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21st International Learning and Technology Conference, L and T 2024

作者： Shigdar, Basmah Binsalim, Haya Almatrafi, Lina Badaam, Salma Barkat, Enfel Effat University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Saudi Arabia's Vision 2030 emphasizes renewable energy to drive sustainable development, particularly leveraging solar power due to abundant sunshine. However, integrating solar photovoltaic (PV) systems into existing grids presents multifaceted challenges. Voltage regulation, power quality issues, and reverse power flow disruption due to solar intermittency demand effective management strategies. Challenges, including voltage fluctuations, power quality issues, reverse power flow disruptions, and solutions like energy storage, advanced inverter control, demand response, and grid modernization, are explored to mitigate these challenges. A literature review and methodologies are presented, including PV generator modeling and power flow calculations. The proposed methodology is validated through case studies and a simulation based on the Institute of electrical and Electronics Engineers (IEEE) 30-Bus System, showcasing differences in voltage and angle outputs across nodes with integrated PV generators. © 2024 IEEE.

关键词： Power quality

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Real-Time Evaluation of Human Inertial properties in Immersive Simulators for Moth type Boats 6

Real-Time Evaluation of Human Inertial properties in Immersi...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Todesca, Davide Sergenti, Carol Giulietti, Nicola Carnevale, Marco Dept. Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9798350394634

The aim of this work is to present a system designed for real-time markerless evaluation of human inertial properties, to be used in a Human In The Loop (HITL) simulator of a Moth class sailboat. The method is based on human body pose estimation algorithm used for 3D skeleton reconstruction from a multi-camera acquisition. The exploitation of a markerless system enables ease of use compared to marker-based systems, and reduces signal’s disturbances compared to Inertia Measurement Units (IMUs) for body pose reconstruction. The use of multi-camera setup and the integration of YOLOv8-pose model results with image triangulation techniques reduce occlusion occurrence, measurements drift and complexity. The 3D pose obtained is then used to estimate the Center of Mass (CoM) position and the Tensor of Inertia (ToI) of the skipper. The system allows for a real-time computation of the inertial properties of the skipper, thus enabling a real-time update of the mass matrix of the numerical model running the human-in-the loop simulator. This study shows the results achievable by the system in terms of inertia estimation for different skipper’s poses, and compares the cases in which the skipper is modelled as a point mass with real-time position updates with the case in which the full inertia tensor of the skipper is considered. Results show how in contexts like those of Moth class sailboat simulators the possibility to take into account and to vary in real-time the tensor of inertia of the sailor increases the accuracy achievable in the dynamic simulation. © 2018 IEEE.

关键词： Cameras

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Electromagnetic Power Harvesting by electrically Small, Resonant, Parasitic, Superdirective Arrays Designed via Characteristic Mode Theory 5

Electromagnetic Power Harvesting by Electrically Small, Reso...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Kaifas, Theodoros N. F. Democritus University of Thrace Dept. of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350352863

The work at hand provides a comprehensive study of Electromagnetic Power Harvesting via Metamaterial type antennas on the context of the Characteristic Mode Theory. Super-directive, parasitic arrays of electrically small but resonant element radiators - particles are studied, designed and evaluated. The presented designs provide more antenna-gain than conventional ones by invoicing and practically matching the theoretical maximum bounds of super-directivity boosting the rectenna system to its absolute limits, as far as the antenna part is concerned. Furthermore, those arrays apart from their own merit they exhibit the characteristic that can be arranged in large host surfaces to scale for even more increased performance. In the current work we will show that the analysis of harvesting particles via Characteristic Mode Theory can provide in-dept. understanding of the physics involved and assist on performance improvement strategies during relative design circles. © 2024 IEEE.

关键词： characteristic mode theory electromagnetic power harvesting super-directivity

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Thin Gold Film Deposited on PDMS for Stretchable electrical Interconnections and Bio-compatible Applications via Textured 3D Printed Mold

Thin Gold Film Deposited on PDMS for Stretchable Electrical ...

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2024 International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles, E-Textiles 2024

作者： Digregorio, Gabriel Redoute, Jean-Michel Montefiore Institute The University of Liège Dept. of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9783000804298

This study demonstrates that 3D printing significantly enhances the surface roughness of PDMS substrates, leading to a substantial increase in the maximum allowable elongation before a drastic rise in electrical resistance. The PDMS substrates were molded using 3D-printed molds with varying surface treatments to achieve different roughness levels. The untreated samples displayed higher surface roughness, allowing for elongation up to 12.5% before the electrical resistance increased sharply due to the formation of micro-cracks in the gold film. Conversely, smoother surfaces, achieved through ethanol mist treatment, showed higher initial resistance and reduced elongation capacity. This finding underscores the critical role of substrate roughness in stretchable electronics, where optimizing surface characteristics is essential for balancing adhesion and mechanical robustness, thus improving the performance and reliability of bio-compatible and stretchable electrical interconnections. © 2024 Fraunhofer IZM.

