Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Orthogonal Trajectories01:26

Orthogonal Trajectories

134
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
134
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

350
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
350
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

1.0K
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...
1.0K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

586
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
586
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

663
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
663
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

814
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...
814

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Calcined oyster shell powder as a lime substitute in earthen building materials.

Scientific reports·2026
Same author

Longitudinal predictions of visual attention span at school entry for Chinese character reading fluency and reading comprehension via developmentally specific mediation of language-related cognitive skills.

The British journal of developmental psychology·2026
Same author

Mediating mechanisms and urban diversity: How environmental risk perception shapes recycling behavior in China's megacities.

iScience·2026
Same author

Dual regulation of NFS1 by TRIM67-mediated degradation and CEBPA-driven transcription modulates colorectal cancer progression.

Cellular signalling·2026
Same author

Sources of Microbial and Organic Contaminants in the Production of Soybean Whey Protein for Feed and Potential Food Applications.

Food science & nutrition·2026
Same author

Regulatory Mechanism of Ionic Liquids on the Structure and Molecular Weight of the Melamine-Formaldehyde Prepolymer: Experimental and Theoretical Insights.

The journal of physical chemistry. B·2026

相关实验视频

Updated: Mar 15, 2026

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

6.4K

通过CNN-SOFTS-based Coupled Spatio-Temporal Features进行海上轨迹预测.

Yongfeng Suo1, Chunyu Yang1, Gaocai Li1

  • 1Navigation College, Jimei University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了海上轨迹预测的新框架,有效地整合了空间和时间数据. 拟议的方法提高了复杂船舶运动的预测准确性,提高了航行安全.

关键词:
这是一个CNN模型,CNN模型.基于 CNN 软件的框架.在SOFTS模型中,SOFTS模型是有曲线的水道.时空特征是时间空间特征.轨迹的预测和预测.

更多相关视频

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

相关实验视频

Last Updated: Mar 15, 2026

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

6.4K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

科学领域:

  • 海上物流和导航航运输
  • 人工智能和机器学习
  • 数据科学和分析数据科学和分析

背景情况:

  • 准确的海上轨道预测对于航行安全和交通管理至关重要.
  • 现有的方法很难有效地整合自动识别系统 (AIS) 数据中的空间和时间特征,特别是在复杂的场景中.
  • 挑战包括捕捉复杂的船舶运动模式和空间结构.

研究的目的:

  • 通过明确结合空间和时间特征,开发一个高准确度的海上轨迹预测框架.
  • 提高船舶轨迹预测模型的预测准确度和计算效率.
  • 提高内陆河流航行安全和水上交通监测能力.

主要方法:

  • 基于卷积神经网络 (CNN) 的空间编码器被用于抽象空间密度分布和学习全球空间结构.
  • 系列-核心融合时间序列 (SOFTS) 模型结合了角速度,加速和角加速,以捕捉船舶运动状态中的时间依赖.
  • 一个融合回归模块连接空间和时间特征用于轨迹预测.

主要成果:

  • 拟议的框架在验证数据集上实现了0.020的平均平方误差 (MSE) 和0.060的平均绝对误差 (MAE).
  • 该方法在预测准确性和计算效率方面超过了几种先进的时间序列预测模型.
  • 整合动态特征 (角速度,加速度,角加速) 将MSE降低10.22%,MAE降低9.49%.

结论:

  • 开发的框架有效地整合了空间和时间特征,用于优越的海上轨道预测.
  • 包含动态运动功能显著提高了预测性能.
  • 该方法对智能导航系统具有很大的潜力,可以提高安全性和交通管理.