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A High-Gain DC-DC Boost Converter for Solar Microgrid systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

引用

1st International conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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The Integration of Time Series Anomaly Detection into a Smart Home Environment

The Integration of Time Series Anomaly Detection into a Smar...

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10th International conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Kaufman, Eran Hoffner, Yigal Fadila, Adan Masharqa, Amin Mawasi, Nour Department of Software Engineering Shenkar College Israel

ISBN: (纸本)9789897587504

Smart home IoT systems have become integral to modern households. To ensure security and safety, prevent hazards, accidents and health emergencies, optimize resource usage, and maintain system reliability, it is essential to have anomaly detection as an integral part of the home management system. Integrating anomaly detection into the smart home environment requires it to be extended to a comprehensive anomaly management process that can be broken down into several stages: data collection and aggregation, anomaly detection, anomaly assessment, decision-making, action-taking, logging and analysis of anomaly events and responses. Our work focuses on three key contributions. First, we explore anomaly detection algorithms to improve detection accuracy, improve classification, and provide users with detailed information on identified anomalies. Second, we present a step-by-step breakdown of the anomaly management process, highlighting how anomaly detection functions as its critical subprocess. Finally, we provide an in-depth explanation of how this management process is seamlessly integrated into a functional smart home environment, ensuring a cohesive and effective approach to anomaly handling. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Decision making

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Detection and Localization of Malicious Nodes in Internet of Things Based on SDN 18th

Detection and Localization of Malicious Nodes in Internet of...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Jingxu, Xiao Chaowen, Chang Lu, Yuan Yingying, Ma Chenli, Yang Information Engineering University of the Army Strategic Support Force Henan Zhengzhou450004 China

ISBN: (纸本)9783031714665

In order to solve the security threat caused by malicious nodes in the Internet of Things transmission link to the data flow and transmission process, a software Defined Network (SDN) based detection and location method of malicious nodes in the Internet of Things is proposed. The software defined network architecture is applied to the transmission process of data flow between edge nodes. The incoming switch node adds a forwarding verification header to the incoming data packet according to the forwarding path. Each downstream switch node verifies and forwards the data packet based on the header to ensure the integrity of the data packet and the consistency of the forwarding path, and sends the abnormal data packet that fails to pass the verification to the controller. The controller locates the malicious exchange node in the link through the header information. Finally, the proposed method is simulated and evaluated, and the experimental results show that the scheme is effective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： anomaly detection anomaly location Internet of Things packet verification software Defined Network

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Digital Dilemmas: Unpacking the realities of Industry 4.0 for SMEs 16

Digital Dilemmas: Unpacking the realities of Industry 4.0 fo...

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16th International conference on ENTERprise Information systems, CENTERIS 2024 - 12th International conference on Project MANagement, ProjMAN 2024 - 14th International conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Shinde, Sagar Sampaio, Suzana Cormican, Kathryn Enterprise Research Centre and LERO-The Irish Software Research Centre School of Engineering University of Galway H91 TK33 Ireland Federal Rural University of Pernambuco PE Recife Brazil

The transition to Industry 4.0 is complex and fraught with challenges. There is compelling evidence that demonstrates that Small to Medium Sized Enterprises (SMEs) are struggling to implement digital technologies and consequently they are not reaping their associated benefits. Industry 4.0 technologies are often developed by and for large firms, overlooking the unique challenges SMEs face. Despite its importance, there is a dearth of evidence relating to what these unique challenges are and how these digital transformation initiatives are implemented in practice. This research aims to address some of these concerns. We present preliminary results from a structured literature review to identify and categorize the specific challenges faced by SMEs. After screening, 133 papers remained for analysis from which preliminary results were obtained. The results shed light on an important but heretofore neglected area that has implications for scholars. By understanding these challenges, SMEs can tailor their strategies and allocate resources more effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： challenges digital transformation Industry 4.0 SME

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Harnessing the Power of AI with Deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, D. Purushothaman, V. Raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Kalman Filter-Mutual Information Models Suggest Improved Training Protocols for Treating Cortical Blindness 59

Kalman Filter-Mutual Information Models Suggest Improved Tra...

