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
Classification of Signals01:30

Classification of Signals

523
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...
523
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Linear time-invariant Systems01:23

Linear time-invariant Systems

287
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
287
Aggregates Classification01:29

Aggregates Classification

344
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...
344
Associative Learning01:27

Associative Learning

439
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
439

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

Bridging Distribution Gaps in Time Series Foundation Model Pretraining With Prototype-Guided Normalization.

IEEE transactions on neural networks and learning systems·2026
Same author

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Decoupled Hierarchical Distillation for Multimodal Emotion Recognition.

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

EEG-to-gait decoding via phase-aware representation learning.

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

Decoding Covert Speech From EEG by Functional Areas Spatio-Temporal Transformer.

IEEE journal of biomedical and health informatics·2026
Same author

Bioinspired Heat-Induced Viscoelasticity-Switchable Electrodes for Conformal Brain-Computer Interfaces.

Advanced materials (Deerfield Beach, Fla.)·2025
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jul 17, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K

自主监督的对比表示学习用于半监督的时间序列分类.

Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen

    IEEE transactions on pattern analysis and machine intelligence
    |August 28, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了通过时间和上下文对比 (TS-TCC) 进行时间序列表示学习,这是一种从未标记的时间序列数据中学习的新框架. 它的性能与监督方法相提并论,即使使用有限的标记数据.

    更多相关视频

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    568

    相关实验视频

    Last Updated: Jul 17, 2025

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    6.6K
    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    568

    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 时间序列分析时间序列分析

    背景情况:

    • 从没有标记或标记很少的时间序列数据中学习有效的表示是具有挑战性的.
    • 对比的自我监督学习已经成为一种强大的技术,用于从未标记的数据中进行表示学习.
    • 现有的方法可能无法完全捕捉时间序列数据中固有的时间动态和上下文信息.

    研究的目的:

    • 提出一个新的框架,时间序列表示学习通过时间和上下文对比 (TS-TCC),用于从未标记的数据中学习可靠的时间序列表示.
    • 调查时间序列特定数据增强策略在对比学习中的影响.
    • 将框架扩展到半监督设置 (Class-Aware TS-TCC) 以利用有限的标记数据.

    主要方法:

    • 开发了TS-TCC,包括时间对比和上下文对比模块.
    • 引入了特定于时间序列的弱和强数据增强.
    • 拟议的Class-Aware TS-TCC (CA-TCC) 使用伪标签用于半监督学习中的类意识对比损失.
    • 进行了一项关于时间序列数据增强选择的系统研究.

    主要成果:

    • 通过线性评估,TS-TCC通过线性评估学习能够实现与完全监督培训相当的表现的表示.
    • 拟议的框架在少量学习和转移学习场景中表现出高效率.
    • 通过利用有限的标记数据,CA-TCC有效地改善了表示.

    结论:

    • TS-TCC提供了一种强大的方法,用于对时间序列数据进行自我监督的表示学习.
    • 该框架在具有有限标记数据的场景中提供了显著的优势.
    • 提出的方法推进了时间序列表示学习的最新技术.