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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

175
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
175
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
10.9K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

84
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
84
Random Variables01:09

Random Variables

11.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.2K
Sampling Distribution01:12

Sampling Distribution

11.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
11.6K
Sampling Methods: Overview01:06

Sampling Methods: Overview

221
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
221

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 hypergravity on the biomechanics of the musculoskeletal system in human lumbar spine: a numerical study.

Frontiers in bioengineering and biotechnology·2026
Same author

H<sub>2</sub>O<sub>2</sub>-independent oxygen activation via proton-coupled electron transfer for selective hydroxyl radical generation.

Water research·2026
Same author

Recyclable glass fiber-reinforced epoxy copper clad laminates for printed circuit board.

Communications chemistry·2026
Same author

Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: a targeted next-generation sequencing study of bronchoalveolar lavage fluid.

Annals of clinical microbiology and antimicrobials·2026
Same author

Comparative analysis of lipid-lowering mechanisms in efficacy-divergent traditional Chinese medicines.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Sensitivity of various material properties to the intervertebral disc biomechanics.

Computer methods in biomechanics and biomedical engineering·2026

相关实验视频

Updated: May 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

318

基于八度卷积和多阶段生成对立网络的储随机模拟.

Xuechao Wu1, Wenyao Fan2, Shijie Peng3

  • 1School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China. wxc2201710237@126.com.

Scientific reports
|December 31, 2024
PubMed
概括

本研究介绍了OctSinGAN,这是一种用于先进水库模拟的新型混合框架. 它有效地使用多尺度分析和八度卷曲重现复杂的地质特征,提高空间分辨率和精度.

关键词:
生成性的对抗性网络.关节功能损失 关节功能损失多个尺度的空间表示.八度卷积的卷积是八度的储单位 储单位

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.1K

相关实验视频

Last Updated: May 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

318
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.1K

科学领域:

  • 地质科学 地质科学
  • 人工智能的人工智能
  • 储水库工程 储水库工程

背景情况:

  • 传统的随机模拟方法由于计算限制和较低的空间分辨率而与复杂的储存单元作斗争.
  • 生成对抗网络 (GAN) 提供了空间变量高阶统计拟合的潜力,但在使用堆叠的卷积神经网络 (CNN) 时,面临着有限的训练数据和过度拟合的挑战.

研究的目的:

  • 开发一个先进的水库模拟框架,克服传统方法和现有的基于GAN的方法的局限性.
  • 提高复杂地质构成的水库表征的准确性和效率.

主要方法:

  • 一个混合框架,OctSinGAN,将八度卷积和多阶段GAN结合起来,被提出用于水库模拟.
  • 使用单个训练图像 (TI) 进行多尺度表示,采用金字塔结构,捕捉各种尺度的特征.
  • 八度卷积被用于多频特征表示,一个关节损失函数优化了网络参数.

主要成果:

  • OctSinGAN框架证明了在三个不同的训练图像中有效地复制空间变化,通道连接和空间结构.
  • 由OctSinGAN生成的模拟显示与原始训练图像具有很高的相似性,表明性能强.
  • 拟议的方法解决了与以前的方法相关的培训样本不足,高计算消耗和过度装配的问题.

结论:

  • OctSinGAN提供了一种强大而有效的方法,用于高准确度的水库模拟,特别是复杂的地质结构.
  • 该框架通过准确捕捉多尺度特征并提高模拟质量来增强水库特征.
  • 在水库模拟方面的这一进步对地下建模和资源管理具有重大意义.