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

Classification of Systems-I01:26

Classification of Systems-I

150
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
150
Classification of Systems-II01:31

Classification of Systems-II

119
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
119
Functional Classification of Joints01:09

Functional Classification of Joints

3.6K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.6K
Classification of Signals01:30

Classification of Signals

310
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
310
Force Classification01:22

Force Classification

1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Aggregates Classification01:29

Aggregates Classification

289
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
289

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

Using Deep Reinforcement Learning to Decide Test Length.

Educational and psychological measurement·2025
Same author

Evaluation of a Process by which Individual Interest Supports Learning within a Formal Middle School Classroom Context.

International journal of science and mathematics education·2021
Same author

A Two-Level Alternating Direction Model for Polytomous Items With Local Dependence.

Educational and psychological measurement·2020
Same author

The transition to digital presentation of the diagnostic imaging domain of the Part IV examination of the National Board of Chiropractic Examiners.

The Journal of chiropractic education·2020
Same author

Score production and quantitative methods used by the National Board of Chiropractic Examiners for postexam analyses.

The Journal of chiropractic education·2019

相关实验视频

Updated: May 7, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

根据使用功能主要组件集群和神经网络的难度进行项目分类.

James Zoucha1, Igor Himelfarb2, Nai-En Tang2

  • 1University of Northern Colorado, Greeley, CO, USA.

Educational and psychological measurement
|January 6, 2025
PubMed
概括

本研究引入了一种功能数据分析 (FDA) 方法,用于分类测试项目的难度,为视觉检查提供了一种一致的替代方案. 该方法准确地对项目进行分类,提高了受试者分类中的公平性.

科学领域:

  • 心理测量 心理测量 心理测量
  • 统计建模 统计建模
  • 教育测量教育的测量

背景情况:

  • 一致的项目难度对于公平的考核分类至关重要.
  • 主观的视觉检查可能会导致项目难度分类不一致.

研究的目的:

  • 用功能数据分析 (FDA) 来基于难度级别对测试项目进行分类的实用程序.
  • 为项目分类提供经验和一致的方法,增强测试公平性.

主要方法:

  • 使用功能主要组件 (FPC) 将项目特征曲线 (ICC) 分成难度组.
  • 采用神经网络来根据ICC预测项目难度.
  • 将FDA的分类与传统的视觉检查进行比较.

主要成果:

  • 视觉和FDA分类之间的大多数差异仅以一个相邻的难度级别不同.
  • 美国食品和药物管理局将67%的中等到硬的物品分类为更高难度级别.
  • 一个神经网络在预测项目难度方面取得了79.6%的准确性.
  • 神经网络的错误分类与FDA集群相比,也只差别在一个相邻的水平上.

结论:

关键词:
功能性主要组件聚类集群.项目分类 项目分类 分类.神经网络的神经网络的神经网络

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

相关实验视频

Last Updated: May 7, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
  • 美国食品和药物管理局的方法提供了一个高效和实用的程序来根据难度对测试项目进行分类.
  • 这种经验方法提高了教育测试计划的一致性和公平性.
  • 该方法特别有利于测试具有分散受试者群体和各种测试时间表的测试程序.