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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Houqing Wang Yue Zhao Hui Cao Darvish Peyman Dongfang Han Zhuxuan Ma Jinlai Zhang Zhenqi Wang Dept. of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA Dept. of Computer Science and Technology Xinjiang University Urumqi China Dept. of Mechanical and Electrical Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter’s effectiveness in suppressing high-frequency harmonics and introducing power losses. In this paper, a new passive damping method is proposed for the three-phase four-leg shunt active power filter (SAPF) system, by adding resistors between the neutral line and the connection point of the three-phase lines. The selection of the damping resistor is guided by criteria that aim to minimize power losses while ensuring good stability characteristics. The SAPF system with the proposed passive damping demonstrates excellent attenuation of switching frequency harmonics and significant mitigation of interphase switching interference, enhancing harmonic compensation for nonlinear loads. The mathematical model based on single-phase equivalent circuit and control strategy of three-phase four-leg SAPF are analyzed respectively. Simulations and experiments with imbalanced loads are constructed to validate the theoretical analysis.

关键词： Damping Resistors Power filters Passive filters Stability criteria Interference Active filters Harmonic analysis Mathematical models Power harmonic filters

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method 5

Evaluating the Solutions to Predict the Impact of Lung Cance...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sundar, R. Ramadass, Sudhir Meeha, D. Subramanian, Balambigai Shankar, S. Siva Parasa, Gayatri Madanapalle Institute of Technology and Science Dept. of Computer Science and Engineering India Sterck Systems Chennai India Dr. Rangarajan Dr. Sakunthala Engg College Vel Tech Multi Tech Dept. of Information Technology Avadi Chennai India Dept. of Ece Kongu Engineering College Perundurai India Kg Reddy College of Engineering and Technology Dept. of Cse -AIandML Telangana Chilukuru India Koneru Lakshmaiah Education Foundation Dept. of Cse Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665474672

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness. © 2023 IEEE.

关键词： Forecasting

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ATL Model Checking for Analysis of Ethereum Smart Contracts

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 2006-2014页

作者： Nam, Wonhong Kil, Hyunyoung Dept. of Software Korea Aerospace University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

A blockchain is a growing list of cryptographically secured blocks to maintain shared data on decentralized systems, in order to archive transactions between untrusted participants. Smart contracts are computer programs that automatically execute legal events according to the terms of contracts. Although the Ethereum blockchain has been successfully applied to a number of interesting applications, there have been several events that subtle flaws in smart contracts induce a huge amount of financial loss such as the DAO attack. Accordingly, to analyze smart contracts, we propose a novel formal verification technique to employ ATL (Alternating-time Temporal Logic) model checking. Our methodology represents the interaction between users and smart contracts with a two-player game and verify game properties by using MCMAS that is an efficient ATL model checker. © The Korean Institute of Electrical Engineers

关键词： Smart contract

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis

PhishGuard: A Convolutional Neural Network-Based Model for D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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IoT Applications in Blockchain Technology 1

IoT Applications in Blockchain Technology

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Bale, Ajay Sudhir Biswas, Ankur Malik, Suraj Uchoi, Enjula Soni, Sabhyata Uppal Soni, Arun New Horizon College of Engineering Dept. of Ece Bengaluru India Adamas University Dept. of Comp. Sc. & Engg. Kolkata India Meerut India Lovely Professional University Computer Science and Engineering Department India Panjab University Uiet Department of Electrical & Electronics Engineering India Mohali India

ISBN: (纸本)9798350341737

Securing the data through blockchain might be crucial to enhancing the IoT network, since data is an especially important part of the system. Secure storage of the information generated by the IoT network's intelligent gadgets is made possible via the integration of blockchain technology. For blockchain to be widely used in the IoTs, it must first conquer technological and financial barriers. To make the most of the IoT, blockchain technology may require some tweaking. The upcoming Internet of Things (IoT) technical breakthrough could involve this. This work emphasizes on bringing on board the recent trends of IoT applications utilizing Blockchain technology. The challenges faced in doing so are also outlined. We're hoping this work might help elucidate the state of blockchain studies and provide a road map for what comes next. © 2023 IEEE.

