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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Samanta, Indu Sekhar Rout, Pravat Kumar Lenka, Manas Ranjan Cherukuri, Murthy Swain, Kunjabihari Siksha 'o' Anusandhan University Dept. of Computer Science and Engineering Bhubaneswar India Siksha 'o' Anusandhan University Department of Electrical and Electronics Engineering India School of Computer Engineering Kiit Deemed to Be University India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350315769

The Internet of Things (IoT) has been the talk of the town for quite some time now, and its adoption is growing rapidly. With the growth of IoT, there is a need for high-speed communication networks to support a large number of connected devices and the data they generate. The fifth-generation (5G) and sixth-generation (6G) communication networks are being developed to address this need. This research paper aims to analyze the compatibility of IoT with 5G and 6G communication networks. The paper also discusses the potential benefits and challenges of using 5G and 6G in IoT applications © 2023 IEEE.

关键词： Internet of things

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SPD Miscoordination in PV Systems Due to Lightning Strikes 16

SPD Miscoordination in PV Systems Due to Lightning Strikes

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Baluja, Osmel Perez Leon Avila, Bernardo Yaser Garcia Vazquez, Carlos A. Cotfas, Daniel Tudor Cotfas, Petru Adrian Breffe, Orlys E. Torres Transilvania University of Braşov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Brasov Romania Technological University of Havana Department of Electric Power Systems Faculty of Electrical Engineering Havana Cuba

ISBN: (纸本)9798350388107

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes. © 2024 IEEE.

关键词： DC transformers

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4D-Rossler Hyperchaotic System for Image Encryption and Decryption with High Security 10

4D-Rossler Hyperchaotic System for Image Encryption and Decr...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Agarwal, Soumil Byramdharmika, M. Dhathri, Sree Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Engineering and Information Systems Vellore Institute of Technology School of Computer Science Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur India

ISBN: (纸本)9798350353068

In today's digital world, safeguarding data at all times is important, especially with the widespread use of images across various processes. Image encryption is vital in concealing sensitive information through cryptographic techniques, rendering data indecipherable to unauthorized parties. However, existing encryption systems often fall short in terms of both efficiency and security. This research explores chaos theory for image encryption methodologies to address these limitations. A comprehensive study of image encryption fundamentals was conducted, including chaos theory, various chaotic maps, DNA encoding, and encryption system evaluation metrics. Implementation of different chaotic maps such as the logistic map, tent map, and combined logistic-tent map, along with 3D chaotic systems like the Lorenz and Rossler systems, was performed for encryption purposes. Additionally, the 4D extension of the Rossler hyperchaotic system was introduced for enhanced encryption capabilities. Encryption methods were evaluated using metrics such as UACI, NPCR, correlation coefficients, image entropy and histogram analysis, with results compared and tabulated. This research advances image encryption techniques by leveraging chaos theory to achieve heightened security and efficiency in safeguarding image data. © 2024 IEEE.

关键词： Chaotic systems

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, R. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

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Design and Implementation of a MPPT Controller-Based Bidirectional DC-DC Converter for Electric Vehicles Using PUMA Algorithm 4

Design and Implementation of a MPPT Controller-Based Bidirec...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Subramaniam, G. Devayani, G. Gokulapriya, C. Janani, S. Dhanush, T. Shashhwath, S. M. Kumarasamy College of Engineering Electrical and Electronics Engineering Tamil Nadu Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Tamil Nadu Karur India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

The growing need for effective energy management in electric vehicles (EVs) has led to advancements in power conversion technologies, especially in systems that facilitate the use of renewable energy sources and efficient battery management. The design and implementation of bidirectional DC-DC converters in electric cars utilize Maximum Power Point Tracking (MPPT) for their controllers. This approach ensures optimal energy conversion through the PUMA (Perturb and Observe with Modified Algorithm) technique. By intelligently regulating the power flow between a renewable energy source and the vehicle's energy storage system (such as solar panels and batteries), the proposed converter maximizes energy conversion from the renewable source while keeping operational costs low and maintaining ideal battery conditions. A bidirectional converter enables the battery to both charge and discharge, while also providing a seamless interface for energy exchange between the grid, solar panels, and the car. The MPPT controller utilizes the PUMA algorithm, which is known for its faster convergence and greater stability, making it superior to traditional Disturb and Observe (P&O) algorithms. This device includes various components that monitor voltage and current values in real-time, employing adaptive control and improved power management to ensure high-quality energy conversion regardless of external conditions. Simulations and hardware implementations have shown that this method effectively enhances vehicle performance, extends the range of electric vehicles, and optimizes the use of renewable energy sources. The study also addresses several other issues, including the potential for large-scale implementation of modern electric vehicles (EVs), the robustness of the system, and the reduction of voltage and current ripple. © 2025 IEEE.

