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Transfer Learning-Based Fault Classification Methods for Solar Photovoltaic Modules: A Comparative Analysis 6th

Transfer Learning-Based Fault Classification Methods for Sol...

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6th international conference on Intelligent Communication, control and Devices, ICICCD 2024

作者： Supriya Shukla, Ashutosh Manchanda, Mahesh Department of AI and ML COER University Roorkee247667 India Electrical Cluster School of Engineering UPES Dehradun India Department of Computer Science and Engineering GEHU University Dehradun India

ISBN: (纸本)9789819783281

India’s reliance on fossil fuels to meet its energy generation demands has a negative impact on both the environment and public health. In the present energy transition, photovoltaic (PV) technologies have become more widely accepted for commercial use, achieved significant progress in technology, and have a major role to play in reducing the harmful environmental effects of fossil fuel-based power generation. Soiling research on solar PV has notably increased in recent years due to its critical role in reducing the performance of solar PV cells. therefore, to guarantee the amount of power yield, efficient monitoring of the soiling is crucial. It is difficult to manually analyse soiling severity on the panel images due to the non-uniformity of the type and amount of soiling faults. Deep learning models have been successfully applied to the analysis of solar panel images and soiling categorization;nevertheless, this approach may require a significant amount of training time and may lead to gradient descent. To tackle these training issues, we propose a comparative study of two pre-trained neural network architectures, namely, MobileNetV3small and RestNet50, on a publicly available dataset of 875 images of six solar PV fault classes, namely, clean, dust, bird-drop, electrical damage, physical damage, and snow-covered. the experimental results show that MobileNetV3small achieved a better performance of 98%. the results show that MobileNetV3small obtained a promising result of 11% higher classification accuracy than ResNet50, with an accuracy of 86.6%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric losses

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th international conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. the performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. the experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th international conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. the MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. the cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. the experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Smart Grid Solutions for Sustainable Mining: A Cost-Effective Energy Management Framework with Optimization Algorithms

Smart Grid Solutions for Sustainable Mining: A Cost-Effectiv...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sravani Parvathareddy Abid Yahya Lilian Livutse Amuhaya Ravi Samikannu Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Botswana Department of EEE New Era Engineering College Botswana

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research work focuses on conceptualizing and modeling smart grid infrastructure. the project's main objective is to develop a smart grid infrastructure to improve energy management in industrial processes. Regenerate has deployed a system that uses renewable energy sources like solar photovoltaic (PV) arrays and wind turbines combined with batteries for energy storage. this research work describes a method that first creates synthetic data and then uses a polynomial function to precisely model the statistical fluctuations inherent in the output of renewable energy sources. the process is carried out using MATLAB. A simulation model created using Simulink is also applied to build a smart grid that includes dynamic load control, demand side management DSM, and various control system configurations. the simulation demonstrates that it is possible to produce electricity with minimal or no depreciation related to an increased dependency on renewable energy sources. It can also function at a higher ratio of energy output produced by traditional sources than previously credited within New Zealand while having substantially fewer overall expenses associated with carbon emissions.

关键词： Photovoltaic systems Renewable energy sources Demand side management Electricity Mathematical models Smart grids Wind turbines Load flow control Load modeling Synthetic data

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Distributed Event-Triggering Secondary Frequency control of Islanded Microgrids Under DoS Attacks 8th

Distributed Event-Triggering Secondary Frequency Control of...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhou, Lihua Zhang, Changda Du, Dajun Fei, Minrui Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical Engineering Hebei University of Science and Technology Hebei China

ISBN: (纸本)9789819602247

High reliability and efficiency of microgrids are being threatened by cyber attacks. this paper investigates a Secondary Frequency control Scheme (SFCS) for an islanded microgrid with Event-Triggering Mechanism (ETM) under Denial of Service (DoS) attacks. Firstly, the microgrid dynamic model is reviewed and control objectives are formulated. Secondly, a cooperative frequency control scheme combined with distributed event-triggering mechanism is presented to achieve the desired frequency control under DoS attacks. thirdly, the stability and Zeno Behaviour of the closed-loop system are analysed by Lyapunov theory. Finally, simulation results of the microgrid are explored to verify the effectiveness of the proposed frequency control scheme. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Multi-controller Architecture Design and Features in Software - Defined Networking (SDN)

Multi-Controller Architecture Design and Features in Softwar...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： V. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Networking paradigm known as “Software-Defined Networking” (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can't be able to fulfill by a single centralized controller. this causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. the introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done.

