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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...

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相关实验视频

Updated: Jun 11, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

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Published on: April 13, 2016

通过条件生成模型学习地震的地面运动.

Pu Ren1, Rie Nakata2,3,4, Maxime Lacour4,5

  • 1Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. ren.pu@northeastern.edu.

Nature communications
|March 17, 2026
PubMed
概括

人工智能 (AI) 谱图生成器,地面运动条件生成建模 (CGM-GM),可以预测地震地面运动. 这种人工智能工具对改善地震危险评估和基础设施弹性非常有希望.

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Last Updated: Jun 11, 2026

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科学领域:

  • 地质物理学 地质物理学
  • 人工智能的人工智能
  • 地震学 地震学

背景情况:

  • 预测地震地面运动对于地震危险评估和基础设施弹性至关重要.
  • 目前的实证模拟和基于物理的模型等方法由于数据稀疏性,计算强度和复杂的地球结构要求而存在局限性.

研究的目的:

  • 引入人工智能驱动的光谱图生成器,地面运动条件生成建模 (CGM-GM),用于高准确度的地面运动预测.
  • 利用人工智能捕捉空间连续的里埃振幅光谱 (FAS) 和波形属性,而没有明确的物理约束.

主要方法:

  • 使用概率自编码器提取隐藏的时间频率分布.
  • 采用前后分布的变异顺序模型.
  • 将地震强度和地理坐标输入到地面运动条件生成模型 (CGM-GM) 模型中.

主要成果:

  • 基于地面运动条件生成建模 (CGM-GM) 模型成功捕获了空间连续的里埃振幅谱 (FAS).
  • 该模型准确地预测了地震特性,如P和S到达和波形持续时间.
  • 使用旧金山湾区地震记录进行评估,证明了有效的性能.

结论:

  • 对地面运动的条件生成建模 (CGM-GM) 显示出作为对基于物理的模拟和实证地面运动模型的补充工具的潜力.
  • 人工智能方法在地震学中为地震危险评估和基础设施弹性提供了有希望的进步.