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International Renewable Energy Congress

作者： M. Elrashid Z. Rajab M. Elhashane A. Younis J. Gómez-Romero Salah I.S Tnatin F. Mohamed Electrical and Electronic Department Faculty if Engineering Benghazi University Department of Computer Science and Artificial Intelligence University of Granada Electrical Department Faculty of Engineering Omar Al-Mukhtar University Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

This research investigates the modeling, design, and simulation of a microgrid with advanced energy management controller, developed using MATLAB/Simulink. This paper presents a comprehensive analysis, modeling, and simulation of a smart controller for a boost converter, using an artificial neural network (ANN) for maximum power point tracking (MPPT) to enhance power output. The study begins with a comparison between traditional MPPT techniques and the ANN-based approach. These MPPT strategies are compared in terms of their response to dynamic weather conditions (such as temperature and irradiance variations) and their ability to minimize oscillations around the maximum power point. The simulation results demonstrate that the ANN-based MPPT method outperforms the conventional P&O and IC methods, providing faster response times and higher output power. Additionally, the system's performance was evaluated under conditions of power shortage and during battery charging and discharging cycles, showing the effectiveness of the smart controller in maintaining system stability and optimizing energy management.

关键词： Maximum power point trackers Renewable energy sources Microgrids Artificial neural networks Batteries Time factors MATLAB Energy management Voltage control Thermal stability

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Analysis of Distance Measures for WiFi-based Indoor Positioning in Different Settings 2

Analysis of Distance Measures for WiFi-based Indoor Position...

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2nd International Conference on Innovative Research in Applied Science, engineering and technology, IRASET 2022

作者： Duong-Bao, Ninh He, Jing Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9781665422093

Recently, indoor positioning systems based on wireless technologies such as WiFi fingerprinting become more popular. The nearest neighbor-based algorithms using Euclidean distance are very common and used in many fingerprinting systems. Thus, the distance measure is very important and it affects much to the tracking result. In this paper, we present an analytical study of using different distance measures for the weighted K-nearest neighbor algorithm to determine the position of a user. We implement five distance measures and compare the positioning results of each measure to find out the best one. To check the robustness of the measures, we change some settings when creating the radio map in the offline phase such as the number of access points or the distance between two reference points. From the experiments, it is shown that the Chi-Squared distance outperforms other distance measures since it achieves the mean error of 1.13 meters in a simple test case and 1.20 meters in a more complicated test case. Even when we change the settings, Chi-Squared distance remains the best positioning result. © 2022 IEEE.

关键词： Indoor positioning systems

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Optimizing Task Offloading and Cpu Frequency Control in D2d Assisted Mec Network With Energy Harvesting Using Multi-Agent Reinforcement Learning

SSRN

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SSRN 2023年

作者： Mi, Xin He, Huaiwen Shen, Hong School of Computer University of Electronic Science and Technology of China Zhongshan Institute China Computer Science and Engineering School University of Electronic Science and Technology of China Chengdu China Faculty of Applied Sciences Macao Polytechnic University China

Delay-sensitive task offloading remains an open challenging problem in a Device-to-Device (D2D) assisted Mobile Edge Computing (MEC) system with energy harvesting devices due to the dynamic load level at edge nodes and the fluctuation of harvested energy. To address this issue, we propose a joint task offloading and CPU frequency control scheme for delay-sensitive task in a D2D-MEC systems considering multi-slot tasks with varying processing speed and data transmission rate. We model task arrival and service processes using queuing systems, and leverage D2D communication to reduce edge server load and avoid network congestion. We formulate the average task delay optimization as a nonlinear integer programming problem of making task offloading decisions for each generated task at busy mobile devices and CPU frequency decisions at each time slot. To perform sequential decision-making in a large multi-dimensional discrete action space, we propose an efficient Multi-Agent deep reinforcement learning algorithm named MAOC based on Proximal Policy Optimization-based (MAPPO) technique, which works following the centralized training with decentralized execution (CTDE) framework. Our algorithm can efficiently work and converge quickly in non-stationary environments. Experimental results demonstrate that our proposed algorithm outperforms other baseline algorithms. © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Building a Better Metaverse: How Federated Learning is Revolutionizing Virtual Worlds

Building a Better Metaverse: How Federated Learning is Revol...

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Chaddad, Ahmad Wu, Yihang Kateb, Reem School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Ecole de Technologie Superieure The Laboratory for Imagery Vision and Artificial Intelligence MontrealQC Canada Jeddah University Faculty of Computer Sciences and Engineering Jeddah Saudi Arabia Taibah University Saudi Arabia

ISBN: (纸本)9798350302301

The rapid development of artificial intelligence (AI) has been made possible by the swift advancement of technology. Recently, there has been a significant increase in interest in the concept of the Metaverse, which is viewed as the ultimate manifestation of the internet. However, building a vast Metaverse requires a substantial allocation of resources to maintain it. Such circumstances pose several challenges, including the disclosure of sensitive data and the problem of non-independent and homogeneous data distribution. In this study, we will focus on explaining federated learning (FL), a methodology that addresses these issues by training localized models and then consolidating them on a global scale to create the ultimate model without sharing the original data. Additionally, we will also discuss the current challenges and potential developments that should be considered in the upcoming years. © 2023 IEEE.

关键词： Sensitive data

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA 4

An Exploratory Investigation into Sustaining Cybersecurity P...

