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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mass Analyzers: Overview01:13

Mass Analyzers: Overview

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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
807
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

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The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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相关实验视频

Updated: Sep 10, 2025

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Manod:分布式系统的多模式异常检测框架

Wen Liu1, Degang Sun2, Haitian Yang1

  • 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100080, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100080, China.

Neural networks : the official journal of the International Neural Network Society
|August 27, 2025
PubMed
概括

通过分析指标和日志来提高分布式系统的可靠性. 这种多模式的方法显著提高了故障检测的准确性,减少了系统故障.

关键词:
检测异常深度学习分布式系统采用日志模型多模式学习时间序列分析

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科学领域:

  • 计算机科学
  • 软件工程
  • 系统工程

背景情况:

  • 分布式基础设施对于可扩展的应用程序至关重要,需要强大的异常检测以实现系统稳定性.
  • 目前的异常检测方法通常使用单个数据源 (指标或日志),导致不准确和错误的结果.
  • 整合多式数据 (计量和日志) 提供了更全面的系统视图,以改善故障检测.

研究的目的:

  • 提出Manod,一种用于监测分布式系统健康状况的半监督故障检测方法.
  • 利用多式数据 (指标和日志) 进行更准确和可靠的异常识别.
  • 提高检测系统异常的准确性并减少错误的阳性/阴性.

主要方法:

  • 采用基于图的层次编码方法来生成歧视性表示.
  • 使用预先训练的语言模型处理和建模系统指标和日志.
  • 引入一个关闭的注意力融合机制,以有效地整合异构的数据模式.

主要成果:

  • 马诺德在模拟数据集 (D1) 上获得了0.870的高F1分,在现实数据集 (D2) 上获得了0.934分.
  • 提出的方法在故障检测任务中显著优于所有基线模型.
  • 在异常检测中减轻错误阳性和错误负性的有效性.

结论:

  • 通过使用多式联网数据,Manod提供了一种有效的半监督方法来检测分布式系统中的故障.
  • 通过先进的融合技术整合指标和日志,提高了系统健康监测能力.
  • 该方法显示了确保大型软件系统稳定可靠运行的强大潜力.