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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Kyrollos S. Marcus Ibrahim Abdelsalam Mostafa I. Marei Energy & Renewable Energy Engineering Dep. Faculty of Engineering & Technology Egyptian Chinese University Cairo Egypt Electrical and Control Engineering Dep. College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper proposes a grid-tied electric vehicle (EV) charging station architecture with integrated renewable energy sources and battery storage to optimize energy utilization and minimize grid reliance. The proposed charging station has a power management strategy prioritizes renewable energy and battery power to meet EV charging demands, reducing dep.ndence on the grid and minimizing energy costs. A hybrid maximum power point tracking (MPPT) algorithm ensures optimal energy harvesting from the photovoltaic (PV) array under varying solar irradiance conditions. The system's dynamic control strategy, coupled with battery storage, effectively mitigates the impact of PV variability, and maintains stable DC bus voltage, ensuring reliable EV charging services. Simulation results validate the effectiveness in optimizing energy utilization, reducing grid reliance, and maintaining stable operation, contributing valuable insights to the development of sustainable EV charging infrastructure.

关键词： Maximum power point trackers Solar irradiance Renewable energy sources Heuristic algorithms Simulation Electric vehicle charging Hybrid power systems Batteries Voltage control Vehicle dynamics

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Using A novel noisy deep reinforcement learning approach for detecting the cyberattacks in stored hydrogen fuel based AC-DC networks

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International Journal of Hydrogen Energy 2025年 135卷 457-476页

作者： Oveysikian, Ali Abed, Azher M. Davatgar, Shiva Mosallaei, Behrooz Basem, Ali Habib Ali, Sadek Ahmed, Mohsen Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Mechanical Power Technical Engineering Department College of Engineering and Technology Al-Mustaqbal University Hilla Babylon51001 Iraq Department of Electrical Engineering Islamic Azad University Ahvaz Branch Ahvaz Iran The Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Dep. of Electrical Engineering Techniques Al-Amarah University College Maysan Iraq Imam Abdulrahman Bin Faisal University P.O. Box 1982 Eastern Province Dammam31441 Saudi Arabia

Rising hydrogenous renewable energy and distributed storage integration in hybrid AC-DC networks present new challenges to grid reliability and security. Existing detection methods struggle with the complex dynamics of these systems, particularly when faced with sophisticated cyber threats. Voltage attacks, capable of causing widespread outages and equipment damage, are a significant threat. This paper proposes a novel deep reinforcement learning (DRL) approach using Ornstein-Uhlenbeck noise for enhanced voltage attack detection. Our approach addresses the limitations of conventional detection methods that suffer from low accuracy and poor convergence in complex hybrid environments. The Noisy Deep Q-Network (NDQN) leverages this noise exploration technique to achieve efficient state space exploration and smoother convergence during training. An advanced attack model specifically designed to target vulnerabilities in stored hydrogen fuel-based AC-DC systems based on hydrogen fuel resources is also presented. The NDQN approach's effectiveness is evaluated in a realistic and complex hybrid AC-DC environment under the proposed attack model. The results demonstrate significant improvements in detection accuracy (62.44 % higher average return compared to traditional DQN) and robustness compared to existing methods. Additionally, the approach helped maintain system integrity while reducing generation from the electrical grid by 42.7 %, leading to substantial cost savings. This work showcases the combined efficacy of the novel attack model and the NDQN approach, offering a promising solution for bolstering power grid security and reliability. © 2025 Hydrogen Energy Publications LLC

关键词： Reinforcement learning

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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A Three-Phase Totem-Pole AC-DC Converter Based Off-Board Charger for Electric Vehicles

A Three-Phase Totem-Pole AC-DC Converter Based Off-Board Cha...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Tony Mamdouh Ibrahim Abdelsalam Mostafa I. Marei Electrical and Control Engineering Dep. College of Engineering & Technology. Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents the design, implementation, and performance evaluation of a three-phase bridgeless totem-pole off-board charger specifically developed for electric vehicles (EVs). The charger is characterized by a modular architecture that allows for scalability, consisting of three single-phase parallel bridgeless totem-pole AC-DC converters, each capable of supplying one-third of the total rated power. The control system of the charger employs three cascaded control loops to ensure stable operation and facilitate a seamless transition from current control mode to voltage control mode throughout the battery charging process, accommodating a wide range of EV battery configurations. Furthermore, the charger is engineered to support and improve grid power quality, ensuring a sinusoidal grid current waveform and unity power factor, thereby enhancing efficient power transfer while minimizing grid disturbances. The performance of this level ‘3’ charger was assessed and validated through MATLAB/Simulink simulations, with testing conducted on battery bank voltages of 400V, 800V, and lOOOV for three electric vehicles.

关键词： Reactive power Scalability Power quality Process control Electric vehicles Batteries MATLAB Voltage control Vehicle dynamics Testing

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A Multivariate Functional Analysis of Inter-Hands Spatiotemporal Coordination in Human Bimanual Tasks and its Implications for Robotics

A Multivariate Functional Analysis of Inter-Hands Spatiotemp...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied indep.ndently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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A Simplified Current Source Converter SPWM Online Mapping Scheme

A Simplified Current Source Converter SPWM Online Mapping Sc...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Bader N. Alajmi Ibrahim Abdelsalam Mostafa I. Marei Nabil A. Ahmed Electrical Engineering Technology Department College of Technological Studies Shuwaikh Kuwait Electrical and Control Engineering Dep. College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper introduces an online mapping scheme for current source converters (CSC). This scheme is capable of transforming the gating signals from conventional voltage source converters (VSC) utilizing sinusoidal pulse width modulation (SPWM) techniques into signals suitable for CSC operation. The proposed mapping table is designed for straightforward implementation and follows to the operational constraints of CSCs, including the maintenance of continuous input current and the prevention of simultaneous activation of more than one top or bottom switches of the CSC circuit. The effectiveness of the proposed online CSC mapping table and the associated control strategy was evaluated using PSCAD/EMTDC simulation software. The simulation results confirm that the mapping scheme successfully meets the operational constraints of the CSC while producing a high-quality sinusoidal load current with low total harmonic distortion (THD). Furthermore, the proposed control strategy effectively tracks the reference output load voltage, representing satisfactory dynamic performance.

