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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： M. Abinaya P.Kaviya Priya Mohsina Mirza A. Anbumani R. Sathya M. Sethuram Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering P.A College of Engineering and Technology Coimbatore India Department of Electrical and Electronic Engineering Global College of Engineering and Technology Department of Computer Science and Engineering Paavai College of Engineering Coimbatore India Department of Information Technology Kongunadu College of Engineering and Technology Trichy India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore

Web based vote casting is a form of voting in which the voter is able to cast their votes through any web portal wherever in the world. In the Biometric method, voter can cast the votes using their finger impression. In this method the voter can selects the login portal which is particularly designed for an election commission. Each person needs to register the election commission website, using the registration details substantiate themselves before the machine authorize the user to sight the ballot visible on the screen. The voter is then allowed to pick their selected candidate and then cast the votes which would be passed to the election server for the refining process. In “E-Voting” method a voter can cast their votes through online without any difficulty. If all the entries provided by the user are precise user can login the system using the user ID and password voter can cast their votes through online wherever in the world. If an information given by the voter is not correct then the entry will be discarded and it shows the error message. This technique is mainly implemented to reduce the difficulty of the election commission and voter. It is a user-friendly approach, also it avoids the unwanted crowds during the election period. Here, this study has used immutable ledger technology that is a blockchain based application, which provides the security and also increase the efficiency of the voting process.

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Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

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Annual IEEE Conference on Applied Power electronics Conference and Exposition (APEC)

作者： Zheyuan Yi Kai Sun Qiang Li Zengyang Liu Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State Center for Power Electronics Systems University Blacksburg Virginia USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process.

关键词： Heating systems Temperature distribution Accuracy Simulation Computational modeling Thermal analysis High-frequency transformers Optimization Surface treatment Heat transfer

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abdulrahman Takiddin Rachad Atat Muhammad Ismail Katherine Davis Erchin Serpedin Department of Electrical & Computer Engineering Texas A&M University College Station TX USA Electrical & Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Computer Science Tennessee Technological University Cookeville TN USA

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

关键词： Location awareness Convolution Power system stability Chebyshev approximation Benchmark testing Multitasking Graph neural networks

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Anomaly Detection in Conventional Meters and Electricity Consumption Using Optimized Extreme Gradient Boosting

Anomaly Detection in Conventional Meters and Electricity Con...

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Distributed Computing and electrical Circuits and electronics (ICDCECE), IEEE International Conference on

作者： Laith H. Alzubaidi B. Sathyavani Mamatha Bai B G Papiya Dutta N. Naga Saranya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Department of Electronics and Communication Engineering Bharat Institute of Engineering and Technology Hyderabad India Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

With the progress of innovative information technologies, smart energy meters have designed a substantial quantity of real-time electricity consumption. Energy consumption is based on the various factors such as humidity, building area, temperature, habitation and so on and these factors are cooperatively describing the energy meter background. The smart meter supports to capable to identify data abnormalities for efficient monitoring as well as investigation. The anomaly detection supports for better decision making for minimizing the energy consumption. In this research, Machine Learning (ML) approach of Optimized Extreme Gradient Boosting (OXGBoosting) for the detection and classification of anomaly for energy consumption in conventional meters. This research collects the large-scale anomaly dataset for estimating the effectiveness. The proposed method is estimated by various performance metrices and it achieves an accuracy of 0.95, precision of 0.92, recall of 0.92, F1-score of 0.91 and AUC of 0.95 respectively when compared to the previous studies such as Extreme Gradient Boosting (XGB), Two-Class Boosted Decision Tree and NGBoost.

关键词： Meters Energy consumption Temperature Electricity Boosting Feature extraction Smart meters

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A High Performance Switched-Capacitor Based Single-Phase Transformerless Inverter

A High Performance Switched-Capacitor Based Single-Phase Tra...

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International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD

作者： Mohua Biswas Shuvra Prokash Biswas Sudipto Mondal Anas Bin Islam Md. Rabiul Islam Afef Fekih Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh School of Engineering Deakin University VIC Australia School of Electrical Computer & Telecommunications Engineering University of Wollongong NSW Australia Dept. of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA

This paper presents a new circuit structure of five-level switched-capacitor (SC) based transformerless inverter which is ideal for grid-integrated photovoltaic (PV) applications. The main objective of the proposed multilevel inverter (MLI) is to suppress the leakage current close to zero meanwhile maintaining lower number of semiconductor device count. Self-voltage balancing and voltage boosting capability of SCs are also ensured while designing the proposed MLI. Moreover, optimum switching operation ensures low power dissipation of the semiconductor devices while achieving greater efficiency. The proposed MLI along with the control strategy is developed and validated through MATLAB/Simulink computer simulation environment while an extensive loss analysis is conducted in PLECS simulation platform to ensure a strong feasibility of the proposed MLI.

