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

Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

295
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
295
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

280
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
280
Compacting Factor test01:22

Compacting Factor test

190
The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
190
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

14.0K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
14.0K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Parallel-axis Theorem01:06

Parallel-axis Theorem

7.0K
The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
7.0K

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

Protein phosphatase 2A regulates senescence and immunogenicity in medulloblastoma models.

The Journal of clinical investigation·2026
Same author

Self-supervised semantic graph propagation for multi-view clustering.

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

Genomic Subtype Influences BH3 Mimetic Drug Sensitivity and Synergy with Cytotoxic Chemotherapeutics in T-cell Acute Lymphoblastic Leukemia.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Chlorosyl Nitrite (OClNO): An Elusive Intermediate in the Photochemistry of Nitryl Chloride.

Journal of the American Chemical Society·2026
Same author

Antigen Specificity and Cell Engineering Determine CAR T Cell Efficacy in Group 3 Medulloblastoma.

Research square·2026
Same author

Cell type-resolved proteomics reveals intra- and intercellular signaling in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Modeling the impact of budget limitation on the screening and treatment pathway of HPV-induced precancerous cervical lesions.

Mathematical biosciences and engineering : MBE·2026
Same journal

Modeling the effects of trait-mediated dispersal on coexistence of two species: Competition and non-consumptive predator-prey.

Mathematical biosciences and engineering : MBE·2026
Same journal

A close look at the viral reduction rate in target cell limited models.

Mathematical biosciences and engineering : MBE·2026
Same journal

A stochastic agent-based model for simulating tumor-immune dynamics and evaluating therapeutic strategies.

Mathematical biosciences and engineering : MBE·2026
Same journal

Addressing domain shift via imbalance-aware domain adaptation in embryo development assessment.

Mathematical biosciences and engineering : MBE·2026
Same journal

Effect of drug resistance on an HIV epidemic in heterogeneous populations.

Mathematical biosciences and engineering : MBE·2026
查看所有相关文章

相关实验视频

Updated: Jul 21, 2025

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

7.0K

坚固的上限规范双重超图规范的非负矩阵三重因子化.

Jiyang Yu1, Baicheng Pan2, Shanshan Yu3

  • 1College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

Mathematical biosciences and engineering : MBE
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

一种新方法,强大的上限规范双超图规范非负矩阵三因素化 (RCHNMTF),通过处理异常值和学习几何信息来改进数据分析. 这种方法提高了集群性能和数据表示,而不是标准的非负矩阵三因素化.

关键词:
有限制的标准.双重超图的规范化规范化非负矩阵三因子化非负矩阵三因子化强大的集群.

更多相关视频

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

相关实验视频

Last Updated: Jul 21, 2025

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

7.0K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 矩阵因子分解矩阵因子分解

背景情况:

  • 非负矩阵分解 (NMF) 是一种常见的技术.
  • 非负矩阵三因素化 (NMTF) 提供了更多的灵活性,但对噪声和异常值敏感.
  • 现有的方法往往忽略了数据多元体内的几何信息.

研究的目的:

  • 引入一种新的强大的上限规范双超图规范化非负矩阵三因子化 (RCHNMTF) 算法.
  • 解决标准NMTF的局限性,特别是对异常值的敏感性和无法捕捉几何结构的局限性.
  • 为了提高数据表示和集群性能.

主要方法:

  • 使用强大的上限标准来减轻极端异常值的影响.
  • 结合双重超图规范化,利用特征和样本多元组的内在几何信息.
  • 应用直角性约束来实现独特的数据表示和改进的集群.

主要成果:

  • 拟议的RCHNMTF方法显示出对异常值的显著稳定性.
  • 该算法有效地从特征和样本多元体中捕获几何信息.
  • 七个数据集的实验结果证实了RCHNMTF对现有方法的优越性.

结论:

  • RCHNMTF为非负矩阵三因子化提供了一个强大的和有效的方法.
  • 该方法通过处理异常值和利用几何结构来增强数据分析.
  • 在聚类和数据表示任务中,RCHNMTF表现出卓越的性能.