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相关概念视频

Observational Learning01:12

Observational Learning

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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...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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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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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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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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相关实验视频

Updated: Jun 27, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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边缘计算辅助传感和通信系统的强大卸载:深度增强学习方法

Li Shen1, Bin Li1, Xiaojie Zhu2

  • 1School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括

本研究介绍了一种集成的传感,通信和计算 (ISCC) 系统,以减少频谱拥堵和计算负载. 它优化了能源效率的资源,同时使用先进的算法满足感知和延迟需求.

关键词:
计算不确定性计算不确定性深度强化学习的学习.集成通信和传感传感.移动边缘计算移动边缘计算坚固的设计,强大的设计.

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科学领域:

  • 无线通信系统无线通信系统
  • 边缘计算是一种边缘计算.
  • 信号处理 信号处理

背景情况:

  • 频谱拥堵和计算负担是现代无线系统的关键挑战.
  • 综合传感,通信和计算 (ISCC) 系统为解决这些问题提供了统一的方法.
  • 任务复杂性的不确定性对边缘计算中的计算资源配置构成了重大挑战.

研究的目的:

  • 为ISCC系统制定一个强大的资源分配策略,尽量减少能源消耗.
  • 通过优化通信和计算资源来解决计算不确定性.
  • 确保系统性能满足目标识别应用程序的感知和延迟约束.

主要方法:

  • 制定一个强大的通信和计算资源分配问题.
  • 优化传输束成形,卸载比率和计算资源分配.
  • 适应性学习的马尔科夫决策过程 (MDP) 与近接政策优化 (PPO) 的应用.

主要成果:

  • 提出的方法有效地将总能耗降至最低,同时满足感知和延迟约束.
  • 基于MDP的PPO算法展示了快速的训练速度,并确保了延迟合规性.
  • 模拟结果验证了在ISCC系统中提出的方法的稳定性和有效性.

结论:

  • 开发的自适应学习策略为ISCC系统中的资源配置提供了有效的解决方案.
  • 综合方法成功地平衡了通信,传感和计算任务.
  • 这些发现有助于为目标识别提供更有效,更可靠的边缘计算解决方案.