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PV Systems Efficiency Evaluation Using Machine Learning Techniques 8

PV Systems Efficiency Evaluation Using Machine Learning Tech...

引用

8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Wadi, Mohammed Jouda, Mohammed Salemdeeb, Mohammed Husain, Nour Istanbul Sabahattin Zaim University Electrical & Electronics Engineering Dept. Istanbul Turkey Istanbul Sabahattin Zaim University Electrical Engineering Dept. Istanbul Turkey Istanbul Sabahattin Zaim University Computer Engineering Dept. Istanbul Turkey

ISBN: (纸本)9798331531492

Many factors, such as solar irradiance, temperature, angle of incidence, shading, panel degradation, temperature coefficient, and other parameters, can substantially affect the efficiency of photovoltaic (PV) systems. This work presents a concise overview of cooling strategies, temperature coefficient analysis, geographical influence, and advanced materials to explain the effects of solar irradiance and ambient temperature on the efficiency of PV systems. Furthermore, the study examines the two factors of solar irradiance and ambient temperature using two distinct approaches: deterministic techniques and machine learning methods. The results demonstrate that solar irradiance and temperature significantly impact the efficiency of PV models. These results lead to enhancing the performance and reliability of solar energy systems. © 2024 IEEE.

关键词： Solar irradiance

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A Highly Interpretable Deep Equilibrium Network for Hyperspectral Image Deconvolution 48

A Highly Interpretable Deep Equilibrium Network for Hyperspe...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas University of Patras Dept. of Computer Engineering & Informatics Greece Ionian University Dept. of Digital Media and Communication Greece

ISBN: (纸本)9781728163277

In this paper, a novel technique for the hyperspectral image deconvolution problem is developed. First, considering the highly ill-posed nature of the examined problem, it is imperative to incorporate proper priors (regularizers) to capture the strong spectral and spatial dependencies of the hyperspectral images. Then, in light of this, a novel optimization problem is proposed by employing a convolutional neural network to act as a regularizer, which is learnt to reflect the properties of the signals of interest. To solve the proposed optimization problem, we use the half quadratic splitting methodology, thus designing an efficient iterative solver (iteration map). Based on the Deep Equilibrium (DEQ) modeling, which aims to express the proposed iterative solver as an equilibrium (fixed-point) computation, a highly interpretable deep learning-based network is derived, which can be trained endto-end. Extensive numerical results using two publicly available datasets illustrate that the proposed method markedly outperforms other state-of-the-art approaches. © 2023 IEEE.

关键词： deconvolution deep equilibrium models deep unrolling Hyperspectral images learnable regularizers

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Efficient Hardware Implementation of Seizure Detector Using Machine Learning

Efficient Hardware Implementation of Seizure Detector Using ...

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2024 International Conference on Microelectronics, ICM 2024

作者： El-Tanbadawy, Mariam K. Salem, Mohammed A. Megeed Abd Elghany, Mohamed A. German University in Cairo Computer Engineering Dept. Egypt German University in Cairo Electronics Engineering Dept. Egypt Tu Darmstadt Integrated Electronic Systems Lab Germany

ISBN: (纸本)9798350379396

In the past few decades, Seizure Detection has been a vital cornerstone in biomedical research. Both hardware and software implementations have been explored to improve algorithms, detection performance and resources. In this paper, three handcrafted features composed of Coastline Function, Hurst Exponent and Fractal Dimension are used as training data for supervised learning models to build an efficient and sensitive seizure detector. Random Forest classifier exhibits the best accuracy at 98%. Hardware efficiency, too, is exploited through a modified Feature Extractor that combines the aforementioned features. The features are adjusted to reduce their hardware resources on Virtex-7 FPGA. The area utilization is improved by 10%, power utilization is improved by 20% and latency decreased by 32%. © 2024 IEEE.

关键词： Fractal dimension

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Epileptic Seizure Detection using Denoising Autoencoder 4

Epileptic Seizure Detection using Denoising Autoencoder

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Joshi, Sukumar Rout, Pravat Kumar Samanta, Indu Sekhar Cherukuri, Murthy Swain, Kunjabihari Siksha 'o' Anusandhan University Dept. of Electrical Engineering Bhubaneswar India Siksha 'o' Anusandhan University Department of Electrical and Electronics Engineering India Siksha 'o' Anusandhan University Dept. of Computer Science and Engineering Bhubaneswar India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350349856

Epileptic seizures are a common neurological disorder characterized by abnormal brain activity. Early and accurate detection of seizures plays a crucial role in effective treatment and improving the quality of life for individuals with epilepsy. This research paper proposes a innovative strategy for epileptic seizure detection utilizing a denoising autoencoder (DAE). The DAE is a feature extraction technique to enhance the discriminatory power of electroencephalogram (EEG) signals, commonly used for seizure detection. The suggested method's excellent sensitivity and specificity in correctly recognizing epileptic seizures is supported by the results of experiments. © 2024 IEEE.

