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関連する概念動画

Reinforcement Schedules01:24

Reinforcement Schedules

241
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
241
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

731
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Reinforcement01:23

Reinforcement

341
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:
341
Observational Learning01:12

Observational Learning

311
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
311
Feedback control systems01:26

Feedback control systems

419
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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強化学習を用いたエッジIoTにおける遅延シフトによる分散型キュー制御

Viacheslav Kovtun1

  • 1Vinnytsia National Technical University, Vinnytsia, Ukraine. kovtun_v_v@vntu.edu.ua.

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|August 22, 2025
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まとめ
この要約は機械生成です。

この研究は,要求サービスを効率的に管理するためのエッジIoTシステムの適応モデルを導入します. 処理時間を動的に調整することで,不安定なトラフィックでもサービスの品質 (QoS) とエネルギー効率を改善します.

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科学分野:

  • コンピュータ科学
  • 電気工学
  • 応用数学

背景:

  • エッジIoTシステムは,エネルギー効率,応答性,および自己規制に対する要求が増加する課題に直面しています.
  • エッジネットワークの不安定なトラフィック条件は,適応的なサービス管理戦略を必要とします.
  • 既存のモデルには,ダイナミックなQoSとエネルギー管理の要件に対応する柔軟性が欠けていることが多い.

研究 の 目的:

  • エッジIoTシステムの周辺ノードでのリクエストサービスプロセスのモデリングと管理のための適応的なアプローチを開発する.
  • 変動する交通条件下でのエネルギー効率,応答性,および自己規制を強化する.
  • ダイナミックなQoSとエネルギー管理のためのスケーラブルでトラフィック型アグノスティックなソリューションを提供する.

主な方法:

  • デバイスの不可用性を考慮するために,パラメータ化された時間シフトを持つストキャスティックG/G/1モデルが提案されました.
  • サービス品質 (QoS) の指標 (遅延,変動性,損失,エネルギー消費) の分析表現は,シフトパラメータの関数として導出されました.
  • ディープQネットワーク (DQN) ベースの補強学習エージェントは,シフトパラメータの分散型,リアルタイム制御のために実装されました.

主要な成果:

  • 最先端のモデルと比較して平均遅延が17~26%減少した.
  • サービス時間の変動が減少し,ピークロード後の列の回復の安定性が向上しました.
  • 提案されたソリューションはトラフィックタイプ無関係で,多様なエッジアーキテクチャにわたってスケーラブルです.

結論:

  • 適応的なアプローチは,エッジIoTシステムのサービスプロセスを効果的にモデル化し管理し,QoSとエネルギー効率を向上させます.
  • DQNベースのエージェントは,ダイナミックで分散した制御を提供し,リアルタイムのキュー状態に適応します.
  • この結果は,センサーネットワーク,5G/6Gエッジシナリオ,ダイナミックなQoSとエネルギー管理を必要とするシステムに適用できます.