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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Systems-I01:26

Classification of Systems-I

188
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:
188
Aggregates Classification01:29

Aggregates Classification

326
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...
326
Classification of Signals01:30

Classification of Signals

466
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.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
466
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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增强的工业文本分类通过超变的图形引导的全球上下文集成.

Geng Zhang1, Jianpeng Hu1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, Songjiang, China.

PeerJ. Computer science
|January 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于工业文本分类的新方法,该方法使用从预先训练的模型中增强的全球上下文表示. 该方法显著提高了包括工业专利在内的多个数据集的分类准确性.

关键词:
囊网络是一个囊网络.超变量图的超变量图工业应用 工业应用文本信息的力矩阵.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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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
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相关实验视频

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 预先训练有素的模型在文字分类的局部上下文处理方面表现出色.
  • 工业文本数据集,特别是具有小样本大小的数据集,存在独特的分类挑战.
  • 现有的方法往往难以捕捉对工业文本数据至关重要的全球背景.

研究的目的:

  • 提出一种工业文本分类的方法,使用从预先训练的模型中增强的全球上下文表示.
  • 开发一种有效处理工业文本数据集中小样本大小的方法.
  • 为了提高工业文本分类任务的准确性和F1分数.

主要方法:

  • 利用BERT预先训练的模型来提取主要文本表示和局部上下文嵌入.
  • 通过代更新构建一个与BERT嵌入式融合的文本信息矩阵和一个超变量图.
  • 使用囊网络来净化和扩展BERT文本特征,然后与超图形表示进行融合.

主要成果:

  • 拟议的模型在CHIP-CTC数据集上实现了86.82%的准确性和82.87%的F1得分.
  • 在CLUEEmotion2020数据集上,准确率达到61.22%,F1得分为51.56%.
  • 该模型在工业专利数据集上表现出强的表现,准确率为91.84%,F1得分为79.71%,在所有测试数据集中表现优于基线.

结论:

  • 开发的方法有效地解决了工业领域文本的分类问题,特别是在有限的数据的情况下.
  • 全球上下文表示的整合显著提高了分类性能.
  • 该方法为各种工业文本分类应用提供了可靠的解决方案.