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Robust decentralized plug and play voltage tracker of islanded microgrids under loads and lines uncertainties by the invariant ellipsoids

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Journal of King Saud University - engineering Sciences 2024年第6期36卷 409-419页

作者： Bayoumi, Ehab H.E. Soliman, Hisham M. El-Sheikhi, Farag Ali Shafiq, M. Awad, Hilmy Energy and Renewable Energy Department Egyptian Chinese University Cairo Egypt Dept. of Electrical Power Eng. Faculty of Eng Cairo University Cairo Egypt Dept. of Electrical and Electronics Engineering Istanbul Esenyurt University Istanbul Turkey Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Technology Faculty of Technology and Education Helwan University Egypt

Microgrids (MGs) comprising of distributed generation units (DGs) are subjected to plug-and-play operation (PnP), lines (connecting the DGs) parameters uncertainty, and load changes. Robust stability and an authentic operation for islanded microgrids can be guaranteed through a robust decentralized voltage tracker developed in this paper. The proposed controller design has the following merits: 1) fully decentralized, 2) scalable, and 3) maintains robust stability against PnP of DGs, load changes, and lines parameters’ uncertainties. A sufficient condition is developed by linear matrix inequality convex optimization is exploited to solve the problem. The MGs’ changes in load and the line's parameters are modelled as norm-bounded uncertainties. The suggested controller uses local measurements from DGs, i.e., decentralized by the decomposition of the global system into subsystems. For each subsystem, the rest of the system's impact is considered disturbances, whose influence must be minimized. The proposed disturbance rejection control algorithm is based on the method of invariant-ellipsoids. Several time-domain scenarios such as connecting and disconnecting a number of DGs, local load changes, and variation of transmission line parameters are executed to assess the suggested controller's effectiveness, using MATLAB /Sim PowerSystems Toolbox. © 2021 The Authors

关键词： Controllers

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Credit Card Fraud Detection Using Machine Learning- A Comprehensive Review 3

Credit Card Fraud Detection Using Machine Learning- A Compre...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jaswant, T. Venkata Manoj, G. Sai Vamisdhar, V. Aravind, A. Reddy, S. Vasudeva Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Engineering Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798331518592

Credit card fraud is an omnipresent threat in the digital era that cannot be effectively managed without complex detection and prevention strategies. Machine learning has become instrumental for the industry, principally, due to providing tools to analyze transactional information and identify patterns of fraud. The article dwells on the methods, problems, and prospects of their future use. The algorithms are analyzed according to their potential applicability, scalability, and interpretability for fraud detection. This study includes convolutional neural networks and recurrent neural networks are used for utilization of anomaly exposure detection methods to detect unseen fraud with models. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Musical Genres Using Audio Spectrograms 5

Classification of Musical Genres Using Audio Spectrograms

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Abbas, Mohammad Kazim Gupta, Kunal Mudassir, Mohammad Jain, Rishabh Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India Kiet Group Of Institutions Dept. Of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350330861

The classification of music into distinct genres is a valuable undertaking in the realm of multimedia study, as it enables artists, albums, and songs to be categorized based on shared musical characteristics. This study aims to contribute to the field of music genre categorization by proposing an approach that can more accurately classify musical genres compared to existing methodologies. Our approach leverages the attributes extracted from the MK2 audio dataset, which is a compilation of musical genre data that has been collected and processed to facilitate the prediction of song genres. The proposed method employs convolutional neural networks (CNN) and utilizes audio spectrograms as input features. Additionally, this study presents a comparative analysis of our proposed approach with previous research papers, providing insights into the effectiveness of our proposed model. © 2023 IEEE.

关键词： Classification (of information)

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory Data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on engineering informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory Data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory Data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： Data visualization

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A Comprehensive Analysis of Music Genre Classification with Audio Spectrograms using Deep Learning Techniques 5

A Comprehensive Analysis of Music Genre Classification with ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

ISBN: (纸本)9798350330861

This review paper investigates the application of audio spectrograms for categorization of musical genres. The use of feature extraction technique in convolutional neural networks (CNNs) for music genre analysis is highlighted in the discussion of these techniques. The relationship between audio spectrograms and musical subgenres is also observed in this paper, as well as how well audio segmentation methods perform in terms of increasing classification precision. The findings of recent research employing various approaches like CNN, PRCNN, LSTM, SVM, etc are used to conduct a comparative analysis of their effectiveness, highlighting the strengths and weaknesses of each approach. The use of spectrogram for feature extraction and multi-layer CNN proved to be ideal for future researches. © 2023 IEEE.

