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相关概念视频

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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Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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基于深度强化学习的IoV任务卸载决策.

Jing Su1, Yuejun Liu2

  • 1Software School of Anyang Normal University, Anyang, 455002, Henan, China.

Scientific reports
|November 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于车辆任务卸载的智能深度强化学习方案. 它优化了部分卸载和资源分配,大大降低了车载应用程序的延迟和能源消耗.

关键词:
DDPG算法中的一个算法.深度强化学习的学习.车辆的互联网汽车的互联网任务卸载决策任务卸载决策

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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科学领域:

  • 智能运输系统 智能运输系统
  • 边缘计算 边缘计算
  • 机器学习 机器学习

背景情况:

  • 车载应用程序面临资源限制 (计算,存储,能源).
  • 云端协作计算是一个关键的解决方案,但现有的方案缺乏部分卸载和任务优先级.
  • 确定最佳卸载速度和平衡资源分配仍然是一个挑战.

研究的目的:

  • 解决当前云端协作任务卸载方案的局限性.
  • 为动态车辆环境开发一种新的任务卸载决策方案.
  • 根据任务优先级,实现有效的部分卸载和合理的资源分配.

主要方法:

  • 通信,能源消耗,成本,优先级和任务卸载模型的设计.
  • 使用深度强化学习算法的任务卸载决策方案的实施.
  • 基于深度决定性政策梯度 (IDDPG) 的改进方案的开发.

主要成果:

  • 与现有方法相比,拟议的基于IDDPG的方案显著优化了性能.
  • 与DQN相比,实现了59.46%的延迟降低,与DDPG相比,降低了67.39%.
  • 与DQN相比,能源消耗减少了18.37%,与DDPG相比,能源消耗减少了11.76%.

结论:

  • 基于IDDPG的方案有效地处理云边缘环境中的部分卸载和任务优先级.
  • 拟议的方法为车载计算提供了优越的延迟和能源效率.
  • 这项研究为优化互联汽车资源利用提供了强大的解决方案.