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Laminar and Turbulent Flow

Laminar and Turbulent Flow

Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...

Laminar Flow

Laminar Flow

Laminar flow represents a smooth, orderly fluid motion where particles move along parallel paths, resulting in minimal mixing between layers. Streamlined particle paths characterize this flow regime and occur under conditions where viscous forces dominate over inertial forces. The distinction between laminar, transitional, and turbulent flow is primarily determined by the Reynolds number, a dimensionless quantity calculated as:

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相关实验视频

Updated: Apr 30, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

使用WGAN模型进行网络流量数据增强,由LLM指导.

Jumanah Hmoud Alyoubi¹, Miada Almasre¹, Aishah Aseeri¹

¹Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

本研究介绍了一种使用图形条件生成模型和大型语言模型 (LLM) 来创建合成网络流量的新框架. 这种方法有效地解决了物联网 (IoT) 安全分析中的类不平衡,提高了设备识别准确度.

关键词:

物联网设备识别物联网设备识别物联网安全物联网安全物联网安全在法学士 (LLM) 课程中.ML ML ML 在这里.这是一个WGAN车辆.网络流量分类网络流量分类.

相关实验视频

Last Updated: Apr 30, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

科学领域:

网络安全网络安全
人工智能的人工智能
网络工程网络工程

背景情况:

物联网 (IoT) 设备从网络流量中识别面临的挑战是由于严重的类不平衡,这降低了分类器的性能.
合成数据生成是一个潜在的解决方案,特别是在隐私敏感的安全场景中,对真实流量数据的访问有限.

研究的目的:

提出一个新的框架,结合图形条件生成建模和大语言模型 (LLM) 指导,用于生成现实的,语义上有效的合成网络流量.
为改善物联网 (IoT) 安全分析,解决网络流量分类中类不平衡的关键问题.

主要方法:

构建特征关系图 (使用皮尔森相关性,斯皮尔曼等级相关性,相互信息) 来调节瓦斯斯坦GAN (WGAN) 以保持流量结构.
采用LLM来定义和执行类特定的语义约束 (特征范围,属性相关性,协议规则) 标签一致和符合标准的合成数据.
实施双重验证循环,将LLM反和分类器性能评估与SMOTE等传统方法相结合.

主要成果:

拟议的框架通过共同利用结构 (图形) 和语义 (LLM) 调节来产生高保真度合成网络流量.
在使用新方法平衡的数据集时,观察到宏观F1评分和网络流量分类平衡精度的持续改善.
该方法在数据稀缺和隐私受限制的环境中对安全分析具有显著的实用性.

结论:

图形条件生成模型和LLM指导的集成提供了一个强大的方法来生成合成数据,用于不平衡的网络流量分类.
该框架提高了物联网 (IoT) 基础设施中设备识别的可靠性,这对于强大的安全分析至关重要.
该方法为克服网络安全研究和应用中的数据限制和隐私问题提供了可行的解决方案.