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

4.3K
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...
4.3K
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

191
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
191
Transformers in Distribution System01:27

Transformers in Distribution System

101
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
101
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

310
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
310

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

Unifying network connectivity from geodesics to random walks via the random cluster model.

Nature communications·2026
Same author

Stopping Beta-Blockers after Myocardial Infarction.

The New England journal of medicine·2026
Same author

A novel model based on ferroptosis-related lncRNAs for predicting prognosis and adjuvant therapy response in colon adenocarcinoma.

Discover oncology·2026
Same author

ABIGX: A Unified Framework for eXplainable Fault Detection and Classification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Phosphorylation of silkworm thymosin promotes the proliferation of Bombyx mori nucleopolyhedrovirus by facilitating the assembly of microfilaments.

Microbial pathogenesis·2026
Same author

Genome-wide Identification of <i>Phloem Protein 2</i> Genes (<i>PP2</i>s) and Characterization of <i>GhPP2</i>-<i>43</i> in <i>Verticillium wilt</i> Resistance of Cotton (<i>Gossypium hirsutum</i>).

Journal of agricultural and food chemistry·2025

相关实验视频

Updated: Jun 24, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

使用密集的时空变压器网进行跨模式的缺失时间序列归算.

Xusheng Qian1, Teng Zhang1, Meng Miao1

  • 1State Grid Jiangsu Electric Power Company Limited Marketing Service Center, Nanjing 210019, China.

Mathematical biosciences and engineering : MBE
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

从传感器网络中丢失时间序列数据是一个挑战. 一个新的密集的时空变压器网络 (DSTTN) 有效地归算缺失的数据,即使在完整的数据缺失场景中,也实现了最先进的性能.

关键词:
完整的数据 缺少的数据.交叉模式数据融合交叉模式数据融合密集的时空变压器网.时间序列数据归算时间序列数据归算缺少的时间序列数据.

更多相关视频

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.1K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

相关实验视频

Last Updated: Jun 24, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.1K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

科学领域:

  • 数据科学数据科学数据科学
  • 人工智能的人工智能
  • 传感器网络 传感器网络

背景情况:

  • 传感器网络经常因为采样问题或设备故障而缺失时间序列数据.
  • 现有的归算方法难以准确,特别是在完全缺失数据 (CDM) 场景中.

研究的目的:

  • 开发一种准确和可靠的方法来归纳缺失的时间序列数据.
  • 解决当前归算技术的局限性,特别是在CDM情况下.

主要方法:

  • 提出了一种使用密集的时空变压器网络 (DSTTN) 的新型交叉模式归算方法.
  • DSTTN使用堆叠的时空变压器块和密集连接将空间数据嵌入时间序列数据中.
  • 在端到端管道中使用交叉模式约束和图形拉普拉斯规范化来优化模型.

主要成果:

  • 对于随机和非随机丢失的数据,DSTN展示了最先进的归算性能.
  • 该方法在解决具有挑战性的完整数据缺失 (CDM) 问题方面尤其有效.
  • 实验性比较验证了DSTN在各种基线归算模型中的优越性.

结论:

  • DSTTN在时间序列数据归算方面取得了重大进展.
  • 拟议的方法为传感器网络中完整的数据缺失场景提供了可行和有效的解决方案.