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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Venkatesan, P. Gopinath, C. Prabhavathy, P. Department of Electrical and Electronics Engineering India Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sriperumbudur India Anna University Department of Computer Science and Engineering College of Engineering Guindy Chennai India

ISBN: (纸本)9798331535476

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management. © 2024 IEEE.

关键词： Smart power grids

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

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Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi Samikannu K.Vinoth Narasimha Rao Dasari Senthil Kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MDCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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Advancing Brain Tumor Classification: Exploring Two Deep Learning Architectures for Improved Accuracy 16

Advancing Brain Tumor Classification: Exploring Two Deep Lea...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kumar, Vendra Durga Ratna Ethel Muchina, Fadzai Singh, Priyanka Khan Nizami, Tousif Srm University Ap Computer Science and Engineering AndhraPradesh India Srm University Ap Department of Electrical and Electronics Engineering AndhraPradesh India

ISBN: (纸本)9798350383119

A mass of abnormal cells that form inside or outside the brain is called a brain tumor. Adults are at high risk of developing brain tumors, which can cause serious organ dysfunction and even death. Detecting tumors manually is a tedious and difficult method that might yield erroneous findings. As a result, these tumors must be meticulously classified to offer a complete medical diagnosis and design an appropriate treatment plan. Estimating the patient's chances of survival is difficult since tumors are uncommon and can vary greatly in size, location, and history. In order to address these issues, the use of two different deep learning frameworks for multi-class brain tumor classification utilizing Magnetic Resonance Imaging (MRI) data was examined in this study. Significant evaluation metrics, including F1 score, recall, accuracy, and precision, were applied to these models. Both models demonstrated significant improvements over prior brain tumor classification studies, illustrating that deep learning algorithms may be used in the future to accurately diagnose brain tumors and enable medical personnel to make well-informed judgments regarding patients' treatment courses. This study proposes two classification algorithms: ResNet50, that obtained a success rate of 99.39%, and EfficientNetB0, obtained accuracy rate of 99.75%. © 2024 IEEE.

关键词： Brain Tumor Deep Learning EfficientNetB0 MRI Multi-class Classification ResNet50

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DLSTMFRNN – a newly developed network-based deep long short-term memory and recurrent neural network for stock market prediction

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International Journal of Web engineering and Technology 2024年第2期19卷 148-169页

作者： Nagarjun Yadav, V. Pazhanirajan, S. Anil Kumar, T. Department of Computer Science Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Electrical and Electronics Engineering Anurag University Venkatapur Ghatkesar Rd. Telangana Hyderabad500088 India

The stock market (SM) is fundamentally nonlinear in nature and the people invest in SM on the basis of predictions. The SM prediction is a highly challenging and complex process. The classical techniques may not guarantee the prediction reliability. Hence, a deep long short-term memory fused recurrent neural network (DLSTMFRNN) is presented for reliable SM prediction. Here, input time series data is obtained from the database and it is pre-processed utilising missing value imputation. Thereafter, features are extracted and then feds to the feature selection phase, in which the features are selected employing the Soergel metric. Finally, SM prediction is carried out utilising DLSTMFRNN, which is a new network designed by incorporating DLSTM and RNN. The DLSTMFRNN obtained a minimal mean absolute percentage error (MAPE) of 17.65, mean squared error (MSE) of 0.116, root mean square error (RMSE) of 0.341 and relative absolute error (RAE) of 0.156. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Long short-term memory

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Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques 2nd

Photovoltaics Cell Anomaly Detection Using Deep Learning Tec...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Al-Dulaimi, Abdullah Ahmed Hameed, Alaa Ali Guneser, Muhammet Tahir Jamil, Akhtar Department of Electrical and Electronics Engineering Karabuk University Karabuk Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9783031509193

Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning Detection Electroluminescence image detection Solar panel

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Three-Port DC-DC Converter with Galvanic Isolation Using Multi-Winding Transformer 3

Three-Port DC-DC Converter with Galvanic Isolation Using Mul...

