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相关概念视频

Variance01:15

Variance

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 The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the...
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Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

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When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
476
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

649
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
649
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

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Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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A Review on UAS Trajectory Estimation Using Decentralized Multi-Sensor Systems Based on Robotic Total Stations.

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使用B-splines对基于点云的几何表面表示的方差成分估计.

Elisabeth Ötsch1, Corinna Harmening2, Hans Neuner1

  • 1Research Group Engineering Geodesy, Geodesy and Geoinformation, TU Vienna, Vienna, Austria.

Journal of applied geodesy
|July 16, 2025
PubMed
概括

本研究使用差异成分估计 (VCE) 来分离测量和模型不确定性在几何表面近似. 当模型不确定性超过测量不确定性时,可以实现现实的分离.

科学领域:

  • 地质测量是指地质测量.
  • 计算几何学的计算几何学
  • 统计建模 统计建模

背景情况:

  • 评估时代间位移严重依赖于点云对几何表面表示的随机信息.
  • 点云的不确定性可能来自仪表,环境或对象特定的因素,以及近似方法的模型不确定性.

研究的目的:

  • 通过差异成分估计 (VCE) 调查测量和模型不确定性的现实估计和分离.
  • 评估VCE在区分测量 (距离,角度) 和模型不确定性 (共变函数) 之间的有效性.

主要方法:

  • 使用 BIQUE 估计方法对张量积 B-spline 表面近似进行差异成分估计 (VCE).
  • 创建了一个更复杂的B-spline表面,通过人工改变点来模拟模型不确定性.
  • 与测量和模型不确定性相关的分离的重叠方差元件.

主要成果:

  • 发现差异组件在特定条件下是可分离和可估计的.
  • 当模型不确定性超过测量不确定性时,分离成功发生.
  • 在变异共变矩阵 (VCM) 设置中,仅包括受模型偏差影响的点至关重要.

结论:

关键词:
B-spline 的近似值是什么?在 TLS 中使用 TLS.测量不确定性 测量不确定性模型不确定性的不确定性差异组成部分估计估计.

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  • 在几何点云近似中,VCE是解开测量和模型不确定性的可行方法.
  • 这项研究表明了不确定性和数据选择的相对大小对于准确的VCE的重要性.
  • 这些发现有助于更可靠地评估随时间变化的表面变化.