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相关概念视频

The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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相关实验视频

Updated: Jan 21, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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适应性空域分配模型用于城市无人机物流,使用不确定性下的多目标优化.

Yao Zhu1, Xin Sun2, Tongdi Hou2

  • 1Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. yphz5223@outlook.com.

Scientific reports
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了城市无人机物流的混合框架,提高了复杂环境中的效率和安全. DRL-RO模型解决了强大的城市级UAV交通管理的不确定性.

关键词:
适应式的空域分配.深度强化学习的学习.分布性强大的优化优化.多目标优化多目标优化城市无人机 空中飞行器物流 城市无人机

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Design and Construction of an Urban Runoff Research Facility
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Design and Construction of an Urban Runoff Research Facility

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科学领域:

  • 物流与运输科学 物流与运输科学
  • 人工智能和机器人技术
  • 城市规划和城市管理

背景情况:

  • 城市无人机 (UAV) 物流面临着空域有限,需求波动和不确定性的挑战.
  • 静态分配方法对于动态的城市环境是不够的.

研究的目的:

  • 开发城市无人机物流管理的适应性框架.
  • 为应对空域局限性,需求波动和不确定性的挑战.

主要方法:

  • 开发了一个DRL-RO (深度增强学习和离散强大优化) 混合框架.
  • 使用了三层不确定性建模系统和注意力增强的政策网络.
  • 一个改进的MOEA/D-DRL算法被用于帕雷托边界近似.

主要成果:

  • 该框架在深取得了高成功率的次方程计算复杂性.
  • 一个分层的空域管理策略平衡了分配效率,飞行安全和成本.
  • 瓦斯斯坦球束在极端场景中确保了稳定性和可扩展性.

结论:

  • DRL-RO框架为城市无人机交通管理提供了强大的解决方案.
  • 它为城市级无人机系统提供理论支持和技术解决方案.
  • 该研究表明,在无人机物流中有效平衡效率,安全和成本.