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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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PRUNING DEEP CONVOLUTIONAL NEURAL NETWORK USING CONDITIONAL MUTUAL INFORMATION

arXiv

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arXiv 2024年

作者： Vu-Van, Tien Thanh, Dat Du Ho, Nguyen Vu, Mai Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Viet Nam Computer Science Department Loyola University Maryland United States Electrical and Computer Engineering Department Tufts University United States

Convolutional Neural Networks (CNNs) achieve high performance in image classification tasks but are challenging to deploy on resource-limited hardware due to their large model sizes. To address this issue, we leverage Mutual Information, a metric that provides valuable insights into how deep learning models retain and process information through measuring the shared information between input features or output labels and network layers. In this study, we propose a structured filter-pruning approach for CNNs that identifies and selectively retains the most informative features in each layer. Our approach successively evaluates each layer by ranking the importance of its feature maps based on Conditional Mutual Information (CMI) values, computed using a matrix-based Rényi α-order entropy numerical method. We propose several formulations of CMI to capture correlation among features across different layers. We then develop various strategies to determine the cutoff point for CMI values to prune unimportant features. This approach allows parallel pruning in both forward and backward directions and significantly reduces model size while preserving accuracy. Tested on the VGG16 architecture with the CIFAR-10 dataset, the proposed method reduces the number of filters by more than a third, with only a 0.32% drop in test accuracy. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

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The Role of AI in Modern Classroom Management 2

The Role of AI in Modern Classroom Management

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gayaki, Unnati Daronde, Subodh Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Biomedical Engineering Faculty of Engineering and Technology Maharashtra Wardha India

ISBN: (纸本)9798331505745

Artificial Intelligence is changing the way classrooms are managed, encouraging new ideas, making things easier, and helping students learn in their own way. Adaptive platforms, smart tutoring systems, and tools like I-MINDS customize learning experiences to fit different needs while making it easier for teachers. It takes care of jobs like grading, tracking attendance, and analyzing behavior, allowing teachers to concentrate on important thinking skills and emotional growth. AI also enhances teamwork through group tools, promotes fairness, and assists in real-time decision-making. Technologies like VR, AR, and MR allow people to learn through experience, especially with hard subjects. Incorporating AI learning into school curricula readies students for a future driven by AI. Despite issues with data privacy, biased algorithms, and ethical questions, AI is making learning environments inclusive, dynamic, and just. New technology, combined with ways that are focused on people, is how AI seeks to transform education into a holistic and flexible system for learners and teachers alike. © 2025 IEEE.

关键词： Decision making

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The Positive Impact of Artificial Intelligence on Education 1

The Positive Impact of Artificial Intelligence on Education

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1st International Conference on Global Aeronautical engineering and Satellite Technology, GAST 2024

作者： Gourari, Abdelali El Raoufi, Mustapha Skouri, Mohammed Ben Braim, Abdelghani Ait Ezzini, Mustapha Lahsen, Yassine Ait Achki, Samira Cadi Ayyad University Laboratory of Physics High Energy and Astrophysics Faculty of Science Semlalia Marrakech Morocco Cadi Ayyad University Laboratory of Materials Energy and Environment Faculty of Sciences Semlalia Marrakech Morocco Cadi Ayyad University Faculty of Sciences Semlalia Fluid Mechanics and Energetic Laboratory Marrakesh Morocco Cadi Ayyad University Laboratory of Computer Systems Engineering Faculty of Sciences Semlalia Marrakech Morocco Fstg Computer Science Department Marrakech Morocco

ISBN: (纸本)9798350371598

Those interested in artificial intelligence technologies, especially supervised and unsupervised learning in education, know they need considerable data for well-modeled training and high-quality accuracy. However, data access is not easy as it drains time and effort. So, in this paper, we will talk about the impact of artificial intelligence on education in general and how it will affect teachers' role in the future. We will also create an algorithm that will integrate several algorithms sequentially that will add value to this paper and contribute significantly to solving most of the problems that exist in education. Since the reinforcement learning (RL) algorithm is based on trial and error during training, it stores all the good events done in a considerable buffer. Once the RL model is ready, we will collect enough data to be used to train one of the algorithms mentioned earlier (supervised learning and unsupervised learning). © 2024 IEEE.

