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共 2105 条记录，以下是81-90 订阅

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Radar Applications of a Prototype Dual-Band Metamaterial and Metasurface Antenna 6

Radar Applications of a Prototype Dual-Band Metamaterial and...

引用

6th Annual IEEE Research and Applications of Photonics in Defense Conference, RAPID 2023

作者： Hecht, Kristy Amin, Md Shohel Louisos, Dustin Mencagli, Mario Junior Hofmann, Tino Willis, Andrew Touma, Jimmy E. University of North Carolina at Charlotte Electrical and Computer Engineering Dept. CharlotteNC28025 United States University of North Carolina at Charlotte Physics Dept. CharlotteNC28025 United States Air Force Research Laboratory Eglin AFBFL32542 United States

ISBN: (纸本)9781665423526

Current dual-band radars consist of 2 different radar antennas with the same back-end electronics and software. There is great interest in AFRL to develop dual-band radars by using the same antenna, operating at two different frequencies, to achieve the desired efficiency as with traditional systems but without the SWaPC constraints. This article discusses radar applications of a dual-band antenna design made possible by integrating metamaterial and metasurface design elements. The metamaterial component of the design investigates the phase change properties of VO2 as a mechanism for switching the equivalent impedance of the proposed antenna. The meta-surface component of the design investigates the size, arrangement and structure of the antenna's unit cells that allow for both dual-band behavior and beam-steering capabilities. This article discusses the application of the proposed dual-band antenna for detection of and tracking of radar targets. © 2023 IEEE.

关键词： Metamaterials

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Loaded Waveguide Antenna for Undersea Communications

Loaded Waveguide Antenna for Undersea Communications

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Yannan Liu Hossein Naghavi Yasuo Kuga John M. Mower Dept. of Electrical and Computer Engineering University of Washington Seattle Washington Applied Physics Laboratory University of Washington Seattle Washington

A loaded waveguide antenna has been designed, manufactured, and tested to explore potential applications in ocean observation projects. This antenna can operate under high pressure and attenuation in the deep sea with... 详细信息

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

关键词： Antenna measurements Underwater communication Oceans Loading Sea measurements Loaded waveguides Titanium dioxide

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Machine Learning Algorithms for Predicting Chronic Diseases

Machine Learning Algorithms for Predicting Chronic Diseases

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Furkan Buluş Bilal Saoud Ibraheem Shayea Zuleikha Syzdykova Dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey Dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria Dept. of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management.

关键词： Machine learning algorithms Data analysis Prevention and mitigation Medical services Predictive models Prediction algorithms Robustness Bayes methods Decision trees Diseases

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THz-Frequency Free-Standing Bi-Layer wire grid Polarizers Fabricated by Stereolithography

THz-Frequency Free-Standing Bi-Layer wire grid Polarizers Fa...

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Louisos, Dustin Mclamb, Micheal Boreman, Glenn D. Zhang, Jincheng Willis, Andrew Hofmann, Tino Touma, Jimmy E. University of North Carolina at Charlotte Dept. of Physics and Optical Science CharlotteNC28025 United States University of North Carolina at Charlotte Electrical and Computer Engineering Dept CharlotteNC28025 United States Air Force Research Laboratory Eglin AFBFL32542 United States

Finite element simulations of free-standing, bi-layer wire grid polarizers, with dimensions typical for stereo-lithography, show two orders of magnitude improvement over single-layer wire grid polarizers. © 2024 ... 详细信息

ISBN: (纸本)9798350373097

关键词： Stereolithography

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Low-Loss Wireless Implant Telemetry Using Magnetoinductive Waveguides

Low-Loss Wireless Implant Telemetry Using Magnetoinductive W...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Connor Jenkins Asimina Kiourti Dept. of Electrical and Computer Engineering ElectroScience Laboratory The Ohio State University Columbus OH USA

We introduce a novel wireless implant telemetry system that utilizes magnetoinductive waveguides (MIW) to break state-of-the-art boundaries in terms of: (a) transmission loss, and (b) bandwidth. Magnetoinductive waveguides rely on the careful design of a series of resonant loops, where excited current on the transmit loop leads to the current propagating as magnetoinductive waves to the receive loop. A MedRadio (401–406 MHz) MIW is shown to communicate with a $49.3 mm^{3}$ implant at 1 cm dept. with a transmission loss of only 19.4 dB and a bandwidth of 187.4 MHz. This represents an improvement of over 30 dB and 85 MHz over a similar Radio-Frequency (RF) based system. Notably, the reported MIW framework empowers a full wearable system that can be integrated into clothing to handle wireless nodes placed both on and inside the human body.

