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共 63507 条记录，以下是1091-1100 订阅

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Effect of Measurement Uncertainty on PV Output Power Estimation

Effect of Measurement Uncertainty on PV Output Power Estimat...

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international symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, IAT 2024

作者： Mašić, Fatima Džemić, Zijad Šarić, Mirza International Burch University Sarajevo71000 Bosnia and Herzegovina

ISBN: (纸本)9783031716935

The recent increase in distributed generators due to the benefits they bring, such as a decrease in CO2 emissions, deforestation, increase in energy efficiency, and creation of employment opportunities for locals, with photovoltaics (PVs) at the forefront, induced working challenges in the distribution network. In that manner pointing out the importance of distributed grid hosting capacity (HC) estimation, for which output of photovoltaic (PV) is a significant parameter. This paper used comparative analysis to examine the effect of measurement uncertainty on PV output, through irradiance parameter. For this research measurements of the irradiance and temperature of PV device were performed, as well as estimation of measurement uncertainty of irradiance parameter. Obtained values were used for PV system modeling in the Open Distribution System Simulator (OpenDSS) tool. The results of the research contribute to sustainable and renewable energy generation, pointing out the importance of the reduction of measurement uncertainties. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Clean energy

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Design of MAC-layer Protocols for distributed NOMA-based VLC networks 13

Design of MAC-layer Protocols for Distributed NOMA-based VLC...

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13th international symposium on Communication systems, networks and Digital Signal Processing (CSNDSP)

作者： Rallis, Konstantinos G. Papanikolaou, Vasilis K. Diamantoulakis, Panagiotis D. Dowhuszko, Alexis A. Hamalainen, Jyri Karagiannidis, George K. Aristotle Univ Thessaloniki Dept Elect & Comp Engn GR-54124 Thessaloniki Greece Aalto Univ Dept Commun & Networking Espoo 02150 Finland

ISBN: (数字)9781665410441

ISBN: (纸本)9781665410441

In this paper, a visible light communication (VLC) cellular network that utilizes non-orthogonal multiple access (NOMA) is investigated. To deal with the high inter-cell interference attributed to the high-density of VLC network nodes, a medium access control (MAC) scheduler is designed based on a distributed NOMA approach that aims at increasing the performance of cell-edge users, while securing the high data rate requirements of cell-center users in the VLC network. To this end, two practical protocols are proposed to guarantee the increased spectral efficiency and interference mitigation through distributed NOMA. In each protocol, the decoding strategy and power allocation of the users is investigated through a low complexity algorithm that ensures user fairness in the network. The presented analysis is validated through simulations, where the proposed schemes notably outperform the benchmark scheme in which the cell-edge user does not perform successive interference cancellation. Finally, the results showcase valuable insights, mainly on the fact that the decoding strategy in such networks is not as obvious as in conventional downlink NOMA.

关键词： Visible Light Communication Non-Orthogonal Multiple Access decoding strategy interference mitigation distributed NOMA successive interference cancellation

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On Intrinsic Cause and Defense of Adversarial Examples in Deep Neural networks 19th

On Intrinsic Cause and Defense of Adversarial Examples in...

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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European symposium on Research in computer Security, ESORICS 2024

作者： Tasaki, Hajime Chao, Jinhui Department of Information and System Engineering Chuo University Bunkyo Tokyo1-13-27 Kasuga Japan

ISBN: (纸本)9783031823619

The existence of so-called adversarial examples has become a serious threat to Deep Neural networks (DNN) and their applications, especially security-sensitive ones. Explanations and defenses mainly focused on inside of the DNN, e.g. its generalization errors and training defects, have been proposed without much success. Recently, we have shown that the extrinsic cause of adversarial examples is due to embedding geometry of the training data manifold, and adversarial examples mainly exist in the orthogonal complementary spaces of the tangent spaces on the data manifold. In this paper, we prove the intrinsic cause of the adversarial examples, i.e. the existence of non-zero manifold-orthogonal components of every weight almost everywhere, as the counterpart of the extrinsic cause. Investigations showed that weights of the DNN always have nonzero and large manifold-orthogonal components at almost every data point, which explains why small adversarial perturbations can lead to misclassifications in well-trained DNN. Then, based on these mechanisms, we propose a new defense against adversarial examples by extracting the manifold structure of the training data and removing the components orthogonal to the tangent spaces of the manifolds from the input data, which successfully prevents misclassification of DNN for major adversarial example attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Design and implementation of a dedicated 5G communication terminal for distributed power sources 3

Design and implementation of a dedicated 5G communication te...

