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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

169
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:
169
Classification of Systems-II01:31

Classification of Systems-II

134
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
134
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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Updated: Jun 5, 2025

Design and Analysis for Fall Detection System Simplification
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使用机器学习方法将软件安全要求分为机密性,完整性和可用性.

Taghreed Bagies1

  • 1Information Technology, Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究将软件安全要求的分类自动化为保密性,完整性和可用性 (CIA) 三位一体. 具有句子转换器嵌入的支持向量机实现了87%的准确性,改善了要求可追溯性.

关键词:
美国中央情报局,中央情报局.机器学习 机器学习安全要求安全要求.这是一个句子转换器.软件工程 软件工程 软件工程

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科学领域:

  • 软件工程 软件工程 软件工程
  • 信息安全 信息安全
  • 自然语言处理自然语言处理.

背景情况:

  • 安全要求是关键的非功能软件要求,通常基于机密性,完整性和可用性 (CIA) 三位一体.
  • 基于自然语言的要求可能含糊不清,因此很难区分不同的安全维度.
  • 自动化安全要求的分类有助于追踪和确保实施.

研究的目的:

  • 提出和评估自动将安全要求分类到中央情报局三元组的方法.
  • 为了比较功能提取技术 (TF-IDF和句子转换器嵌入) 与机器学习算法相结合.

主要方法:

  • 为每个特征提取技术开发了五种机器学习模型 (SVM,KNN,RF,GB,BNB).
  • 使用术语频率-反向文档频率 (TF-IDF) 和句子转换器嵌入功能提取.
  • 创建了一个网络接口,用于实时分析和分类安全要求.

主要成果:

  • 支持矢量机 (SVM) 模型与句子转换器嵌入相结合,达到87%的最高准确率.
  • 与其他模型相比,这种方法在预测要求的安全维度方面表现优越.
  • TF-IDF和其他机器学习算法表现出不同程度的成功,但SVM-句子-变压器组合的表现优于它们.

结论:

  • 将安全要求自动分类为中央情报局三元组是可行的,对软件工程有好处.
  • 句子转换器嵌入与SVM相结合,为此分类任务提供了一个高度准确的方法.
  • 开发的系统可以提高安全要求管理的效率和可靠性.