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

Plastic Deformations01:19

Plastic Deformations

489
Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
489
Plastic Deformations01:14

Plastic Deformations

492
It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
492
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

502
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
502
Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

665
When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
665
Plasticity00:58

Plasticity

3.1K
Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
3.1K
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

433
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
433

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

Pioneers and paradigms in sprint science: a thematic historical mini review.

Frontiers in sports and active living·2026
Same author

Development and Interpretable Machine Learning-Based Prediction of Cardiovascular Disease Risk in Chinese COPD Patients: An Analysis of the CHARLS Database.

International journal of chronic obstructive pulmonary disease·2026
Same author

Preferences of frail elderly patients with cardiovascular disease for web-based exercise telerehabilitation interventions in China: protocol for a discrete choice experiment study.

BMJ open·2026
Same author

Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.

Current atherosclerosis reports·2026
Same author

Counterfactual Risk Minimization for Out-of-Distribution Generalization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Comparative Analysis of the Transcriptome of the Chicken Breast Muscle at Different Developmental Stages.

Animals : an open access journal from MDPI·2026
Same journal

HiVLR: Hierarchical Vision-Language Reasoning for interpretable zero-shot radiography image understanding.

Medical image analysis·2026
Same journal

FAA-Net: Fetal abdominal anomaly diagnosis in prenatal ultrasound via LLM-enhanced multi-instance learning.

Medical image analysis·2026
Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
查看所有相关文章

相关实验视频

Updated: Feb 19, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

12.0K

变形恢复扩散模型 (DRDM):用于图像处理和合成的实例变形.

Jian-Qing Zheng1, Yuanhan Mo2, Yang Sun3

  • 1The Kennedy Institute of Rheumatology, University of Oxford, UK; Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, UK.

Medical image analysis
|February 17, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了一种用于医学成像的新生成模型,该模型使用变形场来创建逼真的图像. 这种方法确保了解剖学可信性,并改善了对一些任务的数据增强,如少数镜头学习和图像注册.

关键词:
数据增强数据增强生成型模型是一种生成型模型.图像的注册 图像的注册图像合成 图像合成分段化 分段化 分段化 分段化

更多相关视频

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

7.6K
Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
06:20

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging

Published on: April 28, 2022

2.5K

相关实验视频

Last Updated: Feb 19, 2026

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
14:14

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Published on: April 16, 2017

12.0K
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

7.6K
Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
06:20

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging

Published on: April 28, 2022

2.5K

科学领域:

  • 医疗成像医学成像
  • 生成型模型 生成型模型
  • 计算机视觉 计算机视觉

背景情况:

  • 扩散模型对合成医疗图像生成有希望.
  • 现有的方法往往缺乏可解释的对应性,并且可以产生解剖学上不可思议的结果.
  • 需要生成模型来确保解剖学可信性和结构完整性.

研究的目的:

  • 提出一种基于扩散的新型生成模型,即变形恢复扩散模型 (DRDM).
  • 通过强调通过变形场的形态转换来解决当前方法的局限性.
  • 为了改善医学成像应用,生成多样化,解剖学上可信的变形.

主要方法:

  • DRDM使用的是维护拓的变形场生成策略.
  • 它涉及随机采样和多尺度变形速度场 (DVF) 的整合.
  • 该模型被训练来恢复不现实的变形组件,将变形图像恢复到现实的分布.

主要成果:

  • 在保持解剖学可信度的同时,DRDM产生了多样化,大规模的变形.
  • 对心脏MRI和肺CT的实验证明了该模型的功能.
  • 在2D图像分割和3D图像注册任务中观察到性能增长.

结论:

  • 在医学成像中,DRDM为生成建模提供了一种新的方法.
  • 该模型增强了数据增强和合成,改善了下游任务性能.
  • 显而易见的DRDM显示了医疗图像操纵和分析的重大潜力.