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European Conference on Antennas and Propagation, EuCAP

作者： Simone Mencarelli Annalisa Tresoldi Andrew C.M. Austin Michael J. Neve Guglielmo Aglietti Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea-Auckland Space Institute University of Auckland Auckland New Zealand Communication Systems and Networks Group University of Bristol Bristol UK

This paper investigates the electrical characteristics of a thin glass-fibre membrane to be employed as a support and dielectric substrate for a new kind of deployable antenna mechan-ical structure designed for space-borne Synthetic Aperture Radar sensors on nano-satellite platforms. The approach outlined in this paper relies on cost-effective test fixtures built from material samples originally designed for mechanical testing. A simple Time Domain Reflectometry / Transmissometry (TDR/T) procedure enables the extraction of the material's complex permittivity.

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Optimal Economic Dispatch of Power System Networks with Renewable Energy Sources

Optimal Economic Dispatch of Power System Networks with Rene...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Atif M. Mohammad Osama A. Alghamdi Abdullah Ali Alhusssainy Muhyaddin Rawa Abdullah M. Abusorrah Y. A. Al-Turki Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia

This paper compares betwe!en different types of batteries of a grid-connected hybrid power system that utilizes renewable energy sources (PV and Wind Turbines). The hybrid system is located in Yanbu region in Saudi Arabia. Part of the vision of 2030 of Saudi Arabia is to use renewable energy sources to cover a portion of the total electricity production. The required energy to be produced by renewable energy sources is 850 MW for the vision of 2030. To meet such a requirement, a thorough study is carried out to investigate a potential solution. The purpose of this study is to design a hybrid system which contains battery storage and has the lowest cost. Ten different batteries were used for a comparison study to examine the hybrid system with the lowest cost. Hybrid Optimization of Multiple Energy Resources (HOMER) software was used to design the hybrid system. HOMER provides real time evaluation of power systems. The criteria used for decision making purpose are the net present cost (NPC), levelized cost of energy (COE), electricity production, initial capital cost, nominal battery capacity, and battery autonomy. The results indicate that the hybrid system with Zinc Bromine flow battery (electrochemical type) has the lowest cost.

关键词： Renewable energy sources Bromine Costs Production Software Hybrid power systems Batteries

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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Recognition of Food-Texture Attributes Using an In-Ear Microphone 25th

Recognition of Food-Texture Attributes Using an In-Ear Micro...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Papapanagiotou, Vasileios Diou, Christos van den Boer, Janet Mars, Monica Delopoulos, Anastasios Multimedia Understanding Group Department of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Kallithea Greece Department of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Division of Human Nutrition and Health Wageningen University Wageningen Netherlands

ISBN: (纸本)9783030688202

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. © 2021, Springer Nature Switzerland AG.

关键词： Microphones

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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Coherency-Constrained Spectral Clustering for Proactive Load Shedding: A Tri-Level Model

Coherency-Constrained Spectral Clustering for Proactive Load...

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IEEE General Meeting Power& Energy Society

作者： Mario D. Baquedano-Aguilar Arturo Bretas Sean Meyn Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In low-inertia power grids, traditional load-shedding methods can be ineffective, causing instability and unnecessary grid islanding. This work introduces a distributed tri-level optimization model for proactive load shedding to prevent grid islanding considering generator coherency identification. Preventing islanding is crucial because it reduces the need for additional load shedding and transmission line tripping, thereby maintaining frequency stability. This is especially important in systems heavily dependent on non-dispatchable resources like wind and solar. The model employs generator coherency identification through spectral clustering using synchronizing coefficients. The results are then used as constraints in a dynamic graph based on power flow measurements to identify coherent areas for load shedding under highly stressed conditions. This approach offers practical applications and holds promise for real-world use.

关键词： Wind Islanding Power transmission lines Load shedding Power system stability Transmission line measurements Generators Stability analysis Load modeling Stress measurement

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Multiplier analysis of Lurye systems with power signals

Multiplier analysis of Lurye systems with power signals

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IEEE Conference on Decision and Control

作者： William P. Heath Joaquin Carrasco School of Computer Science and Engineering Bangor University Dean St LL57 1UT UK Department of Electrical and Electronic Engineering Control Systems and Robotics Group Nancy Rothwell Building University of Manchester M13 9PL UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean.

关键词： Sensitivity Power measurement Noise Stability analysis Steady-state Time-varying systems Lyapunov methods

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Advanced Signal Processing Techniques for Demagnetization Detection in PM Generators at Variable Speed

Advanced Signal Processing Techniques for Demagnetization De...

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2022 International Conference on electrical Machines, ICEM 2022

作者： Garcia-Calva, T.A. Gyftakis, K.N. Skarmoutsos, G.A. Mueller, M. Morinigo-Sotelo, D. Romero-Troncoso, R. De J. University of Guanajuato Research Group HSPDigital Department of Electronic Engineering SalamancaGTO 36700 Mexico Technical University of Crete School of Electrical and Computer Engineering 73100 Greece University of Edinburgh School of Engineering and the Institute for Energy Systems EdinburghEH9 3FB United Kingdom Universidad de Valladolid Research Group HSPDigital-ADIRE ITAP ValladolidES VLL 47001 Spain Autonomous University of Queretaro Research Group HSPDigital Department of Mechatronics San Juan del RioQRO 76806 Mexico

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

In this paper, demagnetization detection in permanent magnet generators is analyzed using advanced signal processing techniques. The merits of these methods are thoroughly compared against the commonly adopted short-time Fourier transform analysis. Moreover, traditional signal processing techniques may not be suitable for fault detection when the machine operates at variable speed. Under such condition, interference between the fundamental frequency of the machine and the fault harmonic can lead to false alarms. Simulation results demonstrate effective performance and feasibility of the proposed methods for demagnetization detection of permanent generators operating under non-stationary conditions. © 2022 IEEE.

关键词： Spectrum analysis

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Liu, Haoxuan Qi, Nan Li, Xiaojie Boulogeorgos, Alexandros A. A. Tsiftsis, Theodoros A. Xiao, Ming Röning, Juha Nanjing University of Aeronautics and Astronautics Nanjing China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden University of Oulu Biomimetics and Intelligent Systems Group Finland

ISBN: (纸本)9798350368369

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Information-Theoretic Limits on the Performance of Auditory Attention Decoders

Information-Theoretic Limits on the Performance of Auditory ...

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Asilomar Conference on Signals, systems & computers

作者： Ruwanthi Abeysekara Christopher J. Smalt I. M. Dushyanthi Karunathilake Jonathan Z. Simon Behtash Babadi Department of Electrical & Computer Engineering University of Maryland College Park MD Institute for Systems Research University of Maryland College Park MD Human Health & Performance Systems Group MIT Lincoln Laboratory Lexington MA Department of Biology University of Maryland College Park MD

Speaker-specific attention decoding from neural recordings to suppress the acoustic background and extract a target speaker in an in-the-wild multi-speaker conversation scenario poses a cornerstone challenge for advanced hearing devices. Despite several recent advances in auditory attention decoding, most existing approaches fail to reach the real-time performance and attention decoding accuracy required by hearing aid devices. In this work, we aim to quantify fundamental limits on the performance of auditory attention decoding by establishing and computing the trade-off between accuracy and decision window length. We demonstrate the utility of our theoretical bounds in benchmarking the performance of existing widely-used attention decoding algorithms using both simulated and experimentally recorded magnetoencephalography data.

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