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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

167
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
167
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

126
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...
126
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

260
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
260
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

155
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
155
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

186
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
186
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

944
Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
The pressure difference depends on the fluid's velocity and radius of curvature. The pressure variation is minimal in flows with nearly straight streamlines.
944

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

Effect of angiotensin II and angiotensin II type 1 receptor antagonist on the proliferation, contraction and collagen synthesis in rat hepatic stellate cells.

Chinese medical journal·2008
Same author

[Determination of nicotinamide in formula milk powder using liquid chromatography-isotope dilution mass spectrometry].

Se pu = Chinese journal of chromatography·2008
Same author

In vivo tracking of superparamagnetic iron oxide nanoparticle-labeled mesenchymal stem cell tropism to malignant gliomas using magnetic resonance imaging. Laboratory investigation.

Journal of neurosurgery·2008
Same author

Enhancement and broadening of extreme-ultraviolet supercontinuum in a relative phase controlled two-color laser field.

Optics letters·2008
Same author

Screening and breeding of high taxol producing fungi by genome shuffling.

Science in China. Series C, Life sciences·2008
Same author

Reversible self-association of a concentrated monoclonal antibody solution mediated by Fab-Fab interaction that impacts solution viscosity.

Journal of pharmaceutical sciences·2008

相关实验视频

Updated: Sep 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

轮流约束:维护基于深度学习的图像分割的全球形状相似性.

Shengzhe Chen, Zhaoxuan Dong, Jun Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 30, 2025
    PubMed
    概括

    本研究引入了一种用于图像细分的新型轮流方法,增强了形状相似性保护. 新的形状损失可以提高各种深度学习模型的细分精度.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 有效的图像细分需要先前的知识限制,以获得最佳的结果.
    • 现有的方法往往忽略从轮流的角度全球形状相似性.
    • 将轮流先验集成到深层卷积网络中仍然是一个未被探索的领域.

    研究的目的:

    • 建立和数学推导一个轮流约束,以保持图像细分中的全球形状相似性.
    • 提出将这种约束纳入深度神经网络和变异模型的方法.
    • 评估拟议方法在提高细分精度和形状相似性方面的有效性.

    主要方法:

    • 基于可比的轮定义的全球形状相似性.
    • 衍生出一个数学轮流量约束,以保持全球形状相似性.
    • 实现了深度学习框架的形式损失的约束,并将其集成到变化模型中.
    • 通过展开代方案开发了轮流形状相似性网络 (CFSSnet).

    主要成果:

    • 拟议的形状损失显著提高了跨不同数据集和基准模型的细分精度和形状相似性.
    • 形状的损失表明了一般的适应性,有效地与各种网络架构工作.
    • CFSSnet在细分噪音污染图像方面表现出强大,同时保持了全球形状相似性.

    更多相关视频

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.8K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.2K

    相关实验视频

    Last Updated: Sep 13, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.8K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.2K

    结论:

    • 开发的轮流约束及其实现为增强图像分割提供了强大的工具.
    • 拟议的形状损失是改进基于学习的细分框架的多功能组件.
    • 对于图像分割任务,CFSSnet提供了一个强大的解决方案,特别是那些涉及噪音数据和需要形状保存的任务.