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相关概念视频

Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Cognitive Dissonance01:38

Cognitive Dissonance

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Short-distance Transport of Resources02:12

Short-distance Transport of Resources

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Reinforcements in Concrete01:25

Reinforcements in Concrete

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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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Corrosion of Reinforcement01:27

Corrosion of Reinforcement

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The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
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相关实验视频

Updated: Feb 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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一种基于多代理强化学习算法的认知物联网资源分配方法.

Rong Wang1, Yanjin Shen1, Dongtao Wang2

  • 1Hunan Automotive Engineering Vocational University, Zhuzhou, 412000, China.

Scientific reports
|February 7, 2026
PubMed
概括
此摘要是机器生成的。

这项研究优化了对车辆中的认知物联网 (CIoT) 的资源配置,以尽量减少信息时代 (AoI). 改进的多代理近接政策优化 (IMAPPO) 算法大大降低了连接车辆的数据延迟.

关键词:
信息时代是一个信息时代.认知车辆网络是指认知车辆网络.多个代理强化学习学习多个代理强化学习学习资源分配资源的分配.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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科学领域:

  • 无线通信无线通信
  • 物联网的物联网,就是物联网.
  • 人工智能的人工智能

背景情况:

  • 车辆间通信面临来自动态频谱接入和通道条件的挑战.
  • 及时性对于高速车辆网络至关重要,使信息时代 (AoI) 成为一个关键的性能指标.

研究的目的:

  • 在车辆的认知物联网 (CIoT) 网络中尽量减少信息时代 (AoI).
  • 解决联合道选择和功率控制问题,以在高速移动下分配资源.

主要方法:

  • 将问题建模为具有自定义奖励函数的马尔科夫决策过程 (MDP).
  • 使用车辆作为代理的多代理强化学习方法.
  • 为混合动力行动空间提出了改进的多代理近接政策优化 (IMAPPO) 算法,并增强了Actor网络.

主要成果:

  • 拟议的IMAPPO算法有效地管理动态车辆环境中的资源分配.
  • 模拟证实了算法的可行性和有效性,减少了系统AOI.
  • 在降低车辆用户的AOI方面,CIoT资源分配方案显著超过了替代方法.

结论:

  • IMAPPO算法为CIoT车辆网络中的资源分配提供了有效的解决方案.
  • 优化AoI对于联网和自动驾驶汽车的实时应用至关重要.
  • 这项研究促进了未来汽车通信系统的智能资源管理.