Institute (NUSRI) Singapore Institute of Microelectronics (IME) Agency for Science Technology and Research Singapore National Centre for Advanced Integrated Photonics Singapore

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Photonic integrated circuits provide compact and efficient solutions for multimodal spectroscopic sensors. However, the resulting sensory data is highly complex and contains significant redundancy. To circumvent high communication cost for redundancy, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal edge computing system. A microring resonator crossbar is used as photonic processor with 5-bit accuracy. The in-situ processing of sensory data is demonstrated in this system, achieving a classification accuracy of 94.27% across 45 different classes. The multimodal edge computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the photonic data processing capability.

关键词： Temperature sensors Accuracy Multimodal sensors Redundancy Sensor systems Silicon Temperature control Optoelectronic and photonic sensors Photonics Edge computing

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A Review of Non-fungible Tokens Applications in the Real-world and Metaverse 9th

A Review of Non-fungible Tokens Applications in the Real-wor...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Far, Saeed Banaeian Bamakan, Seyed Mojtaba Hosseini Qu, Qiang Jiang, Qingshan Department of Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Management Sciences Yazd University Yazd89195-741 Iran Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The growth of the popularization of blockchain technology has caused new concepts to emerge. Non-Fungible Token (NFT) is one of these concepts introduced based on blockchain technology in 2017. As newly-emerged concepts, NFTs have rapidly become a popular technology in industry and academic research. NFTs currently have various applications in the industry, which are reviewed in the present study. They have also attracted a broad spectrum of research. Metaverse is another exciting blockchain-based technology that enables people to connect with digital avatars in a virtual universe. The importance of this study is to identify new applications of NFTs in the Metaverse making NFTs more applicable. Assisting with identity management and ownership right of digital assets are examples of NFT applications in the Metaverse that are explained in this study in detail. Ultimately, this study presents some future directions of NFT applications in industry and academic research. © 2022 The Authors. Published by Elsevier B.V.

关键词： Blockchain

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Microwave Time Quasicrystals

Microwave Time Quasicrystals

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Conference on Lasers and Electro-Optics (CLEO)

作者： Kyungmin Lee Minwook Kyung Jagang Park Yung Kim Hyuckjoon Cho Joonhee Choi Bumki Min Department of Physics Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Electrical Engineering and Computer Sciences University of California Berkeley California USA Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Electrical Engineering Stanford University Stanford CA USA

We experimentally observe the discrete time-quasicrystalline phase in microwave temporal crystals. In addition, it was confirmed that the microwave time quasicrystal is robust against spatial defects to a certain degree.

ISBN: (纸本)9781957171258

关键词： Microwaves Optical properties Photonic crystals Printed circuit boards Resonant modes Split ring resonators

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