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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Warchol, Dawid Oszust, Mariusz Department of Computer and Control Engineering Faculty of Electrical and Computer Engineering Rzeszów University of Technology W. Pola 2 Rzeszów35-959 Poland

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches. © 2022 The Authors. Published by Elsevier B.V.

关键词： Time series

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Development of Path Tracking and Obstacle Avoidance based on an Advanced Potential Field controller

Development of Path Tracking and Obstacle Avoidance based on...

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2023 International Symposium on Electrical and Electronics engineering, ISEE 2023

作者： Tran, Duc Thien Nguyen, Quang Chien Pham, Duc Huy Nguyen, Trong Tai Luu, Nguyen Phong Ho Chi Minh City University of Technology and Education Automatic Control Department Ho Chi Minh City700000 Viet Nam Faculty of Electrical & Electronics Engineering Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9798350315486

This paper proposes a new method that combines path tracking and obstacle avoidance for a differential drive mobile robot based on the Advanced Potential Field controller (APFC) to increase stability and safety for robot operation. In conventional methods, the transition between path following and obstacle avoidance is typically achieved through a simple switch, which does not ensure the robot returns to the original path after avoiding the obstacle. The proposed methods integrate the Repulsive Potential Field (RPF) and Attractive Potential Field (APF), effectively solving this issue and enhancing smooth navigation in static environments. The RPF is utilized to steer the robot away from obstacles, while the APF is employed to redirect the robot back to its path after obstacle avoidance. The algorithm is verified on the designed robot, and experimental results provide efficiency in achieving autonomous robot navigation. © 2023 IEEE.

关键词： Mobile robots

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Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

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Journal of Electrical Systems and Information Technology 2024年第1期11卷 1-27页

作者： Sarhan, Amany Abdel-Rahem, Rowyda Darwish, Bassel Abou-Attia, Arwa Sneed, Ahmed Hatem, Shahd Badran, Awatef Ramadan, Mohamed Department of Computer and Control Engineering Tanta University Tanta Egypt Department of Artificial Intelligence Faculty of Artificial Intelligence Delta University Dakahlia Egypt

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.

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Integrating machine learning and sentiment analysis in movie recommendation systems

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Journal of Electrical Systems and Information Technology 2024年第1期11卷 1-25页

作者： Sarhan, Amany M. Ayman, Hager Wagdi, Mariam Ali, Bassant Adel, Aliaa Osama, Rahf Department of Computer and Control Engineering Tanta University Tanta Egypt Department of Artificial Intelligence Faculty of Artificial Intelligence Delta University Gamassa Egypt

The fast growth of the film business, along with an ever-increasing number of movie options, has highlighted the need for better recommendation algorithms. This study investigates the application of sentiment analysis in a movie recommendation system with the goal of improving the user experience. The importance of this sector stems from its ability to bridge the gap between user interests and the vast number of cinematic products, addressing individual emotional states and preferences. Researchers choose to generate movie recommendations based on the sentiments conveyed by viewers’ reviews of the movies. Sentiment-based movie recommendation system research employs techniques such as natural language processing and hybrid models with the goal of increasing user satisfaction. To this purpose, the integration of advanced machine learning algorithms such as cosine similarity, support vector machine, and Naive Bayes improves recommendation systems with sentiment analysis. Cosine similarity improves movie recommendations by recognizing minor user preferences, while support vector machines and Naive Bayes enhance sentiment analysis by offering a nuanced interpretation of textual attitudes. Through trials, the proposed system employs two public datasets for sentiment analysis, namely the TMDB5k dataset and the Reviews dataset, and makes predictions (positive, negative, or neutral) based on the content of the review through conducting sentiment analysis on the text using the Viscous Accretion Disk Evolution Resource (VADER) approach. The findings, based on users’ feedback, are more accurate and informative regarding movie quality, where SVM accuracy is 99.28% and Naïve Bayes accuracy is 96.60% when used with VADER sentiment analysis.

