Vehicular Ad Hoc networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Servi...
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Aiming at the impact of node faults on normal business operation in computernetworks, a log information-driven fault prediction method is proposed. By constructing an efficient deep learning model and introducing a c...
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This article aims to build an educational innovation network management system based on artificial intelligence technology to address the challenges and opportunities faced in the field of education. Due to the develo...
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ISBN:
(纸本)9798400718267
This article aims to build an educational innovation network management system based on artificial intelligence technology to address the challenges and opportunities faced in the field of education. Due to the development of society and advancement of technology, traditional education methods are no longer applicable to today's society. However, due to the many defects and deficiencies in the traditional education management system, educational innovation has become particularly difficult. Therefore, this article studies the use of artificial intelligence technology to construct an educational innovation network management system. The experimental results show that the education innovation network management system based on artificial intelligence technology performs well in resource utilization and response time, and has higher resource utilization and shorter response time compared to traditional education management systems. In summary, this study provides a new approach for reform and innovation in the field of education, and it is hoped that this study can contribute to the development and progress of the education sector.
This study is dedicated to constructing a surrogate model of the shock wave field using physics-informed neural networks, aiming to achieve accurate reconstruction of the shock wave field with a low amount of data. Th...
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Graph neural network (GNN) models are capable of capturing the intrinsic structure and semantic relationships within data and this mechanism grants them substantial potential advantages in the field of computer vision...
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To address the challenges in the field of 3D object detection, such as complex network architectures, large network parameter counts, and the prevalent use of sparse convolution which is not conducive to edge deployme...
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To improve the accuracy of the YOLOv8n algorithm for object detection, this paper proposes an enhanced YOLOv8n algorithm. This algorithm introduces the CARAFE module, TripleAttention mechanism, and MultiSEAMHead modul...
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With the evolution of modern networks, network operators are able to flexibly deploy Service Function Chains (SFCs) in accordance with network policy requirements to achieve desired purposes. At the same time, to redu...
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ISBN:
(纸本)9798350376975;9798350376968
With the evolution of modern networks, network operators are able to flexibly deploy Service Function Chains (SFCs) in accordance with network policy requirements to achieve desired purposes. At the same time, to reduce the latency of processing packets through SFCs, an increasing number of network operators are designing SFCs to operate in parallel. In addition, many service functions are designed to be stateful, making the processing behavior of packets state-dependent, but the state is complex and arbitrary, which introduces greater complexity and uncertainty and makes the verification of SFCs behavior more difficult. To verify the correctness of stateful parallel SFCs, a formal verification method named SPV is proposed in this paper. SPV first defines the correctness attribute of stateful parallel SFCs, and then uses Coloured Petri Nets (CPN) for offline modeling of stateful parallel SFCs. This model describes the topological structure and behavior of the stateful parallel SFCs and enhances the expressiveness of the CPN model with Standard ML Language (ML) functions. In experiments, by analyzing the reachability graph and state space of the stateful parallel SFC's CPN model, the correctness attribute of the stateful parallel SFCs are verified, completing the correctness verification of stateful parallel SFCs. Moreover, these results also demonstrate the potential of CPN-based modeling for the verification of SFCs in stateful networks.
Road lane segmentation and classification play a crucial role in autonomous vehicles. Lane segmentation helps localize the position of the lane boundaries and the category information provides assistance for making de...
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Encrypted network traffic classification plays an important role in today's network security and performance optimization. Through the classification and identification of network traffic, it can better monitor an...
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