关键词： Elongation

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing the U-Net Network for Segmenting the Left Ventricle from Cine-MRI Scans 4

Utilizing the U-Net Network for Segmenting the Left Ventricl...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Malhotra, Sonal Thapliyal, Siddhant Aggarwal, Priyanshi Kumar, Mukesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350387490

In this research, we studied the U-Net segmentation model for identifying and demonstrating addresses related to cardiac left ventricle contour in cine-MRI images. Efficacy of the model is evaluated across different stages such as preprocessing, training, validation and robustness testing along with comparisons to conventional methods and other deep learning architectures. But the preprocessing includes standardized pixel intensities, and by data augmentation using rotation, flip method or scaling techniques. The training and validation stages clearly show the robust consolidation of our model with little overfitting, guaranteeing decent performance on different datasets. The developed model is robust by passing the test against noise/ artifacts, and providing a very good segmentation in extreme situations so that there are no accuracy outliers represented. Comparative analyses show better sensitivity-specificity trade-offs and precision-recall characteristics versus other methods. In general, a U-Net model developed here works well for cardiac MRI analysis and can be used as an effective tool to help automatic LV segmentation accurate and efficient in clinical applications which will improve diagnosis of heart diseases from imaging points. © 2024 IEEE.

关键词： Deep learning

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A Comparative Study of Machine Learning Methods for Intrusion Detection 10

A Comparative Study of Machine Learning Methods for Intrusio...

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10th International Conference on electrical and Electronics engineering, ICEEE 2023

作者： Sibai, Fadi N. Asaduzzaman, Abu Sibai, Ahmad Gulf University for Science and Technology Electrical & Computer Engineering Dept. Mishref Kuwait Wichita State University Electrical & Computer Engineering Dept. WichitaKS United States University of South Florida Computer Science Dept. TampaFL United States

ISBN: (纸本)9798350304299

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely, neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic gradient descent SGD. Using the NSL-KDD data set, these ML techniques were trained and tested to correctly classify the network and operating system records of this dataset into one of 24 possible attacks. The performances of these ML methods were analyzed and compared, with the random forest method performing at the top. To the best of our knowledge, this is the first work on investigating more than 4 ML classifiers on this data set in one single work and using the same set of tools. © 2023 IEEE.

关键词： Intrusion detection

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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eXtended Reality Services for Outdoor Cultural Spaces: System Design and Evaluation Framework Using Unbiased Biometric Data 15

eXtended Reality Services for Outdoor Cultural Spaces: Syste...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Koukopoulos, Zois Koukopoulos, Dimitrios Fidas, Christos University of Patras Interactive Technologies Lab Dept. of Electrical & Computer Engineering Patras Greece University of Patras Dept. of History-Archaeology Patras Greece University of Patras Dept. of Electrical & Computer Engineering Patras Greece

ISBN: (纸本)9798350368833

The concept of enriching the visible reality with more information has led to the development of the eXtended Reality technology, where digital elements like text, images, 3D representations and virtual scenarios, are embedded to the real world and become visible to humans via mobile devices. Cultural Heritage is embracing eXtended Reality and tries to find new ways to harvest its merits in an endless struggle to disseminate cultural information to the public and the next generations. In this paper, we are presenting an innovative intelligent Environment, operating in outdoor cultural spaces and offering personalized eXtended Reality cultural experiences to visitors. The Environment aims to support the financial sustainability of cultural heritage institutes by attracting visitors and especially younger ones, as well as create new sources of revenue for cultural heritage professionals by allowing them to create XR experiences, without the help of IT professionals and exploiting them in an online market of XR cultural experiences. We propose a novel framework to evaluate the user experience by collecting biometric data (brain activity and eye behavior) from the visitors of outdoor cultural spaces and exploring their cognition and emotions while using the new Environment. © 2024 IEEE.

关键词： Biometric Data eXtended Reality Outdoor Cultural Spaces Sustainability

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Robust decentralized plug and play voltage tracker of islanded microgrids under loads and lines uncertainties by the invariant ellipsoids

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Journal of King Saud university - engineering sciences 2024年第6期36卷 409-419页

作者： Bayoumi, Ehab H.E. Soliman, Hisham M. El-Sheikhi, Farag Ali Shafiq, M. Awad, Hilmy Energy and Renewable Energy Department Egyptian Chinese University Cairo Egypt Dept. of Electrical Power Eng. Faculty of Eng Cairo University Cairo Egypt Dept. of Electrical and Electronics Engineering Istanbul Esenyurt University Istanbul Turkey Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Technology Faculty of Technology and Education Helwan University Egypt

Microgrids (MGs) comprising of distributed generation units (DGs) are subjected to plug-and-play operation (PnP), lines (connecting the DGs) parameters uncertainty, and load changes. Robust stability and an authentic operation for islanded microgrids can be guaranteed through a robust decentralized voltage tracker developed in this paper. The proposed controller design has the following merits: 1) fully decentralized, 2) scalable, and 3) maintains robust stability against PnP of DGs, load changes, and lines parameters’ uncertainties. A sufficient condition is developed by linear matrix inequality convex optimization is exploited to solve the problem. The MGs’ changes in load and the line's parameters are modelled as norm-bounded uncertainties. The suggested controller uses local measurements from DGs, i.e., decentralized by the decomposition of the global system into subsystems. For each subsystem, the rest of the system's impact is considered disturbances, whose influence must be minimized. The proposed disturbance rejection control algorithm is based on the method of invariant-ellipsoids. Several time-domain scenarios such as connecting and disconnecting a number of DGs, local load changes, and variation of transmission line parameters are executed to assess the suggested controller's effectiveness, using MATLAB /Sim PowerSystems Toolbox. © 2021 The Authors

关键词： Controllers

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