This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit bal...
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Attacks on cloud infrastructure and services have become a significant concern due to the increasing reliance on cloud computing. Various types of attacks pose threats to the cloud ecosystem. Denial-of-Service (DoS) a...
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ISBN:
(纸本)9798350308266;9798350308259
Attacks on cloud infrastructure and services have become a significant concern due to the increasing reliance on cloud computing. Various types of attacks pose threats to the cloud ecosystem. Denial-of-Service (DoS) attacks aim to disrupt services by overwhelming the cloud infrastructure with excessive traffic, rendering them inaccessible. Distributed Denial-of-Service (DDoS) attacks take this a step further by utilizing a network of compromised devices to launch coordinated attacks, causing widespread disruptions. Traditional rule-based detection methods may struggle to keep pace with the sophistication and diversity of modern DoS attacks. Machine learning algorithms, on the other hand, can learn from historical attack data, network behavior, and anomalies to detect and classify malicious traffic accurately. In this paper, we propose a classifier based on deep learning algorithms that can detect modern sophisticated DoS attacks more accurately, enabling early detection and timely response. The proposed classifier demonstrated high detection rates, often exceeding 99%, in effectively identifying DoS attacks on cloud infrastructure.
We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by ...
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Major depressive disorder (MDD) is a common and socially significant psychiatric disorder with extremely complex pathologic mechanisms. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) ha...
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Human action recognition (HAR) is a computer vision technique used to understand the activity of the action performed in the scene. Computer vision technology has become popular and is applied in various areas like su...
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Aiming at the problems of blurred edge contours and low overall clarity of lensless images in the process of super-resolution reconstruction, this paper proposes an image super-resolution network MAR-Net combining mul...
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The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to...
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ISBN:
(纸本)9798350348439;9798350384611
The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Meanwhile, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.
With the ongoing progress of the contemporary economy and technology, there has been a notable surge in the quantity of vehicles in China. Consequently, there is a pressing requirement for an automated, secure, depend...
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Blockchain's immutability, while a core feature, can pose challenges in cases involving sensitive information or compliance with legal regulations, hindering its development. Many subsequent works designed editabl...
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The rapid advancement of software solutions in the industry has brought significant ethical concerns, ranging from data privacy issues to algorithmic bias and cybersecurity threats. Addressing these concerns requires ...
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