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6th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2024

作者： Mohamed, Amal Amin Kamel, Salah Hassan, Mohamed H. Tolba, Mohamed A. Ahmed Adam, Ahmed Hamed Aswan University Department of Electrical Engineering Aswan Egypt Nuclear Research Center Egyptian Atomic Energy Authority Reactors Department Cairo Egypt School of Automation Chongqing University Chongqing China

ISBN: (纸本)9798350382891

To achieve an environmentally responsible and emission-free society, researchers are working towards replacing fossil fuels with renewable energy sources. In this pursuit, this study proposes the use of Mountaineering Team-Based Optimization (MTBO) to address optimal power flow (OPF) issues associated with unpredictable wind turbines (WT) and Flexible AC Transmission System (FACTS) devices. The proposed approach employs Weibull probabilities to calculate WT's output power, along with other control variables such as the anticipated real electricity production of WT, the production power of thermal power units, and the electrical voltage values of each bus that generates electricity. The primary objective function to be optimized is the total generating cost of the network, which includes the penalty and reserve costs for under-and overestimating WT. The updated IEEE 30 bus test network is used to evaluate the proposed method strategy for OPF difficulties. The success and efficiency of the MTBO technique in resolving the OPF issue are evaluated by comparing the findings with those of other meta-heuristic optimization methodologies using similar testing methodology, control variables, and system conditions. The results show that the MTBO method performs well compared to other methods, making it a viable option for solving OPF issues. © 2024 IEEE.

关键词： Wind power

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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A Comparative Study of Deep Learning Models for Image Super-Resolution Across Various Magnification Levels

A Comparative Study of Deep Learning Models for Image Super-...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Soni, Jayesh Gurappa, Subhash Upadhyay, Himanshu Applied Research Center Florida International University Miami United States Florida International University Department of Electrical and Computer Engineering Miami United States

ISBN: (纸本)9798350391213

Image Super-Resolution (SR) is a vital image processing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imaging, forensic analysis, satellite imagery, etc. Despite its significance, there are still issues with existing SR techniques, such as handling unknown degradation, generalizing across various domains, and recovering the fine details, especially at high scaling factors. Traditional approaches like interpolation and reconstruction-based methods produce poor results, which leads to the use of deep learning models that have demonstrated notable advancements. However, problems like computational complexity, performance drop on real-world data, and dependency on fixed scaling factors remain. In this research, we conduct a comparative analysis of SR models, namely Super-Resolution Convolutional Neural Networks (SRCNN), SwinIR, and Real-ESRGAN, across multiple image domains (e.g., Animals, Cars, Faces, Humans, Landscapes, Ships) using different scaling factors (e.g., x 2, x 3, x 4) and metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Learned Perceptual Image Patch Similarity (LPIPS), Objective Difference Image (ODI) Score, Tenengrad sharpness and Laplacian Variance. This approach helps us better understand the performance of various deep-learning models, guiding further improvements to ensure they are efficient, robust, and applicable to real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Antoniadis, Christos Katsalis, Kostas Tyrovolas, Dimitrios Tegos, Sotiris A. Ioannidis, Sotiris Diamantoulakis, Panagiotis D. Karagiannidis, George K. Liaskos, Christos DOCOMO Euro-Labs Munich Germany Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanon University of Ioannina Computer Science Engineering Department Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (纸本)9798331507428

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future. © 2024 IEEE.

关键词： Resource allocation

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RescueAid: Smartphone-Aided Situational Awareness For Emergency Response 24

RescueAid: Smartphone-Aided Situational Awareness For Emerge...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Laoudias, Christos Kolios, Panayiotis Ellinas, Georgios University of Cyprus Kios Research and Innovation Center of Excellence Cyprus University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350341010

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform's user interface the location traces and associated images collected during a real field exercise. © 2023 IEEE.

关键词： Smartphones

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The Application of Machine Learning Techniques to Evaluate the Impact of Transmission Line compensation on Fault Classification 4

The Application of Machine Learning Techniques to Evaluate t...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. Department of Electrical Power Engineering South Africa Department of electronics and computer engineering South Africa Durban University of Technology Smartgrid Research Center Steve Biko Campus Durban South Africa

ISBN: (纸本)9798350353266

The use of machine learning represents a significant advancement in the field of detecting defects in transmission lines. This study looks at how machine learning methods can be used to analyze the impacts of compensation in transmission lines in fault detection and classification. The main focuses is on the misclassification percentage based on different types of faults, including Line to Line (L-L), Line to Ground (L-G), Double Line to Ground (L-L-G), and Three-phase faults (L-L-L). The study shows that introducing series capacitors in the fault loop will affects the impedance seen by the relay, and makes the fault classification task more challenges as the compensation level increases. see for line compensation alters the impedance measured by relays, resulting in a more complex fault classification technique as compensation levels increases. The first model is a two-bus system developed to test the R/X response at different types of faults. There after transmission line compensation is added at 50% and 100% compensation. Data is collected from these models and implemented on machine learning based classifier to assess the fault classification accuracy in the presence of transmission line compensation. It is concluded that transmission line compensation does affects the performance of the protection system and three phase faults are the most affected in terms of accuracy when the capacitive reactance (introduced by compensation) is present in the fault loop. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Load Frequency Control of Two-Area Interconnected Power System Using Fuzzy-I Controller

