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arXiv 2023年

作者： Bolin, David Simas, Alexandre B. Wallin, Jonas Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Statistics Lund University Sweden

Whittle-Matérn fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equation. Unlike earlier covariance-based approaches for specifying Gaussian fields on metric graphs, the Whittle-Matérn fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties of the model class, particularly the consistency and asymptotic normality of maximum likelihood estimators of model parameters and the necessary and sufficient conditions for asymptotic optimality properties of linear prediction based on the model with misspecified parameters. The covariance function of the Whittle-Matérn fields is generally unavailable in closed form, and they have therefore been challenging to use for statistical inference. However, we show that for specific values of the fractional exponent, when the fields have Markov properties, likelihood-based inference and spatial prediction can be performed exactly and computationally efficiently. This facilitates using the Whittle-Matérn fields in statistical applications involving big datasets without the need for any approximations. The methods are illustrated via an application to modeling of traffic data, where allowing for differentiable processes dramatically improves the *** Codes Primary 62M30, secondary 62M05, 60G60, 35R02 Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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Comparative Study Between Unscented Kalman Filter and Multi-Layer Perceptron Applied in an Electric Vehicle Simulation with Pack Parameters Generated from the Database

Comparative Study Between Unscented Kalman Filter and Multi-...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Kawe Monteiro de Souza José Rodolfo Galvão Jorge Augusto Pessatto Mondadori Fernanda Cristina Corrêa Paulo Broniera Junior Maria Bernadete de Morais França Dept. Postgraduate Degree in Electrical Engineering State University of Londrina (UEL) Londrina Brazil Graduate Program in Electrical Engineering (PPGEE) Federal University of Technology (UTFPR) Ponta Grossa Brazil Laboratory LAPSEE-PIM Senai Institute of Inf. and Communication Technology (ISTIC) Londrina Brazil

In electric vehicles (EVs), monitoring battery cells ensures safety and longevity. The Battery Management System (BMS) is responsible for this task, using variables such as State of Charge (SoC). This paper presents a comparative study between the Unscented Kalman Filter (UFK) and Multilayer Perceptron Neural Network (PMC) methods applied to a pack using a simulation of a vehicle under the FTP-75 cycle with reference SoC using Coulomb Counting (CC). The Neural Network performed better with an RMSE value of 0.418 compared to 0.0593 for UFK.

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers

Observing Generator Performance by Finite Element Analysis C...

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technology and Policy in Energy and Electric Power (ICT-PEP), International Conference on

作者： Muhammad Afnan Habibi Mochamad Farhan Ali Irfani Langlang Gumilar Arya Kusumawardana Aripriharta Angger Baskoro Study Program of Power Plant Engineering Technology Universitas Negeri Malang Malang Indonesia Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Dept. Electrical Engineering and Computer Sciences National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies.

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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ghoneim, Sherif S. M. Baz, Mohammed Alzaed, Ali Zewdie, Yohannes Tesfaye Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. © The Author(s) 2025.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

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Compact Complementary Split Ring Resonator Based Sensor for Non-Invasive Glucose monitoring

Compact Complementary Split Ring Resonator Based Sensor for ...

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Telecommunications Conference (ITC-Egypt), International

作者： Adnan Zahran Youssif Ayman Aly Angie R. Eldamak Dalia N. Elsheakh Electronics and Communication Systems Engineering Program Faculty of engineering Ain Shams University Cairo Egypt Electronics and Communication Systems Engineering Dept. Faculty of engineering Ain Shams University Cairo Egypt Electrical Dept. Faculty of Engineering and Technology Badr University in Cairo Badr City Cairo Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper presents a novel approach for noninvasive glucose monitoring using a Compact Complementary Split Ring Resonator (CCSRR) based sensor. The sensor design is initially simulated using High-Frequency Structure Simulator (HFSS) software, and then experimental validation is done. We carried out simulations with and without a finger phantom model to closely resemble conditions in real situations. The sensor exhibits high sensitivity and accuracy in detecting glucose concentrations within blood samples. Vector Network Analyzer was used along with the sensor to validate the simulation results. The results demonstrate close agreement between simulation and experimental data, affirming the effectiveness of the proposed sensor design for non-invasive glucose monitoring.

关键词： Sensitivity Split ring resonators Fingers Phantoms Network analyzers Vectors Glucose

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Planning and Verification of a Cloud-Based Monitoring DC Microgrid System Using the Greedy Algorithm

Planning and Verification of a Cloud-Based Monitoring DC Mic...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Jenn-Jong Shieh Chia-Wei Lu Chang-Hua Lin Hwa-Dong Liu Dept. of Electrical Engineering Feng Chia University Taichung Taiwan R.O.C. Dept. of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan R.O.C. Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei Taiwan R.O.C.

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

This paper aims to implement a cloud-based monitoring DC microgrid system suitable for communities by integrating a simulated utility grid system (SUGS), battery energy storage system (BESS), solar power generation system (SPGS), and cloud-based front-end monitoring interface and technology. Additionally, the paper utilizes Long Short-Term Memory (LSTM) model to predict the next day's load curve and employs a solar simulator to simulate the daily variations in solar irradiance. Furthermore, to enhance economic benefits during peak and off-peak time-of-use (TOU) pricing periods, this paper adopts the concept of local selection using a greedy algorithm to optimize energy allocation between SUGS, BESS, and SPGS through cloud computing. Finally, a derating case study is conducted through simulations and experiments to verify the economic value and feasibility of the proposed greedy algorithm in a community-based DC microgrid.

关键词： Greedy algorithms Economics Cloud computing Load forecasting Microgrids Solar power generation Resource management Monitoring Long short term memory Testing

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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