关键词： Reinforcement learning

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Autoencoder-Enhanced Roommate Recommendation System

Autoencoder-Enhanced Roommate Recommendation System

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Khaled Zahran Abdallah Amir Magy Elbanhawy Ingy Ghanem Amir El-Ghamry Khaled M. Fouad Ibrahim F. Moawad Faculty of Computer Science and Engineering New Mansoura University Egypt Faculty of Computer and Information Sciences School of Engineering and Computer Science Mansoura University University of Hertfordshire Hosted by Global Academic Foundation Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

This proposed system is designed for creating a new way of giving personalized recommendations by focusing on people's behaviors and preferences. The system uses traditional machine learning algorithms integrating it with deep learning techniques to get the most out of data and suggest recommendations which are designed according to each individual's unique preferences. A deep learning autoencoder is used to learn a lower-dimensional representation of the data, with an assurance on feature extraction and reconstruction accuracy. The encoded features are passed to be clustered using KMeans, with the effectiveness of clustering estimated through internal validation metrics such as silhouette score, Calinski-Harabasz index, and Davies-Bouldin index. Also, t-Distributed Stochastic Neighbor Embedding (t-SNE) is utilized for visualizing clustered data in a simple manner. Additionally, a silhouette plot is given to provide a visual representation of the silhouette scores across clusters, highlighting the degree of cohesion within clusters and separation between them. Finally, using cosine similarity that identifies similar users within the same cluster.

关键词： Measurement Deep learning Visualization Machine learning algorithms Focusing Data visualization Clustering algorithms Feature extraction Indexes Recommender systems

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Simulation of the Functional Characteristics of Low-Voltage Switching Devices, Based on the Example of Automatic Circuit Breakers

Simulation of the Functional Characteristics of Low-Voltage ...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Petrov, Almaz Radikovich Ivanovna Gracheva, Elena Sinyukova, Tatiana Valtchev, Stanimir Miceli, Rosario Rahman, Aqeel Ur Kazan State Power Engineering University Departm. of Power Supply of Industrial Enterprises Kazan Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Cts University Nova Faculty Fct and Uninova Lisbon Portugal University of Palermo Department of Engineering Italy

ISBN: (纸本)9798350348378

The article discusses the main features of mechanical, chemical, thermal, and electrical processes occurring in the contact zones of the surfaces of contact connections of electrical devices. The parameters determining the value of power losses and power consumption in low voltage switching equipment are analyzed. The main features of the characteristics of the topology and technical condition in the operating conditions of the equipment of shop low-voltage industrial power supply networks are shown. A comparative analysis of the technical characteristics of automatic circuit breakers. Using catalog data, the dependences of active power losses in circuit breakers on rated currents are established. Algorithms have been developed and the obtained dependencies have been modeled using approximating functions. The standard deviation of the compiled approximating functions is calculated. Analytical expressions of the dynamics of power losses per pole in the studied machines of some companies of the function of the rated current are presented. The graphical dependencies of the investigated parameters of low-voltage equipment are presented. The developed models are recommended to be used to increase the reliability of the assessment and refinement of the amount of active power and electricity losses in low-voltage electrical networks of industrial power supply systems. © 2023 IEEE.

关键词： Electric circuit breakers

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Efficient Optimization of Energy Consumption at Home through Machine Learning

Efficient Optimization of Energy Consumption at Home through...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sharada Prasad N Lakshmi Shree M S Manjunatha S Vipul Vekariya Ganga D Benal Harshal Patil Department of Electrical and Electronics Engineering RNS Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption.