关键词： Total harmonic distortion Power engineering Software packages Simulation Prevention and mitigation Switches Pulse width modulation Regulation Voltage control Voltage-source converters

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Interleaved Multi-Port Non-Isolated DC/DC Converter for Green Hydrogen Production with PV/PEM Electrolyzer System

Interleaved Multi-Port Non-Isolated DC/DC Converter for Gree...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Ahmed M. Elbakly Ibrahim Abdelsalam Mostafa I. Marei Energy Researches & Studies Department Ministry of Electricity & Renewable Energy Cairo Egypt Electrical and Control Engineering Dep. College of Engineering & Technology. Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper introduces and examines a multi-port non-isolated DC-DC converter designed for the integration of photovoltaic (PV) systems with a DC grid and the generation of green hydrogen via a Proton Exchange Membrane (PEM) electrolyzer. This system is particularly effective when the available PV power surpasses the power demands of the DC bus. In such scenarios, the excess power is converted into green hydrogen using PEM technology. The proposed multi-port nonisolated DC-DC converter features a two-phase interleaved boost converter to transfer power from the PV system to the DC bus, integrated with a two-phase interleaved buck converter that supplies the necessary power to the PEM during the discharge phase of the interleaved boost converter. Two control loops are established to regulate the proposed converter. The first control loop manages the interleaved boost converter to achieve Maximum Power Point Tracking (MPPT) from the PV system. The second control loop governs the interleaved buck converter to channel the surplus PV power into green hydrogen production through PEM technology. The performance of proposed multiport system is tested using PSCAD/EMTDC simulation software.

关键词： Maximum power point trackers Solar irradiance Protons Power engineering Buck converters Power demand Simulation Production Green hydrogen Software

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Interference Estimation via Model-Based Deep Learning in Grant-Free Networks

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IEEE Wireless Communications Letters 2025年

作者： Pereira, Diogo Oliveira, Rodolfo da Costa, Daniel Benevides Kim, Hyong Instituto de Telecomunicações Lisbon1049-001 Portugal Universidade Nova de Lisboa Dep. de Engenharia Electrotécnica e de Computadores Faculdade de Ciências e Tecnologia Caparica2829-516 Portugal Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Carnegie Mellon University Electrical and Computer Engineering PittsburghPA United States

This paper presents a novel approach for estimating interference distribution parameters in grant-free access networks using a model-based deep learning (DL) framework. Our method integrates the precision of analytical models with the adaptability of deep learning algorithms. Specifically, we employ an analytical model to generate labeled data, which enhances the deep learning model’s ability to estimate interference levels. Through extensive validation, we demonstrate that our approach accurately estimates interference across a broad range of scenarios, including operating regions not covered during the model’s training. Moreover, our method also estimates the spatial density of interfering nodes, making it a valuable tool for interference management in grant-free access networks. This methodology offers a robust solution for improving interference estimation accuracy, aiding decision-making at the Medium Access control (MAC) and physical layers in grant-free access schemes. © 2025 IEEE. All rights reserved.

关键词： Contrastive Learning

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Dynamic process scheduling using genetic algorithm

Dynamic process scheduling using genetic algorithm

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2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017

作者： Hussein, Qasim Mohammed Hasoon, Armaneesa Naaman Control and Computer Engineering Dep. Tikrit University Tikrit Iraq Computer Sciences Dep. Tikrit University Tikrit Iraq

ISBN: (纸本)9781538629628

There is a necessity to schedule the system and user processes by the operating system to getting maximum utilization of the CPU and obtaining high throughput. This paper presents a dynamic scheduling algorithm that make use of genetic algorithm operations for scheduling multi non-preemptive task on uniprocessor system to get minimum average waiting time and average turnaround time. The algorithm dep.nd on construct fitness function that take in its consideration the dead time of process to avoid the starvation problem, in addition to obtaining maximum throughput. The results from proposed algorithm simulation by matlab (8.3) illustrates the successful of this method to obtain optimal result by giving the minimum average turnaround time for executing the given processes. © 2017 IEEE.

关键词： Genetic algorithms

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Segmentation of nuclei in cancer tissue images: Contrasting active contours with morphology-based approach

Segmentation of nuclei in cancer tissue images: Contrasting ...

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8th IEEE International Conference on BioInformatics and Bioengineering, BIBE 2008

作者： Cataldo, Santa Di Ficarra, Elisa Acquaviva, Andrea Macii, Enrico Dep. of Control and Computer Engineering of Politecnico di Torino Italy Dep. of Computer Science Universita di Verona Italy

ISBN: (纸本)9781424428458

In this paper we present a fully automated morphology-based technique for segmentation of nuclei in cancer tissue images and we compare it with a common technique for biomedical image processing, namely active contours. We discuss the limitations of active contours in the processing of immunohistochemical images characterized by heterogeneously stained nuclear region and noise caused by the presence of multiple tissue layers in the sample. We describe the integration of the proposed approach in a fully automated protein activity quantification tool. Finally, we demonstrate and motivate through extensive experiments that our fully automated morphology-based approach provides better accuracy compared to various active contours implementations.

关键词： Morphology

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