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3rd international Conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Gao, Zunhai Lu, Hongxia Zhou, Kang Yu, Yue School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China Economics and Management School Hubei Business College Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (纸本)9781510683143

With the increasing integration of renewable energy into the power grid, the demand for efficient communication systems for managing and controlling distributed power sources has become urgent. Here, we introduce the design and implementation of a 5G communication terminal tailored specifically for the unique requirements of distributed power sources. The proposed terminal aims to improve communication reliability, latency, and throughput, thereby optimizing the overall performance of the distribution network, and designs a set of self-feedback fault elimination. The system architecture, key features, and experimental results were discussed in detail, demonstrating the feasibility and effectiveness of the proposed solution. © 2024 SPIE.

关键词： Power distribution networks

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Research on Speaker Recognition Based on the ECAPA-TDNN Method 9

Research on Speaker Recognition Based on the ECAPA-TDNN Meth...

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9th international symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Gan, Zhenye Zhang, Xu Northwest Normal University College of Physics and Electronic Engineering Lanzhou China Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Lanzhou China

ISBN: (纸本)9798350388404

Speaker recognition, also known as speaker identification, encompasses two major functions: speaker identification and speaker verification. It is a critical branch of speech processing. The aim of this technology is to recognize or verify the identity of a speaker. Speaker recognition technologies are widely applied in various domains, including security authentication, law enforcement, and personalized interaction systems. Despite their extensive application, speaker recognition technologies face numerous challenges in practical applications such as noise interference and recording quality. Given the limited research on speaker recognition for ethnic minorities within the country, this study employs an improved ECAPATDNN model and conducts experimental validation on a selfconstructed Tibetan speaker recognition dataset. The experimental results indicate an Equal Error Rate (EER) of 0.013, a True Positive Rate (TPR) of 0.992, and a False Positive Rate (FPR) of 0.006, with the threshold set at 0.30. © 2024 IEEE.

关键词： Deep neural networks

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FedRAL: Cost-Effective distributed Annotation via Federated Reinforced Active Learning

FedRAL: Cost-Effective Distributed Annotation via Federated ...

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2024 IEEE international Conference on Big Data, BigData 2024

作者： Lazaridis, Yannis Kastellos, Anestis Psaltis, Athanasios Daras, Petros Centre for Research and Technology Hellas Thessaloniki Greece

ISBN: (纸本)9798350362480

This paper addresses the challenge of reducing annotation costs in distributed learning environments, particularly in systems with limited data and computational resources, such as those found in edge devices. We propose Federated Reinforced Active Learning, a framework that integrates Federated Learning with Reinforced Active Learning to optimize data labeling under strict cost constraints. The method is designed for small-scale networks where data is sparse, and minimal training epochs are available. By utilizing reinforcement learning within active learning, the system selects the most informative data samples, allowing for efficient training while significantly reducing the need for extensive annotations. This approach is particularly suited for environments where minimizing both annotation and computational costs is critical, such as in applications where cost efficiency and resource limitations are top priorities. The proposed method is evaluated on the CIFAR-10 and CIFAR-100 datasets using ResNet18, across 5 and 10 clients. Results demonstrate that the method significantly reduces annotation costs and improves learning outcomes, making it an ideal solution for cost-sensitive distributed systems. © 2024 IEEE.

关键词： Federated learning

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Enhancing Intrusion Detection in Smart Grids: A Machine Learning-based Defense Against DDoS Attacks with Imbalance-Aware Optimization 3

Enhancing Intrusion Detection in Smart Grids: A Machine Lear...

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3rd international Conference on Electronics and Renewable systems, ICEARS 2025

作者： Mahaveerakannan, R. Kumar, T. Rajesh Balamanigandan, R. Vasarao, Parnandi Srinu Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Swarnandhra College of Engineering and Technology Department of Computer Science and Engineering West Godavari District Andhra Pradesh Narsapur India

ISBN: (纸本)9798331509675

The presence of numerous bidirectional communication devices connecting customers to the grid makes smart grid networks particularly vulnerable to network attacks. As an instance of a network attack that could impact the smart grid, a distributed denial of service (DDoS) attack occurs when a compromised grid communiqué devices or nodes send a flood of false data or requests to the smart grid network. This can cause problems with smart meters, data servers, and the state estimator, which in turn affects the services that end users receive. The problem of protecting the network from distributed denial of service (DDoS) attacks is best addressed by methods that rely on machine learning. One major drawback of using machine learning-based approaches is that models need to be retrained every time new types of attacks appear. Despite the fact that DDoS attack detection has long relied on Intrusion Detection systems (IDS), previous research has been limited due to a number of factors. Actually, it is highly discouraged to interfere with the regular functioning of the smart grid. to suggest using reconstructive deep learning methods to identify DDoS attacks efficiently and with minimal downtime in order to tackle this difficulty. In order to identify typical attacks on a communication network, this research introduces a shallow neural network with just 110 artificial neurones triggered by ReLU. to suggest a better attack-sharing loss function to handle unbalanced learning, which will allow for the training of such tiny neural networks. A Lungs Performance-based Optimisation (LPO) was devised using the suggested model to hone the parameters. In smart grid settings, our suggested approach improves the scalability and dependability of IDS and provides useful insights for building strong IDS based on machine learning. This enables organisations to strengthen their defences against harmful cyber-attacks and proactively reduce security risks. © 2025 IEEE.

