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

Gradually Varying Flow01:29

Gradually Varying Flow

32
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
32
Rapidly Varying Flow01:24

Rapidly Varying Flow

49
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
49
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

56
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...
56
Introduction to Types of Flows01:23

Introduction to Types of Flows

816
Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
Two-dimensional flow involves changes in both length and height, as seen in...
816
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

59
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...
59
Flow Table Test01:12

Flow Table Test

122
The flow table test is an established method used to assess the workability of concrete, particularly useful for evaluating highly flowable concrete mixes. This test employs an apparatus that consists of a wooden board topped with a steel plate, collectively weighing 35 pounds. The board is connected to a base via a hinge and measures 27.6 inches on each side.
Concrete is placed within a truncated cone mold that is 8 inches high with an 8-inch base diameter and a 5-inch top diameter. The...
122

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

Domain Adaptation With Additional Features via Label-Aware and Graph-Based Fused Gromov-Wasserstein Optimal Transport.

Neural computation·2026
Same author

Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy.

Discover nano·2025
Same author

Rational partitioning of spectral feature space for effective clustering of massive spectral image data.

Scientific reports·2024
Same author

ATNAS: Automatic Termination for Neural Architecture Search.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Cost-effective framework for gradual domain adaptation with multifidelity.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Gaussian Process Koopman Mode Decomposition.

Neural computation·2022
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
查看所有相关文章

相关实验视频

Updated: Jun 3, 2025

A Human Cerebral Organoid Model of Neural Cell Transplantation
08:58

A Human Cerebral Organoid Model of Neural Cell Transplantation

Published on: July 21, 2023

1.1K

通过规范化流程逐步调整域名.

Shogo Sagawa1,2, Hideitsu Hino3,4

  • 1Department of Statistical Science, Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan sagawa@ism.ac.jp.

Neural computation
|January 9, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了规范化流程,以改善当存在大域差距时的逐渐域适应. 该方法通过转换目标域分布来提高分类性能,克服传统自我训练方法的局限性.

更多相关视频

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.4K
Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation
12:05

Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

Published on: November 3, 2018

11.5K

相关实验视频

Last Updated: Jun 3, 2025

A Human Cerebral Organoid Model of Neural Cell Transplantation
08:58

A Human Cerebral Organoid Model of Neural Cell Transplantation

Published on: July 21, 2023

1.1K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.4K
Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation
12:05

Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

Published on: November 3, 2018

11.5K

科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 标准域调整方法在源数据和目标数据域之间存在显著差异.
  • 逐步域名适应使用中间域名,但在有限的中间步骤和大域名距离的情况下经常失败.

研究的目的:

  • 为了解决在具有挑战性的领域差距下逐步域名适应的失败.
  • 提出一种新的方法,利用无监督领域适应中的规范化流.

主要方法:

  • 拟议的方法使用规范化流来学习从目标域分布到高斯混合分布的转换,利用源域.
  • 这种方法保持了无监督域调整框架.

主要成果:

  • 在真实世界数据集上的实验证明了该方法在缓解由大域差距引起的问题方面的有效性.
  • 与现有方法相比,拟议的技术显著提高了分类性能.

结论:

  • 规范化流提供了一个可行的解决方案,以逐渐的域适应问题与大域差距.
  • 该方法成功地提高了对挑战无监督域调整场景的分类准确性.