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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Acceleration Vectors01:30

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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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Relative Motion Analysis using Rotating Axes01:25

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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...
450
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
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一种基于适应性核心值差异的AIS轨迹压缩方法Douglas-Peucker算法.

Ting Zhang1,2, Zhiming Wang1, Peiliang Wang3,4

  • 1Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.

Scientific reports
|September 13, 2024
PubMed
概括
此摘要是机器生成的。

一个新的自适应算法,ACTD-DP,通过动态调整值来改善轨迹压缩. 这种方法实现了高压缩比,同时保持了轨迹形状完整性,优于传统方法.

关键词:
这就是ACTD-DP算法.这就是AISIS.船的轨道是船的轨道.轨道压缩的轨道压缩.

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科学领域:

  • 海洋技术 海洋技术
  • 数据科学数据科学数据科学
  • 计算机科学 计算机科学

背景情况:

  • 像Douglas-Peucker (DP) 这样的传统轨迹压缩算法使用静态值,限制了适应性.
  • 自动识别系统 (AIS) 数据产生大量的轨迹信息.

研究的目的:

  • 为增强轨迹压缩提出一个自适应的核心值差异-DP (ACTD-DP) 算法.
  • 改进现有算法中静态值的局限性.

主要方法:

  • 使用课程值预处理AIS数据,以丢弃多余的点.
  • 量化压缩点与值,并通过曲线拟合建立功能关系.
  • 分析函数曲线特征以解决核心值和核心值差异.
  • 引入一个压缩因子来确定最佳核心值差异.

主要成果:

  • 该ACTD-DP算法实现了87.53%的平均压缩比 (ACR) 和23.20%的平均长度损失比 (ALLR).
  • 与四个其他算法相比,它在压缩比和轨迹形状完整性方面表现出了优越的性能.
  • 在四个不同的轨迹数据集中证实了稳定性和适用性.

结论:

  • 该ACTD-DP算法为船舶轨迹提供了卓越的压缩效果.
  • 它有效地平衡了高压缩比,同时保持了轨迹形状.
  • 该算法表现出良好的稳定性和适用性,使其适合于现实世界的场景.