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3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah A. Alobaidi, Abdulraheem H. Altaf, Aatif Kumar, Anil Kumar, Mahendra Alghamdi, Sultan King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah Saudi Arabia Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar India

ISBN: (纸本)9798350304732

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance. © 2023 IEEE.

关键词： Fuzzy logic controller integral controller load frequency control optimization technique parameter estimation

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UV C Sterilization Autobot 4

UV C Sterilization Autobot

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4th Research, Invention, and Innovation Congress: Innovative electricals and Electronics, RI2C 2023

作者： Meesomklin, Suwaluck Nipatchai, Nipat Ayuthaya, Kullaya Seneewong Na Akkaraekthalin, Prayoot Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Rd. Bangsue Bangkok Thailand

ISBN: (纸本)9798350330618

Due to the spread of the COVID1 9 virus, the research team is seeking ways to reduce its transmission as well as that of various bacteria within buildings using Ultraviolet-C (UVC) radiation, which is effective in killing viruses. The research team has designed and built an automatic robot to kill disease-causing germs inside the building using UVC lamps. The robot is driven by two wheels and can be controlled in three modes: 1) remotely controlled by radio waves, or 2) controlled by a mobile phone/computer through a Wi-Fi signal, which allows the user to see a live stream image from the robot's front camera, or 3) automatically controlled to move parallel to walls (Wall keeping mode) using PD control. The distance between the robot and the wall, measured by ultrasonic sensors, is maintained at 20- 40 cm, after which the UVC lamps are turned on at distance of 26- 50 cm for 12. 5-18 seconds to kill germs in areas such as door handles and elevator buttons, reducing the risk of transmission through contact with surfaces within the building. This UVC autoboot has effective eradication bacteria 100% within 30 minutes. The UVC autobot has effective eradication bacteria in yeast 99.99% for approximately 30 minutes. ©2023 IEEE.

关键词： Robots

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Boosting the Performance of Transformer Architectures for Semantic Textual Similarity

arXiv

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arXiv 2023年

作者： Rep, Ivan Čeperić, Vladimir Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Croatia

Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, and DeBERTaV3 cross-encoders by approaching the problem as a binary classification task or a regression task. We combine the outputs of the transformer models and use handmade features as inputs for boosting algorithms. Due to worse test set results coupled with improvements on the validation set, we experiment with different dataset splits to further investigate this occurrence. We also provide an error analysis, focused on the edges of the prediction range. © 2023, CC BY.

关键词： Semantics

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Tracking changes using Kullback-Leibler divergence for the continual learning

Tracking changes using Kullback-Leibler divergence for the c...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Basterrech, Sebastian Wozniak, Michal VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of concept drift, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model's performance and negatively affects the achieved prediction quality. Most current methods apply statistical learning and similarity analysis over the raw data. However, similarity analysis in streaming data remains a complex problem due to time limitation, non-precise values, fast decision speed, scalability, etc. This article introduces a novel method for monitoring changes in the probabilistic distribution of multi-dimensional data streams. As a measure of the rapidity of changes, we analyze the popular Kullback-Leibler divergence. During the experimental study, we show how to use this metric to predict the concept drift occurrence and understand its nature. The obtained results encourage further work on the proposed methods and its application in the real tasks where the prediction of the future appearance of concept drift plays a crucial role, such as predictive maintenance. © 2022 IEEE.

关键词： Forecasting

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A Game-Theoretic Framework for Distributed Load Balancing: Static and Dynamic Game Models

arXiv

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arXiv 2025年

作者： Fardno, Fatemeh Rasoul Etesami, S. The Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to coordinate with each other, yet the goal is to balance the load among servers in a distributed manner. First, we provide a static game formulation in which each player holds a job with a certain processing requirement and wants to schedule it fractionally among a set of heterogeneous servers to minimize its average processing time. We show that this static game is a potential game and admits a pure Nash equilibrium (NE). In particular, the best-response dynamics converge to such an NE after n iterations, where n is the number of players. We then extend our results to a dynamic game setting, where jobs arrive and get processed in the system, and players observe the load (state) on the servers to decide how to schedule their jobs among the servers in order to minimize their averaged cumulative processing time. In this setting, we show that if the players update their strategies using dynamic best-response strategies, the system eventually becomes fully load-balanced and the players' strategies converge to the pure NE of the static game. In particular, we show that the convergence time scales only polynomially with respect to the game parameters. Finally, we provide numerical results to evaluate the performance of our proposed algorithms under both static and dynamic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Queueing networks

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Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

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computers, Materials & Continua 2022年第2期70卷 3281-3294页

