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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Gatla, Ranjith Kumar Prasad K, N. V Sridhar, Patthi Kumar, A Satish Reddy, K Narender Chatterjee, Sayanti Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Telangana Hyderabad India Sri Sunflower College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665402392

Fault diagnosis in Multilevel Inverters (MLI) has taken worthwhile attention into account in this era. Researchers, Scholar, Engineers and Scientist are paying august attentions towards fault diagnosis in multilevel inverter they are also ascertaining new methods and techniques to diagnose accurate faults in minimum time. To develop fast and precise detection to make system more reliable and efficient, the performance evaluation of the drive under different fault conditions is required. This paper presents a detailed performance analysis of the Cascaded H-bridge Multilevel Inverter (CMLI) fed Permanent Magnet synchronous Motor (PMSM) under different fault (single and multiple switch faults) conditions. It will help to design a fast and accurate fault diagnosis technique to prevent the system failure. © 2022 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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A Joint Communication and Computation Design for Probabilistic Semantic Communications

arXiv

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arXiv 2024年

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Zhang, Zhaoyang Poor, H. Vincent College of Information Science and Electronic Engineering Zhejiang University China Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed where the solutions for the receive beamforming matrix of the BS, transmit power of each user, and semantic compression ratio of each user are obtained stage by stage. Numerical results validate the effectiveness of our proposed scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Game Theory Applied to Deception in Network Security

Game Theory Applied to Deception in Network Security

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computing, Internet of Things and Microwave Systems (ICCIMS), International Conference on

作者： David Haighton Sylvain Leblanc Electrical and Computer Engineering Department Royal Military College of Canada Kingston Ontario Canada

ISBN: (数字)9798350351736

ISBN: (纸本)9798350351743

The Cyber Kill Chain is a concept used to model the steps an intruder must perform in order to achieve their objectives. It comprises a series of steps that must be accomplished by the attacker [1]. Most defense mechanisms in use are directed towards later steps in the cyber kill chain and not disrupting it early [2]. Deception is a growing field of research that aims to disrupt the intruder's reconnaissance phase and prevent the attack's success without any systems being compromised. In a game, at least two players seek to maximize their numerical score, which is called utility or payoff. [3]. This paper summarizes and discusses the application of Game Theory for deception in Cyber Security. More specifically, this paper will examine how deception is modeled and used to defend against intrusions. This survey consists of a background on the Cyber Kill Chain, the Applications of Game Theory in Cyber Security, Modeling Deception in using Game Theory, and Potential Areas for Research. The key finding in this survey is that Game Theory is an effective tool in creating deception policies, and more research is needed in this field.

关键词： Surveys Microwave integrated circuits Computational modeling Reconnaissance Network security Microwave theory and techniques Numerical models Game theory computer crime Microwave FET integrated circuits

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Visualizing Volt-Var Distributions in Large-Scale Electric Grid Models

Visualizing Volt-Var Distributions in Large-Scale Electric G...

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Annual North-American Power Symposium

作者： Sanjana Kunkolienkar Jordan Cook Thomas J. Overbye Department of Electrical and Computer Engineering Texas A&M University College Station TX

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

This paper analyzes the North American and syn-thetic power grid models by examining their voltage and reactive power output distributions. Histograms are used to visualize this difference. The North American grids, managed by various utilities and independent operators, exhibit considerable vari-ability in voltages and generator settings. In contrast, synthetic grids display uniform voltage levels and centralized control, indicating a simplified design approach. Statistical measures are presented to quantify these differences with the objective of making synthetic grids realistic.

关键词： Visualization Reactive power Histograms Analytical models Power grids Generators North America Voltage control Centralized control

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Smart Quarantine Environment Privacy through IoT Gadgets Using Blockchain

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Intelligent Automation & Soft computing 2023年第3期35卷 3021-3036页

作者： Nitish Pathak Shams Tabrez Siddiqui Anjani Kumar Singha Heba G Mohamed Shabana Urooj Abhinandan R Patil Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110078India Department of Computer Science Jazan UniversityJazan45142Saudi Arabia Department of Computer Science Aligarh Muslim UniversityAligarh202002India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP O Box 84428Riyadh11671Saudi Arabia Sanjay Ghodawat University KolhapurMaharashtra416118India

The coronavirus,formerly known as COVID-19,has caused massive global *** a precaution,most governments imposed quarantine periods ranging from months to years and postponed signiﬁcantﬁnancial ***,governments around the world have used cutting-edge technologies to track citizens’*** of sensors were connected to IoT(Internet of Things)devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps,privacy and security issues regarding data transmission and memory space *** this study,we suggest a block-chain-based methodology for safeguarding data in the billions of devices and sen-sors connected over the *** trial secrecy and safety qualities are based on cutting-edge *** evaluate the proposed model,we recom-mend using an application of the system,a Raspberry Pi single-board computer in an IoT system,a laptop,a computer,cell phones and the Ethereum smart contract *** models ability to ensure safety,effectiveness and a suitable budget is proved by the Gowalla dataset results.