Robust Load Frequency Control of Two-Area Interconnected Pow...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Sindi, Hatem F. King Abdulaziz University K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350331790

The paper proposes a Fuzzy-I controller for optimal load frequency control (LFC) in a two-area interconnected power system. The proposed controller is composed of a fuzzy logic controller (FLC) and an integral (I) controller. To achieve a better dynamic response for the LFC, the integral controller and frequency bias parameters of the secondary loop are tuned using the Particle Swarm Optimization (PSO) technique. To boost the efficiency of the optimization, a proposed objective function using the integral of squared error (ISE) and settling time is used. A comparative study and analysis are conducted between the base system controller, optimized I controller, and the proposed controller under three different scenarios of load disturbances. The robustness of the proposed controller is shown by performing a sensitivity analysis for various system parameters and operating conditions. Moreover, the performance of the proposed controller was compared with the previous LFC approaches published in the literature. The simulation outcomes demonstrate that the proposed method has an outstanding performance. The work results are presented as time-domain simulations using MATLAB/SIMULINK tool. © 2023 IEEE.

关键词： Controllers

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Highly efficient ternary organic solar cells with excellent open-circuit voltage and fill factor via precisely tuning molecular stacking and morphology

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Science China Chemistry 2024年第3期67卷 963-972页

作者： Dinghong Sun Zhenyu Chen Jianfeng Zhang Wei Song Jingyu Shi Jintao Zhu Yuanyuan Meng Fei Jin Shuncheng Yang Ziyi Ge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Faculty of Material Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049China Department of Chemical and Environmental Engineering University of Nottingham NingboNingbo 315100China

The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.

关键词： alkylbenzene side-chain morphology regulation ternary blends organic solar cells

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Factors Affecting the Performance of FastICA Algorithm for Decomposition of High-Density Surface Electromyogram 9th

Factors Affecting the Performance of FastICA Algorithm for D...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Castilhos, Mateus Augusto Schneider Elias, Leonardo Abdala Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas SP Campinas Brazil

ISBN: (纸本)9783031494031

The motor unit (MU) physiology and the neural control of human movement can be studied via surface electromyogram (sEMG) decomposition techniques, which estimate the discharge times of MUs during a given contraction. The recent decomposition methods are based on the blind source separation (BSS) methods, and some approaches such as independent component analysis (ICA) can be applied to solve the problem. This study aims to investigate the influence of several parameters of the fast independent component analysis (FastICA) algorithm in the decomposition process of high-density sEMG. The extension factor, the number of iterations, and the method of initialization of the separation vector were explored for three evaluation metrics, namely, the execution time of decomposition, the absolute number of unique decomposed MUs, and the ratio of the number of unique MUs to the total number of extracted MUs. The number of iterations increased the execution time of decomposition and the absolute number of unique decomposed MUs. Besides, the method of initialization of the separation vector had little influence on the execution time of decomposition, an optimal value can be achieved for the absolute number of unique decomposed MUs. Moreover, the repeated convergence of the FastICA algorithm to the same source was mainly affected by the number of iterations. The study has reinforced the importance of evaluating the parameter combinations to achieve a better FastICA decomposition performance based on the chosen evaluation metric. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Blind source separation

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Controllable Microparticle Spinning via Light without Spin Angular Momentum

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Physical Review Letters 2024年第25期132卷 253803-253803页

作者： Yi-Jing Wu Pan-Pan Yu Yi-Fan Liu Jing-Han Zhuang Zi-Qiang Wang Yin-Mei Li Cheng-Wei Qiu Lei Gong Department of Optics and Optical Engineering Hefei National Research Center for Physical Sciences at the Microscale Department of Electrical and Computer Engineering

The spin angular momentum (SAM) of an elliptically or circularly polarized light beam can be transferred to matter to drive a spinning motion. It is counterintuitive to find that a light beam without SAM can also cause the spinning of microparticles. Here, we demonstrate controllable spinning of birefringent microparticles via a tightly focused radially polarized vortex beam that has no SAM prior to focusing. To this end, the orbital Hall effect is proposed to control the radial separation of two spin components in the focused field, and tunable transfer of local SAM to microparticles is achieved by manipulating the twisted wavefront of the source light. Our work broadens the perspectives for controllable exertion of optical torques via the spin-orbit interactions.

关键词： Angular momentum of light Light-matter interaction Polarization of light Structured light Optical tweezers

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