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on Control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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A Deep Learning Approach For Cassava Leaf Disease Diagnosis

SSRN

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SSRN 2023年

作者： Azeta, Ambrose Jonathan, Kingsley Guembe, Blessing Nwadoziokwu, Endurance Department of Computer Science Namibia University of Science and Technology Namibia Department of Electrical and Electronics Engineering Covenant University Ogun Ota Nigeria Department of Computer and Information Sciences Covenant University Ogun Ota Nigeria Department of Electrical and Electronics Engineering Niger Delta University Bayelsa Amassoma Nigeria

The main challenge for cassava plants is their susceptibility to a large number of unknown diseases and viral strains. These viral strains can completely stop crop output, necessitating the need to accurately identify notable infections to adequately address crop disease emergencies. Therefore, it is important to deploy Artificial Intelligence (AI) computerized decision support systems to help farmers efficiently diagnose cassava leaf diseases. This study proposed a Convolutional Neural Networks (CNN) model to detect cassava leaf disease at an early stage. An efficient solution for the detection of cassava leaf disease from its image dataset is proposed in this study. The argumentation of data was used to address the problem of the limited dataset, and then, Convolutional Neural Networks were fine-tuned for the classification of cassava leaf disease. The National Crops Resources Research Institute (NaCRRI) was utilized to train the proposed model. The result shows that the proposed model achieved a prediction accuracy of 97.28 which outperformed existing models in literature. The results indicate that the proposed model performed well in detecting cassava disease and it is capable of diagnosing cassava leaf diseases. © 2023, The Authors. All rights reserved.

关键词： Crops

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Sliding Mode Current Control Strategy for Nine-Switch Converter 2

Sliding Mode Current Control Strategy for Nine-Switch Conver...

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2nd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2021

作者： Gulbudak, Ozan Gokdag, Mustafa Komurcugil, Hasan Karabuk University Department of Electrical Electronics Engineering TR Eastern Mediterranean University Computer Engineering Dept Gazimagusa Via Mersin TRNC

ISBN: (纸本)9781665443197

This paper proposes a sliding mode control strategy for regulating dual-load fed by a nine-switch converter. The nine-switch converter consists of two three-phase output terminals, and the nine-switch converter can control dual-loads. The nine-switch converter contains fewer switching devices compared to the two-parallel voltage-source inverters. The nines witch converter offers a more compact ac-drive system where multiple load control is required regarding power stage weight and size. Nevertheless, attaining a fully independent control for individual load is a challenging task. The poor closed-loop design causes an unpleasant circulating current between separate load stages. Therefore, the possibility of the interaction between different load stages is the primary closed-loop design consideration. This study proposes the sliding mode control to obtain a reliable energy conversion operation method for a dual output nine-switch converter. Sliding mode control methodology is comprehensively explained, and solid design steps are provided. The simulation work verifies the theoretical framework of the designed sliding mode controller. The performed simulation works demonstrate that multiple ac loads are well regulated by the proposed control method. © 2021 IEEE.

关键词： Sliding mode control

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Machine Learning Techniques for Predictive Maintenance in Industrial IoT Systems

Machine Learning Techniques for Predictive Maintenance in In...

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Science, Technology, engineering and Management (ICSTEM), International Conference on

作者： G. Nixon Samuel Vijayakumar Vipin Sharma S. Pradeep Pratheeba C T. Suresh Balakrishnan Ramprasad Jat Department of Physics R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Mechanical Engineering Medi-Caps University Indore Madhya Pradesh India Department of Electronics and Communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Engineering Government Engineering College Jhalawar Rajasthan India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The purpose of this exploration is to probe implicit operations of machine literacy ways for prophetic conservation within the environment of Industrial Internet of Effects (IIoT) grounded systems. As a result of the explosion of detector data in artificial settings, prophetic conservation has developed as an essential system for reducing time-out and maximizing the performance of means. The purpose of this study is to probe a variety of machine literacy algorithms, including retrogression, bracket, and anomaly discovery, which are specifically designed to examine streaming detector data and anticipate faults in outfits before they do. In addition, it investigates the difficulties that are connected with the perpetration of prophetic conservation results in IIoT systems. These difficulties include problems with data quality, the interpretability of models, and the scalability of the results. The purpose of this study is to give perceptivity into the utility and limitations of colorful machine learning approaches in the process of perfecting the trustability and effectiveness of artificial ministry. These perceptivity are handed through a complete examination of being exploration and case studies.

关键词： Scalability Machine learning Detectors Explosions Internet Environmental management Probes

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Secure Training Protocols for Machine Learning Models in Adversarial Network

Secure Training Protocols for Machine Learning Models in Adv...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Akshay Varkale Deepti Upodhyay Snehal Sathwara Ajit Kumar Mishra Department of Computer Science & Engineering IES University Bhopal Madhya Pradesh India Department of Computer Science & Engineering IES College of Technology Bhopal Madhya Pradesh India Computer Engineering RK University Department of Electrical & Electronics Engineering IES College of Technology Bhopal Madhya Pradesh India

In the realm of secure training protocols for machine learning models within adversarial networks, the proposed methodology encompasses three key algorithms: Federated Learning with Differential Privacy (FedDP), Adversarial Training with Robust Optimization (AdvRO), and Privacy-Preserving Homomorphic Encryption Training (PPHET). Each algorithm plays a critical role in enhancing data privacy, model robustness, and reliability during the model training process. The study evaluates these methods in comparison to traditional approaches, emphasizing the importance of data privacy, robustness, and computational efficiency. This research presents a secure training protocol for machine learning models in adversarial networks, with a focus on data privacy, model robustness, and reliability assurance. The proposed method combines federated learning with differential privacy, adversarial training with robust optimization, and privacy-preserving homomorphic encryption training. These approaches ensure data privacy, enhance model robustness, and protect against adversarial threats. We compare the proposed method with traditional methods and evaluate their performance across various parameters, such as data privacy, robustness to adversarial attacks, and scalability. The results demonstrate the effectiveness of the proposed approach in achieving secure and trustworthy machine learning models in adversarial networks.

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