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Enhancing Power Quality in Distribution Systems through Bi-directional Solid-State Transformers 3

Enhancing Power Quality in Distribution Systems through Bi-d...

引用

3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Ghambirlou, Khaled Esamil SH Abosnina, Adel Ali Moschopoulos, Gerry Engineering Western University Dept. Electrical and Computer London Canada College of Electrical and Electronics Technology Benghazi Libya

ISBN: (纸本)9798331540319

Power quality issues, including voltage sags, swells, harmonic distortion, oscillatory voltage transients, etc. can degrade equipment performance, leading to inefficiencies and costly disruptions. Studying these issues and introducing solutions are essential for maintaining the reliability and efficiency of modern distribution systems. This paper examines and contrasts the benefits of a bidirectional three-stage solid-state transformer (SST) in mitigating power quality issues to traditional transformers. SST introduces reactive power support to improve the system quality by controlling active and reactive power. This paper investigates key power quality issues-voltage sag, swell, harmonic distortion, and oscillatory transients-arising from capacitor bank energizing, focusing on the novel application of solid-state transformers (SSTs) in mitigating these effects. Additionally, it examines SST-based fault isolation across various fault types (three-phase, double line-to-ground, and line-to-ground). Several simulations have been performed using the MATLAB/Simulink platform to highlight the benefits of the Solid-State Transformer (SST) over conventional transformers and cascade multiple-active-bridge SST (CMABSST) types. The proposed SST employs active and reactive power control to alleviate these effects, allowing the SST controller to manage reactive power effectively and provide the required support that makes the system stable and reliable. © 2024 IEEE.

关键词： Distribution transformers

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DigiPramaan: Blockchain Based Certificate Management System 8

DigiPramaan: Blockchain Based Certificate Management System

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Swami, Prathamesh Vhatkar, Kapil Ambekar, Yash Singh, Kartikey Kaware, Yashovardhan Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350391770

With more people taking online courses and earning certifications, there are great new opportunities to improve skills. But, this digital trend has also led to a problem with fake certificates. To address this problem, we propose a robust certificate management system utilizing blockchain technology and smart contracts. Our system comprises three key components: certificate creation, authenticity verification, and a user-friendly digital storage solution. Blockchain technology is at the core of our system, ensuring that each certificate is immutable and trustworthy. We utilize cryptographic hashes as unique identifiers for each certificate, which detect any tampering by resulting in a different hash. Additionally, our system offers secure digital storage based on cloud technology, allowing users to efficiently manage and access their certificates. We also emphasize features for certificate revocation and updates, enhancing the system's security and adaptability. In conclusion, our blockchain based system offers a comprehensive and secure solution to the issue of fake certificates in the digital era. © 2024 IEEE.

关键词： Blockchain

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Ontology Mapping-Based Semantic Reasoning with OPC UA for Heterogeneous Industrial Devices

Ontology Mapping-Based Semantic Reasoning with OPC UA for He...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science in Lyle School of Engineering DallasTX United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9781665410205

The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2024 IEEE.

关键词： Structured Query Language

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Classifying Temporomandibular Disorder and Trigeminal Neuralgia using Self-Supervised and Deep Learning Approach 4

Classifying Temporomandibular Disorder and Trigeminal Neural...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Saigaonkar, Swati Narawade, Vaibhav Ramrao Adik Institute of Technology D Y Patil Deemed to be University Dept. of Computer Engineering Nerul India

ISBN: (纸本)9798350353266

The study explores the challenges of accurately diagnosing Temporomandibular Disorder (TMD) and Trigeminal Neuralgia (TN), two conditions that often result in severe facial pain. Proper assessment of pain characteristics is essential to avoid misdiagnosis and ensure effective treatment for these conditions, and clinical notes play a significant role in distinguishing between these disorders as they contain rich information. The model was initially trained using a selfsupervised learning approach, where descriptions of both TMD and TN were fed using a Masked Language Modeling (MLM) technique. A custom deep learning model was built. This approach was chosen because the quantity of clinical notes from the MIMIC-III database alone was insufficient to train a model capable of precisely distinguishing between the two diseases. The accuracy obtained is significantly high with a value of 0.98 as against fine-tuned baseline model DISTILBERT. For rare diseases, often the problem data scarcity arises which could be tackled using the suggested approach. © 2024 IEEE.

关键词： Federated learning

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Real-Time AI Driven Shooting Posture Assessment and Correction for Professional and Military Training Using Machine Learning, OpenCV, and MediaPipe 4

Real-Time AI Driven Shooting Posture Assessment and Correcti...

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

作者： Sarswat, Dhairya Moradabad Institute of Technology Aktu University Computer Science and Engineering Dept. U.P. Moradabad India

ISBN: (纸本)9798350387490

Shooting is one of the most popular Olympic Games in India As for now Shooting contributed 4 out of 35 Olympic medals for the country. Thus, the presented paper aims to expose some advanced training methodologies in order to achieve the maximum potential of this kind of sport. This paper also describes an implementation of an AI-based system for shooting posture evaluation and feedback by employing computer vision and sophisticated posture recognition methods in real-time control system. For image acquisition and processing, the system incorporates OpenCV and, for body landmarks detection, it uses MediaPipe's pose estimation models. It therefore computes outstanding angles as well as changes in relation to these postures to allow users to correct their positioning. Refinement of the training process in the usage of weapons is evident by adoption of the proposed solution that has displayed efficient concerning the professional shooters, military personnel, and the target consumers. The ability of the system to give real-time accurate feedback on the participants' posture while at the same time increasing the training quality and performance efficiency minimizes the gap between conventional and contemporary technological approaches in shooting training. This research also discusses the utility of the system in multiple shooting disciplines and situations. Through the use of real-time video data and through the utilization of sophisticated machine learning algorithms, the system is able to tailor feedback depending on the shooting position or shooting posture or tactic that the user signifies to the system. Thus, the proper combination of real-time posture assessment guarantees that users get necessary feedback instantly and apply it in the process of enhancing their technique. The research therefore establishes the role of the system in enhancing training efficiency and effectiveness;though this study could be relevant to other fields such as security, the application

关键词： Feedback

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Al-Safi, Mohammed G. S. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Dept. of Computer Technology Engineering Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Accounting Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Kurdistan Region Iraq

ISBN: (纸本)9798350369359

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper. © 2023 IEEE.