关键词： Ring signature blockchain IoT encryption smart contract decryption authentication privacy-preserving

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Enhancing Power Flow Studies Through Representative Scenario Selection

Enhancing Power Flow Studies Through Representative Scenario...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Jordan Cook Maximo Briones Farnaz Safdarian Thomas Overbye Department of Electrical and Computer Engineering Texas A&M University College Station TX

This paper presents an approach to scenario selection with the goal of improving the accuracy of power flow simulations, particularly with vast datasets involving load and weather variables. With large power systems and large amounts of available data, it is computationally expensive to choose important scenarios with a higher impact on the operation, considering load and weather for renewable generation output. Using the K-Means method for clustering, representative points are strategically chosen to simulate various solar, wind, and load conditions. The two selected representative points include an average and an outlier. Choosing these two points allows for baseline data analysis as well as anomalies, which can cause stress in the grid. The method is then demonstrated in this paper to show its functionality and how it captures the diversity of a dataset. The resulting clusters help finding interesting scenarios by addressing the variability that is inherent in power systems. This leads to improving grid reliability by preparing for a range of scenarios.

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IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第3期70卷 5161-5177页

作者： K.R.Uthayan G.Lakshmi Vara Prasad V.Mohan C.Bharatiraja Irina V.Pustokhina Denis A.Pustokhin Vicente García Díaz Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Information Technology QIS College of Engineering&TechnologyOngole523001India Department of Electronics and Communications Engineering Saranathan College of EngineeringTrichy620012India Department of Electrical and Electronics Engineering SRMInstitute of Science and TechnologyChennai603203India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Science University of OviedoOviedo33003Spain

Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,***,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV *** the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC *** UAV aerial imagery,efficient image representation is crucial for the purpose of scene *** existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average ***,the current research work introduces a new DL-enabled aerial scene classificationmodel *** presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further ***,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial *** is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration *** order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is ***,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial *** performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS *** proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.

关键词： Artificial intelligence mobile edge computing unmanned aerial vehicles deep learning optimization

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Prediction of Tuberculosis Patients' Treatment Outcomes Using Multinomial Naive Bayes Algorithm and Class-Imbalanced Data

Prediction of Tuberculosis Patients' Treatment Outcomes Usin...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Foh, Wei Lian Willian Ang, Sau Loong Lim, Chia Yean Alaga, Arvindran A-L Yeap, Gik Hong UOW Malaysia Department of Computing & Creative Media KDU Penang University College Penang Malaysia Universiti Sains Malaysia School of Computer Sciences Penang Malaysia Hospital Sultanah Bahiyah Respiratory Department Alor Setar Malaysia UOW Malaysia School of Engineering Computing & Built Environment KDU Penang University College Penang Malaysia

ISBN: (纸本)9798350331790

Tuberculosis (TB) is a severe and highly contagious disease that affects millions of people worldwide. The current TB treatment programs are challenging to complete for many patients due to numerous factors, including limited human resources and financial resources. To address these challenges, a solution is needed to aid in resource allocation strategies. This study suggests a machine learning methodology for predicting the treatment outcomes of TB patients. This will enable healthcare facilities to optimize resource allocation based on the prediction made. A large multi-variate TB patient dataset from the Brazilian Information System for Notifiable Disease (SINAN) was used in this study, containing attributes related to patient characteristics, clinical information, and laboratory data. The proposed model used the Naive Bayes algorithm due to its simplicity and efficiency in predicting treatment outcomes. The dataset was pre-processed, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to overcome the class imbalance issues in the dataset. The combination of the borderline SMOTE and Naive Bayes algorithms on the preprocessed dataset was found to have achieved the highest levels of accuracy among the combinations sampled. This demonstrated the capability of the new algorithm in predicting the treatment outcome of TB patients. The proposed model could assist healthcare providers in implementing more targeted follow-ups and more appropriate resource allocation strategies to improve the overall treatment outcome of TB patients. © 2023 IEEE.

关键词： Patient treatment

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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An Efficient Framework for EEG Signals Processing Using Backpropagation Multilayer Convolutional Neural Network for Brain Tumor Diagnosis 3

An Efficient Framework for EEG Signals Processing Using Back...

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3rd Odisha International Conference on electrical Power engineering, Communication and computing Technology, ODICON 2024

作者： Dash, Santamati Rayshan, Ahmed Raheem Hussain, Moiz Abdul Harika, P. Lakshmi Jawad, Israa Abed School of Sports and Yogic Sciences Kiit University Odisha India College of Veterinary Medicine University of Al-Qadisiyah Department of Physiology and Pharmacology Iraq Padmashri Dr. V.B. Kolte College of Engineering Electrical Engineering Department Malkapur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Bowrampet Hyderabad India AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq

ISBN: (纸本)9798350354379

The average patient's life expectancy is shortened by a Brain Tumour (BT), one of the most dangerous and aggressive diseases. The prognosis for patients with BTs is worse when they get inadequate medical therapy or a wrong diagnosis. An electroencephalogram (EEG) records electrical activity in the brain in a non-invasive manner. Several modern imaging methods including MRI, SPECT, and PET, EEG offer superior tumor-specific diagnoses. This study offers a diagnostic method for identifying if an EEG signal is cancerous or normal. Initially, EEG signals were first used to capture brain data, which was then preprocessed using the stationary wavelet transform (SWT) method. The features of the brain data are then selected using a heuristic assessment. To extract features, a Grey-Level Co-Occurrence Matrix (GLCM) is utilized. Furthermore, we suggest a novel BackPropagationMultilayer Convolutional Neural Network (BP-MCNN) approach for BT diagnostics for tumor categorization. A set of database samples was used to provide the neural network with the statistical information that had been extracted from the EEG signal during the training phase. After being trained, a neural network can accurately categorize a test set of samples into normal or cancerous. The suggested approach provides superior classification accuracy than the current methods, according to experimental data. © 2024 IEEE.

关键词： Electroencephalography

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