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59th Annual conference on Information Sciences and systems, CISS 2025

作者： Pack, Christopher C. Pack, Charles D. McGill University Montreal Neurological Institute Quebec Canada Monmouth University Computer Science/Software Eng. Red BankNJ United States

ISBN: (纸本)9798331513269

Vision training protocols, especially those for subjects with brain impairments, often employ a form of visual discrimination in which specific stimuli must be identified over the course of many days. Trainers can control exercise difficulty by modifying the stimuli in several ways, e.g., Gaussian smoothing, affine transformations, adding pixel noise or varying contrast and brightness. Typically, subjects improve performance in training, but such gains do not generalize afterwards. In a previous paper, we introduced a Kalman Filter model of vision training that, when coupled with a Shannon-type information-theoretic assessment technology, "mutual information", might be used to assess a protocol's potential for lasting acuity gains. Our empirical observation, based on about 300 training protocols with only one image (stimulus) pair, was that including correlated noise, as a training feature, seemed to improve the chances of a good generalization outcome. In this paper, using a sample of more than 3000 vision training protocols, with 5 image pairs of various complexities (entropies), 11 pixel noise levels, 8 pixel noise correlation types and 7 image distortion strategies, we extend and enhance the previous arguments. That is, we believe our models can be used to better understand which protocols may inhibit or enhance perceptual learning and, hence, show the way to improved brain training for individuals with cortical blindness. This would be a significant breakthrough since, currently, vision training, for this condition, is often ineffective. We also discuss how one might apply simple statistical techniques to interpolate, extrapolate or optimize protocol features. Future work, targeted for 2025, will focus on laboratory experiments that will allow us to explore and validate our ideas and observations. © 2025 IEEE.

关键词： Affine transforms

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Deep Variational Sequential Monte Carlo for High-Dimensional Observations

Deep Variational Sequential Monte Carlo for High-Dimensional...

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2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： van Nierop, Wessel L. Shlezinger, Nir van Sloun, Ruud J.G. Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Dept. of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9798350368741

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution. © 2025 IEEE.

关键词： Particle Filters Sequential Monte Carlo Variational Inference

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Investigating the Benefits of Interoperability in 5G-Enabled Health Care Management Models 5th

Investigating the Benefits of Interoperability in 5G-Enabled...

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5th International conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Devrani, Rekha Batra, Raman Gobi, N. Chaudhary, Chetan Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Interoperability, or correspondence between at least two programming applications, is a vital improvement in 5G innovation that can have critical ramifications for medical services the executive’s models. The efficiency with which health care professionals can access various patient data points, manage their workflow, and provide higher-quality care is enhanced by software applications’ ability to interact with one another. Furthermore, interoperability can assist with lessening medical services costs, work on understanding results, and work with better correspondence between medical cares suppliers. This paper investigates existing examinations on interoperability and the potential advantages it could bring to 5G Based medical services the executives models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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An Online Integrated Development Environment for Automated Programming Assessment systems

An Online Integrated Development Environment for Automated P...

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17th International conference on computer Supported Education, CSEDU 2025

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9789897587467

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Teaching

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Anomaly Detection in Retinal Imagery Using Variational Autoencoders 24

Anomaly Detection in Retinal Imagery Using Variational Autoe...

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7th International conference on Computational Intelligence and Intelligent systems, CIIS 2024

作者： Lebaka, Kagiso Blessing Sithungu, Siphesihle Philezwini Academy of Computer Science and Software Engineering University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

This paper situates itself within the broader medical imaging field, focusing on applying generative modelling techniques for anomaly detection in retinal images. Given the complexity of retinal structures, detecting subtle abnormalities that signal disease requires models that can learn intricate patterns and discern deviations. This study explores the use of a Variational Autoencoder (VAE) to reconstruct healthy retinal images and identify anomalies through reconstruction loss and latent space analysis. With the use of a dataset with an imbalance of healthy and abnormal samples, the model was trained exclusively on healthy images to capture a baseline of normal retinal features. The findings suggest that while the VAE can effectively reconstruct broader structural patterns, it needs to work on finer details, impacting its sensitivity in anomaly detection. These results highlight the challenges inherent in applying generative models to medical diagnostics and emphasise the need for advanced techniques, such as contrastive learning, that may enhance feature extraction. Thus, this paper contributes to the ongoing discourse on the suitability of generative models for medical imaging and supports continued research into more nuanced methods to meet the specific demands of retinal anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： Ophthalmology

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