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

Network Function of a Circuit01:25

Network Function of a Circuit

294
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
294
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

93
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
93
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Mean From a Frequency Distribution01:11

Mean From a Frequency Distribution

16.8K
Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
When such a data set is encountered,...
16.8K
Relative Frequency Distribution00:55

Relative Frequency Distribution

11.0K
A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
11.0K

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

Cluster-guided adversarial graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Emergence of Lineage E.4 and Structural Plasticity of A28L Protein in Mpox Virus: Characterization of LCR7 Length Polymorphisms Across Lineages - Shenzhen City, Guangdong Province, China, 2023-2025.

China CDC weekly·2026
Same author

Genomic characterization of a large-scale chikungunya outbreak in China.

The Journal of infection·2026
Same author

Amniotic membrane transplantation combined with cryotherapy vs. lamellar keratoplasty for medically refractory peripheral ulcerative keratitis: a retrospective cohort study.

Frontiers in medicine·2026
Same author

Control of Circularly Polarized Luminescence in Cholesteric Luminescent Liquid Crystals: From FRET to Exciton Coupling.

Angewandte Chemie (International ed. in English)·2026
Same author

Wastewater-based surveillance and early warning-forecasting framework for norovirus: a two-year longitudinal study in Shenzhen, China.

Frontiers in microbiology·2026

相关实验视频

Updated: Jul 10, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

在频域群众分析的通用特征函数损失.

Weibo Shu, Jia Wan, Antoni B Chan

    IEEE transactions on pattern analysis and machine intelligence
    |November 23, 2023
    PubMed
    概括

    本研究引入了一种新的频率域方法来进行人群分析,使用通用特征函数损失 (GCFL) 来更好地监督人群密度图的学习. 这种方法提高了人群计数和本地化任务的准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 现有的人群密度图学习方法依赖于空间信息,这些信息通常是松散的组织和分散的.
    • 空间领域的监管限制了从人群地图中提取全面的监管信息.

    研究的目的:

    • 为群众分析开发一种新的损失函数,利用频率域进行改进的监督.
    • 为了解决人群密度地图学习中的空间域监督的局限性.

    主要方法:

    • 为群众分析设计了一个通用的特征性功能损失 (GCFL).
    • 从密度或点图转换空间信息到频率域使用扩展特征函数.
    • 基于地图的精心组织的频率内容计算的损失.

    主要成果:

    • 与分散的空间信息相比,频域表示提供了组织良好的层次信息.
    • GCFL在人群计数,人群定位和杂人群计数任务中表现出有效性.
    • 对基准数据集的实证结果显示,GCFL在最先进的 (SOTA) 损失和SOTA方法的竞争力方面的优势.

    结论:

    • 在频率领域的监管为人群分析提供了更有效的方法.

    更多相关视频

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
    10:52

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

    Published on: April 13, 2016

    8.8K
    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
    06:40

    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

    Published on: June 15, 2018

    10.2K

    相关实验视频

    Last Updated: Jul 10, 2025

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    42.9K
    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
    10:52

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

    Published on: April 13, 2016

    8.8K
    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
    06:40

    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

    Published on: June 15, 2018

    10.2K
  • 对于各种人群分析任务,GCFL提供了一种强大而可适应的工具,其性能优于现有的方法.