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

作者： Abdullah Huda, Mahfuzul Brayyich, Mohammed Haroon, Noor Hanoon Khan, Abdul Azeem Verma, Rajan University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq Negara Brunei Darussalam Faculty of Islamic Technology Brunei Lovely Professional University Division of Research and Development Phagwara144411 India KKR & KSR Institute of Technology & Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services. © 2024 IEEE.

关键词： Personnel training

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CanSat: Design, Development, and Deployment of Open-Source Weather Monitoring Nano-Satellite

CanSat: Design, Development, and Deployment of Open-Source W...

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Digital Futures and Transformative Technologies (ICoDT2), International Conference on

作者： Talha Alam Mazhar Javed Abdur Rahman Qureshi Abdullah All Mamun Anik Electronic Engineering University of Engineering and Technology Peshawar Abbottabad Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Science and Technology Topi Pakistan Computer science The University of Haripur Haripur Pakistan Mechanical Engineering University of Huddersfield Queensgate United Kingdom

ISBN: (数字)9798350391862

ISBN: (纸本)9798350391879

Environmental monitoring and educational accessibility face significant challenges due to the complexity and high cost of traditional satellite systems. Addressing these issues requires a cost-effective solution that enables comprehensive study and intervention to improve environmental conditions. In this paper, we introduce CanSat, a compact satellite designed for educational and research purposes, utilizing off-the-shelf components and a 3D-printed frame to streamline operations and focus on critical processes such as modeling and communication. Equipped with a microcontroller (Esp32) and an RF communication (LoRa Module). CanSat efficiently processes and transmits data, including real-time video, temp, and %RH (% Relative humidity), to ground stations. The CanSat’s performance was observed through successful data transmission and global accessibility of data visualization and analysis. The results indicated reliable operation, with data visualization and analysis accessible worldwide through customized web pages and real-time tracking capabilities, demonstrating CanSat’s potential as a user-friendly and cost-effective platform for near-space exploration

关键词： Wireless communication Radio frequency Nanosensors Satellite broadcasting LoRa Data visualization Web pages Streaming media Reliability engineering Real-time systems

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A Novel Hybrid Coil Design and Implementation for Wireless Power Transfer Systems

SSRN

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SSRN 2023年

作者： Pashaei, Ali Aydin, Emrullah Ozdemir, Mehmet Akif Kosesoy, Yusuf Aydemir, Mehmet Timur Computer Engineering Faculty of Engineering and Natural Science Istanbul Rumeli University Istanbul Turkey Electric-Electronic Engineering Faculty of Engineering and Natural Science Malatya Turgut Ozal University Malatya Turkey Electric-Electronic Engineering Faculty of Engineering Gazi University Ankara Turkey Electrical Engineering Faculty of Engineering Eindhoven University of Technology Eindhoven Netherlands Electric-Electronic Engineering Faculty of Engineering and Natural Science Kadir Has University Istanbul Turkey

Wireless Power Transfer (WPT) has been drawing a lot attention in the last ten years in parallel with the market increase in electric vehicles. Although conductive charging methods are still the preferred ones, WPT based charging systems are eyed as clean and flexible alternatives of tomorrow. At the center of these systems are transmitting and receiving coils and there are different coil types proposed in the literature. This paper proposes a square-hexagonal hybrid coil structure to increase the magnetic coupling by shaping the magnetic field. In addition, this design aims to minimize the coupling coefficient variation for the misaligned coils which is one of the most important problems in WPT systems. 3D model of the coils is created and analyzed by ANSYS, Maxwell. Compared to the conventional square coil structure the coupling coefficient of the proposed structure is less affected from misalignment on the x and y axis, and as a result, it has a better efficiency. In addition, a WPT system operating at 5- W, 85-kHz was designed and tested in a laboratory environment. FEA analyzes and experimental application results largely overlap, and accordingly, the coil-to-coil efficiency of our WPT system was 93.5% and the overall efficiency of the system was 87%. © 2023, The Authors. All rights reserved.

关键词： Energy transfer

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

arXiv

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arXiv 2025年

作者： Chen, Xiaolu Huang, Chenghao Zhang, Yanru Wang, Hao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Data Science and AI Faculty of IT Monash Energy Institute Monash University MelbourneVIC3800 Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Shenzhen Institute for Advanced Study of UESTC Shenzhen China

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods. © 2025, CC BY.

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Abnormal wind speed detection and prediction: methodology and case study

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Intelligent Marine technology and Systems 2025年第1期3卷 1-10页

作者： Yang, Yuting Zhang, Cong Lam, Kin-Man Sun, Xin Xue, Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Faculty of Data Science City University of Macau Macao China College of Engineering Ocean University of China Qingdao China

Accurate wind speed prediction is crucial for conserving power resources and enhancing power utilization efficiency. However, deviations from typical wind patterns can introduce errors into predictions, potentially leading to imbalances between wind power supply and demand. Consequently, developing a model to forecast abnormal wind speeds is essential. To address this, we leverage the microcanonical multifractal formalism algorithm to detect abnormal wind speeds. In this paper, we integrate ensemble empirical mode decomposition, phase space reconstruction, and long short-term memory (LSTM) networks to predict these anomalies. Initially, wind speed data is meticulously pre-processed to generate datasets for one-hour, one-day, and non-zero wind speeds. Subsequently, LSTM networks are used to forecast abnormal wind speeds. Evaluations of our methodology across different datasets demonstrate its effectiveness, particularly excelling in one-hour forecasts.

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