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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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V2G-Auth: Lightweight Authentication and Key Agreement Protocol for V2G Environment Leveraging Physically Unclonable Functions

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 66-78页

作者： Reddy, Alavalapati Goutham Babu, Ponnuru Raveendra Odelu, Vanga Wang, Li Kumar, Sathish A.P. Department of Mathematics and Computer Science Fontbonne University St. LouisMO63105 United States Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh 534101 India Department of Electrical Engineering and Computer Science Cleveland Sate University ClevelandOH44115 United States Department of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City517646 India

Electric vehicle (EV) is a fast-growing technology that could help reduce greenhouse gas emissions in the energy and transportation sectors. The characteristics of EVs allow for rapid smart city expansion and serve as a key weapon in the emergence of vehicle-to-grid (V2G) technology. The entities, such as EVs and charging stations, connect with one another in order to gather data about the status of the EV and the environment in which it is traveling. The exchange of information among entities in V2G over a public channel may be exposed to security threats. The user’s devices are also vulnerable to physical attacks since they are left unprotected at their end. Hence, we suggest a lightweight protocol for key agreement and mutual authentication between entities operating in a V2G environment. Additionally, we assess the proposed scheme using the Scyther tool to conduct formal and informal security analyses in order to demonstrate how effectively it can counteract known security attacks. Finally, the performance study reveals that the suggested protocol provides a communication and computational benefit when compared to relevant protocols. © 2023 Institute of electrical and Electronics Engineers. All rights reserved.

关键词： Vehicle-to-grid

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio University of Twente Department of Applied Mathematics Enschede 7500AE Netherlands University of Twente Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science Enschede 7500AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome 00185 Italy

ISBN: (纸本)9798331513283

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE. © 2025 IEEE.

关键词： Target drones

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Portable Embedded Deployment of 5G Standalone (SA) System with Software-Defined Radio 28

Portable Embedded Deployment of 5G Standalone (SA) System wi...

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28. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 28th VDE ITG Conference on Mobile Communication: Technologies and Applications

作者： John, Fabian Hauschild, Sebastian Hellbrück, Horst University of Applied Sciences Lübeck – Technische Hochschule Lübeck Germany Department of Electrical Engineering and Computer Science Center of Excellence CoSA Germany

ISBN: (纸本)9783800763832

We present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware in this paper. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low-power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next-generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance, indoor coverage range, and power consumption of our system with measurements. We show that our system achieves acceptable data rates, and is very space and energy-efficient with an average power consumption of ≈ 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work. © 2024 Mobilkommunikation: Technologien und Anwendungen - 28. VDE ITG-Fachtagung. All rights reserved.

关键词： Software radio

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A Comparative Analysis of Goal-Oriented Requirements engineering and Model-Based Systems engineering Frameworks for Managing Requirements of Systems of Systems 25

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alhajhassan, Suhair Murrar, Samer Samhan, Ahmad Odeh, Mohammed Elrashidi, Ali Applied Science Private University Software Engineering Department Amman Jordan Applied Science Private University Computer Science Department Amman Jordan Zarqa University Software Engineering Department Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs);GORE-based approach to Energy Management Systems (EnMS);Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services;GORE and reference architecture approach for microgrid systems;and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems. © 2024 IEEE.

关键词： Conceptual design

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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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How Large Language Models are Transforming Teachers' Assessment of Student Competency: A Case Study on LLM-Based Report Writing

How Large Language Models are Transforming Teachers' Assessm...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Dongyub Kim, Daejung Loeser, Martin Seo, Kyoungwon Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul Korea Republic of Seoul National University of Science and Technology Department of Environmental Engineering Seoul Korea Republic of Electrical Engineering and Mechatronics ZHAW Zurich University of Applied Sciences Department of Computer Science Winterthur Switzerland

ISBN: (纸本)9798331510756

Evaluating student competencies is critical to fostering academic success, but the traditional process of assessment and report generation is time-consuming and labor-intensive for teachers. The emergence of Large Language Models (LLMs) presents an opportunity to automate this process, potentially reducing the workload for educators. However, concerns such as hallucinations in LLM outputs and the lack of usability studies involving teachers raise questions about their reliability and practical application. In response, we developed an LLM-based report writing system specifically designed for real-time competency evaluation. To assess its effectiveness, we conducted a case study involving five educational experts, each with over 15 years of experience. These experts used the system to generate student competency reports and found them to be both sensible and specific. While they expressed concerns about issues like insufficient detail, they recognized the system's significant time-saving benefits. Our findings demonstrate that LLMs can positively impact teachers' competency assessments by streamlining the reporting process, offering valuable support for educators. © 2025 IEEE.

关键词： Students

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On the convergence of projected alternating maximization for equitable and optimal transport

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The Journal of Machine Learning Research 2024年第1期25卷 16456-16488页

作者： Minhui Huang Shiqian Ma Lifeng Lai Department of Electrical and Computer Engineering University of California Davis CA Department of Computational Applied Mathematics and Operations Research Rice University Houston TX

This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In the discrete distributions case, the EOT problem can be formulated as a linear program (LP). Since this LP is prohibitively large for general LP solvers, (Scetbon et al., 2021) suggests to perturb the problem by adding an entropy regularization. They proposed a projected alternating maximization algorithm (PAM) to solve the dual of the entropy regularized EOT. In this paper, we provide the first convergence analysis of PAM. A novel rounding procedure is proposed to help construct the primal solution for the original EOT problem. We also propose a variant of PAM by incorporating the extrapolation technique that can numerically improve the performance of PAM. Results in this paper may shed lights on block coordinate (gradient) descent methods for general optimization problems.

关键词： equitable and optimal transport fairness saddle point problem projected alternating maximization block coordinate descent acceleration rounding

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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