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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

149
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,
149
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
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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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...
106
Labeling Emotion01:20

Labeling Emotion

142
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
142
Aggregates Classification01:29

Aggregates Classification

327
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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相关实验视频

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一个混合深度学习情绪分类系统,使用多模式数据.

Dong-Hwi Kim1, Woo-Hyeok Son1, Sung-Shin Kwak1

  • 1Department of Computer Science, Dankook University, 152 Jukjeon-ro Campus, Suji-gu, Yongin-si 16890, Republic of Korea.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了韩语的混合深度学习情绪分类系统 (HDECS),其性能优于现有模型. HDECS有效地处理多式联网数据,以改善各种应用中的情绪识别.

关键词:
贝尔特 (BERT) 公司深度学习是一种深度学习.情绪的分类 情绪的分类这是一个多式联络模式.

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相关实验视频

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 语言 情感 识别 语言

背景情况:

  • 情绪分类对于人工智能和个性化服务至关重要.
  • 现有的单模态方法面临着语调,文本结构和生理信号变化的挑战.
  • 韩语语言提出了独特的NLP挑战,比如主题省略和间距.

研究的目的:

  • 提出一种混合多式联络深度学习系统 (HDECS),用于增强韩语情感分类.
  • 解决单模态情绪分析在口语环境中的局限性.
  • 使用多式联络数据来提高情绪识别准确度.

主要方法:

  • 为多式联运韩国数据重新训练LSTM和CNN模型,直到预测协议超过0.75.
  • 从预测生成情感句子来增强脚本数据.
  • 在增强数据集上使用BERT进行最终情绪预测.

主要成果:

  • 与KLUE/roBERTa模型相比,HDECS案例模型显示出更高的性能.
  • 在分级交叉度 (CCE) 中取得了0.5的改进,在准确度中获得了0.09,在F1得分中获得了0.11.
  • 该模型有效地整合了多式联络信息,以进行强大的情绪分类.

结论:

  • 拟议的HDECS在韩国情感分类方面取得了重大进展.
  • 混合方法可以适应各种语言和区域语音特征的情感分类.
  • HDECS对需要细微的情感理解的现实应用具有前景.