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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

434
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...
434
State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

704
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...
704
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

880
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...
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相关实验视频

Updated: Jan 17, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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时间频率协作学习不平衡的船舶运动数据与缺失的标签在海状态估计.

Shuxin Li, Mengna Liu, Xu Cheng

    IEEE transactions on cybernetics
    |September 25, 2025
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    概括
    此摘要是机器生成的。

    BalanceSSE通过解决类不平衡和缺失数据,通过使用半监督学习 (SSL) 改进了海洋状态估计. 这种新的方法提高了船舶运动数据集的准确性.

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    科学领域:

    • 海洋工程和海洋学.
    • 人工智能和机器学习
    • 数据科学是数据科学.

    背景情况:

    • 半监督学习 (SSL) 对于海洋状态估计 (SSE) 至关重要,但与不平衡和不完整的船只运动数据作斗争.
    • 现有的SSE伪标签方法受到诸如高阶层不平衡和缺失数据等挑战的限制.
    • 需要强大的SSL方法来处理船舶运动分析中的数据缺陷是很重要的.

    研究的目的:

    • 引入BalanceSSE,这是一种针对类不平衡船舶运动数据的新型半监督学习方法,用于海水状态估计.
    • 解决现有的SSL方法在处理缺失数据和船舶运动数据集中的类不平衡方面的局限性.
    • 提高深度学习模型在海洋状态估计中的准确性和适用性.

    主要方法:

    • 动态推算 (DIT):通过权衡不同的数据维度,动态推算不完整的船舶运动数据.
    • 不平衡时间频率学习 (ITFL):利用时间频率协作学习来生成伪标签,以及用于伪标签选择的自适应性信心策略.
    • 集群Prox分类器 (CL):通过集群和基于近距离的分类来提高伪标签和估计的准确性.

    主要成果:

    • 与最先进的方法相比,BalanceSSE在UCR和船舶运动数据集上的表现优越.
    • 废弃性研究证实了每个模块 (DIT,ITFL,CL) 对BalanceSSE的整体有效性的重大贡献.
    • 提出的方法成功地处理了类不平衡和不完整的数据,改善了海水状态估计.

    结论:

    • BalanceSSE为半监督的海洋状态估计提供了强大而有效的解决方案,特别是在存在数据挑战的情况下.
    • 集成动态归算,失衡学习和高级分类显著提高了估计性能.
    • 这项工作通过为现实世界船舶运动数据提供更适用和更准确的SSL框架,推进了海洋状态估计领域.