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関連する概念動画

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

478
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...
478
Corrosion of Reinforcement01:27

Corrosion of Reinforcement

586
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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Updated: Feb 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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マルチエージェント強化学習アルゴリズムに基づく認知IoTリソース割り当て方法

Rong Wang1, Yanjin Shen1, Dongtao Wang2

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

Scientific reports
|February 7, 2026
PubMed
まとめ
この要約は機械生成です。

この研究は、情報エージ（AoI）を最小化するために、車両内の認知IoT（CIoT）のリソース割り当てを最適化します。改良型マルチエージェント近接方策最適化（IMAPPO）アルゴリズムは、接続された車両のデータ遅延を大幅に削減します。

キーワード:
情報エージ認知車両ネットワーキングマルチエージェント強化学習リソース割り当て

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科学分野:

  • ワイヤレス通信
  • IoT（モノのインターネット）
  • 人工知能

背景:

  • 車両間通信は、動的なスペクトルアクセスとチャネル条件によって課題に直面しています。
  • 高車速ネットワークでは適時性が重要であり、情報エージ（AoI）は重要なパフォーマンス指標となっています。

研究 の 目的:

  • 車両向けの認知IoT（CIoT）ネットワークにおける情報エージ（AoI）を最小化すること。
  • 高車速移動下でのリソース割り当てのための共同チャネル選択と電力制御に対処すること。

主な方法:

  • カスタム報酬関数を持つマルコフ決定プロセス（MDP）として問題をモデル化すること。
  • 車両をエージェントとするマルチエージェント強化学習アプローチを採用すること。
  • ハイブリッドアクションスペースのための改良型アクターネットワークを備えた改良型マルチエージェント近接方策最適化（IMAPPO）アルゴリズムを提案すること。

主要な成果:

  • 提案されたIMAPPOアルゴリズムは、動的な車両環境におけるリソース割り当てを効果的に管理します。
  • シミュレーションは、システムAoIを削減する上でのアルゴリズムの実現可能性と有効性を確認します。
  • CIoTリソース割り当てスキームは、車両ユーザーのAoIを削減する上で、代替方法よりも大幅に優れています。

結論:

  • IMAPPOアルゴリズムは、CIoT車両ネットワークにおけるリソース割り当ての効果的なソリューションを提供します。
  • AoIの最適化は、接続および自律走行車におけるリアルタイムアプリケーションにとって重要です。
  • この研究は、将来の車両通信システムのためのインテリジェントなリソース管理を進歩させます。