关键词： Performance evaluation Scalability Process control Computer architecture Telecommunication traffic Coherence Software Reliability Software defined networking Mobile computing

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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Advances in Computer science, electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Preeti Naval V Pushparajesh Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. the main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. the most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. there are several intriguing aspects of this age. the most pressing problem that requires solving using suitable forensic methods is security. High-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. this article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services.

关键词： 6G mobile communication Cloud computing High performance computing Forensics Machine learning Communications technology Fraud Security Standards Monitoring

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Optimizing Wind Energy Output Using a SEPIC Converter and Graph Neural Network (GNN)-Based control Strategy

Optimizing Wind Energy Output Using a SEPIC Converter and Gr...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Ms.V. Manimegalai V.S. Arjun Karthic Ms.M.K. Aparna Ms.R. Aruna Ms.V. Mohanapriya Ms.A. Gayathri Department of Electrical and Electronics Engineering Sri Krishna College of Technology Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study presents an optimized wind energy conversion system for increased energy output through the use of Graph Neural Networks (GNNs) and machine learning. In order to increase efficiency, the system incorporates a SEPIC converter and keeps track of torque, acceleration, and RPM for the turbine. the GNN model uses historical and current wind speed data to dynamically modify turbine operation in order to reduce losses. Reliability and consistent performance are guaranteed by automated alerts and remote monitoring for problems like dust buildup. this method establishes a standard for sustainable wind energy systems and greatly increases energy utilization. the integration of GNNs and SEPIC converters enhances wind turbine energy efficiency by dynamically optimizing performance and reducing losses.

关键词： Torque Wind speed Wind farms Graph neural networks Wind turbines Reliability Sustainable development Wind energy conversion Standards Remote monitoring

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

A Machine Learning Framework for Skin Cancer Detection using...

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international Image Processing, Applications and Systems conference (IPAS)

作者： Mary Linda Panakal Augustine Sotirios Spanogianopoulos Mhd Saeed Sharif Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London London UK School of Electrical and Mechanical Engineering University of Portsmouth Portsmouth UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

this paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. this research aims to improve skin cancer diagnosis. this work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. this discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

关键词： Deep learning Machine learning algorithms Image processing Transfer learning Medical diagnosis Reliability Skin cancer Image classification

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3D Printed Solar Cell: A Short Review 6th

3D Printed Solar Cell: A Short Review

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6th international conference on Intelligent Communication, control and Devices, ICICCD 2024

作者： Singh, Ankit Devi, Velpuri Leela Jately, Vibhu Singh, Sunaina Dahima, Vartika Dobriyal, Shreshth Kapoor, Praveen Department of Electrical and Electronics Engineering University of Petroleum and Energy Studies Dehradun248007 India Department of Chemistry Applied Science University of Petroleum and Energy Studies Dehradun248007 India Librarian University of Petroleum and Energy Studies Dehradun248007 India

ISBN: (纸本)9789819783281

In the recent development of solar cells, new methods are introducing day by day one of the recent developments in area of PV 3D printed solar cell is attracting everyone. Currently, the PV sector is experimenting with 3D printing technologies to tackle the challenges associated with manufacturing solar cells which are used in PV modules. 3D laser printing possesses the capacity to attain consistency in layering over extensive surfaces, exceptional exploitation of materials with minimal wastage, moreover adaptability in the direction of integrated sheet-to-sheet (S2S) and roll-to-roll (R2R) methods. the main goal of our paper is to point out the major problems with the current methods of making solar cells and stress the benefits of using 3D printing as an alternative procedure. this study offers a thorough examination of the many techniques used in the production of solar cells, specifically highlighting the usage of 3D printing technology in this process. the limitations of traditional solar cell production processes, the difficulties in solar cell development, and the potential for future advancements are furthermore emphasized. We suggest that 3D printing method is a suitable option for creating solution-based solar cells on a large scale, with efficient use of materials and high flexibility. the findings may prompt further exploration and inquiries into the potential uses of this technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D printed solar cell EHD FDM R2R S2S

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