关键词： Vehicles

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Youtube Transcript Summarizer 5

Youtube Transcript Summarizer

引用

5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Panthagani, Vijaya Babu Duggempudi, Vijaya Deepika Reddy Kommera, Naga Lakshmi Vakkalagadda, Nikhita Vignan's Foundation for Science Technology and Research Dept. Computer Science and Engineering A.P Guntur India

ISBN: (纸本)9798350395235

In the modern world, a video transcript summarizer offers a wide range of applications. It draws attention to the crucial points in the video. YouTube videos are watched in large quantities by people for a variety of reasons, including enjoyment, education, obtaining crucial information, and pursuing personal hobbies. Finding a video with any meaningful information about a subject is incredibly tough because most videos are just buffers for unimportant information. The main goal in the majority of situations is to get useful information from the video. In order to generalize YouTube video transcripts for abstractive text summary without sacrificing the essential details and substance, this research has developed a video summarization system based on Natural Language Processing and Machine Learning. The goal of this project is to make the writing for the videos shorter. © 2024 IEEE.

关键词： Machine learning

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Improved Semi Supervised Image Classification using GAN

Improved Semi Supervised Image Classification using GAN

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Kushwaha, Mohit Nandanwar, Amit Kumar Joy, Ashley Varghese Choudhary, Jaytrilok Singh, Dhirendra Pratap Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering M.P Bhopal India

ISBN: (纸本)9798350348460

Image classification is the process of categorizing images into one or more pre-defined classes or categories based on the visual content of the image. The goal of image classification is to create a model that can accurately predict the class of an input image that it has not seen before. In this paper, a semi-supervised model has been proposed for image classification that uses Generative Adversarial Network (GAN) that can give excellent results using only few labelled training data. © 2024 IEEE.

关键词： CIFAR-10 GAN Image classification MNIST Semi-supervised learning

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Recent Trends in Brain Tumor Prediction Using Machine Learning and Deep Learning 16

Recent Trends in Brain Tumor Prediction Using Machine Learni...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Hariyani, Gulshan Mantri, Shamla Dr. Vishwanath Karad MIT-World Peace University Dept. of Computer Engineering and Technology Pune India

ISBN: (纸本)9798331505264

A spontaneous growth of abnormal cells in brain tissues that increases the pressure inside the skull is called a brain tumor. This tumor can be non-cancerous or cancerous based on characteristics, but at most can cause life-threatening if not treated in the early stages. Machine learning and deep learning approaches have become a potent tool in the field of medical imaging in recent years, providing brain tumor identification and classification with increased automation and accuracy. This review explores the recent advancements in machine learning and deep learning models, which have shown better results in brain tumor prediction. Mainly the type of images that are considered for brain tumor capturing are Magnetic Resonance Imaging (MRI), as these images provide great insights into brain tissues, so in this paper, we are referring to MRI-type images only. This paper also covers various segmentation techniques mentioned in multiple studies, as it is a time-consuming job for radiologists to deeply understand MRI images and highlight the tumor present. In this paper, we have also mentioned what metrics are used to analyze these models, like accuracy, sensitivity, specificity, and more, to know if the models are robust. © 2024 IEEE.

关键词： Tumors

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AI-Driven Network Intrusion Detection and Resource Allocation in Real-World O-RAN 5G Networks 30

AI-Driven Network Intrusion Detection and Resource Allocatio...

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30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024

作者： Tsourdinis, Theodoros Makris, Nikos Korakis, Thanasis Fdida, Serge Dept. of Electrical and Computer Engineering University of Thessaly Sorbonne Universite CNRS LIP6 Volos Greece Dept. of Electrical and Computer Engineering University of Thessaly Centre for Research and Technology Hellas CERTH Volos Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece Sorbonne Universite CNRS LIP6 Paris France

ISBN: (纸本)9798400704895

5G technology, the latest advancement in mobile networks, promises increased data speeds, reduced latency, and enhanced capacity. However, network performance and user experience can be critically impacted by malicious traffic, identified as anomaly traffic and intrusion methods. Addressing these challenges requires optimized resource sharing and robust network security measures. In this paper, we present an AI/ML-driven Network Intrusion Detection framework with dynamic resource allocation and user management within the O-RAN architecture. Our Anomaly Traffic Detector (ATD) enhances network security by mitigating Denial of Service (DoS) attacks through an xApp that classifies network traffic in real-time and dynamically adjusts network resources and user connections. Experimental evaluations show that our system effectively maintains low latency under attack conditions, nearly doubles the throughput for legitimate users, and reduces average CPU usage by up to 15%. We use as reference platforms the OpenAirInterface, and FlexRIC for programming the slice and user connectivity decisions at the RAN level, and evaluate our scheme under real-world settings in a testbed environment. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Network intrusion

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