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3rd IEEE International Conference on Power electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Paul, Preenu Jose, Babita R. Shahana, T.K. Abraham, Chikku Mathew, Jimson School of Engineering Cochin University of Science and Technology Division of Electronics Engineering Kerala India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Kerala India Indian Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350387193

A three port DC-DC converter that employs push-pull topology with isolation is presented. The converter transfers power from two DC input sources using a multi-winding transformer to the load. The high gain of the converter is achieved using quasi - switched boost (qSB) network, switched capacitor (SC) topology, multi winding transformer turn's ratio and voltage doubler circuit (VDR). The converter operation, steady state analysis deriving output voltage and comparison with similar topologies are presented. MATLAB/ Simulink results justify the converter operation and analysis. The underlined features of proposed converter are (i) high voltage conversion ratio (ii) galvanic isolation (iii) reduced utilization of semiconductor devices and (iv) capability to combine multiple input sources. © 2024 IEEE.

关键词： Capacitor bank

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A Novel Method for Monitoring and Controlling the electrical Parameters in Industrial Applications Using IoT Technology 1

A Novel Method for Monitoring and Controlling the Electrical...

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1st IEEE International Conference on Smart and Sustainable Developments in electrical engineering, SSDEE 2025

作者： Muthusamy, Suresh Duraisamy, Sarathkumar Ramachandran, Meenakumari Vadivel, Surendar Kavitha, V. Kumar, T. Suresh Ramanathan, Praveen Kumar Ravikumar, Rahul Rajendran Mohanraj, Sanjay Balaji Department of Electrical and Electronics Engineering Perundurai Tamil Nadu Erode India Department of Electrical and Electronics Engineering Tiruval Kavaraipettai Gummidipoondi India Department of Computer Science and Engineering Erode Thindal India AVS Engineering College Department of Computer Science and Engineering Salem India Department of Electrical and Electronic Engineering Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798331542108

The proposed system will introduce an IoT-based smart energy monitoring solution to enhance real-time tracking and management of electrical parameters, using the ESP32 Wi-Fi module as the core processing unit. The system integrates current and voltage sensors for accurate measurement of electricity consumption. These sensors capture electrical parameters processed and calibrated by the ESP32 so that power usage and energy consumption can be considered in their calculation. The processed data is then transmitted to the cloud, where users can now monitor and control their energy consumption using the Blynk mobile application. For example, this innovative system features live monitoring of electrical parameters, customizable alerts for abnormal consumption, and remote device control via relay module. These are then integrated into the system, which is useful in the management of energy for residential and commercial purposes, thus contributing to the conservation of energy that promotes sustainability and sound energy practice. © 2025 IEEE.

关键词： Electric power measurement

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MIMO IMPEDANCE MODELLING OF GRID FOLLOWING INVERTER WITH LOCAL VOLTAGE CONTROL 14

MIMO IMPEDANCE MODELLING OF GRID FOLLOWING INVERTER WITH LOC...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Saridaki, Georgia Kotsampopoulos, Panos Hatziargyriou, Nikos Luo, Xi Batzelis, Efstratios Singh, Abhinav School of Electrical and Computer Engineering National Technical University of Athens Greece School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9781837242689

In recent years, in response to the global climate crisis, inverter-based energy resources (IBRs) have been massively integrated into power systems. Their effective operation and control lie on a wide variety of inner, outer, and synchronization control loops which have been reported to interact and compromise power system's stability. In this study, a Multi-Input-Multi-Output (MIMO) impedance model for a three-phase grid following inverter with local voltage control has been proposed. This linear time invariant model takes into consideration the delay and the coupling between the control blocks and serves as a simpler and more computationally efficient method to capture oscillations and interactions in time and frequency domain. It can predict instabilities caused by the different control layers along with the frequencies of the oscillations. The effect of the delay, the current control tuning parameters and the interactions between the current controller and the local voltage controller have been highlighted. The results have been validated in time and frequency domain simulations in SIMULINK environment. © The Institution of engineering & Technology 2024.

关键词： Frequency domain analysis

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