关键词： Wireless communication Radio frequency Wearable computers Magnetic resonance Implants Electromagnetic waveguides Bandwidth

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Statistical Characterization of Cavity Quality Factor via the Stochastic Green's Function Approach

Statistical Characterization of Cavity Quality Factor via th...

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2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023

作者： Lin, Shen Shao, Yang Peng, Zhen Addissie, Bisrat D. Drikas, Zachary B. University of Illinois at Urbana-Champaign Dept. Electrical and Computer Engineering UrbanaIL61801 United States Tactical Electronic Warfare Division U.S. Naval Research Laboratory WashingtonDC20375 United States

ISBN: (纸本)9798350309768

There has been a strong interest in statistically characterizing the cavity quality factor (Q-factor) for large, complex enclosures. While there are existing methods for analyzing the Q-factor statistics due to distributed losses, there is currently little discussion about the statistical cavity Q-factor caused by localized losses, such as aperture leakage and absorptive loading. This paper presents a physics-oriented, hybrid deterministic-stochastic model that calculates the probability distribution of cavity Q-factor. The research work is evaluated and validated through representative experiments. © 2023 IEEE.

关键词： Stochastic systems

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PV-Power Forecasting using Machine Learning Techniques

PV-Power Forecasting using Machine Learning Techniques

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IEEE International Conference on Electro-Information Technology

作者： Kazi Abdullah Al Arafat Kode Creer Anjan Debnath Temitayo O. Olowu Imtiaz Parvez Dept. of Computer Sci and Eng Atish Dipankar University of Science and Technology Dhaka Bangladesh Dept. of Computer Science Utah Valley University Orem UT Dept. of Electrical Engineering and Computer Science OR Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Solar energy forecasting plays a pivotal role in the efficient utilization of renewable energy resources for sustainable power generation. This study delves into the domain of solar-power forecasting, employing a comprehensive analysis of machine learning models. The primary objective is to evaluate and compare the performance of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Multi-Layer Perceptron (MLP), and Linear Regression (LR) models in predicting solar energy production. Through a comprehensive evaluation of individual model performance, the study provides nuanced insights into the strengths and limitations of each forecasting approach. Results indicate that the Multy-Layer Perceptron (MLP) model excels in accuracy, exhibiting low root mean square error (RMSE) and high correlation among the parameters. The Gated Recurrent Unit (GRU) model demonstrates competitive performance, while the Recurrent Neural Network model showcases strengths in multiple metrics. Additionally, MLP and GRU models display superior predictive accuracy, emphasizing their efficacy in solar energy forecasting.

关键词： Renewable energy sources Recurrent neural networks Correlation Accuracy Machine learning algorithms Solar energy Machine learning

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Methodology for Evaluating Equivalent Models for the Dynamic Analysis of Power Systems

Methodology for Evaluating Equivalent Models for the Dynamic...

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Power Tech Conference

作者： Georgios A. Barzegkar-Ntovom Eleftherios O. Kontis Theofilos A. Papadopoulos Panagiotis N. Papadopoulos Dept. of Electrical and Computer Engineering Power Systems Laboratory Democritus University of Thrace Xanthi Greece Power Systems Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Electronic and Electrical Engineering Institute for Energy and Environment University of Strathclyde Glasgow Scotland U.K.

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed.

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Cyberattack Classification in Smart Grid Distribution Substations using a Novel Ensemble Bagging Learning Technique

Cyberattack Classification in Smart Grid Distribution Substa...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Victor O. Ijeh Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Smart grid incorporates the communication networking that enables the exchange of information among the monitoring and controlling devices. Such incorporation of the communication networking into the electricity grid infrastructure poses the risk of cyberattacks that target the critical assets within such an infrastructure. Most of the existing research focuses on the detection of such cyberattacks but without identifying the type of the attacks. This can result in overlooked threats and misdirected the necessary countermeasures. Recognizing the attack’s type is essential for timely responses and strategic planning against future threats, thereby enhancing the resilience of the smart grid. In this paper, a Fine Tree Bagging-based Ensemble Learning (FTBE) technique is proposed to detect and classify the different types of cyberattacks and power quality disturbances. The salient features of the attacks’ types are highlighted, which helps in identifying the types of the attack following the detection process.

关键词： Training Accuracy Substation automation Uncertainty Scalability Smart grids Ensemble learning

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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