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相关概念视频

Distributed Loads: Problem Solving01:21

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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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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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When analyzing the behavior of structures, engineers often rely on the concept of equilibrium. This refers to the state where all forces and moments acting on a system balance each other, resulting in no net movement or rotation. In many cases, equilibrium can be described by a set of standard equations. However, in some situations, alternative sets of equilibrium equations must be used to describe the system's behavior accurately.
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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The deflection of a simply supported beam that carries a central point load can be analyzed using structural mechanics principles, particularly by applying Castigliano's theorem. This theorem relates the displacement at the load application point to the partial derivatives of the strain energy in the structure. The simply supported beam with a point load at its center has symmetric reaction forces at the supports, each bearing half of the load. The bending moment at any point along the beam...
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分布式在线学习算法用于非合作游戏的不平衡图形.

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    此摘要是机器生成的。

    本研究介绍了多代理系统中受约束的在线非合作游戏的新算法,实现了泛化纳什平衡的亚线性动态遗憾和约束违反.

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    科学领域:

    • 控制理论 控制理论
    • 游戏理论 游戏理论
    • 优化优化 优化优化

    背景情况:

    • 研究复杂的多代理系统,其成本和约束在时间上有所变化.
    • 解决了在线游戏的新奇性,以及不平衡的二进制图,这是现有研究中的一个差距.
    • 强调了随时间变化的合非线性不平等约束的挑战.

    研究的目的:

    • 开发一种分布式学习算法,用于在受约束的在线非合作游戏中寻找变化的通用纳什平衡 (GNE).
    • 在动态遗憾和约束违规方面分析拟议算法的性能.
    • 通过在线电力市场游戏来证明算法的适用性.

    主要方法:

    • 一个分布式学习算法,结合了梯度下降,投影和原始-双元方法.
    • 亚线性动态遗憾和约束违规行为的分析.
    • 使用在线电力市场游戏进行示例案例研究.

    主要成果:

    • 拟议的算法实现了亚线性动态遗憾和约束违规.
    • 在研究的游戏设置中,成功地寻找变化的通用纳什平衡 (GNE).
    • 在动态的在线市场场景中表现出实际的实用性.

    结论:

    • 开发的算法提供了一个有效的解决方案,限制在线非合作游戏超过不平衡的二进制图.
    • 建立了对动态遗憾和约束违规行为的理论保证.
    • 在动态环境中分析和控制复杂的多代理系统提供了有价值的框架.