关键词： Feature extraction

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Deep Equilibrium Models Meet Federated Learning 31

Deep Equilibrium Models Meet Federated Learning

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

ISBN: (纸本)9789464593600

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Deep learning

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Increasing the RES Hosting Capacity of the Cyprus Distribution System

Increasing the RES Hosting Capacity of the Cyprus Distributi...

引用

CIRED 2024 Vienna Workshop

作者： Therapontos, Phivos Konstantinou, Rafail Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781837242672

The Renewable Energy Sources (RES) penetration in the power system of Cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the Cyprus distribution system and the strategic plan of the Cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined. © The Institution of engineering & Technology 2024.

关键词： Distribution transformers

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Frame Acceleration and Super Resolution for video Enhancement Using GAN 8

Frame Acceleration and Super Resolution for video Enhancemen...

引用

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

作者： Lokhande, Sharayu Maity, Ankush Lenka, Sourabh Kumar Pious, Roshan Choudhary, Vishal Pawar, Shilpa Army Institute of Technology Dept. of Computer Engineering Pune India Army Institute of Technology Dept. of Electronics & Telecommunication Pune India

ISBN: (纸本)9798350391770

Low-resolution (LR) videos are common in various applications, including surveillance and video conferencing, primarily due to bandwidth constraints or older recording technologies. This research introduces a novel approach for enhancing the quality of LR videos by leveraging Generative Adversarial Networks (GANs) equipped with MeanShift loss, perceptual loss, Total Variation (TV) Loss, and a modified VGG19 architecture. By processing each frame of an LR video through our model, we achieve significant improvements in visual quality, detail sharpness, and temporal consistency, resulting in high-resolution (HR) videos that are both visually pleasing and realistic. The conventional approach to video enhancement, particularly when employing deep learning models like GANs, involves processing high volumes of data, which can be computationally intensive and time-consuming. This challenge is compounded in real-time applications or environments lacking powerful graphical processing units (GPUs). Our research proposes a novel solution by extracting frames from low-resolution (LR) videos, enhancing each frame individually, and then recombining them into a high-resolution (HR) video. This method offers a significant reduction in computational requirements, making high-quality video enhancement accessible on less capable hardware and suitable for real-time processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Utilization Of Data Analytics in Analysing Audio and Crowd Video Content 9

Utilization Of Data Analytics in Analysing Audio and Crowd V...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Anshy Kumar, Manoj Saxena, Aditi GLA University Dept. of Computer Engineering and Applications Mathura India GLA University Dept. of Electronics and Communication Engineering Mathura India

ISBN: (纸本)9798350397376

The development of growing variety of useful sources of data, the creation of a number of methods for machine learning, and the development of web of things and advanced analytics enable people and policy maker with new tools for predictive services however an interconnected system also has a deficit that will help in reducing building costs and allow new forms of facilities this is a case in point for an applied big data analysis framework of things and smart grid for web applications, this paper presents a summary and description of big data and the internet of the creation of *** paper provides a comparative analysis of audio and crowd video content an intelligent grid case study that shows the high standards of big data analysis *** evaluation processing, analysis and reporting for comprehension and optimization gather and analyzes, of learner data in their contexts and in the condition in which it is generated. © 2023 IEEE.

关键词： Big data

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Voltage Sag Investigation for Induction Motor Startup in Distribution Systems using Solid State Transformer 15

Voltage Sag Investigation for Induction Motor Startup in Dis...

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15th International Renewable Energy Congress, IREC 2025

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

ISBN: (纸本)9798331516772

Dealing with voltage sags during motor startup is crucial because they can delay power restoration and lead to widespread failures for industrial loads and distribution systems. A novel approach is introduced to investigate voltage sag in distribution networks using solid-state transformers (SSTs) instead of traditional ones. The induction motor startup model is employed to simulate voltage sags, accurately representing the characteristics influenced by conventional industrial loads. The inherent capabilities of SSTs are leveraged to mitigate these voltage sag issues effectively. This study focuses on analyzing voltage sags caused by the activation of induction motors in power systems, with particular emphasis on SST technology, which actively regulates voltage during motor starting by controlling reactive power. The selected distribution system is implemented and tested using MATLAB/Simulink. The results will be compared with previous research on voltage sag during motor startup, demonstrating the improvements and effectiveness of using SSTs in managing power quality issues. This study emphasizes the potential of SSTs to improve the stability and reliability of power systems during the challenging conditions of motor startup. © 2025 IEEE.

关键词： Distribution transformers

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