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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

1.6K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.6K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.7K
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...
1.7K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.3K
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...
1.3K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.1K
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

408
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
408
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
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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相关实验视频

Updated: Jan 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

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贝叶斯深度强化学习用于不确定性量化和适应性支持优化在深度基础坑工程中的贝叶斯深度强化学习.

Weiming Gu1

  • 1Architectural Engineering Institute, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. gwming0228@126.com.

Scientific reports
|October 9, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了深层基础坑支的智能框架,结合了贝叶斯推理和深度强化学习. 该系统通过优化支持和降低成本,提高了地质工程的安全性和效率.

关键词:
适应性优化适应性优化贝叶斯的推理 贝叶斯的推理深的基础坑深的基础坑.深度强化学习的学习.多物理合器不确定性量化不确定性的量化.

相关实验视频

Last Updated: Jan 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

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科学领域:

  • 地质技术工程 地质技术工程
  • 计算力学 计算力学 计算力学
  • 人工智能的人工智能

背景情况:

  • 深层基础坑需要强大的支持系统来管理复杂的多物理相互作用.
  • 传统方法往往缺乏适应性和精确的不确定性量化.

研究的目的:

  • 开发一个完整的框架,用于确定不确定性量化和适应性支持优化深度基础坑系统.
  • 通过智能自动化提高地质工程施工的安全性,效率和成本效益.

主要方法:

  • 贝叶斯推理 (马尔科夫链蒙特卡罗) 与深度强化学习的整合.
  • 开发一个多物理合数值模型 (机械-液压-热).
  • 实时监控数据的整合用于适应性支持调整.

主要成果:

  • 实现了高预测准确度 (R2=0.91) 和可靠性 (覆盖概率=96.8%).
  • 墙壁位移 (35%) 和表面沉积 (42%) 的显著减少.
  • 报告了18%的成本节约,12%的建筑时间缩短,零安全事故.

结论:

  • 这种新的框架比传统的决定性方法提供了更高的性能.
  • 智能适应性支持优化可提高变形控制和施工效率.
  • 这项研究为先进的城市地质工程提供了实用工具.