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Silicon carbide-graphene nanocomposite electrodes for enhanced desalination via capacitive deionization

引用

Ceramics International 2025年

作者： Yousef, Ayman Hameed, R.M. Abdel Maafa, Ibrahim M. Abutaleb, Ahmed El-Halwany, M.M. Department of Chemical Engineering College of Engineering and Computer Sciences Jazan University Jazan45142 Saudi Arabia Chemistry Department Faculty of Science Cairo University Giza Egypt Engineering and Technology Research Center Jazan University P.O.Box 114 Jazan82817 Saudi Arabia Department of Mathematics and Physics Engineering Faculty of Engineering Mansoura University El-Mansoura Egypt Faculty of Engineering Mansoura National University Egypt

Graphene nanoflakes [GNFs] were ultrasonically modified with silicon carbide nanopowder with subsequent hydrothermal heating at 120 °C. A number of these SC-GNFs nanocomposites was fabricated based on altering their metallic carbide content [5, 10 and 15 wt%] in relation to that of carbonaceous substrate. The chemical composition, morphology and interaction with water droplets of SC-GNFs nanostructures were examined using suitable physical characterization tools. The ability of these carbide containing graphene nanomaterials to desalinate water samples was evaluated through varied electrochemical measurements. Outstanding specific capacitances could be measured at different SC-GNFs nanocomposites when related to those of bare components of SiC and GNFs. Incorporating 10 wt% SiC between graphene nanoflakes could significantly promote their capacitance by about nine folds. Galvanostatic charge-discharge experiments of SC-GNFs nanocomposite revealed good stability attitude within longer periods. Increased salt removal efficiencies were also demonstrated at all SC-GNFs nanopowders up to 83.33 % when contrasted to those of SiC [10.42 %] and GNFs [12.50 %]. These findings at graphene nanoflakes surfaces after their treatment with SiC nanoparticles might suggest further studies for relevant metallic carbides onto convenient carbonaceous based supports for valuable CDI systems. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Deionized water

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Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion

arXiv

引用

arXiv 2024年

作者： Ghafoor, Mehwish Mahmood, Arif Bilal, Muhammad Department of Computer Science Information Technology University Lahore Pakistan Center of Excellence in Intelligent Engineering Systems Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

In the field of 3D Human Pose Estimation from monocular videos, the presence of diverse occlusion types presents a formidable challenge. Prior research has made progress by harnessing spatial and temporal cues to infer 3D poses from 2D joint observations. This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation, even in the presence of severe occlusions. Confronting the issue of occlusion-induced missing joint data, we propose a temporal interpolation-based occlusion guidance mechanism. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Each intermediate-view undergoes spatial refinement through a self-refinement schema. Subsequently, these intermediate-views are fused to yield the final 3D human pose estimation. The entire system is end-to-end trainable. Through extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets, our method’s performance is rigorously evaluated. Notably, our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements. The code is available here: https://***/MehwishG/DTF. © 2024, CC BY-NC-ND.

关键词： 3D modeling

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An Effective Evaluation of SONARS using Arduino and Display on Processing IDE 1

An Effective Evaluation of SONARS using Arduino and Display ...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Rani, B Kezia Singh, Kamlesh Kalpanadevi, D. Patra, Jyothi Prasad Srinivas, T. Aditya Sai Hkbk College of Engineering Department of Ece Bangalore India Vasavi College of Engineering Department of Information Technology Hyderabad India GraphicbEra Hill University Department of Computer Science and Engineering Dehradun India Kalasalingam Academy of Research and Education Krishnan Koil Department of Computer Applications Tamilnadu India Odisha University of Technology and Research Outr Faculty of Electrical odisha Bhubaneswar India G. Pulla Reddy Engineering College Department of Computer Science and Engineering Andhra pradesh Kurnool India

ISBN: (纸本)9798350397840

Sonars are used by SAR teams to identify and retrieve submerged objects like cars and planes. In the event of extensive searches, where sonar operators may become fatigued and overlook the possible item, strategies for the programmable characterisation or identification of the object may be invaluable. Current sonar technology may be used for more than just navigation;it can also be used to locate fish, townships, and create maps of the ocean floor. The power of the transmitter, the quality of the transducer, the sensitivity of the collector, and the design of the acoustic correspondence are the four key aspects that affect the appearance of a sonar system. While current sonar frameworks have limited functionality-such as a two-layered screen and a view of less than 360 degrees of the surrounding environment improved sonar frameworks with a three-layered screen, joined to new PC frameworks, and a 360-degree view of the surrounding environment are on the horizon. Companies now face substantial challenges while attempting oceanic research and inspections. To keep up with the requirements of current maritime businesses, better underwater devices and more precise estimations are always being created and modified. © 2022 IEEE.

关键词： Sonar

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