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Adaptive Sliding Mode control for a Boost Converter with Constant Power Load

Adaptive Sliding Mode Control for a Boost Converter with Con...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： D. Zambrano-Prada A. El Aroudi L. Vázquez-Seiszdedos O. Lopez-Santos R. Haroun L. Martínez-Salamero Department of Electrical Electronic and Automatic Control Engineering School of Electrical and Computer EngineeringCampus Sescelades Group of Automatic Control and Industrial Electronics (GAEI) Rovira i Virgili University Tarragona Spain

Output voltage regulation in a boost converter with Constant Power Load (CPL) can be obtained by means of sliding-mode control with a linear estimation loop of the output power. The estimation procedure is a simple integrator of the output voltage error and confers an adaptive nature on the switching regulator. Two switching surfaces of quadratic type are considered to induce the sliding motions, and the corresponding conditions for the existence of both sliding mode and stability of the equilibrium point are derived. The resulting controller can be implemented using simple analog electronics requiring operational amplifier-based circuits in both cases plus a multiplier in one case and a multiplier and a divider in the other case. PSIM and MATLAB simulations show a fast recovery and zero steady-state output voltage error in response to large-signal disturbances in the output voltage and the load power.

关键词： Renewable energy sources Estimation Switches Stability analysis Steady-state Circuit stability Behavioral sciences

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Feature Selection via Genetic Multiobjective Optimization with Fuzzy Rejection Mechanisms 17

Feature Selection via Genetic Multiobjective Optimization wi...

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17th IEEE International Conference on Intelligent computer Communication and Processing, ICCP 2021

作者： Cimpanu, Corina Ferariu, Lavinia Dumitriu, Tiberius Gheorghe Asachi Technical University of Iaşi Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (纸本)9781665409766

In pattern identification, embedded Feature Selection (FS) determines a detailed data description and an efficient and accurate classification. Most Genetic Algorithm (GA) based optimization procedures tackle the minimization of a classifier's error rate. Independent of how feature selection procedures are configured, either in Single (SOO) or Multi-Objective Optimization (MOO) manners, the major problem with all classifiers is multidimensionality and quantity of redundant and noisy data recordings. This paper compares six GA-based optimizations as viable solutions for an accurate and efficient assessment of working memory load levels during arithmetic computations based on Electroencephalogram (EEG) data. An objective layout analysis of a randomly generated population motivates both SOO and MOO further explorations. The single objective error-based optimization, the aggregation of error, and the number of selected features illustrate the limitations of SOOs. The third SOO incorporates a fuzzy rejection mechanism for unnecessary features. The baseline for MOO comparison is Deb's NSGA. Two new MOO procedures for feature selection are proposed: a MOO Fuzzy Rejection of Irrelevant Features (MOO-FRIF) and a Fuzzy Progressive Deletion of Irrelevant Features (MOO-FPDIF). Both new MOO approaches gradually eliminate or discourage the EEG features that negatively influence the classifier. Two classifiers address the problem distinctively, namely Random Forests (RF) and a multi-class Support Vector Machine (SVM) with one vs. one comparison mechanism and RBF kernel. © 2021 IEEE.

关键词： Multiobjective optimization

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs 3

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Syed, Mohd Khizar Chandra Behera, Prakash Josephson, P Joel Khan, Shamim Ahmad Kholmukhamedov, Timur Ponni Valavan, M. Kohinoor college Department of Physics Maharashtra India St. Claret College Department of Science Bangalore India Malla Reddy Engineering College Department of ECE Telangana Medchal Malkajgiri India Glocal School of Science & Technology Glocal University Uttar Pradesh India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798350390728

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems. © 2024 IEEE.

关键词： State of charge

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Experimental Research of Modes of Operation of Local Autonomous Sources of Energy Supply with Induction Generator 4

Experimental Research of Modes of Operation of Local Autonom...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zachepa, Iurii Zachepa, Nataliia Khrebtova, Oksana Kobiliansky, Boris Turaev, Ilya Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Ukrainian Engineering Pedagogics Academy Department of Electromechanical and Computer Systems Kharkov Ukraine

ISBN: (纸本)9798350346831

The paper presents the results of experimental studies of autonomous power supply sources formed according to the 'internal combustion engine-induction generator' scheme. Autonomous local sources of energy supply according to this scheme are formed from existing electromechanical and electrotechnical equipment of other functional purposes only for the period of electricity demand. Such sources of electricity are universal for welding and other technological equipment of military and civil, including agricultural, machinery. According to the results, the viability of the proposed concept of creating autonomous power supply sources based on motor vehicles and electrical equipment has been proven. The limits of guaranteed excitation and subsequent trouble-free operation of autonomous local sources of power supply with power to consumers of various categories have been established. © 2022 IEEE.

关键词： Electric loads

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