关键词： Network intrusion

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Probing the Convergence of Vehicular Edge Metaverse and 6G: Blockchain-enabled Framework 18

Probing the Convergence of Vehicular Edge Metaverse and 6G: ...

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18th IEEE international Conference on Advanced networks and Telecommunications systems, ANTS 2024

作者： Kumar, Shree Chaudhary, Rajat Budhiraja, Ishan Bennett University School of Computer Science and Engineering Technology Greater Noida India

ISBN: (纸本)9798350391725

The convergence of vehicular networks and the Meta-verse, referred to as the Vehicular Edge Metaverse is set to revolutionize both transportation and in-car experiences. This paradigm shift envisions vehicles not only as modes of transport but also as immersive gateways to the Metaverse, offering passengers interactive and engaging experiences while on the move. The advent of 6G, with its ultra-fast speeds and minimal latency is crucial in enabling the real-time data synchronization and low-latency communication required for a seamless Vehicular Edge Metaverse experience. Additionally, blockchain technology can play a key role in ensuring secure and transparent data sharing and transactions within this ecosystem, fostering trust and enabling new business models. This paper explores the potential of this convergence, discussing the key enabling technologies such as 6G, edge computing, and blockchain, and their role in addressing the challenges of real-time synchronization, resource management, and security. Furthermore, we discuss the potential applications like augmented reality-enhanced driving assistance and immersive in-car entertainment, highlighting the transformative impact of the Vehicular Edge Metaverse on the future of transportation. © 2024 IEEE.

关键词： Blockchain

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Feature Reduction-based Intrusion Detection (FRID) in Satellite-Terrestrial Integrated networks 5

Feature Reduction-based Intrusion Detection (FRID) in Satell...

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5th international Conference on High Performance Big Data and Intelligent systems, HDIS 2023

作者： Yao, Lin Tian, Ziyuan Deng, Jing Wu, Guowei Li, Li DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Dalian University of Technology Dalian China The Department of Computer Science UNC Greensboro UNC NC Greensboro27412 United States Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350394160

Satellite-Terrestrial Integrated networks (STINs) integrate satellite networks, the Internet, and mobile wireless networks and are able to provide powerful services to users. In STIN, satellite gateways play an important role of connecting space-based networks to terrestrial-based networks, but can be under frequent attacks such as distributed Denial of Services (DDoS) attack. Therefore, intrusion detections become essential in STINs. In this work, we propose a feature reduction-based intrusion detection (FRID) technique with the help from deep reinforcement learning. In FRID, only features that are highly distinguishable, robust, and independent are selected for the later stages. Experimental results show the accuracy, recall, FAR, and F1 scores comparing FRID with several state-of-the-art schemes and demonstrate its superiority. ©2023 IEEE.

关键词： Intrusion detection

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The Smart Optimization of Security systems in Wireless Personal Sensor networks 2

The Smart Optimization of Security Systems in Wireless Perso...

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2nd IEEE international Conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Das, Sunanda Sindhu Madhuri, G. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Wireless personal sensor networks (WPSNs) are an emerging technology that provides a platform for monitoring and collecting data from a variety of sources. These networks are used to monitor environmental, health, and even lifestyle factors. As the use of WPSNs continues to grow, it is important to consider the security implications of using these networks. WPSNs rely on a variety of technologies to ensure secure communication between devices. These include encryption, authentication, secure protocols, and secure key management. Encryption is used to protect data from being intercepted and decoded by an unauthorized user. Authentication is used to verify the personality of the user and the device attempting to access the WPSN. Secure protocols are used to ensure secure communication between the devices and the server. Finally, secure key management is used to manage and store the encryption keys used to protect the data. In addition to encryption, authentication, and secure protocols, WPSNs must also be designed with a robust access control system. Access control systems are used to limit access to the WPSN based on user credentials and other factors. This assist ensure that authorized users alone can access the data stored on the WPSN. © 2023 IEEE.

关键词： Authentication

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