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3rd International Conference on Emerging electronics and Automation, E2A 2023

作者： Kushagra, C. Kirtimaan, S. Rushit, P. Jane Joshita, K. Divya Navamani, J. Lavanya, A. Department of Computer Science Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819730896

Currently, IoT rules many unmanned applications to improve supervision and productivity. The proposed work concentrates on the need for a cooling system for solar Photovoltaic (PV) panels to enhance its efficiency. An increase in temperature will reduce the efficiency of the solar panel. The cooling system should be monitored, automated, and controlled without human intervention. This is achieved by developing a web App by which the cooling system can be controlled automatically. DHT11–Temperature and Humidity Sensor is used to monitor the temperature of the panel. If the module temperature increases more than 35 °C to 60 °C, the sensor sends the signal to the actuator to cool the PV panel, which will increase the output power. This technique is proposed for solar rooftop home applications where the grey water can be treated and used for cooling the solar panel rather than using fresh, clean water. Finally, a web-interface is done to cool the solar panel with water pumping through the tubes below the panel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cooling IoT Solar Temperature Web interface

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Channels engineering in Magnetic Recording: From Theory to Practice

IEEE BITS the Information Theory Magazine

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IEEE BITS the Information Theory Magazine 2023年第3期3卷 6-49页

作者： Garani, Shayan Srinivasa Vasic, Bane Indian Institute of Science Department of Electronics Systems Engineering Bengaluru560012 India University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Information theory, coding theory, and signal processing have significantly shaped magnetic read/write channels engineering through a chronological sequence of innovations and research advancements, cognizant of the underlying physical processes. In this magazine article, we provide an overview of magnetic recording technologies leading to the channels engineering aspects, central to this article. We survey important coding and signal processing algorithms along with some design architectures that have made it to practice within hard disk drives for magnetic recording channels. The push toward realizing ultrahigh densities (> 4 Tb/in2>) on magnetic disks with ultrahigh throughput rates (>10 Gb /s) necessitates the development of native two-dimensional (2-D) coding and signal processing algorithms and architectures within the framework of 2-D magnetic recording (TDMR) along with read head engineering and changes to the recording physics. We also provide an overview of novel channels engineering solutions covering all aspects of TDMR channels driven from a systems science perspective. The innovations and research advances described in this article may serve a broad engineering audience in other areas as well. © 2021 IEEE.

关键词： Magnetic recording

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Techno Assessment of Distribution System with Integration of Different Types of Distributed Generators

Techno Assessment of Distribution System with Integration of...

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2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Gupta, Babita Sudabattula, Suresh Kumar Mishra, Sachin Dharavat, Nagaraju BVRIT HYDERABAD College of Engineering for Women Department of Electrical and Electronics Engineering Hyderabad India School of Electronics and Electrical Engineering Lovely Professional University India School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350366051

The distribution system's use of decentralized energy resources has lately risen to satisfy electricity demand. However, when these sources are properly integrated, they lead to positive outcomes such as reduced power loss (PLoss), improved voltage profile, and reduced operational expenses. The aim of this paper is to decrease PLoss by considering the power factor, active power, and reactive power of distributed generators (DGs). The optimal locations of DGs can be determined using the Voltage Stability Index(VSI) approach, while the sizes for these DGs can be determined using the firefly Algorithm(FA). The proposed method is demonstrated on IEEE 33-bus *** findings of the proposed method significantly improve the voltage profile and decrease PLoss of the RDN. The results indicate that the best arrangement of DG placement (type3) can reduce the PLoss by 94.26% and improve the VSI from 0.689 p.u to 0.9640 p.u and voltage at the weakest bus from 0.9131 p.u to 0.9921 p.u. Further, FA is compared with the Flower Pollination Algorithm (FPA) and Dragonfly Algorithm (DA) in optimizing DGs location and ratings to enhance the DN performance. Therefore, a proven and effective optimization strategy is recommended for incorporating Distributed Energy Resources (DERs) into Distribution Systems (DS) using VSI and FA techniques. The results indicate that the suggested method can improve the distribution system's performance by PLoss and enhancing the voltage profile. © 2024 IEEE.

关键词： Distributed energy

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Evaluation of the Explainable AI-NLP Framework for Text Categorization

Evaluation of the Explainable AI-NLP Framework for Text Cate...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Singh, Amar Preet Gupta, Gaurav Soumya, K. Noida Institute Of Engineering And Technology Department Of Electrical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

Although there has been a decrease in interpretability, significant progress has lately been achieved in developing state-of-the-art models. This assessment provides a thorough analysis of Explainable AI (XAI) within the framework of Natural Language Processing (NLP). This study examines the fundamental classification of descriptions in addition to the many techniques for producing and displaying explanations. The purpose of the survey is to provide a comprehensive study of the many approaches and strategies used to clarify the models' predictions, making it a helpful tool for people who develop NLP models. Additionally, we draw attention to the gaps in the current literature and urge further investigation into potential avenues for this crucial field of study. An experiment is also conducted and the results are discussed in this article. © 2024 IEEE.

关键词： Natural language processing systems

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics

Hybrid Edge-Cloud Approach for Renewable Energy Management U...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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A novel facial emotion recognition model using segmentation VGG-19 architecture

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International Journal of Information Technology (Singapore) 2023年第4期15卷 1777-1787页

作者： Vignesh, S. Savithadevi, M. Sridevi, M. Sridhar, Rajeswari Department of Electrical and Electronics Engineering National Insistute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Insistute of Technology Tiruchirappalli India

Facial Emotion Recognition (FER) has gained popularity in recent years due to its many applications, including biometrics, detection of mental illness, understanding of human behavior, and psychological profiling. However, developing an accurate and robust FER pipeline is still challenging because multiple factors make it difficult to generalize across different emotions. The factors that challenge a promising FER pipeline include pose variation, heterogeneity of the facial structure, illumination, occlusion, low resolution, and aging factors. Many approaches were developed to overcome the above problems, such as the Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) histogram. However, these methods require manual feature selection. Convolutional Neural Networks (CNN) overcame this manual feature selection problem. CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models. In this paper, we propose a novel CNN architecture by interfacing U-Net segmentation layers in-between Visual Geometry Group (VGG) layers to allow the network to emphasize more critical features from the feature map, which also controls the flow of redundant information through the VGG layers. Our model achieves state-of-the-art (SOTA) single network accuracy compared with other well-known FER models on the FER-2013 dataset. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Emotion recognition FER dataset VGG

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Impact of different voltage source models on antenna current and input impedance within the boundary element method formalism 24

Impact of different voltage source models on antenna current...

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24th International Conference on Applied Electromagnetics and Communications, ICECOM 2023

作者： Susnjara, Anna Poljak, Dragan University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Department of Electronics and Computer Science Split Croatia

ISBN: (纸本)9798350313017

This paper investigates the differences in dipole current and input impedance due to different source models in the framework of boundary element formalism. The half-wavelength and full-wavelength dipole antennas are placed in free space and above a lossy medium. Antenna current and input impedance are obtained by numerically solving the Pocklington's integro-differential equation via Galerkin-Bubnov Indirect Boundary Element Method (GB-IBEM). Two different feeding structures are used as excitation sources, a delta gap source model usually used under GB-IBEM formalism and a magnetic frill source model. Different segment numbers are also used. The results indicate that the current magnitude in case of half-wavelength dipole antenna varies to a relatively great extent depending on the applied voltage generator model. Input impedance does not vary due to different source models, however, its value changes greatly with segment number. © 2023 IEEE.

关键词： Boundary element method

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