关键词： Supply chains

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Adaptive Strategies for Maximizing Spectral Efficiency in dual branch MRC Systems Facing non-Uniform Fading Patterns

Adaptive Strategies for Maximizing Spectral Efficiency in du...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Rashmi Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

The presented research paper investigates the development and performance of adaptive strategies for non-uniform fading patterns in dual-branch Maximal Ratio Combining systems. The study employs key enabling strategies, including power control, adaptive modulation, and antenna selection, with the purpose of improving the spectral efficiency and reliability of wireless communication systems. Conducting simulations in MATLAB and Python, this paper presents a comprehensive analysis using heatmaps, 3D surface plots, and dynamic simulation outputs to show the effect of the strategies under different Signal-to-Noise Ratios and fading conditions. The outcomes, illustrated in a clear-cut manner in the visuals, reveal that the adaptive strategies lead to a drastic reduction in the Bit Error Rates and a more optimal distribution of power across the branches, ensuring highly effective performance even in the case of the most challenging non-uniform fading patterns. Ultimately, the research findings show sight into the necessity of adaptive strategies to ensure proper coverage and quality of communication in non-uniform fading conditions, which novel research could benefit from.

关键词： Fading channels Solid modeling Analytical models Adaptive systems Three-dimensional displays Spectral efficiency Power control Bit error rate Modulation Signal to noise ratio

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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

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Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Valluri Ayyapa Kesava Sriram Kothamasu Priyusha Killaru Saadhivik Muddana Suresh Aala Vamsi Gutha Mukkara Prasana Kumar Sunil Chinnadurai Dept.of Electronics and Communication Engineering SRM University AP India Dept. of Computer Science and Engineering SRM University AP India Dept. of Computer Science SRM University AP India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research.

关键词： Measurement Accuracy Computational modeling Mortality Feature extraction Reliability Root mean square Cancer Hyperspectral imaging Synthetic data

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Integration of Q-Learning with Maximal Ratio Combining to Enhance IoT Communications in Dynamic Fading Environments

Integration of Q-Learning with Maximal Ratio Combining to En...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

This paper serves to show how Q-learning can be used in complement with Maximal Ratio Combining to better IoT communication systems under dynamic fading, high-density networks, and severe interference sources. The ML-enhanced MRC demonstrated promising improvements in signal strength and quality, allowing for performance that was either above or at least equal to the regular MRC. The ML-enhanced presented the added benefit of dynamic adaptation, modifying its performance to meet real-life needs by adapting to issues such as increased density and interference. These results support that integrating Q-learning into MRC-based frameworks significantly increases the reliability and efficiency of IoT communication protocols. Future investigations will aim to improve ML algorithms more appropriate for devices with few resources and evaluate their potential with state-of-the-art technology to foster future applications across intelligent IoT networks. These approaches have the potential to support more efficient operations and increased connectivity in IoT systems.

关键词： Fading channels Performance evaluation Q-learning Protocols Diversity reception Interference Metrology Robustness Real-time systems Internet of Things

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Performance study of an optimized lead-free Cs3Sb2I9-based perovskite solar cell using SCAPS-1D

Performance study of an optimized lead-free Cs3Sb2I9-based p...

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2022 IEEE Green Technologies Conference, GreenTech 2022

作者： Datta, Shamik Hossain, Md Maruf Nuzhat, Samiha Debnath, Ananya Huq, Hasina University Of Texas Rio Grande Valley Dept. Of Electrical And Computer Engineering EdinburgTX United States Shahjalal University Of Science And Technology Dept. Of Electrical And Electronic Engineering Sylhet Bangladesh Chittagong University Of Engineering And Technology Dept. Of Electronics And Telecommunication Engineering Chittagong Bangladesh

ISBN: (纸本)9781665467681

Commercialization of perovskite solar cells as clean source of energy can augment reduction of carbon footprint. Nonetheless, lead incorporation in the conventional lead cation based perovskites poses an environmental threat. Physical proximity of living organisms to lead point sources exhibits a wide spectrum of negative consequences. Antimony(Sb) is widely considered as one of the promising lead alternatives for its suitable electronic configuration and non-toxic nature. In this study, we have explored the photovoltaic performance of Cs3Sb2I9 as absorber for the first time using SCAPS-1D software. A planar, regular architecture with device configuration of FTO/TiO2/Cs3Sb2I9 /NiOx/Au has been proposed and numerically simulated. Two key material parameters of the absorber layer: thickness and bandgap have been modified to inspect their effect on device performance. Also, the effect of temperature change on photovoltaic performance has been studied. The best optimized device yielded a maximum power conversion efficiency of 17.49%, open-circuit voltage Voc of 1.4 V, short-circuit current density Jsc of 15.33 mA/cm2 and fill factor FF of 80.95%. © 2022 IEEE.

关键词： Perovskite

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Malware Detection across Clusters

Malware Detection across Clusters

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjushree C V A.N. Nandakumar Dept. of information science & Engineering Vemana Institute of Technology India Dept. of Computer science & Engineering City Engineering College India

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures.

关键词： Deep learning Knowledge engineering Java Simulation Neural networks Software algorithms Clustering algorithms

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