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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

296
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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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

265
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Classification of Systems-II01:31

Classification of Systems-II

240
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,
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Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

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According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
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相关实验视频

Updated: Sep 10, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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开发基于SAO结构的自动标记主题建模技术分析

Minyoung Park1, Sunhye Kim2, Byungun Yoon3

  • 1Master student, Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, Seoul, Korea.

PloS one
|August 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的主体-动作-对象 (SAO) 方法,用于专利中的主题建模,改进技术趋势分析. 该方法提高了自动标记的准确性,并为技术管理提供了更深入的语义洞察力.

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相关实验视频

Last Updated: Sep 10, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Automatic Identification of Dendritic Branches and their Orientation
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科学领域:

  • 信息科学
  • 技术管理
  • 计算语言学

背景情况:

  • 主题建模对于识别技术趋势和预测进步至关重要.
  • 专利文件中现有的自动标签方法在量化评估和捕捉技术层次上存在局限性.
  • 传统的方法往往忽视了技术的功能意义.

研究的目的:

  • 在专利文件中引入基于主体行动对象 (SAO) 的主题建模方法.
  • 通过使用"SAO袋"概念克服现有的自动标签方法的局限性.
  • 提高技术趋势分析的准确性和可解释性.

主要方法:

  • 开发了一种自动标记方法,结合了SAO主题建模和评分.
  • 综合文本总结和网络分析以进行全面评估.
  • 纳入基于合作专利分类 (CPC) 的分类层次结构以提高可解释性.

主要成果:

  • 拟议的模型在技术背景下捕捉功能意义方面表现出有效性,使用ROUGE,相关性,覆盖范围和歧视得分进行评估.
  • 基于SAO的方法提高了主题标签的准确性,并提供了更深入的语义洞察力.
  • 层次结构增强了对技术发展和趋势的看法.

结论:

  • 基于SAO的自动标签方法为专利文件中的主题建模提供了更准确和更易于解释的方法.
  • 这种方法有助于更有效的技术管理,创新和政策制定.
  • 这项研究为技术预测和诊断的精细方法奠定了基础.