作者： Nazar Hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Data Science Noroff University CollegeNorway Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543KSASaudi Arabia Department of natural and engineering sciences College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11421Saudi Arabia Faculty of Engineering Industrial Engineering DepartmentKing Saud UniversityRiyadh11421Saudi Arabia

Agriculture is an important research area in the field of visual recognition by *** diseases affect the quality and yields of ***-stage identification of crop disease decreases financial losses and positively impacts crop *** manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly *** this work,we propose a new framework for the recognition of cucumber leaf *** proposed framework is based on deep learning and involves the fusion and selection of the best *** the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and *** fine-tuned models are trained using deep transfer *** are extracted in the later step and fused using a parallel maximum fusion *** the later step,best features are selected usingWhale Optimization *** best-selected features are classified using supervised learning algorithms for the final classification *** experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method.

关键词： Cucumber diseases database preparation deep learning parallel fusion features selection

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Enhancing Classroom Engagement using Real-Time Student Attention Monitoring with YOLOv9 10

Enhancing Classroom Engagement using Real-Time Student Atten...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Gunawan, Teddy Surya Muzarudin, Muhammad Amirul Faiz Kartiwi, Mira Yusoff, Nelidya Md International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

This research investigates the utilization of YOLOv9 in classrooms to monitor student attention in real time, thereby addressing the challenge of sustaining student engagement. The inability of conventional teaching methods to maintain students' attention frequently necessitates the implementation of dynamic modifications to instructional strategies. The goal is to create a system that accurately identifies students' attention levels. The system was trained and tested on annotated datasets using the YOLOv9c and YOLOv9e models, resulting in precision, recall, and mAP scores that demonstrated high effectiveness. A dashboard from Streamlit offered real-Time and cumulative insights, which allowed for immediate pedagogical adjustments. The results showed that YOLOv9c had a higher precision (0.876) and YOLOv9e had a superior recall, underscoring their respective strengths. However, despite a few misclassifications, the system's robust performance suggests its potential as a transformative educational tool. Future enhancements will focus on integrating additional data sources, reducing misclassifications, and improving algorithm scalability and accuracy to foster a more adaptive learning environment and showcase the system's potential for diverse educational applications. © 2024 IEEE.

关键词： Students

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Development of a Blockchain-Integrated Medical Device for Enhanced Data Integrity 10

Development of a Blockchain-Integrated Medical Device for En...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Gunawan, Teddy Surya Roslan, Nadiah Karmila Kartiwi, Mira Yusoff, Nelidya Md International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

Integrating blockchain technology with medical devices is crucial for addressing healthcare data integrity and security challenges. Traditional data storage methods are vulnerable to tampering and unauthorized access, compromising patient safety and privacy. This study develops a blockchain-integrated medical device system to create a tamper-resistant framework. The primary objectives are to secure medical device measurements using blockchain, evaluate the system's security against data tampering and unauthorized access, and assess its performance. The methodology includes deploying smart contracts on the Ethereum blockchain to store hashed and encrypted medical data, simulating data generation with a Raspberry Pi, and implementing user authentication through Flask and JSON Web Tokens (JWT). The system achieved an average transaction throughput of 7.72 TPS and a latency of approximately 0.13 seconds, demonstrating robust tamper resistance. These findings indicate that blockchain integration can significantly enhance data integrity and security in healthcare, improving patient outcomes and data privacy within medical ecosystems. © 2024 IEEE.

关键词： Blockchain

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A Decision Support System to Optimize Food Set Determination for Nutrient Requirement Consumers

A Decision Support System to Optimize Food Set Determination...

引用

Information engineering (ACIE), IEEE Asia Conference on

作者： Wisute Ongcunaruk Pornthipa Ongkunaruk Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Engineering Faculty of Engineering Kasetsart University Bangkok Thailand

Currently, consumers have a variety of food restrictions such as food allergies, high blood pressure, hyperlipidemia, obesity, or being overweight, allergies, diabetes, or heart disease. The objective of this research was to develop an integer programming and decision support system of consumption for a consumer with dietary restrictions. An integer programming model was formulated to determine the optimal food set for nutrient requirement consumers and the problem will be solved by the solver function in Excel. The database of the program included food menu nutrients such as carbohydrates, protein, sugar, fibre, minerals, and vitamins as well as calories and price. The users can input their nutrient constraints such as an upper and lower limit on the amount of nutrients required, the amount of food consumed in certain food categories, undesirable food or food containing allergens, the type of menu, and the budget limitation. Next, we created a user interface in Excel as a decision support system for convenient use by consumers. The users can simply fill in their requirements, and the program will show the best answer corresponding to the data entered in a short time. The program will generate a menu that minimizes costs such that all constraints are satisfied in the output sheet. In the future, the program can be used in hospitals, day-care centres, or vending machines.

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