关键词： Adaptive control systems

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A Deep Learning Approach for Classification of Vitiligo and Scar Images 10

A Deep Learning Approach for Classification of Vitiligo and ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajesh, E. Madanagopal, C. John, Reovwin Thanikaiselvan, V. Tamizharasi, T. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Skin diseases such as vitiligo present significant challenges in accurate diagnosis due to their similarities with other conditions, often requiring time-consuming assessments by medical professionals. Leveraging advancements in deep learning and medical image processing, this research proposes a novel approach to efficiently and accurately classify these conditions. The proposed model comprises multiple convolutional layers for extracting intricate features from skin images, followed by max-pooling layers to downsample spatial dimensions while retaining essential information. Additionally, fully connected layers enable complex feature learning and classification. This architecture, with six convolutional layers progressively increasing in depth and spatial reduction through max-pooling, culminates in three fully connected layers for precise classification. This comprehensive design ensures effective classification between vitiligo and scar images. By utilising this architecture, our model achieves a high training accuracy of 93.53% and precision of 96.50%, demonstrating its effectiveness in discriminating between vitiligo and scar images. This approach holds promise for enhancing diagnostic accuracy and efficiency in clinical settings, contributing to improved patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Principal component analysis

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Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 471-481页

作者： Shengyuan Liu Tianhan Zhang Zhenzhi Lin Yilu Liu Yi Ding Li Yang the School of Electrical Engineering Zhejiang UniversityHangzhou 310027China the Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA the School of Electrical Engineering Shandong UniversityJinan 250061China Electrical and Electronics Systems Research Division Oak Ridge National LaboratoryOak RidgeTN 37830USA

Controlled islanding plays an essential role in preventing the blackout of power *** there are several studies on this topic in the past,no enough attention is paid to the uncertainty brought by renewable energy sources(RESs)that may cause unpredictable unbalanced power and the observabilit>T of power systems after islanding that is essential for back-up black-start ***,a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming(MISOCCP)is proposed to address these ***,the uncertainty of RESs is characterized by their possibility distribution models with chance constraints,and the requirements,e.g.,system observability,for rapid back-up black-start measures are also ***,a law of large numbers(LLN)based method is em-ployed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex ***,case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed *** results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

关键词： Controlled islanding second-order cone chance-constrained programming renewable energy source(RES) black-start observability.

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Action Recognition for Multiview Skeleton 3D Data Using NTURGB+D Dataset

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computer Systems science & engineering 2023年第12期47卷 2759-2772页

作者： Rosepreet Kaur Bhogal V.Devendran School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144411India School of Computer Science Engineering Lovely Professional UniversityPhagwara144411India

Human activity recognition is a recent area of research for *** recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety,monitor indoor and outdoor activities,develop Tele immersion systems,or detect abnormal activity *** dimensions(3D)skeleton data is robust and somehow *** to this,it is one of the popular choices for human action *** paper proposed using a transversal tree from 3D skeleton data to represent videos in a *** proposed two neural networks:convolutional neural network recurrent neural network_1(CNN_RNN_1),used to find the optimal features and convolutional neural network recurrent neural network network_2(CNN_RNN_2),used to classify *** deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network(CNN),Long short-term memory(LSTM)and Bidirectional Long shortterm memory(BiLSTM)*** systemefficiently achieves the desired accuracy over state-of-the-art models,i.e.,88.89%.The performance of the proposed model compared with the existing state-of-the-art *** NTURGB+D dataset uses for analyzing experimental *** is one of the large benchmark datasets for human activity ***,the comparison results show that the proposed model outperformed the state-ofthe-art models.

关键词： Activity recognition multiview LSTM BiLSTM NTURGB+D

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DeepFake Image Detection and Classification using EfficientNet Model

DeepFake Image Detection and Classification using EfficientN...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Singh, Sunny Sarala, P. Chandra, Saroj Kumar Kumar, Mikkili Dileep St. Peter's Engineering College Department of Csm Hyderabad India Malla Reddy Engineering College Electrical and Electronics Engineering Hyderabad India Op Jindal University Computer Science and Engineering Raigarh India St. Peters Engineering College Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350373783

Deep learning models such as Vision Transformer (VIT) and EfficientNet, have brought significant advancements to computer vision tasks like image classification, object detection, and image generation. In this paper, comparative analysis of VIT and EfficientNet models has been done with more attention of on their architectural disparities, training procedures, and performance characteristics. Deep learning models like Vision Transformer (VIT) and EfficientNet have revolutionized computer vision. VIT uses self-attention techniques instead of convolutional layers to capture global relationships but with higher computational. EfficientNet models, with compound scaling offer a trade-off between accuracy and efficiency. EfficientNet models are computationally fast with competitive accuracy and is suitable for resource-limited contexts. The paper suggests choosing the best model based on specific use cases and resource limitations. Quantitative analysis of the present work has been done using confusion matrix. It has been observed that EfficientNet models are providing higher performance ratio. © 2024 IEEE.

关键词： Contrastive Learning

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