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

Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

317
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
317
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

57
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
57
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

99
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
99
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

533
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
533

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

Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods.

Materials (Basel, Switzerland)·2025
Same author

Innovative artificial intelligence for practice management in medical healthcare.

European heart journal·2025
Same author

AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection.

IEEE transactions on neural networks and learning systems·2024
Same author

Multi-class boosting for the analysis of multiple incomplete views on microbiome data.

BMC bioinformatics·2024
Same author

Balancing Protection and Quality in Big Data Analytics Pipelines.

Big data·2024
Same author

A toolbox of machine learning software to support microbiome analysis.

Frontiers in microbiology·2023

相关实验视频

Updated: Jun 26, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.4K

GAP-LSTM:用于地理分布式预测的基于图形的自相对应保护网络.

Massimiliano Altieri, Roberto Corizzo, Michelangelo Ceci

    IEEE transactions on neural networks and learning systems
    |May 17, 2024
    PubMed
    概括

    我们开发了GAP-LSTM,这是传感器网络的新预测方法. 它通过有效处理来自多个来源的复杂时空数据来提高准确性.

    科学领域:

    • 计算机科学 计算机科学
    • 数据科学数据科学数据科学
    • 传感器网络 传感器网络

    背景情况:

    • 预测对于地理分布式传感器网络的决策支持至关重要.
    • 挑战包括多变量数据,多个节点和时空自相关性,限制了当前的方法.
    • 现有的方法往往无法同时解决这些复杂问题,从而影响预测的准确性.

    研究的目的:

    • 为地理分布式传感器网络设计的新型预测方法GAP-LSTM提出建议.
    • 从多个节点的多变量数据中有效地利用时空自相关性.
    • 在复杂的传感器网络环境中提高预测的准确性和建模能力.

    主要方法:

    • 开发了GAP-LSTM,集成图形卷积,基于注意力的长期短期记忆 (LSTM) 和2D卷积.
    • 利用潜存储器状态来捕捉复杂的时空依赖关系.
    • 采用了一种技术组合,以协同利用数据特征.

    主要成果:

    • 与现实世界交通,能源和污染数据集的最先进方法相比,GAP-LSTM表现出卓越的性能.
    • 一项废除研究证实了GAP-LSTM方法的每个组成部分的显著贡献.
    • 该方法提供可解释的可视化,以帮助领域专家.

    更多相关视频

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.0K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    相关实验视频

    Last Updated: Jun 26, 2025

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
    09:32

    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    12.4K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.0K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    结论:

    • 通过有效处理多变量时空数据,GAP-LSTM为地理分布式传感器网络提供了增强的预测功能.
    • 拟议的方法代表了对复杂传感器网络应用的准确和洞察力预测的重大进步.
    • 图形卷积和基于注意力的LSTM的整合为这个领域的未来研究提供了一个强大的框架.