关键词： Feature extraction

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Identity Authentication Techniques for Unmanned Ships Communication Based on MITRE ATT&CK Framework

Identity Authentication Techniques for Unmanned Ships Commun...

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2023 IET International Conference on engineering Technologies and Applications, ICETA 2023

作者： Chang, Shih-Hao He, Junxian Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

ISBN: (纸本)9781839539848

With the development of the smart ship industry, unmanned ship technology is rapidly evolving. In this paper, our objective is to analyze potential risks associated with identity authentication technology in unmanned ship communication systems. To effectively address these risks, we propose the utilization of the MITRE ATT&CK framework to organize and implement defense strategies. This framework offers a comprehensive and systematic approach to understanding potential threats and designing appropriate defensive measures. To validate the effectiveness of our proposed defense framework, we have also conducted simulation experiments. These experiments assist us in evaluating the efficiency and reliability of the strategies in real-world scenarios. © The Institution of engineering & technology 2023.

关键词： Internet of things

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Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification 38

Unified Mechanism-Specific Amplification by Subsampling and ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Schuchardt, Jan Stoian, Mihail Kosmala, Arthur Günnemann, Stephan Dept. of Computer Science Munich Data Science Institute Technical University of Munich Germany Dept. of Engineering University of Technology Nuremberg Germany

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results. © 2024 Neural information processing systems foundation. All rights reserved.

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Cost-aware and Time-Varying Request Rate-based Dynamic Cache Provisioning over CCDN 17

Cost-aware and Time-Varying Request Rate-based Dynamic Cache...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Malaviya National Institute of Technology Jaipur Manipal University Jaipur Dept. of Computer Science & Engineering India Malaviya National Institute of Technology Jaipur Dept. of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350307672

Today's Internet users depend on Cloud-based Content Delivery Network (CCDN) providers to efficiently use resources and minimize content delivery latency. However, time-varying request rates make incoming requests unpredictable, resulting in higher miss rates in CCDNs. Thus, dynamically allocating cache instances to mitigate request rate spikes is necessary to maintain Quality of Service (QoS). Therefore, by utilizing the Request Count Window (RCW) cache and a dynamic instantiation mechanism, this article aims to efficiently manage a changing request window and store objects according to a caching policy. To improve cache performance, we propose a new dynamic selective caching policy that caches only the most frequently requested objects over the past L requests. To further enhance dynamic cache provisioning and reduce the duration of resource allocation, a variable time slot mechanism based on the current request rate is introduced. Finally, we present a cost model that accounts for caching and delivery costs due to missed occurrences. The proposed method is extensively compared with the Least Recently Used (LRU) caching policy and the RCW cache with selective caching based on Kth request cache insertion policies. Experimental results demonstrate that the proposed method outperforms the LRU, selective caching on the values 1 and 2 of K by 22.04%, 12.11%, and 9.31%, and by 29.50%, 16.71%, and 3.03% in terms of the average total cost using permanent cache sizes of 2 and 5, respectively. © 2023 IEEE.

关键词： Dynamics

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Leveraging Information and Communication technology (ICT) To Combat Corruption: A Systematic Review

Leveraging Information and Communication Technology (ICT) To...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oluwambo Tolulope, Olowe Ariyo Adebiyi, Ayodele Akindeji, Kayode Landmark University Dept. of Computer Science Omuaran Nigeria Durban University of Technology Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350358155

Corruption, a global phenomenon disproportionately affecting developing nations, undermines governance and economic development. This systematic literature review examines the role of Information and Communications technology (ICT) in mitigating corruption. Despite conflicting evidence, ICT interventions, including transactional and transparency reforms, offer promising avenues for combating corrupt practices. The review highlights the diverse methodologies employed, with a significant focus on AI-related approaches. Collaboration among researchers, policymakers, and stakeholders is essential for developing comprehensive strategies to address corruption effectively, particularly leveraging ICT advancements. © 2024 IEEE.

关键词： Artificial intelligence

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kumari, Trishla Kumar, Rakesh Dwivedi, Rajendra Kumar Madan Mohan Malviya University of Technology Dept. of Computer Science and Engineering Gorakhpur India Madan Mohan Malviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent). © 2023 IEEE.

关键词： Blockchain

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